Juvenile survival in birds is difficult to estimate, but this vital rate can be an important consideration for management decisions. We estimated juvenile survival of cooperatively breeding Florida Scrub-Jays (Aphelocoma coerulescens) in a landscape degraded by fire suppression and fragmentation using data from marked (n = 325) and unmarked juveniles (n = 1,306) with an integrated hierarchical Bayesian model. To assess the combined analyses, we also analyzed these datasets separately, with a Cormack–Jolly–Seber model (marked) and a young model (unmarked). Our data consisted of monthly censuses of territorial family groups from Florida Scrub-Jay populations in East Central Florida collected over a 22-year period. Juvenile survival was estimated from July when young Florida Scrub-Jays begin developing independence to March when they become first-year individuals and grouped according to the habitat quality class of their natal territory that were based on shrub height (with intermediate shrub heights being optimal and short and tall shrub heights being suboptimal) and the presence of sandy openings (the preferred open having many sandy openings; closed not having enough). Parameter estimates in the combined analysis were intermediate to the separate analyses. Notable differences among the separate analyses were that suboptimal habitat survival was lower in the unmarked analysis, the unmarked analysis showed a linear effect of time not seen in the marked analysis, and there was an effect of male breeder death in the marked but not unmarked analysis. The combined data analysis provided more inference than did either dataset analyzed separately including juveniles in optimal-closed territories unexpectedly had higher survival than those in optimal-open, survival increased through time, and male breeder death had a negative effect on survival. This study suggests that optimal-closed habitat may play an important role in juvenile Florida Scrub-Jay survival perhaps by providing better cover from predators and warrants further investigation for management implications.

Habitat quality within territories was classified annually by overlaying territory maps on digital orthorectified aerial photography. Visual classification specified textures that corresponded to vegetation height (Breininger and Carter 2003); we binned territories into the defined habitat classifications. Sociobiological covariates for each territory were quantified from annual demographic data. Helper count was taken in March (at beginning of breeding season), sibling count and cohort count were taken in July (at beginning of capture history). Male death and female death that occurred between March and the following July was coded as binary (1 = death during year). Two types of survival data were utilized. In the first type (hereafter the marked dataset), all nestlings were banded 11 days after hatching using a numbered metal leg band and a unique combination of color bands allowing identification of individuals. The marked dataset was collected from 1988 to 2009 as part of a long-term demographic monitoring effort of the scrub-jay population  occurring on Merritt Island National Wildlife Refuge/John F. Kennedy Space Center (MINWR/KSC). During this period, the populations under study were intensively monitored, and we attempted to locate all nesting attempts before the penultimate egg was laid, and thus clutch size and age of nestlings were known (Carter et al. 2011). Nests were visited weekly to determine status and count eggs or nestlings until failure or fledging. Thereafter, we conducted a monthly census within each territory to record observations of banded birds. In the second data type (hereafter the unmarked dataset), young were not banded in the nest, but a monthly census like that for the marked dataset was conducted within each territory to observe the total number of unmarked young alive with the family group. The unmarked dataset was collected from 1988 to 2014 as part of a larger and less intensive monitoring effort (i.e. no nest monitoring) of the scrub-jay metapopulation in East Central Florida (Breininger et al. 1995) and on MINWR/KSC (Figure 1).

Bayesian models were fit using Markov Chain Monte Carlo (MCMC) methods using the program JAGS 4.3.0 implemented in R (R Core Team 2020) with the package jagsUI and visualized using R packages coda, ggmcmc, and bayesplot.