Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species

Autoři: Catherine Leigh aff001;  Grace Heron aff001;  Ella Wilson aff001;  Taylor Gregory aff001;  Samuel Clifford aff004;  Jacinta Holloway aff001;  Miles McBain aff001;  Felipé Gonzalez aff005;  James McGree aff001;  Ross Brown aff001;  Kerrie Mengersen aff001;  Erin E. Peterson aff001
Působiště autorů: ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Australia aff001;  Institute for Future Environments, Queensland University of Technology, Brisbane, Australia aff002;  School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology. Brisbane, Australia aff003;  London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom aff004;  School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology. Brisbane, Australia aff005;  ARC Centre of Excellence for Robotic Vision (ACRV), Australia aff006;  School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology. Brisbane, Australia aff007
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0217809


Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species.

Klíčová slova:

Conservation science – Cryptic speciation – Fresh water – Latitude – Recombinase polymerase amplification – Rivers – Species delimitation – Virtual reality


1. Tilman D, Clark M, Williams DR, Kimmel K, Polasky S, Packer C. Future threats to biodiversity and pathways to their prevention. Nature. 2017;546: 73. doi: 10.1038/nature22900 28569796

2. Díaz S, Settele J, Brondízio E, Ngo HT, Guèze M, Agard J, et al. Summary for policymakers of the global assessment report on biodiversity and ecosystem services–unedited advance version. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. 2019. Available from:

3. Ondei S, Brook BW, Buettel JC. Nature’s untold stories: an overview on the availability and type of on-line data on long-term biodiversity monitoring. Biodivers Conserv. 2018;27: 2971–87.

4. Adams‐Hosking C, McBride MF, Baxter G, Burgman M, De Villiers D, Kavanagh R, et al. Use of expert knowledge to elicit population trends for the koala (Phascolarctos cinereus). Divers Distrib. 2016;22:249–62.

5. Thompson W, editor. Sampling rare or elusive species: Concepts, designs, and techniques for estimating population parameters. Island Press; 2004.

6. Nekaris KA, Blackham GV, Nijman V. Conservation implications of low encounter rates of five nocturnal primate species (Nycticebus spp.) in Asia. Biodivers Conserv. 2008;17: 733–747.

7. Sequeira AM, Roetman PE, Daniels CB, Baker AK, Bradshaw CJ. Distribution models for koalas in South Australia using citizen science‐collected data. Ecol Evol. 2014;4:2103–14. doi: 10.1002/ece3.1094 25360252

8. Theobald EJ, Ettinger AK., Burgess HK, DeBey LB, Schmidt NR, Froehlich HE, et al. Global change and local solutions: Tapping the unrealized potential of citizen science for biodiversity research. Biol Conserv. 2012;181: 236–244.

9. Bird TJ, Bates AE, Lefcheck JS, Hill NA, Thomson RJ, Edgar GJ, et al. Statistical solutions for error and bias in global citizen science datasets. Biol Conserv. 2014; 173, 144–154.

10. Brown G, Rhodes J, Lunney D, Goldingay R, Fielding K, Garofano N, et al. The influence of sampling design on spatial data quality in a geographic citizen science project. T GIS. 2019; doi: 10.1111/tgis.12568

11. Law BS, Brassil T, Gonsalves L, Roe P, Truskinger A, McConville A. Passive acoustics and sound recognition provide new insights on status and resilience of an iconic endangered marsupial (koala Phascolarctos cinereus) to timber harvesting. PloS ONE. 2018;13:e0205075. doi: 10.1371/journal.pone.0205075 30379836

12. Gonzalez LF, Montes GA, Puig E, Johnson S, Mengersen K, Gaston KJ. Unmanned Aerial Vehicles (RPASs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors. 2016;16: 97.

13. Seymour AC, Dale J, Hamill M, Halpin PN, Johnston DW. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery. Sci Rep. 2017; 45127. doi: 10.1038/srep45127 28338047

14. Vercelloni J, Clifford S, Caley MJ, Pearse AR, Brown R, James A, et al. Using virtual reality to estimate aesthetic values of coral reefs. Roy Soc Open Sci. 2018;5: 172226.

15. Qu C, Brinkman WP, Wiggers P, Heynderickx I. The effect of priming pictures and videos on a question–answer dialog scenario in a virtual environment. Presence 2013;22: 91–109.

16. Harman J, Brown R, Johnson D. Improved memory elicitation in virtual reality: new experimental results and insights. In IFIP Conference on Human-Computer Interaction 2017 (pp. 128–146). Springer, Cham.

17. Brown R, Bruza P, Heard W, Mengersen K, Murray J. On the (virtual) getting of wisdom: Immersive 3D interfaces for eliciting spatial information from experts. Spatial Stat. 2016;18: 318–31.

18. Albert I, Donnet S, Guihenneuc-Jouyaux C, Low-Choy S, Mengersen K, Rousseau J, et al. Combining expert opinions in prior elicitation. Bayesian Anal. 2012;7: 503–532.

