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Detecting small and cryptic animals by combining thermography and a wildlife detection dog

  • 1.

    Cuthill, I. C. Camouflage. J. Zool. 308, 75–92 (2019).

    • Article
    • Google Scholar
  • 2.

    Merilaita, S. & Stevens, M. Crypsis through Background Matching. [Stevens, M. & Merilaita, S. (ed.)] Animal Camouflage: Mechanisms and Function. 17–33. (Cambridge University Press, 2011).

  • 3.

    Booth, C. L. Evolutionary significance of ontogenetic colour change in animals. Biol. J. Linn. Soc. 40, 125–163 (1990).

    • Article
    • Google Scholar
  • 4.

    Vine, S. J. et al. Comparison of methods to detect rare and cryptic species: a case study using the red fox (Vulpes vulpes). Wildlife Res. 36, 436–446 (2009).

    • Article
    • Google Scholar
  • 5.

    Gu, W. & Swihart, R. K. Absent or undetected? Effects of non-detection of species occurrence on wildlife–habitat models. Biol. Conserv. 116, 195–203 (2004).

    • Article
    • Google Scholar
  • 6.

    Sousa-Silvaa, R., Alvesa, P., Honradoab, J. & Lombaac, A. Improving the assessment and reporting on rare and endangered species through species distribution models. Global Ecol. Conserv. 2, 226–237 (2014).

    • Article
    • Google Scholar
  • 7.

    Klare, U., Kamler, J. F. & Macdonald, D. W. A comparison and critique of different scat‐analysis methods for determining carnivore diet. Mammal Rev. 41, 294–312 (2011).

    • Article
    • Google Scholar
  • 8.

    Trolliet, F., Huynen, M., Vermeulen, C. & Hambuckers, A. Use of camera traps for wildlife studies. A review. Biotechnol. Agron. Soc. 18, 446–454 (2014).

    • Google Scholar
  • 9.

    Barja, I., Navarro-Castilla, A. & Pérez, L. Effectiveness and Applications of Hair Traps for the Study of Wild mammal populations. Pol. J. Ecol. 64, 409–419, https://doi.org/10.3161/15052249PJE2016.64.3.010 (2016).

    • Article
    • Google Scholar
  • 10.

    Mitchell, W. F. & Clarke, R. H. Using infrared thermography to detect night-roosting birds. J. Field Ornithol. 90, 39–51 (2019).

    • Article
    • Google Scholar
  • 11.

    Austin, V. I., Ribot, R. F. H. & Bennett, A. T. D. If waterbirds are nocturnal are we conserving the right habitats? Emu. 116, 423–427 (2016).

    • Article
    • Google Scholar
  • 12.

    Havens, K. J. & Sharp, E. J. Thermal imaging techniques to survey and monitor animals in the wild, a methodology (Elsevier 2016).

  • 13.

    Croon, G. W., McCullough, D. R., Olson, C. E. & Queal, L. M. Infrared scanning techniques for big game censusing. J. Wildlife Manage. 32, 751–759 (1968).

    • Article
    • Google Scholar
  • 14.

    Ditchkoff, S. S., Raglin, J. B., Smith, J. M. & Collier, B. A. From the Field: capture of white-tailed deer fawns using thermal imaging technology. Wildl. Soc. Bull. 33, 1164–1168 (2005).

    • Article
    • Google Scholar
  • 15.

    Butler, D. A., Ballard, W. B., Haskell, S. P. & Wallace, M. C. Limitations of thermal infrared imaging for locating neonatal deer in semiarid shrub communities. Wildl. Soc. Bull. 34, 1458–1462 (2006).

    • Article
    • Google Scholar
  • 16.

    Focardi, S., De Marinis, A. M., Rizzotto, M. & Pucci, A. Comparative evaluation of thermal infrared imaging and spotlighting to survey wildlife. Wildl. Soc. Bull. 29, 133–139 (2001).

    • Google Scholar
  • 17.

