From wildlife management to biodiversity assessment Using camera trap by-catch data to infer species richness
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Abstract
Camera traps are widely used in wildlife management to monitor focal species such as ungulates. However, the large amount of data on non-target species is rarely analyzed systematically. In this study, we examine whether management-oriented camera trap surveys can be used to infer broader biodiversity patterns. Using camera trap data from four study sites, we recorded 57 species, including 25 mammals and 32 birds. Species richness varied between sites but this was not primarily driven by total sampling effort. Despite substantially greater effort invested, a long-term opportunistic survey detected only slightly more species than a short-term survey using systematic random placement, consistent with spatial coverage, camera density and area size jointly influencing species detection rather than sampling duration alone. We analyzed species accumulation using Michaelis–Menten models fitted to taxa that could be reliably detected by camera traps. Systematic random surveys reached species saturation more rapidly and exhibited faster accumulation than opportunistic designs. The proportion of mammal species listed under the EU Habitats Directive remained consistent across sites, suggesting that camera traps can effectively detect species of conservation significance, even when deployed for management purposes. Late detections of rare or hard-to-detect species disrupted saturation at one site, illustrating the sensitivity of accumulation models to detectability. Overall, our results suggest that camera trap surveys targeting specific species can provide valuable additional information on biodiversity, and that by-catch data should be systematically integrated into wildlife monitoring programs. Systematic random designs may offer efficiency advantages for species inventories, although this comparison is observational and confounded by differences in area size and camera density.
Keywords
Camera traps, EU habitats directive, Michaelis-Menten equation, species accumulation rate
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References
[1] H. M. Pereira et al., “Essential biodiversity variables,” Science, vol. 339, no. 6117, pp. 277–278, 2013, doi: https://doi.org/10.1126/science.1229931
[2] D. B. Lindenmayer and G. E. Likens, “The science and application of ecological monitoring,” Biological Conservation, vol. 143, no. 6, pp. 1317–1328, 2010, doi: https://doi.org/10.1016/j.biocon.2010.02.013
[3] IPBES, “Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services,” E. Brondizio, S. Diaz, J. Settele, and H. T. Ngo, Eds., 2019, doi: https://doi.org/10.5281/zenodo.3831673
[4] D. E. Wilson, Measuring and Monitoring Biological Diversity. Washington: Smithsonian Institution Press, 1996.
[5] D. I. MacKenzie, J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm, “Estimating site occupancy rates when detection probabilities are less than one,” Ecology, vol. 83, no. 8, pp. 2248–2255, 2002, doi: 10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2
[6] P. Taberlet, E. Coissac, M. Hajibabaei, and L. H. Rieseberg, “Environmental DNA,” Molecular Ecology, vol. 21, no. 8, pp. 1789–1793, 2012, doi: https://doi.org/10.1111/j.1365-294X.2012.05542.x
[7] R. Gibb, E. Browning, P. Glover‐Kapfer, and K. E. Jones, “Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring,” Methods in Ecology and Evolution, vol. 10, no. 2, pp. 169–185, 2019, doi: https://doi.org/10.1111/2041-210X.13101
[8] A. F. O’Connell, J. D. Nichols, and K. U. Karanth, “Camera traps in animal ecology,” Tokyo: Springer Japan, 2011, doi: https://doi.org/10.1007/978-4-431-99495-4
[9] A. C. Burton et al., “REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes,” Journal of Applied Ecology, vol. 52, no. 3, pp. 675–685, 2015, doi: https://doi.org/10.1111/1365-2664.12432
[10] T. G. O’Brien, J. E. M. Baillie, L. Krueger, and M. Cuke, “The Wildlife Picture Index: monitoring top trophic levels,” Animal Conservation, vol. 13, no. 4, pp. 335–343, 2010, doi: https://doi.org/10.1111/j.1469-1795.2010.00357.x
[11] T. R. Hofmeester, J. P. G. M. Cromsigt, J. Odden, H. Andrén, J. Kindberg, and J. D. C. Linnell, “Framing pictures: A conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi-species comparison,” Ecology and Evolution, vol. 9, no. 4, pp. 2320–2336, 2019, doi: https://doi.org/10.1002/ece3.4878
[12] F. Iannarilli, J. Erb, T. W. Arnold, and J. R. Fieberg, “Evaluating species-specific responses to camera-trap survey designs,” Wildlife Biology, vol. 2021, no. 1, 2021, doi: https://doi.org/10.2981/wlb.00726
[13] A. E. Magurran, “Measuring biological diversity,” Malden, MA: Blackwell Pub., 2004.