19. Carr NL, Rodgers AR, Kingston SR, Hettinga PN, Thompson LM, Renton JL, et al. Comparative woodland caribou population surveys in Slate Islands Provincial Park, Ontario. Rangifer. 2012;32: 205–17.

20. Woosnam-Merchez O, Cristescu R, Dique D, Ellis B, Beeton R, Simmonds J, et al. What faecal pellet surveys can and can't reveal about the ecology of koalas Phascolarctos cinereus. Aust Zool. 2012;36: 192–200.

21. Department of the Environment. EPBC Act Referral Guidelines for the Vulnerable Koala (Combined Populations of Queensland, New South Wales and the Australian Capital Territory). Commonwealth of Australia. 2014. Available from:

22. McAlpine C, Lunney D, Melzer A, Menkhorst P, Phillips S, Phalen D, et al. Conserving koalas: a review of the contrasting regional trends, outlooks and policy challenges. Biol Conserv. 2015;192: 226–236.

23. Gonzalez F, Johnson S. Standard operating procedures for UAV or drone based monitoring of wildlife. Proceedings of UAS4RS 2017 (Unmanned Aircraft Systems for Remote Sensing). University of Tasmania, Hobart, Australia. 2017.

24. Logan City Council. Logan City Council Flora and Fauna Species Sightings. 2017. Available from:

25. Dique DS, de Villiers DL, Preece HJ. Evaluation of line-transect sampling for estimating koala abundance in the Pine Rivers Shire, south-east Queensland. Wildlife Res. 2013;30: 127–133.

26. Cristescu RH, Scales KL, Schultz AJ, Miller RL, Schoeman DS, Dique D, et al. Environmental impact assessments can misrepresent species distributions: A case study of koalas in Queensland, Australia. Animal Conserv. 2018; doi: 10.1111/acv.12455

27. Specht RL. Vegetation. In: Leeper GW, editor. The Australian environment. 4th ed. Melbourne: CSIRO-Melbourne University Press. 1970. pp. 44–67.

28. McAlpine CA, Rhodes JR, Callaghan JG, Bowen ME, Lunney D, Mitchell DL, et al. The importance of forest area and configuration relative to local habitat factors for conserving forest mammals: A case study of koala in Queensland, Australia. Biol Conserv. 2006;132: 153–165.

29. Law B, Caccamo G, Roe P, Truskinger A, Brassil T, Gonsalves L, et al. Development and field validation of a regional, management‐scale habitat model: a koala Phascolarctos cinereus case study. Ecol Evol. 2017;7:7475–7489. doi: 10.1002/ece3.3300 28944032

30. Department of Environment and Science. Wooded Extent and Foliage Projective Cover–Queensland 2013. State of Queensland (Department of Environment and Science). 2018b. Available from:

31. Callaghan J, McAlpine C, Mitchell D, Thompson J, Bowen M, Rhodes J, et al. Ranking and mapping koala habitat quality for conservation planning on the basis of indirect evidence of tree-species use: a case study of Noosa Shire, south-eastern Queensland. Wildlife Res. 2011;38:89–102.

32. Department of Environment and Science. Remnant 2015 Broad Vegetation Groups—Queensland. State of Queensland (Department of Environment and Science). 2018. Available from:

33. Neldner VJ, Niehus RE, Wilson BA, McDonald WJF, Ford AJ, Accad A. The Vegetation of Queensland. Descriptions of Broad Vegetation Groups. Version 4.0. Queensland Herbarium, Department of Environment and Science. 2019.

34. Department of Natural Resources and Mines. Waterways. 2017. Available from:

35. Logan City Council. Logan City Council Footpath Network. 2017. Available from:

36. R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from:

37. Pebesma EJ, Bivand RS. Classes and methods for spatial data in R. R News. 2005;5: 9–13.

38. Bivand RS, Pebesma E, Gómez-Rubio V. Applied spatial data analysis with R. Second edition. New York: Springer; 2013.

39. Hijmans RJ. geosphere: Spherical Trigonometry. R package version 1.5–7. 2017. Available from:

40. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K. cluster: Cluster Analysis Basics and Extensions. R package version 2.0.7–1. 2018. Available from:

41. Low-Choy S, O'Leary R, Mengersen K. Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models. Ecology. 2009; 90: 265–277. doi: 10.1890/07-1886.1 19294931

42. O’Hagan A. Expert knowledge elicitation: subjective but scientific. Am Stat. 2019;73:69–81.

43. Low Choy S, Murray J, James A, Mengersen KL. Indirect elicitation from ecological experts: from methods and software to habitat modelling and rock-wallabies. In The Oxford Handbook of Applied Bayesian Analysis 2010 (pp. 511–544). Oxford University Press.

44. Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006;29: 129–151.

45. Ferrari S, Cribari-Neto F. Beta regression for modelling rates and proportions. J Appl Stat. 2004;31: 799–815.

46. O’Leary RA, Low-Choy S, Murray JV, Kynn M, Denham R, Martin TG, et al. Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby Petrogale penicillata. Environmetrics. 2009;20: 379–398.

