https://doi.org/10.5061/dryad.bcc2fqzk0

Description of the data and file structure

Nest survival

Data were collected in 2016 - 2018 at 29 forest fragments by locating and monitoring Wood Thrush nests. the largest nestling (by mass) in the nest had a blood sample taken for genetic sexing and was equipped with a uniquely coded radio transmitter (Lotek NTQB-6, 1.5-1.7g; ~ 1 yr. battery life; 12.7 sec. burst rate) using a figure-eight leg loop harness. Only one nestling was tagged at 131 of the 160 nests (82%) however, 2 nestlings were tagged at 29 (18%) nests. Of the 189 tagged nestlings (2016 n = 47, 2017 n = 66, 2018 n = 76) there was an even sex ratio of males to females (95:94).

The percent forest cover was calculated using open access Wooded Area land cover data layer available through Land Information Ontario from Ministry of Natural Resources and Forestry (2018) and buffering each nest at the 3 spatial scales (500 m, 2 km, and 5 km from the nest to represent home ranges of different potential predators). Nest distance to forest edge and the presence/absence of Brown-headed Cowbird eggs/young were added as predictor variables.

Daily nest survival rate (DSR) was calculated with the package RMark 2.2.4 in R. This model requires data input of the length of breeding season, age of the nest when first found, date when last checked and active (i.e., eggs/nestlings alive), and the fate of the nest (successful versus preyed upon). Nests were classed as successful if at least one nestling fledged. To test for effects of nest age (e.g., days since first egg was laid for each nest) on nest survival each nest’s age on each day of the nesting season was provided, which allows for both time of season and nest age to be used as temporal predictors of nest survival. Models were run in a two-stage hierarchical modeling process. First, models were fit to determine if there was a change in DSR based on temporal sources of variation which included using nest age, year, and both linear and a quadratic term for time. The best fit temporal model (nest age) was subsequently used as the base model for the predictor variables that included forest fragment size, percent forest cover at 3 different spatial scales (500 m, 2 km, and 5 km from the nest), nest distance to forest edge, and nest parasitism. The full model set included models with nest age plus one predictor variable, models with nest age plus two predictor variables (as both additive and interactive models) and models with nest age plus three additive predictor variables.

Nestling Survival data Excel file:

Refer to RMark book on methods for nest survival analysis: FirstFound (day the nest was first found), LastPresent l(ast day that a chick was present in the nest) , LastChecked (last day the nest was checked), Fate (fate of the nest; 0=hatch and 1=depredated), Freq (the frequency of nests with this data), AgeDay1 (age of the nest on the first occasion), AgeFound, FirstEggDa (first egg date), JulianFirstFound (julian date for nest when first found)

• FragSize = natal forest fragment size (in hectares)
• DistToEdge = distance (in m) from forest edge to nest
• BHCO = nest parasitism by Brown-headed Cowbird (0=not parasitized, 1=parasitized)
• ForCover2 = % forest cover within 2km radius of the nest
• ForCover5 = % forest cover within 5km radius of the nest
• FCover500 = % forest cover within 500m radius of the nest

Fledgling Survival

Data from both the manual tracking and the Motus tower detections were used to model fledgling survival and the probability of detecting a bird given that it was alive (p). Fledgling survival was estimated using the Burnham model with the RMark package in R. This model incorporates both live detections on, and dead recoveries between set time intervals, which, in this study was every 4 days. The Burnham model includes a term for fidelity (F) to better estimate survival since individuals may permanently emigrate away from the study area where live encounters are taking place, and so would not be detectable even if still alive. For Wood Thrush fledgling survival (1-16 days old), F was set to 1 because all dead recoveries and live encounters occurred within the study area. Radio-tagged birds were detectable dead or alive by manual tracking on or near the natal territory and detectable alive by Motus prior to the onset of fall migration. The Burnham model also considers the probability that birds who died are encountered as dead (“r”). Known fate models consider that all dead birds can be located via radio-telemetry (e.g., r = 1), but in practice, fledglings not old enough to be independent are sometimes not detected either alive or dead so their fate is unknown.