    Nottingham, C. M., Glen, A. S. & Stanley, M. C. Snacks in the city: the diet of hedgehogs in Auckland urban forest fragments. New Zea. J. Ecol. 43, 3374 (2019).

    • Google Scholar
  • 18.

    Mattsson, B. J. & Niemi, G. J. Using thermal imaging to study forest songbirds. The Loon 78, 74–77 (2006).

    • Google Scholar
  • 19.

    Wasser, S. K. et al. Scat detection dogs in wildlife research and management: application to grizzly and black bears in the yellowhead ecosystem, alberta, canada. Can. J. Zool. 82, 475–492 (2004).

    • Article
    • Google Scholar
  • 20.

    Wasser, S. K. et al. Using detection dogs to conduct simultaneous surveys of northern spotted (strix occidentalis caurina) and barred owls (strix varia). Plos One. 7, 1–8 (2012).

  • 21.

    Cristescu, R. H. et al. Accuracy and Efficiency of Detection Dogs: A Powerful New Tool for Koala Conservation and Management. Scientific Reports 5, 1–6 (2015).

  • 22.

    Cablk, M. E. & Heaton, J. S. Accuracy and reliability of dogs in surveying for desert tortoise (gopherus agassizii). Ecol. Appl. 16, 1926–1935 (2006).

  • 23.

    Nielsen, T. P., Jackson, G. & Bull, C. M. A nose for lizards; can a detection dog locate the endangered pygmy bluetongue lizard (Tiliqua adelaidensis)? T. Roy. Soc. South Aust. 140, 234–243 (2016).

    • Google Scholar
  • 24.

    Robertson, H. A. & Fraser, J. R. Use of trained dogs to determine the age structure and conservation status of kiwi Apteryx spp. populations. Bird Conserv. Int. 19, 121-129 (2009).

  • 25.

    Duggan, J. M., Heske, E. J., Schooley, R. L., Hurt, A. & Whitelaw, A. Comparing detection dog and livetrapping surveys for a cryptic rodent. J. Wildl. Manage. 75, 1209–1217 (2011).

    • Article
    • Google Scholar
  • 26.

    Bray, Y., Devillard, S., Marboutin, E., Mauvy, B. & Péroux, R. Natal dispersal of European hare in France. J. Zool. 273, 426–434 (2007).

    • Article
    • Google Scholar
  • 27.

    Broekhuizen, S. & Maaskamp, F. Behavior of does and leverets of the european hare (lepus europaeus) whilst nursing. J. Zool. 191, 487–501 (1980).

    • Article
    • Google Scholar
  • 28.

    Marboutin, E., Bray, Y., Peroux, R., Mauvy, B. & Lartiges, A. Population dynamics in european hare: breeding parameters and sustainable harvest rates. J. Appl. Ecol. 40, 580–591 (2003).

    • Article
    • Google Scholar
  • 29.

    Olesen, C. R. & Asferg, T. Assessing potential causes for the population decline of european brown hare in the agricultural landscape of europe – a review of the current knowledge. National Environmental Research Institute, Technical report No. 600, Ministry of the Environment, Copenhagen, Denmark (2006).

  • 30.

    Zellweger-Fischer, J. Schweizer feldhasenmonitoring 2015. Schweizerische Vogelwarte, Sempach, Switzerland (2015).

  • 31.

    Bray, Y., Champely, S. & Soyez, D. Age determination in leverets of european hare lepus europaeus based on body measurements. Wildl. Biol. 8, 31–39 (2002).

    • Article
    • Google Scholar
  • 32.

    R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Version 3.3.2, http://www.R-project.org/ (2013).

  • 33.

    Bates, D., Maechler, M. B., Bolker, S. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48 (2015).

    • Article
    • Google Scholar
  • 34.

    Lazarevic, L. Improving the efficiency and accuracy of nocturnal bird surveys through equipment selection and partial automation. Dissertation, Brunel University (2010).

  • 35.

