Incorporating detectability of threatened species into environmental impact assessment

Conservation Biology

Volumn 29, Issue 1, 2015, Pages 216-225

  Garrard, Georgia E ^a   Bekessy, Sarah A ^a   Mccarthy, Michael A ^b   Wintle, Brendan A ^b  

^a RMIT UNIVERSITY   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Biological surveys; False absence; Pimelea spinescens; Time to detection

Indexed keywords

BIOLOGICAL SURVEY; CONSERVATION PLANNING; DETECTION METHOD; DICOTYLEDON; ENDANGERED SPECIES; ENVIRONMENTAL IMPACT ASSESSMENT; EXTINCTION RISK;

AUSTRALIA; MELBOURNE; VICTORIA [AUSTRALIA];

ANIMALIA; PIMELEA;

ENDANGERED SPECIES; ENVIRONMENT; ENVIRONMENTAL PROTECTION; GRASSLAND; PHYSIOLOGY; PLANT DISPERSAL; PROBABILITY; PROCEDURES; THEORETICAL MODEL; THYMELAEACEAE;

CONSERVATION OF NATURAL RESOURCES; ENDANGERED SPECIES; ENVIRONMENT; GRASSLAND; MODELS, THEORETICAL; PLANT DISPERSAL; PROBABILITY; THYMELAEACEAE;

EID: 84921349032     PISSN: 08888892     EISSN: 15231739     Source Type: Journal    
DOI: 10.1111/cobi.12351     Document Type: Article

References (33)

1. Bornand, C. N., M. Kéry, L. Bueche, and M. Fischer. 2014. Hide-and-seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence. Methods in Ecology and Evolution 5:433-442.
2. Carter, O., and N. Walsh. 2006. National recovery plan for the spiny rice-flower, Pimelea spinescens subspecies spinescens. Victorian Government Department of Sustainability and Environment, Melbourne.
3. Chen, G., M. Kéry, J. Zhang, and K. Ma. 2009. Factors affecting detection probability in plant distribution studies. Journal of Ecology 97:1383-1389.
4. Cox, D. R., and D. Oakes. 1984. Analysis of survival data. Chapman and Hall, London.
5. Department of Sustainability and Environment. 2005. Spiny Rice-flower, Pimelea spinescens subsp. spinescens: a nationally threatened species of the grassland community. Victorian Government Department of Sustainability and Environment, Melbourne.
6. Department of the Environment, Water, Heritage and the Arts. 2009a. Significant impact guidelines for the critically endangered spiny rice-flower (Pimelea spinescens subsp. spinescens). EPBC Act Policy Statement 3.11, Commonwealth of Australia.
7. Department of the Environment, Water, Heritage and the Arts. 2009b. Significant impact guidelines for the vulnerable growling grass frog (Litoria raniformis). EPBC Act Policy Statement 3.14, Commonwealth of Australia.
8. Doub, J. P. 2012. The Endangered species act: history, implementation, successes, and controversies. CRC Press, Boca Raton, Florida.
9. Garrard, G. 2009. Dealing with imperfect detectability in biological surveys for native grassland management, PhD thesis, RMIT University.
10. Garrard, G. E., S. A. Bekessy, M. A. McCarthy, and B. A. Wintle. 2008. When have we looked hard enough? A novel method for setting minimum survey effort protocols for flora surveys. Austral Ecology 33:986-998.
11. Garrard, G. E., M. A. McCarthy, N. S. G. Williams, S. A. Bekessy, and B. A. Wintle. 2012. A general model of detectability using species traits. Methods in Ecology and Evolution 4:45-52.
12. Gelman, A. and J. Hill. 2007. Data Analysis using regression and multilevel/hierarchical models. Cambridge University Press, New York.
13. Guillera-Arroita, G., J. J. Lahoz-Monfort, E. J. Millner-Gulland, R. P. Young, and E. Nicholson. 2010. Using occupancy as a state variable for monitoring the Critically Endangered Alaotran gentle lemur Hapalemur alaotrensis. Endangered Species Research 11:157-166.
14. Hauser, C. E., and M. A. McCarthy. 2009. Streamlining "search and destroy": cost-effective surveillance for invasive species management. Ecology Letters 12:683-692.
15. Joseph, L. N., C. Elkin, T. G. Martin, and H. P. Possingham. 2009. Modeling abundance using N -mixture models: the importance of considering ecological mechanisms. Ecological Applications 19:631-642.
16. Kery, M. 2002. Inferring the absence of a species-a case study of snakes. Journal of Wildlife Management 66:330-338.
17. Kéry, M., and K. B. Gregg. 2003. Effects of life-state on detectability in a demographic study of the terrestrial orchid Cleistes bifaria. Journal of Ecology 91:265-273.
18. Lunn, D., D. Spiegelhalter, A. Thomas, and N. Best. 2009. The BUGS project: evolution, critique and future directions. Statistics in Medicine 28:3049-3067.
19. MacKenzie, D. I., J. D. Nichols, N. Sutton, K. Kawanishi, and L. L. Bailey. 2005. Improving inferences in population studies of rare species that are detected imperfectly. Ecology 86:1101-1113.
20. MacKenzie, D., J. Nichols, and G. Lachman. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248-2255.
21. McCarthy, M. A. 2007. Bayesian methods for ecology, Cambridge University Press, Cambridge.
22. McLean, I. F. G., A. D. Wight, and G. Williams. 1999. The role of legislation in conserving Europe's threatened species. Conservation Biology 13:966-969.
23. Pollock, K. H., J. D. Nichols, T. R. Simons, G. L. Farnsworth, L. L. Bailey, and J. R. Sauer. 2002. Large scale wildlife monitoring studies: statistical methods for design and analysis. Environmetrics 13:105-119.
24. Royle, J. A. 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108-115.
25. Slade, N. A., H. M. Alexander, and W. Dean Kettle. 2003. Estimation of population size and probabilities of survival and detection in Mead's milkweed. Ecology 84:791-797.
26. Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van der Linde. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64:583-639.
27. Tyre, A. J., B. Tenhumberg, S. A. Field, D. Niejalke, K. Parris, and H. P. Possingham. 2003. Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecological Applications 13:1790-1801.
28. US Fish and Wildlife Service. 1997. Coastal California Gnatcatcher (Polioptila californica californica) presence/absence survey guidelines. US Fish and Wildlife Service.
29. US Fish and Wildlife Service. 2009. General Ecology And Survey Protocol For Determining Presence/Absence And Abundance For The Desert Tortoise - Mojave Population, in Desert Tortoise Field Manual, US Fish and Wildlife Service.
30. Wenger, S. J., and M. C. Freeman. 2008. Estimating species occurrence, abundance and detection probability using zero-inflated distributions. Ecology 89:2953-2959.
31. Wintle, B. A., M. A. McCarthy, K. P. Parris, and M. A. Burgman. 2004. Precision and bias of methods for estimating point survey detection probabilities. Ecological Applications 14:703-712.
32. Wintle, B. A., R. P. Kavanagh, M. A. Mccarthy, and M. A. Burgman. 2005. Estimating and dealing with detectability in occupancy surveys for forest owls and arboreal marsupials. Journal of Wildlife Management 69:905-917.
33. Wintle, B. A., T. V. Walshe, K. M. Parris, and M. A. McCarthy. 2012. Designing occupancy surveys and interpreting non-detection when observations are imperfect. Diversity and Distributions 18:417-424.