N. W. Byer1,3, B. N. Reid1,4, R. A. Seigel2, and M. Z. Peery1 Moving beyond apparent success: techniques for analyzing nest survival data N. W. Byer1,3, B. N. Reid1,4, R. A. Seigel2, and M. Z. Peery1 1 Department of Forestry and Wildlife Ecology, University of Wisconsin–Madison, Madison, WI 53706, USA; 2 Department of Biological Sciences, Towson University, Towson, MD 21252-0001, USA 3Correspondence: e-mail, nbyer@wisc.edu 4 Present Address: Department of Herpetology, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024-5192, USA Nest Survival Data An Example with Two Sensitive Turtle Species Study design choice and nest survival Study design often interferes with our ability to accurately determine success rates May detect nests after they are laid May monitor nests infrequently after they are laid Failing to account for study design limitations can lead to biased estimates of nest success Alternative Techniques A number of techniques exist that can handle these study design limitations While most were developed by ornithologists, they can be easily applied to herpetological datasets We provide guidance on how to select from a subset of those techniques available to herpetologists, as well as an example of one such technique (Shaffer 2004) in action. Study Species and Systems Bog Turtle (G. muhlenbergii) Federally threatened Very small! Nests in sedge/grass Nesting data from MD Blanding’s Turtle (E. blandingii) Candidate for federal listing Medium-bodied Nests in loose substrates Nesting data from WI Figure 2: On left, the Bog Turtle with study counties circled on a map of MD. On right, the Blanding’s Turtle with study county circled on a map of WI. Figure 1: Nests found by location nesting turtles (left) may be detected at different rates than nests found depredated (right). Analyses and Results Data Analysis Used a variant of logistic regression: the logistic exposure model (Shaffer 2004) Variables: Temperature, Precipitation, Nesting Day, Nest Age, Site, and Year Model selection to select from variable combinations Results Top models Bog Turtles – effects of nest age and maximum temperature Blanding’s Turtles – effects of nest age, nesting day, and minimum temperature Opposite effects of nest age for each species! Use table 1 to determine if study constraints prevent you from using apparent nest success calculations. If so, then use table 2 to select techniques based on covariates of interest. Figure 3: Effects of nest age and maximum temperature on nest success in the top Bog Turtle model. Dashed lines indicate 95% confidence intervals. Figure 3: Effects of nest age, day of nesting, and minimum temperature on nest success In the top Blanding’s Turtle model. Dashed lines indicate 95% confidence intervals. Study Constraint Recommendation Detectability of nests depends on fate Issue: Differences in detectability will bias nest survival estimates. Do not use: apparent nest success. Age of nests unknown Issue: Left truncation. Do not use: apparent nest success, the product method, or survival time analyses. Nests checked sporadically/ infrequently Issue: Not all nest fates or failure dates may be known. Note: Methods will vary in their ability to account for uncertainty in failure dates or fates. Covariates of interest Methods to consider (with citations) Group-level effects only Unbiased estimators (Mayfield 1961, 1975; Johnson 1979; Hensler and Nichols 1981) Group-level and age-related effects only The product method (Klett and Johnson 1982) Mayfield-based logistic regression (Aebischer 1999) Group-level, individual-level, and age-related effects Survival time analyses (Aldridge and Brigham 2001; Nur et al. 2004) Advanced modelling techniques (Dinsmore et al. 2002; Shaffer et al. 2004; Converse et al. 2013) Conclusions and Future Directions By incorporating nest survival analysis techniques designed for other taxa into our existing repertoire of techniques, herpetologists can begin to consider more advanced questions about the impacts of environmental factors on nest survival in a variety of nest-laying herpetofaunal groups. More work is needed to determine the extent to which herpetologists should be worried about: Detectability of nests: does our ability to detect nests ever depend on the fate of the nest (and how do we treat nests discovered depredated?) Left truncation: do we ever discover nests after nests are laid (potentially by observing females guarding broods)? Nest monitoring: once we incorporate these more modern methods, how frequently do we need to check nests to address study objectives? Acknowledgments Thanks to : S. Smith of Maryland DNR; undergraduates (including P. Riddle, A Swichtenberg, S. Tomke, and R. Klausch); and the Wisconsin DNR staff (including R. Thiel, W. Hall Jr., R. Paisley, and B. Searles). Funding provided by: the USDA Hatch Act Research Fund; the EPA-STAR program; the Towson University Department of Biological Sciences; the U.S. Fish and Wildlife Service; and the Maryland Department of Natural Resources. Field work for E. blandingii was approved by the University of Wisconsin–Madison Animal Care and Use committee (assurance # 3368-01) and by the Wisconsin Department of Natural Resources (Endangered and Threatened Resources permit # 681). Field work for G. muhlenbergii was approved by the Towson University IACUC committee (IACUC FR#021913 RS-04) and the Maryland Department of Natural Resources (Permit #53959). Table 1: Typical constraints that may exist for herpetological studies, with analytical issues and recommendations for proceeding. Table 2: Recommended analytical methods for different study objectives, based on covariate choice.