Modeling nest-survival data: a comparison of recently developed methods that can be implemented in MARK and SAS

By Jay J. Rotella, S. Dinsmore, T. Shaffer. Animal Biodiversity and Conservation, 2004

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Estimating nest success and evaluating factors potentially related to the survival rates of nests are key aspects of many studies of avian populations. A strong interest in nest success has led to a rich literature detailing a variety of estimation methods for this vital rate. In recent years, modeling approaches have undergone especially rapid development. Despite these advances, most researchers still employ Mayfield’s ad-hoc method (Mayfield, 1961) or, in some cases, the maximum-likelihood estimator of Johnson (1979) and Bart & Robson (1982). Such methods permit analyses of stratified data but do not allow for more complex and realistic models of nest survival rate that include covariates that vary by individual, nest age, time, etc. and that may be continuous or categorical. Methods that allow researchers to rigorously assess the importance of a variety of biological factors that might affect nest survival rates can now be readily implemented in Program MARK and in SAS’s Proc GENMOD and Proc NLMIXED. Accordingly, use of Mayfield’s estimator without first evaluating the need for more complex models of nest survival rate cannot be justified. With the goal of increasing the use of more flexible methods, we first describe the likelihood used for these models and then consider the question of what the effective sample size is for computation of AICc. Next, we consider the advantages and disadvantages of these different programs in terms of ease of data input and model construction; utility/flexibility of generated estimates and predictions; ease of model selection; and ability to estimate variance components. An example data set is then analyzed using both MARK and SAS to demonstrate implementation of the methods with various models that contain nest-, group- (or block-), and time-specific covariates. Finally, we discuss improvements that would, if they became available, promote a better general understanding of nest survival rates.

Subfields: Ecology, Genetics, Parasitology

View Paper: https://www.semanticscholar.org/paper/dd05a300b57c01bb2e9405b66fd8a161d18502eb

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Citations in the corpus

Nest Site Selection in a Hot Desert: Trade-Off Between Microclimate and Predation Risk (2008, positive): “Values reported are means ± SD. Nest predation data were analyzed with the Nest Survival module in program MARK 4.3 (White and Burnham 1999, Rotella 2005), which allows modeling of daily survival rate as a function of continuous and categorical variables (Dinsmore et al. 2002, Rotella et al. 2004).”
On Improving Telemetry-Based Survival Estimation (2006, methodology): “My recommendation is analogous to that proposed for estimating avian nest survival rates, where until recently the Mayfield and related estimators were used extensively despite their known restrictive assumptions and limitations (Williams et al. 2002, Rotella et al. 2004).”
MIXED-GRASS PRAIRIE PASSERINES EXHIBIT WEAK AND VARIABLE RESPONSES TO PATCH SIZE (2006, methodology): “We used the eff ective sample size (Rotella et al. 2004) to calculate AIC c and considered the model with the lowest AIC c score to be the best-fi ing model.”
Effects of Spring Body Condition and Age on Reproduction in Mallards (Anas Platyrhynchos) (2008, methodology): “We included the random effect of study site (SITE; Rotella et al. 2004) to account for unmeasured variation characterizing individual sites because sites were randomly selected from a population of potential study sites.”
TIME-SPECIFIC VARIATION IN PASSERINE NEST SURVIVAL: NEW INSIGHTS INTO OLD QUESTIONS (2005, methodology): “We used the eff ective sample size (n; Rotella et al. 2004) to compute AICc (i.e. n = total number of days that nests were known to survive + total number of intervals in which a failure occurred).”
Modeling avian nest survival in program mark (2007, methodology): “As noted by Dinsmore et al. (2002), currently no method is available for estimating extra-binomial variation (over-dispersion) in typical nest-survival studies, and this is an area where additional research is needed (Rotella et al. 2004; Johnson, this volume).”