Testing the consistency of wildlife data types before combining them: The case of camera traps and telemetry

Ecology and Evolution

Volumn 4, Issue 7, 2014, Pages 933-943

  Popescu, Viorel D ^a   de Valpine, Perry ^a   Sweitzer, Rick A ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Great Basin Institute   (United States)

Author keywords

Camera trap; Capture rate; Data consistency; Detection probability; Fisher; Home range; Pekania pennanti; Sierra Nevada; Telemetry; Wildlife monitoring

Indexed keywords

EID: 84897947764     PISSN: None     EISSN: 20457758     Source Type: Journal    
DOI: 10.1002/ece3.997     Document Type: Article

References (28)

1. Baddeley, A., M. Berman, N. I. Fisher, A. Hardegen, R. K. Milne, D. Schuhmacher, et al. 2010. Spatial logistic regression and change-of-support in Poisson point processes. Electron. J. Stat. 4:1151-1201.
2. Bates, D., M. Maechler, and B. Bolker. 2012. lme4: Linear mixed-effects models using S4 classes. R package version 0.9-0. Available at http://CRAN.R-project.org/package=lme4 (accessed 13 October 2012).
3. Besbeas, P., S. N. Freeman, B. J. T. Morgan, and E. A. Catchpole. 2002. Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58:540-547.
4. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model inference. 2nd ed. Springer, Berlin.
5. Efford, M. 2004. Density estimation in live-trapping studies. Oikos 106:598-610.
6. Fieberg, J., R. H. Rieger, M. C. Zicus, and J. S. Schildcrout. 2009. Regression modelling of correlated data in ecology: subject specific and population averaged response patterns. J. Appl. Ecol. 46:1018-1025.
7. Gopalaswamy, A. M., J. A. Royle, M. Delampady, J. D. Nichols, K. U. Karanth, and D. W. Macdonald. 2012. Density estimation in tiger populations: combining information for strong inference. Ecology 93:1741-1751.
8. Ivan, J. S., G. C. White, and T. M. Shenk. 2013. Using auxiliary telemetry information to estimate animal density from capture-recapture data. Ecology 94:809-816.
9. Johnson, H. E., L. S. Mills, J. D. Wehausen, and T. R. Stephenson. 2010. Combining ground count, telemetry, and mark-resight data to infer population dynamics in an endangered species. J. Appl. Ecol. 47:1083-1093.
10. Karanth, K. U., and J. D. Nichols. 1998. Estimation of tiger densities in India using photographic captures and recaptures. Ecology 79:2852-2862.
11. Lewis, J. C., R. A. Powell, and W. J. Zielinski. 2012. Carnivore translocations and conservation: insights from population models and field data for fishers (Martes pennanti). PLoS ONE 7:e32726.
12. Mazerolle, M. 2012. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 1.24, Available at http://CRAN.R-project.org/package=AICcmodavg (accessed 15 October 2012).
13. McCulloch, C. E., S. R. Searle, and J. M. Neuhaus. 2008. Generalized, linear, and mixed models. 2nd ed. Wiley Interscience, Hoboken, NJ.
14. 2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4:133-142.
15. R Core Team. 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at http://www.R-project.org/ (accessed 5 June 2012).
16. Rodgers, A. R., A. P. Carr, H. L. Beyer, L. Smith, and J. G. Kie. 2007. HRT: Home Range Tools for ArcGIS. Version 1.1. Ontario Ministry of Natural Resources, Centre for Northern Forest Ecosystem Research, Thunder Bay, Ontario, Canada. Available at http://flash.lakeheadu.ca/~arodgers/hre/ (accessed 23 January 2013).
17. Royle, J. A., and R. M. Dorazio. 2008. Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities. Academic Press, San Diego, CA.
18. Royle, J. A., and K. V. Young. 2008. A hierarchical model for spatial capture-recapture data. Ecology 89:2281-2289.
19. Royle, J. A., R. B. Chandler, K. D. Gazenski, and T. A. Graves. 2013. Spatial capture-recapture models for jointly estimating population density and landscape connectivity. Ecology 94:287-294.
20. Slauson, K. M., J. Baldwin, W. J. Zielinski, and M. Schwartz. 2009. Estimating detection probabilities for fishers using non-invasive methods, and implications for survey protocols. Pp. 68 Final Report. U.S. Forest Service, Pacific Southwest Research Station, Redwood Sciences Laboratory, Arcata, CA.
21. Sollmann, R., B. Gardner, A. W. Parsons, J. J. Stocking, B. T. McClintock, T. R. Simons, et al. 2013a. A spatial mark-resight model augmented with telemetry data. Ecology 94:553-559.
22. Sollmann, R., B. Gardner, R. B. Chandler, D. B. Shindle, D. P. Onorato, J. A. Royle, et al. 2013b. Using multiple data sources provides density estimates for endangered Florida panther. J. Appl. Ecol. 50:961-968.
23. Thompson, C. M., R. A. Green, J. Sauder, K. L. Purcell, R. Sweitzer, and J. M. Arnemo. 2012. The use of radio telemetry in Martes research: techniques and technologies. Pp. 284-319 in K. B. Aubry, W. J. Zielinski, M. G. Raphael, G. Proulx, S. W. Buskirk, eds. Biology and conservation of martens, sables, and fishers: a new synthesis. Cornell Univ. Press, Ithaca, New York, NY.
24. Wand, M. P., and M. C. Jones. 1995. Kernel smoothing. Chapman and Hall, London, U.K.
25. Weir, R. D., M. Phinney, and E. C. Lofroth. 2012. Big, sick, and rotting: why tree size, damage, and decay are important to fisher reproductive habitat. For. Ecol. Manage. 265:230-240.
26. Zhao, F., R. A. Sweitzer, Q. Guo, and M. Kelly. 2012. Characterizing habitats associated with fisher den structures in the Southern Sierra Nevada, California using discrete return lidar. For. Ecol. Manage. 280:112-119.
27. Zielinski, W. J., R. L. Truex, G. A. Schmidt, F. V. Schlexer, K. N. Schmidt, and R. H. Barrett. 2004. Home range characteristics of fishers in California. J. Mammal. 85:649-657.
28. Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev, and G. M. Smith. 2009. Mixed effects models and extensions in ecology with R. Springer, New York, NY.