    Voigt, U. Zur Raumnutzung und Mortalitätsursachen bei Junghasen (Lepus europaeus). [Lang, J., Godt, J. & Rosenthal G. (eds.)] Ergebnisse der “Fachtagung Feldhase – der aktuelle Stand der Hasenforschung” Kassel, Germany. 83–92 (Tauer: lutra – Verlags- und Vertriebsgesellschaft, 2010).

  • 36.

    Conover, M. R. Predator–Prey Dynamics – The Role of Olfaction. (CRC Press, 2007).

  • 37.

    Murphy, E. C., Russell, J. C., Broome, K. G., Ryan, G. J. & Dowding, J. E. Conserving New Zealand’s native fauna: a review of tools being developed for the Predator Free 2050 programme. J. Ornithol. 160, 883–892 (2019).

    • Article
    • Google Scholar
  • 38.

    Hui-Min, Lin. et al. Fire Ant-Detecting Canines: A Complementary Method in Detecting Red Imported Fire Ants. Journal of Economic Entomology 104(1), 225–231 (2011).

  • 39.

    Boonstra, R., Krebs, C. J., Boutin, S. & Eadie, J. M. Finding mammals using far-infrared thermal imaging. J. Mamm. 75, 1063–1068 (1994).

    • Article
    • Google Scholar
  • 40.

    Galligan, E. W., Bakken, G. S. & Lima, S. L. Using a thermographic imager to find nests of grassland birds. Wildl. Soc. Bull. 31, 865–869 (2003).

    • Google Scholar
  • 41.

    McCafferty, D. J. The value of infrared thermography for research on mammals: previous applications and future directions. Mammal Rev. 37, 207–223 (2007).

    • Article
    • Google Scholar
  • 42.

    Cilulko, J., Janiszewski, P., Bogdaszewski, M. & Szczygielska, E. Infrared thermal imaging in studies of wild animals. Eur. J. Wildlife Res. 59, 17–23 (2013).

    • Article
    • Google Scholar
  • 43.

    Tosini, G. & Avery, R. Intraspecific variation in lizard thermoregulatory set points: A thermographic study in. Podarcis muralis. J. Therm. Biol. 18, 19–23 (1993).

    • Article
    • Google Scholar
  • 44.

    Tattersall, G. J. & Cadena, V. Insights into animal temperature adaptations revealed through thermal imaging. Imaging Sci. J. 58, 261–268 (2010).

    • Article
    • Google Scholar
  • 45.

    Allison, N. L. & Destefano, S. Equipment and Techniques for Nocturnal Wildlife. Studies. Wildlife Soc. B. 34, 1036–1044 (2006).

    • Article
    • Google Scholar
  • 46.

    Burke, C. et al. Optimizing observing strategies for monitoring animals using drone-mounted thermal infrared cameras. Int. J. Remote Sens. 40, 439–467 (2019).

  • 47.

    Karp, D. Preweaning Behaviour and Mortality in Wild Brown Hare Leverets (Lepus europaeus), PhD thesis, University of Zurich (2019).

  • 48.

    Linchant, J., Lisein, J., Semeki, J., Lejeune, P. & Vermeulen, C. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Rev. 45, 239–252 (2015).

    • Article
    • Google Scholar
  • 49.

    Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M. & Hanson, L. Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology. Front. Ecol. Environ. 14, 241–251 (2016).

    • Article
    • Google Scholar
  • 50.

    Chabot, D. & Bird, D. M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in? J. Unmanned Vehicle Sys. 3, 137–155 (2015).

    • Article
    • Google Scholar
  • 51.

    Gonzalez, L. F. et al. Unmanned aerial vehicles (uavs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors. 16, 1–18 (2016).

    • Article
    • Google Scholar
  • 52.

    Reed, S. E., Bidlack, A. L., Hurt, A. & Getz, W. M. Detection Distance and Environmental Factors in Conservation Detection Dog Surveys. J. Wildlife Manage. 75, 243–251 (2011).

    • Article
    • Google Scholar
  • 53.

