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

English version

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
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0217809

Souhrn

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

Zdroje

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

Souhrn

Klíčová slova:

Zdroje

PLOS One

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