Abstract
Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the biting-insect hypothesis and other hypotheses for nesting failure in this reintroduced population; resulting inferences will support ongoing efforts to manage this population via an adaptive management approach. Wider application of our approach offers promise for modeling the effects of other temporally varying, but imperfectly observed covariates on nest survival, including the possibility of modeling temporally varying covariates collected from incubating adults.
Highlights
Nest success – the probability that a nest will produce at least one individual – is a key vital rate affecting the evolution, ecology, and management of avian populations
Ecology and Evolution published by John Wiley & Sons Ltd
To carry out the analysis for the biting-insect hypothesis, we developed a novel daily nest survival model to account for missing insect population indices from carbon dioxide traps
Summary
Nest success – the probability that a nest will produce at least one individual – is a key vital rate affecting the evolution, ecology, and management of avian populations. The Mayfield method instead considers daily nest survival (S): S1⁄41À y; D where y is the number of nest failures observed, and D is exposure days – the sum across nests of days in which each nest is monitored, from initial detection to termination. Johnson (1979), Hensler and Nichols (1981), and Bart and Robson (1982) developed likelihood functions for daily nest survival [reviewed by Williams et al (2002)] that addressed the problem of uncertain failure date. The mortality hazard rate is assumed to be constant over the life of the nest (i.e., age-constant survival), or if age-dependent variation in survival is of interest, the assumption is that nests can be aged without error at first detection (e.g., Dinsmore et al 2002). The case of age- or stage-dependent survival with unknown nest age has been considered (Heisey and Nordheim 1995; He et al 2001; Pollock and Cornelius 2001; He 2003; Stanley 2004; Cao et al 2008)
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