All fledglings that were determined to be alive ≥ 16 days after fledging by Motus automated telemetry detections were coded as such in the encounter history on the last live encounter day (16 days). Similar to nest survival, a two-stage modelling process was implemented. The first stage of modelling determined the top temporal model for p while holding survival (S) and r constant. The probability of detecting a tagged fledgling given that it was alive (p) was modelled with year, linearly with age, fledge date, and two age groups (age2 - 0-8d, 9-16d; age3 - 0-8d, 9-12d, 13-16d). Each parameter was modeled separately and with an additive model of year and fledge date, and the constant model for a total of 13 models Next, the top p model was used to model S. Predictor variables modelled for S included a change in survival between calendar year (Year), linearly changing with age (Age), non-linearly changing with age (Age²), time-dependent (Time) and 3 age categories of 2 (age2 - 0-8d, 9-16d), 3 (age3 - 0-8d, 9-12d, 13-16d), and 4 (age4 - 0-4d, 5-8d, 9-12d, 13-16d) age groupings, and the constant model for a total of 26 models. A quadratic term for time (Age²) was included to model S (and not p) as fledgling survival with respect to age may not be linear because flight capability increases rapidly during the first week after fledging.

The top temporal model (S (Age) p (age3+ Year) r (constant) F (=1)) was then used as the base model to model S with the set of covariates that related to the key predictions. The covariates in these models included forest fragment size, % forest cover at 3 different spatial scales (500 m, 2 km, and 5 km from the nest), body condition at the time of tagging, and 2 vegetation measurements that included mean number of trees with >30cm DBH, and % shrub and sapling cover in the understory as the predictor variables of survival. The full model set included models with Age plus one predictor variable, models with Age plus two predictor variables, and the constant model for a total of 38 models (as both additive and interactive models).

Fledgling Survival Excel file

• Occurrences - refer to RMark book on methods
• Freq - *t*he frequency of birds with this particular encounter history - see Mark book
• S_Mass = scaled mass (body condition) Body condition was calculated using a scaled mass index with mass and tarsus length. The scaled mass index was used by adjusting mass to standardized body length measure that is positively correlated with mass on a log-log scale.
• Fdate = nestling fledge date
• FragSize= natal fragment size (in hectares)
• Fcover500 = % forest cover within 500m radius of the nest
• Fcover2 =% forest cover within 2km radius of the nest
• Fcover5 = % forest cover within 5km radius of the nest
• Scover = % shrub and sapling cover
• Trees = trees with >30cm DBH (average number of trees per nesting territory)
• TagID = nano tag ID

Departures Analysis

Motus tower data records were accessed using the motus and motusData R packages and cleaned following guidelines provided by Birds Canada (2022). We examined juvenile migration departure dates using a linear regression (glm function, package lme4) and gamma distribution (link=”log”) in program R. Additive models were run with natal forest fragment size, fledge date, body condition at time of fledging, sex, and year as predictor variables.

Departure Excel file

• FragSize= natal fragment size (in hectares)
• FlDate = Julian date of when nestling fledged the nest
• Sex = sex of tagged nestling
• S_Mass = scaled mass (body condition)
• DDate = Julian date of fall migration departure

PreMigration, Migration and Survival

The pre-migration survival period was defined as >16 days after fledging to fall migration period, migration/wintering survival was the period after fall departure to spring arrival, and annual survival from fledging the nest to spring return. Pre-migration survival was determined using Motus tower detections that were made after Aug. 25 (date of earliest fall migration departure) each year (n = 133). For migration/wintering and apparent annual survival analyses, birds that were detected the following spring through Motus.

Survival was modelled using a linear regression with glm (family binomial with logit link) in the R package lme4. The probability of survival was modeled as the response variable with forest fragment size, fledge date (for pre-migration and annual survival only), departure date on fall migration (for migration/over-wintering survival only), nestling body condition, sex, and year as predictors.

Excel files

• TagID = nano tag ID
• S_Mass = scaled mass (body condition). Body condition was calculated using a scaled mass index with mass and tarsus length. The scaled mass index was used by adjusting mass to standardized body length measure that is positively correlated with mass on a log-log scale.
• FragSize= natal fragment size (in hectares)
• FlDate = Julian date of when nestling fledged the nest
• Sex = sex of tagged nestling
• Outcome = not detected (NS or ND) or detected (S)