[14] L. Michaelis and M. L. Menten, „Die Kinetik der Invertinwirkung,“ Biochem. Z., vol. 49, pp. 333–369, 1913.
[15] K. Barreiro, L. Benestan, C. Moritz, S. Ducatez, J.-C. Gaertner, J. Le Luyer, and C. J. Monaco, “Species richness variation in marine and terrestrial fauna across widespread, fragmented territories: Assessing inherent challenges of data scarcity at local and regional scales,” Scientific Reports, vol. 15, no. 1, p. 21043, 2025, doi: https://doi.org/10.1038/s41598-025-06631-4
[16] Y. Zou et al., “Fragmentation increased in over half of global forests from 2000 to 2020,” Science, vol. 389, no. 6765, pp. 1151–1156, 2025, doi: https://doi.org/10.1126/science.adr6450
[17] X. Si, R. Kays, and P. Ding, “How long is enough to detect terrestrial animals? Estimating the minimum trapping effort on camera traps,” PeerJ, vol. 2, e374, 2014, doi: https://doi.org/10.7717/peerj.374
[18] K. M. O’Connor, L. R. Nathan, M. R. Liberati, M. W. Tingley, J. C. Vokoun, and T. A. G. Rittenhouse, “Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset,” PLOS ONE, vol. 12, no. 4, e0175684, 2017, doi: https://doi.org/10.1371/journal.pone.0175684
[19] A. Caravaggi et al., “A review of camera trapping for conservation behaviour research,” Remote Sensing in Ecology and Conservation, vol. 3, no. 3, pp. 109–122, 2017, doi: https://doi.org/10.1002/rse2.48
[20] F. Rovero and R. Kays, “Camera trapping for conservation,” in Conservation Technology, S. A. Wich and A. K. Piel, Eds. Oxford, U.K., and New York, NY: Oxford University Press, 2021, pp. 79–104, doi: https://doi.org/10.1093/oso/9780198850243.003.0005
[21] K. S. Williams et al., “Utilizing bycatch camera-trap data for broad-scale occupancy and conservation: a case study of the brown hyaena Parahyaena brunnea,” Oryx, vol. 55, no. 2, pp. 216–226, 2021, doi: https://doi.org/10.1017/S0030605319000747
[22] L. Scotson, G. Fredriksson, D. Ngoprasert, W.-M. Wong, and J. Fieberg, “Projecting range-wide sun bear population trends using tree cover and camera-trap bycatch data,” PLOS ONE, vol. 12, no. 9, e0185336, 2017, doi: https://doi.org/10.1371/journal.pone.0185336
[23] W. Kilian, F. Müller, and F. Starlinger, Die Forstlichen Wuchsgebiete Österreichs – Eine Naturraumgliederung nach waldökologischen Gesichtspunkten, 1994. [Online]. Available: https://bfw.ac.at/300/pdf/1027.pdf
[24] J. M. Rowcliffe, J. Field, S. T. Turvey, and C. Carbone, “Estimating animal density using camera traps without the need for individual recognition,” Journal of Applied Ecology, vol. 45, no. 4, pp. 1228–1236, 2008, doi: https://doi.org/10.1111/j.1365-2664.2008.01473.x
[25] S. Beery, D. Morris, and S. Yang, “Efficient pipeline for camera trap image review,” arXiv preprint arXiv:1907.06772, 2019. [Online]. Available: https://arxiv.org/pdf/1907.06772
[26] S. Greenberg, “Timelapse: An image analyzer for camera traps,” 2019. [Online]. Available: https://timelapse.ucalgary.ca/
[27] H. Hendry and C. Mann, “Camelot – intuitive software for camera trap data management,” bioRxiv, 2017, doi: https://doi.org/10.1101/203216
[28] Wageningen University, “Agouti.” [Online]. Available: https://agouti.eu/
