Reservoir Area of Influence and Implications for Fisheries Management

REFERENCES

• Aas, O., and O. Onstad. 2013. Strategic and temporal substitution among anglers and white-water kayakers: the case of an urban regulated river. Journal of Outdoor Recreation and Tourism 1-2:1–8.
   Google Scholar
• Arlinghaus, R., and T. Mehner. 2004. A management-orientated comparative analysis of urban and rural anglers living in a metropolis (Berlin, Germany). Environmental Management 33:331–344.
   PubMed Web of Science ®Google Scholar
• Brown, G. Jr., and R. Mendelsohn. 1984. The hedonic travel cost method. Review of Economics and Statistics 1984:427–433.
   Web of Science ®Google Scholar
• Calenge, C. 2006. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling 197:516–519.
   Web of Science ®Google Scholar
• Carlin, C., S. A. Schroeder, and D. C. Fulton. 2012. Site choice among Minnesota Walleye anglers: the influence of resource conditions, regulations and catch orientation on lake preference. North American Journal of Fisheries Management 32:299–312.
   Web of Science ®Google Scholar
• De Freitas, D. M., S. G. Sutton, J. M. Moloney, E. J. I. Lédée, and R. C. Tobin. 2013. Spatial substitution strategies of recreational fishers in response to zoning changes in the Great Barrier Reef Marine Park. Marine Policy 40:145–153.
   Web of Science ®Google Scholar
• Donthu, N., and R. T. Rust. 1989. Estimating geographic customer densities using kernel density estimation. Marketing Science 8:191–203.
   Web of Science ®Google Scholar
• Friedman, A. B. 2012. taRifx.geo: collection of various spatial functions. R package, version 1.0.4. Available: http://CRAN.R-project.org/package=taR-ifx.geo. (December 2014).
   Google Scholar
• Hunt, L. M. 2005. Recreational fishing site choice models: insights and future opportunities. Human Dimensions of Wildlife 10:153–172.
   Google Scholar
• Jakus, P. M., M. Downing, M. S. Bevelhimer, and J. M. Fly. 1997. Do sportfish consumption advisories affect reservoir anglers’ site choice? Agricultural and Resource Economics Review 26:196–204.
   Google Scholar
• Kernohan, B. J., R. A. Gitzen, and J. J. Millspaugh. 2001. Analysis of animal space use and movements. Pages 126–168 in J. J. Millspaugh and J. M. Marzluff, editors. Radio tracking and animal populations. Academic Press, San Diego, California.
   Google Scholar
• Malvestuto, S. P. 1996. Sampling the recreational creel. Pages 591–623 in B. R. Murphy and D. W. Willis, editors. Fisheries techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland.
   Google Scholar
• Martin, D. R., C. J. Chizinski, K. M. Eskridge, and K. L. Pope. 2014. Using posts to an online social nework to assess fishing effort. Fisheries Research 157:24–27.
   Google Scholar
• Microsoft. 2013. Bing maps. Microsoft, Redmond, Washington. Available: http://www.microsoft.com/maps/. (September 2013).
   Google Scholar
• R Development Core Team. 2012. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
   Google Scholar
• Schramm, H. L. J., P. D. Gerard, and D. A. Gill. 2003. The importance of environmental quality and catch potential to fishing site selection by freshwater anglers in Mississippi. North American Journal of Fisheries Management 23:512–522.
   Web of Science ®Google Scholar
• Seaman, D. E., J. J. Millspaugh, B. J. Kernohan, G. C. Brundige, K. J. Raedeke, and R. A. Gitzen. 1999. Effects of sample size on kernel home range estimates. Journal of Wildlife Management 63:739–747.
   Web of Science ®Google Scholar
• Seaman, D. E., and R. A. Powell. 1996. An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology 77:2075–2085.
   Web of Science ®Google Scholar
• Silverman, B. 1986. Density estimation for statistics and data analysis. Chapman and Hall, London.
   Google Scholar
• Sutton, S. G., and R. B. Ditton. 2005. The substitutability of one type of fishing for another. North American Journal of Fisheries Management 25:536–546.
   Web of Science ®Google Scholar
• Vokoun, J. C. 2003. Kernel density estimates of linear home ranges for stream fishes: advantages and data requirements. North American Journal of Fisheries Management 23:1020–1029.
   Web of Science ®Google Scholar
• Wang, Y, Z. Sun, L. Pan, T. Wang, and D. Zhang. 2013. The application of kernel estimation in analysis of crime hot spots. Pages 1379–1385 in E. Qui, J. Shen, and R. Dou, editors. The 19th international conference on industrial engineering and engineering management. Springer, Berlin.
   Google Scholar
• Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70:164–168.
   Web of Science ®Google Scholar
• Xie, Z., and J. Yan. 2008. Kernel density estimation of traffic accidents in a network space. Computers Environment and Urban Systems 32:396–406.
   Web of Science ®Google Scholar