    Homan, H. J., Linz, G. M. & Peer, B. D. Dogs increase recovery of passerine carcasses in dense vegetation. Wildlife Soc. B. 29, 292–296 (2011).

    • Google Scholar
  • 54.

    Leigh, K. A. & Dominick, M. An assessment of the effects of habitat structure on the scat finding performance of a wildlife detection dog. Methods Ecol. Evol. 6, 745–752 (2015).

    • Article
    • Google Scholar
  • 55.

    Gutzwiller, K. J. Minimizing Dog-Induced Biases in Game Bird Research. Wildlife Soc. B. 18, 351–356 (1990).

    • Google Scholar
  • 56.

    Cablk, M. E., Sagebiel, J. C., Heaton, J. S. & Valentin, C. Olfaction-based Detection Distance: A Quantitative Analysis of how far away dogs recognize tortoise odor and follow it to source. Sensors-Basel. 8, 2208–2222 (2008).

  • 57.

    Jenkins, E. K., DeChant, M. T. & Perry, E. B. When the Nose Doesn’t Know: Canine Olfactory Function Associated With Health, Management, and Potential Links to Microbiota. Front. Vet. Sci. 5, 56, https://doi.org/10.3389/fvets.2018.00056 (2018).

  • 58.

    Dahlgren, D. K. et al. Use of dogs in wildlife research and management. [Silvy, N. J. (ed.)] The wildlife techniques manual. 140–153 (The John Hopkins University Press, 2012).

  • 59.

    Gabrielsen, G. W., Blix, A. S. & Ursin, H. Orienting and freezing responses in incubating ptarmigan hens. Physiol Behav. 34, 925–934 (1985).

  • 60.

    Espmark, Y. & Langvatn, R. Development and habituation of cardiac and behavioral responses in young red deer calves (Cervus elaphus) exposed to alarm stimuli. J. Mammal. 66, 702–711 (1985).

    • Article
    • Google Scholar
  • 61.

    Jacobsen, N. K. Alarm bradycardia in white-tailed deer fawns (Odocoileus virginianus). J. Mammal. 60, 343–349 (1979).

    • Article
    • Google Scholar
  • 62.

    Autenrieth, R. E. & Fichter, E. On the behaviour and socialization of pronghorn fawns. Wildl. Monogr. 42, 3–11 (1975).

    • Google Scholar
  • 63.

    French, S. P. & French, M. G. Predatory behavior of grizzly bears feeding on elk calves in yellowstone national-park. Bears: their biology and management, a selection of papers from the eighth international conference on bear research and management. Victoria, British Columbia, Canada. 335–341 (1989).

  • 64.

    Johnson, D. E. Biology of the elk calf, cervus canadensis nelsoni. J. Wildl. Manage. 15, 396–410 (1951).

    • Article
    • Google Scholar
  • 65.

    Walther, F. R. Flight behaviour and avoidance of predators in thomsons gazelle (gazella thomsoni). Behav. 34, 184–221 (1969).

    • Article
    • Google Scholar
  • 66.

    DeMatteo, K. E., Davenport, B. & Wilson, L. E. Back to the basics with conservation detection dogs: fundamentals for success. Wildlife Biol. 1, https://doi.org/10.2981/wlb.00584 (2019).

  • 67.

    Jamieson, L. T. J., Baxter, G. S. & Murray, P. J. Identifying suitable detection dogs. Appl. Anim. Behav. Sci. 195, 1–7 (2017).

    • Article
    • Google Scholar
  • 68.

    Jamieson, L. T. J., Baxter, G. S. & Murray, P. J. Who’s a Good Handler? Important Skills and Personality Profiles of Wildlife Detection Dog Handlers. Animals. 8, 222 (2018).

    • Article
    • Google Scholar
  • 69.

    Beebe, S. C., Howell, T. J. & Bennett, P. C. Using Scent Detection Dogs in Conservation Settings: A Review of Scientific Literature Regarding Their Selection. Front. Vet. Sci. 3, 96, https://doi.org/10.3389/fvets.2016.00096 (2016).


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