Annual variation in juvenile recruitment is an important component of duck population dynamics, yet little is known about the factors affecting the probability of surviving and breeding in the first year of life. Two hypothesized mechanisms to explain annual variability are indirect carry-over effects (COEs) from conditions experienced during the prefledging period and direct effects from climatic conditions during the postfledging period. We used Cormack-Jolly-Seber models to estimate apparent survival and detection rates of 643 juvenile female lesser scaup (Aythya affinis) marked just prior to fledging at Red Rock Lakes National Wildlife Refuge in southwestern Montana, USA, 2010-2018. We evaluated COEs from hatch date, a hatch date × spring phenology interaction, and conspecific duckling density in addition to a direct climatic effect of winter conditions (indexed by the El Niño Southern Oscillation [ENSO]) and spring habitat conditions on the study area. We used growth data from a subset (n = 190) of known-aged ducklings to estimate the influence of hatch date and conspecific density on prefledging growth to help identify mechanisms underlying COEs. Prefledging growth and juvenile apparent survival were negatively related to measures of conspecific duckling density. We found evidence that detection probability varied annually for juvenile (but not adult) scaup, possibly representing decisions to delay breeding and not return to or remain at the study site in their first year of life. Like with apparent survival, there was suggestive evidence that detection probability decreased with increasing duckling density in the previous year. Hatching date was weakly negatively related to detection probability, but unrelated to apparent survival, whereas neither vital rate was related to winter ENSO index. Our results are consistent with a process where density-dependent growth rates in the prefledging period carry over to influence fitness in subsequent life-cycle stages. If this pattern generalizes to other systems, this density COE may have important implications for our understanding of duck population dynamics and reaffirms the importance of maintaining abundant brood-rearing habitats in conservation and management of ducks.

Hibernation is associated with long lifespan: on average, hibernating mammals live 15% longer than non-hibernators of equivalent mass. We investigated how survival varies with sex, season, and the deployment of biologgers in arctic ground squirrels [Urocitellus parryii (Richardson, 1825)], a widely-distributed northern hibernator. The duration of hibernation in arctic ground squirrels differs markedly by sex: females hibernate 30% longer each year than males, a behavioural trait that could positively affect female survival. Additionally, males engage in aggressive territorial and food cache defense in spring and fall, which may decrease survival in this sex. From 13 years of mark-recapture data, we estimated apparent survival of arctic ground squirrels in Arctic Alaska using Cormack-Jolly-Seber models in Program MARK. We found that females had higher annual survival ["φ" ̂Fannual = 0.753 (0.469; 0.913 C.I.)] than males ["φ" ̂Mannual = 0.546 (0.416; 0.670)], with a maximum observed lifespan (10 years) that exceeded that of males (six years). We also show that biologger use and implantation did not significantly impact survival. Quantifying basic arctic ground squirrel demographics from this well-studied population illustrates how sex-specific hibernation parameters may influence lifespan differences in male and female arctic ground squirrels and provides support for the safety of biologging devices.

Informing conservation and management decisions for habitats frequented by species of high management interest often face the challenge of limited resources for conducting wildlife surveys. When surveys are focused on local areas or sparsely distributed species, it may also be difficult to obtain counts sufficient for implementing abundance models that account for imperfect detection. With replicated aerial surveys collected within a 70.25-km 2 portion of the Eastern Alaska Range, Alaska, USA, during the summers of 2013-2015, we estimated daily abundance of Dall's sheep using two different estimation methods: Bayesian N-mixture models and Poisson regression models. We then compared estimates of relative abundance from both model types while paying special attention to the assumption of closure within individual survey units. With abundance estimates obtained from individual survey days, we then estimated the average number of Dall's sheep within the survey area for the period 1 July-1 October. Daily ewe abundance followed a quadratic pattern, with 10-20 ewes being within our survey area in early July and late September, and approximately 90 ewes within the survey area in mid-August. Lamb-to-ewe ratios averaged 0.2 from July-September, while ram-to-ewe ratios averaged 0.4 from July until mid-August before increasing to about 1.0 by the end of September. These results indicate that our survey area is an important habitat to local Dall's sheep populations when lambs are vulnerable to predators. Accordingly, human recreation and military training within the survey area should be minimized 1.5-3.0 months after parturition to minimize disturbance. We also found that N-mixture models displayed a pattern of abundance estimates that increased in magnitude as model complexity increased. We thus recommend an a priori approach to N-mixture model construction that balances the risk of overfitting models to modest data against the risk of fitting models that do not explain heterogeneity in abundance and detection probability. Lastly, we suggest simple improvements to replicated, aerial surveys for species like Dall's sheep focused on reducing violations of the closure assumption within individual survey units, which can reduce bias of density estimates obtained with N-mixture models.

1. Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with Bayesian multivariate mixed effects models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery indicates increasingly additive harvest mortality. The power of mixed effects models to detect non-zero correlations has rarely been evaluated and these few studies have not focused on a common data type in the form of tag recoveries. 2. We assessed the power of multivariate mixed effects models to estimate negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit mixed effects models to a mallard (Anas platyrhychos) tag-recovery dataset and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate a method of calculating effective sample size for capture-recapture data. 3) Different priors lead to different inference about additive harvest when we fit our models to the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations resulted in correlation estimates with substantial bias and imprecision. Many correlation estimates spanned the available parameter space (–1,1) and were biased towards zero. Only one prior combined with our most intensive monitoring scenario allowed our models to consistently recover negative correlation without bias. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. 4) The inadequacy of prior distributions and sample size combinations typically assumed adequate for robust inference represents a concern in the application of Bayesian mixed effects models for the purpose of informing harvest management. Our analysis approach provides a means for examining prior influence and sample size on mixed-effects models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.