47. Wood SN. Generalized additive models: An Introduction with R. 2nd edition. New York: Chapman and Hall/CRC. 2017.

48. Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10: 101–129.

49. Koricheva J, Gurevitch J, Mengersen K, editors. Handbook of meta-analysis in ecology and evolution. Princeton University Press; 2013.

50. Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv. 1997;24: 38–49.

51. Lahoz-Monfort JJ, Guillera-Arroita G, Wintle BA. Imperfect detection impacts the performance of species distribution models. Global Ecol Biogeogr Lett. 2014;23: 504–515.

52. Welsh AH, Lindenmayer DB, Donnelly CF. Fitting and interpreting occupancy models. PloS ONE. 2013;8:e52015. doi: 10.1371/journal.pone.0052015 23326323

53. Guillera‐Arroita G. Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities. Ecography. 2017;40:281–295.

54. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, et al. Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecol Appl. 2009;19: 181–97. doi: 10.1890/07-2153.1 19323182

55. Mengerson K, Peterson E, Clifford S, Ye N, Kim J, Tomasz Bednarz RB, et al. Modelling imperfect presence data obtained by citizen science. Environmetrics. 2017;28: e2446.

56. Guisan A, Tingley R, Baumgartner JB, Naujokaitis‐Lewis I, Sutcliffe PR, Tulloch AI, et al. Predicting species distributions for conservation decisions. Ecol Letters. 2013;16: 1424–1435.

57. Chen IC, Hill JK, Ohlemuller R, Roy DB, Thomas CD. Rapid range shifts of species associated with high levels of climate warming. Science. 2011;333: 1024–1026. doi: 10.1126/science.1206432 21852500

58. Baxter PWJ, Possingham HP. Optimizing search strategies for invasive pests: Learn before you leap. J Appl Ecol. 2011;48: 86–95.

59. Melzer A, Cristescu R, Ellis W, FitzGibbon S, Manno G. The habitat and diet of koalas (Phascolarctos cinereus) in Queensland. Aust Mammal. 2014;36: 89–199.

60. Pfeiffer A, Melzer A, Tucker G, Clifton D, Ellis W. Tree use by koalas (Phascolarctos cinereus) on St Bees Island, Queensland-report of a pilot study. P Roy Soc Queensland. 2005;112: 47.

61. Cristescu RH, Rhodes J, Frere C, Banks PB. Is restoring flora the same as restoring fauna? Lessons learned from koalas and mining rehabilitation. J Appl Ecol. 2013;50: 423–431.

62. Ellis WA, Melzer A, Green B, Newgrain K, Hindell MA, Carrick FN. Seasonal-variation in water flux, field metabolic-rate and food-consumption of free-ranging koalas (Phascolarctos-Cinereus). Aust J Zool. 1995;43: 59–68.

63. Seabrook L, McAlpine C, Baxter G, Rhodes J, Bradley A, Lunney D. Drought-driven change in wildlife distribution and numbers: a case study of koalas in south west Queensland. Wildlife Res. 2011;38: 509–524.

64. Lee KE, Seddon JM, Johnston S, FitzGibbon SI, Carrick F, Melzer A, et al. Genetic diversity in natural and introduced island populations of koalas in Queensland. Aust J Zool. 2013;60: 303–10.

65. Reckless HJ, Murray M, Crowther MS. A review of climatic change as a determinant of the viability of koala populations. Wildlife Res. 2018 Jan 30;44(7):458–70.

66. Briscoe NJ, Handasyde KA, Griffiths SR, Porter WP, Krockenberger A, Kearney MR. Tree-hugging koalas demonstrate a novel thermoregulatory mechanism for arboreal mammals. Biol Letters. 2014;10: 20140235.

67. Johnson CN, Balmford A, Brook BW, Buettel JC, Galetti M, Guangchun L, et al. Biodiversity losses and conservation responses in the Anthropocene. Science. 2017;356: 270–275. doi: 10.1126/science.aam9317 28428393

68. Turschwell MP, Balcombe SR, Steel EA, Sheldon F, Peterson EE. Thermal habitat restricts patterns of occurrence in multiple life-stages of a headwater fish. Freshw Sci. 2017;36: 402–414.

69. Allen AM, Singh NJ. Linking movement ecology with wildlife management and conservation. Frontiers Ecol Evol. 2016;3: 155.

70. Proença V, Martin LJ, Pereira HM, Fernandez M, McRae L, Belnap J, et al. Global biodiversity monitoring: from data sources to essential biodiversity variables. Biol Conserv. 2017;213: 256–263.

71. Corcoran E, Denman S, Hanger J, Wilson B, Hamilton G. Automated detection of koalas using low-level aerial surveillance and machine learning. Sci Rep. 2019;9: 3208. doi: 10.1038/s41598-019-39917-5 30824795

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2019 Číslo 12