No abstract

The migratory population of the king rail (Rallus elegans) has declined dramatically during the past 40 years, emphasizing the need to identify habitat requirements of this species to help guide conservation efforts. To assess distribution and habitat use of king rails along the Illinois and Upper Mississippi valleys, USA, we conducted repeated call‐broadcast surveys at 83 locations in 2006 and 114 locations in 2007 distributed among 21 study sites. We detected king rails at 12 survey locations in 2006 and 14 locations in 2007, illustrating the limited distribution of king rails in this region. We found king rails concentrated at Clarence Cannon National Wildlife Refuge, an adjacent private Wetlands Reserve program site, and B. K. Leach Conservation Area, which were located in the Mississippi River floodplain in northeast Missouri. Using Program PRESENCE, we estimated detection probabilities and built models to identify habitat covariates that were important in king rail site occupancy. Habitat covariates included percentage of cover by tall (>1 m) and short (>1 m) emergent vegetation, percentage of cover of woody vegetation, and interspersion of water and vegetation (2007 only) within 50 m of the survey location. Detection probability was 0.43 (SE = 0.12) in 2006 and 0.35 (SE = 0.03) in 2007 and was influenced by observer identity and percentage of cover by tall herbaceous vegetation. Site occupancy was 0.11 (SE = 0.04) in 2006 and 0.14 (SE = 0.04) in 2007 and was negatively influenced most by percentage of cover by woody vegetation. In addition, we found that interspersion of vegetation and water was positively related to occupancy in 2007. Thus, nesting king rails used wetlands that were characterized by high water‐vegetation interspersion and little or no cover by woody vegetation. Our results suggest that biologists can improve king rail habitat by implementing management techniques that reduce woody cover and increase vegetation‐water interspersion in wetlands.

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Understanding causes of nest loss is critical for the management of endangered bird populations. Available methods for estimating nest loss probabilities to competing sources do not allow for random effects and covariation among sources, and there are few data simulation methods or goodness-of-fit (GOF) tests for such models. We developed a Bayesian multinomial extension of the widely used logistic exposure (LE) nest survival model which can incorporate multiple random effects and fixed-effect covariates for each nest loss category. We investigated the performance of this model and the accompanying GOF test by analysing simulated nest fate datasets with and without age-biased discovery probability, and by comparing the estimates with those of traditional fixed-effects estimators. We then exemplify the use of the multinomial LE model and GOF test by analysing Piping Plover Charadrius melodus nest fate data (n = 443) to explore the effects of wire cages (exclosures) constructed around nests, which are used to protect nests from predation but can lead to increased nest abandonment rates. Mean parameter estimates of the randomeffects multinomial LE model were all within 1 sd of the true values used to simulate the datasets. Age-biased discovery probability did not result in biased parameter estimates. Traditional fixed-effects models provided estimates with a high bias of up to 43% with a mean of 71% smaller standard deviations. The GOF test identified models that were a poor fit to the simulated data. For the Piping Plover dataset, the fixed-effects model was less well-supported than the random-effects model and underestimated the risk of exclosure use by 16%. The random-effects model estimated a range of 1-6% probability of abandonment for nests not protected by exclosures across sites and 5-41% probability of abandonment for nests with exclosures, suggesting that the magnitude of exclosure-related abandonment is site-specific. Our results demonstrate that unmodelled heterogeneity can result in biased estimates potentially leading to incorrect management recommendations. The Bayesian multinomial LE model offers a flexible method of incorporating random effects into an analysis of nest failure and is robust to age-biased nest discovery probability. This model can be generalized to other staggered-entry, time-to-hazard situations.