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Biodiversity and Conservation

, Volume 21, Issue 3, pp 729–744 | Cite as

Integrating variability in detection probabilities when designing wildlife surveys: a case study of amphibians from south-eastern Australia

  • Stefano CanessaEmail author
  • Geoffrey W. Heard
  • Kirsten M. Parris
  • Michael A. McCarthy
Original Paper

Abstract

Occupancy-based monitoring programs rely on survey data to infer presence or absence of the target species. However, species may occupy a site and go undetected, leading to erroneous inference of absence (‘false absence’). If detectability is influenced by the time of year or weather conditions, survey protocols can be adjusted to minimize the chance of false absences. In this study, detection probabilities for three amphibian species from south-eastern Australia were modelled using a Bayesian approach. For aural surveys, we compared basic models, which only included effects of survey date, duration and time of day on detection, to models including additional effects of weather. Model selection using deviance information criterion (DIC) suggested that the basic model was the most parsimonious for Crinia signifera, while models including relative humidity and water temperature were most supported for Limnodynastes dumerilii and L. tasmaniensis respectively. When predictive performance was assessed by cross validation, DIC results were largely matched for C. signifera and L. dumerilii, while models of detection for L. tasmaniensis were indistinguishable, AUC scores suggesting inadequate performance. We show how results such as these can be used to design surveys, developing protocols for individual surveys and estimating the number of surveys required under those protocols to achieve a threshold cumulative probability of detection. Conservation managers can use these models to maximize the efficiency of surveys. This will improve the accuracy of occupancy data, and reduce the risk of misdirected conservation actions resulting from false absences.

Keywords

Amphibians Aural surveys Detection probability Model selection Monitoring Occupancy Predictions Seasonality Survey planning 

Notes

Acknowledgements

Data for the 2006–2007 season were collected in collaboration with Michael Scroggie and Brian Malone. The collection of these data was made possible by funding from the Department of Sustainability of Environment, the Growling Grass Frog Trust Fund (consisting of DSE, Australian Gas Ltd. and Friends of Merri Creek) and La Trobe University. It was undertaken according to the provisions of the Victorian Wildlife Act 1975 (Research Permit No. 10001816). Data for the 2009 season were collected in collaboration with Blaire Dobiecki and a number of field assistants. Data collection was made possible by funding from the University of Melbourne and was approved from the Animal Ethics Committee of the University of Melbourne (project ID 0911299.1). Manuscript preparation was supported by an Australian Research Council Linkage Grant (LP0990161) with the Australian Research Centre for Urban Ecology, Growling Grass Frog Trust Fund, Melbourne Museum, Melbourne Water, Parks Victoria and Victorian Department of Sustainability and Environment. Helpful comments were received from the editor and two anonymous referees.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Stefano Canessa
    • 1
    Email author
  • Geoffrey W. Heard
    • 1
  • Kirsten M. Parris
    • 1
  • Michael A. McCarthy
    • 1
  1. 1.School of BotanyUniversity of MelbourneVICAustralia

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