The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans
Abstract
The theory of predation risk effects predicts behavioral responses in prey when risk of predation is not homogenous in space and time. Prey species are often faced with a tradeoff between food and safety in situations where food availability and predation risk peak in the same habitat type. Determining the optimal strategy becomes more complex if predators with different hunting mode create contrasting landscapes of risk, but this has rarely been documented in vertebrates. Roe deer in southeastern Norway face predation risk from lynx, as well as hunting by humans. These two predators differ greatly in their hunting methods. The predation risk from lynx, an efficient stalk‐and‐ambush predator is expected to be higher in areas with dense understory vegetation, while predation risk from human hunters is expected to be higher where visual sight lines are longer. Based on field observations and airborne LiDAR data from 71 lynx predation sites, 53 human hunting sites, 132 locations from 15 GPS‐marked roe deer, and 36 roe deer pellet locations from a regional survey, we investigated how predation risk was related to terrain attributes and vegetation classes/structure. As predicted, we found that increasing cover resulted in a contrasting lower predation risk from humans and higher predation risk from lynx. Greater terrain ruggedness increased the predation risk from both predators. Hence, multiple predators may create areas of contrasting risk as well as double risk in the same landscape. Our study highlights the complexity of predator–prey relationship in a multiple predator setting. Synthesis In this study of risk effects in a multi‐predator context, LiDAR data were used to quantify cover in the habitat and relate it to vulnerability to predation in a boreal forest. We found that lynx and human hunters superimpose generally contrasting landscapes of fear on a common prey species, but also identified double‐risk zones. Since the benefit of anti‐predator responses depends on the combined risk from all predators, it is necessary to consider complete predator assemblages to understand the potential for and occurrence of risk effects across study systems.
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“The 3D structure of vegetation is an important determinant of where predators make kills [16,17].”
From Effects of Vegetation Structure on the Location of Lion Kill Sites in African Thicket (2016) · cites this paper as [17]
In the citing paper
“In particular, the 3D structure of vegetation, as viewed by predators and prey (their viewshed), has been found to be important in predator-prey relationships [16,17].”
From Effects of Vegetation Structure on the Location of Lion Kill Sites in African Thicket (2016) · cites this paper as [17]
Citations
Cites
Behavioral decisions made under the risk of predation: a review and prospectus
Lima, S. L., & Dill, L. M. (1990) · Canadian Journal of Zoology · doi:10.1139/z90-092 · Cited by 8,196
Næsset, E. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00290-5 · Cited by 1,437
Remote sensing for biodiversity science and conservation
Turner, W., Spector, S., Gardiner, N., Fladeland, M. M., Sterling, E. J., & Steininger, M. (2003) · Trends in Ecology & Evolution · doi:10.1016/s0169-5347(03)00070-3 · Cited by 1,408
Emergent impacts of multiple predators on prey.
Sih, A., Englund, G., & Wooster, D. (1998) · Trends in Ecology & Evolution · doi:10.1016/s0169-5347(98)01437-2 · Cited by 1,362
Relationships between direct predation and risk effects
Creel, S., & Christianson, D. (2008) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2007.12.004 · Cited by 1,238
Wolves, elk, and bison: reestablishing the "landscape of fear" in Yellowstone National Park, U.S.A.
Laundré, J. W., Hernández, L., & Altendorf, K. B. (2001) · Canadian Journal of Zoology · doi:10.1139/z01-094 · Cited by 1,216
Sappington, J. M., Longshore, K. M., & Thompson, D. B. (2007) · Journal of Wildlife Management · doi:10.2193/2005-723 · Cited by 913
Effects of Predator Hunting Mode on Grassland Ecosystem Function
Schmitz, O. J. (2008) · Science · doi:10.1126/science.1152355 · Cited by 583
Lidar: shedding new light on habitat characterization and modeling
Vierling, K. T., Vierling, L. A., Gould, W. A., Martinuzzi, S., & Clawges, R. M. (2008) · Frontiers in Ecology and the Environment · doi:10.1890/070001 · Cited by 527
Recolonizing Carnivores and Naïve Prey: Conservation Lessons from Pleistocene Extinctions
Berger, J., Swenson, J. E., & Persson, I.-L. (2001) · Science · doi:10.1126/science.1056466 · Cited by 482
Research works printed but not linked: 2
- Arnemo J. M., 2012, Biomedical protocols for free‐ranging brown bears, wolves, wolverines and lynx
- Brøseth H., 2012, Number of family groups, population estimate and population development of lynx in Norway for 2012, 23
Cited by
Sorted by citations
Advances in animal ecology from 3D-LiDAR ecosystem mapping
Davies, A. B., & Asner, G. P. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.10.005 · Cited by 379
Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences
Eitel, J. U. H., Höfle, B., Vierling, L. A., Abellán, A., Asner, G. P., Deems, J. S., Glennie, C., Joerg, P. C., LeWinter, A. L., Magney, T. S., et al. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.08.018 · Cited by 322
Diel predator activity drives a dynamic landscape of fear
Kohl, M. T., Stahler, D. R., Metz, M. C., Forester, J. D., Kauffman, M. J., Varley, N., White, P. J. C., Smith, D. W., & MacNulty, D. R. (2018) · Ecological Monographs · doi:10.1002/ecm.1313 · Cited by 315
Paws without claws? Ecological effects of large carnivores in anthropogenic landscapes
Kuijper, D. P. J., Sahlén, E., Elmhagen, B., Chamaillé‐Jammes, S., Sand, H., Lone, K., & Cromsigt, J. P. G. M. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2016.1625 · Cited by 295
Self-citationFeral Cats Are Better Killers in Open Habitats, Revealed by Animal-Borne Video
McGregor, H. W., Legge, S., Jones, M. E., & Johnson, C. N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133915 · Cited by 244
Metadata
- Authors
- Lone, K., Loe, L. E., Gobakken, T., Linnell, J. D. C., Odden, J., Remmen, J., & Mysterud, A.
- Affiliations
- Norwegian University of Life Sciences (NO)
- Venue
- Oikos
- Publisher
- Wiley
- Published
- 2014-01-14
- Volume
- 123
- Issue
- 6
- Pages
- 641-651
- ISSN
- 0030-1299 (ISSN-L)
- 1600-0706
- OpenAlex
- W2100886239
- Semantic Scholar
- 012248c6a1c96678549bb3d35a3de2eb536f178a
- Cited by
- 227
- References
- 48
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 34 · 2023: 12 · 2022: 29 · 2021: 21 · 2020: 21 · 2019: 18 · 2018: 16 · 2017: 21 · 2016: 18 · 2015: 14 · 2014: 7
- FWCI
- 17.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Oikos (publishedVersion)
- Funders
- Norges Forskningsråd
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Environmental Science › Ecology › Animal Ecology and Behavior Studies
- Physical Sciences › Environmental Science › Ecology › Wildlife-Road Interactions and Conservation
- Keywords
- roe deer, lynx, human hunting, predation risk, landscape of fear, antipredator behavior, habitat cover, terrain ruggedness, boreal forest, predator-prey interactions
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