[29] T. R. Hofmeester et al., “Using by‐catch data from wildlife surveys to quantify climatic parameters and timing of phenology for plants and animals using camera traps,” Remote Sensing in Ecology and Conservation, vol. 6, no. 2, pp. 129–140, 2020, doi: https://doi.org/10.1002/rse2.136
[30] S. Edwards, S. Cooper, K. Uiseb, M. Hayward, B. Wachter, and J. Melzheimer, “Making the most of by-catch data: Assessing the feasibility of utilising non-target camera trap data for occupancy modelling of a large felid,” Afr. J. Ecol., vol. 56, no. 4, pp. 885–894, 2018, doi: https://doi.org/10.1111/aje.12511
[31] M. V. Mazzamuto, M. Lo Valvo, and S. Anile, “The value of by-catch data: how species-specific surveys can serve non-target species,” Eur. J. Wildl. Res., vol. 65, no. 5, Art. no. 1310, 2019, doi: https://doi.org/10.1007/s10344-019-1310-6
[32] J. D. C. Linnell, V. Salvatori, and L. Boitani, Guidelines for Population Level Management Plans for Large Carnivores in Europe: Final Draft, May 2007. Large Carnivore Initiative for Europe report prepared for the European Commission, Contract 070501/2005/424162/MAR/B2, 2007.
[33] P. Kaczensky, G. Chapron, M. von Arx, D. Huber, H. Andrén, and J. D. C. Linnell, Status, Management and Distribution of Large Carnivores—Bear, Lynx, Wolf & Wolverine—in Europe. Part 2: Species Country Reports. Large Carnivore Initiative for Europe report prepared for the European Commission, pp. 1–72, 2013.
[34] A. Soyumert, “Camera-trapping two felid species: Monitoring Eurasian lynx (Lynx lynx) and wildcat (Felis silvestris) populations in mixed temperate forest ecosystems,” Mammal Study, vol. 45, no. 1, p. 41, 2020, doi: https://doi.org/10.3106/ms2019-0046
[35] P. Hájková, B. Zemanová, K. Roche, and B. Hájek, “An evaluation of field and noninvasive genetic methods for estimating Eurasian otter population size,” Conservation Genetics, vol. 10, no. 6, pp. 1667–1681, 2009, doi: https://doi.org/10.1007/s10592-008-9745-4
[36] E. Fairfax and E. E. Small, “Using remote sensing to assess the impact of beaver damming on riparian evapotranspiration in an arid landscape,” Ecohydrology, vol. 11, no. 7, e1993, 2018, doi: https://doi.org/10.1002/eco.1993
[37] P. J. Otte, T. R. Hofmeester, J. Dekker, B. Jonge Poerink, and C. Smit, “Optimizing small mustelid monitoring: Enclosed camera traps increase detection of the smallest carnivores,” Ecological Solutions and Evidence, vol. 6, no. 4, e70120, 2025, doi: https://doi.org/10.1002/2688-8319.70120
[38] P. Palencia, J. Vicente, R. C. Soriguer, and P. Acevedo, “Towards a best‐practices guide for camera trapping: assessing differences among camera trap models and settings under field conditions,” Journal of Zoology, vol. 316, no. 3, pp. 197–208, 2022, doi: https://doi.org/10.1111/jzo.12945
[39] M. Beukes, T. Perry, D. M. Parker, and N. Mgqatsa, “Refining Camera Trap Surveys for Mammal Detection and Diversity Assessment in the Baviaanskloof Catchment, South Africa,” Wild, vol. 2, no. 2, p. 15, 2025, doi: https://doi.org/10.3390/wild2020015
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