DOI: 10.5061/dryad.fn2z34tss
title: Data from: Inter-annual climate variation influences nest initiation date and nest productivity of the Red-cockaded Woodpecker at the northwestern edge of its range

description: Climate change, including directional shifts in weather averages and
 extremes and increased inter-annual weather variation, is influencing
 demograhy and distributions for many bird species. We examined how
 temperature and precipitation coinciding with multiple nesting seasons
 affected overall nesting success and productivity for
 two red-cockaded woodpecker (Dryobates borealis, RCW) populations
 at the species' northwestern range periphery. We used 26 years of
 nesting data (1991-2016) from the two RCW populations to determine if
 inter-annual weather variation has affected nesting pehnology and
 productivity. We conducted analyses at both broad nesting periods (30 and
 60 days before nesting; 40 days overalpping the nesting period up to
 fledging) and short windows to capture the effects of temperature and
 precipitation extremes on individual nests. For both RCW populations,
 warmer early spring temperatures generally advanced nesting and increased
 clutch size and fledgling number. However, effects of average
 precipitation varied depending on amount and duration of precipitation in
 different time periods. At the nest level, temperature and precipitation
 extremes were unrelated to nest success and loss of nestlings (brood
 loss). Our results indicate that RCWs are responding to the effects of
 climate change in various ways, with warming trends having a positive
 benefit on the species at its northwestern range periphery.
description: Nesting data (and associated weather variables) for each
 population of Red-cockaded Woodpeckers were compiled and formatted into
 Excel .CSV tables from a combination of hand-written field notes and
 annual summary reports housed within the Oklahoma Department of Wildlife
 Conservation and the U.S. Forest Service. The statistical code contained
 in the text document was written by the lead author (M. Fullerton) using
 the open-source statistical program R and R Studio
 (https://www.R-project.org/). We combined data for nesting variables
 across all nests for each year and treated years as replicates (n=25 years
 for Oklahoma; n=23 years for Arkansas). Nesting variables assessed at this
 population level included, for first nests (i.e., excluding renests
 following nest failure), median nest initiation date (median date first
 egg was laid across all nests for a given year; treated as Julian date for
 analyses), average clutch size, and average estimated number of nestlings
 fledged. We used the median for nest initiation date. We defined 3 time
 windows for which all weather variables—including average daily maximum
 and daily minimum temperatures, as well as total precipitation—were
 separately calculated. We defined 30 and 60 day windows preceding the
 median first initiation date across all years for each population.
 Further, we defined a 40 day window with a start date based on the same
 median first nest initiation date as above. This 40 day period was
 selected to capture the average egg-laying period, incubation period, and
 period from hatching to fledging.For population-level analyses, we used
 generalized linear models with weather variables as fixed effects and a
 Gamma distribution with a log link. For nest initiation and clutch size
 analyses, we tested weather variables for the two windows (30 and 60 days)
 prior to nest initiation. For the fledgling analysis, we tested weather
 variables for the 40 day window from nest initiation to fledging. We
 conducted pairwise correlation tests among all predictor variables for
 each population and analysis. Before creating a global model, each
 predictor variable from a correlated pair was tested for correlation
 against the response variable of interest. Further, we tested independent
 effects of predictor variables. We used a backwards stepwise model
 selection approach, beginning with the global model containing all
 uncorrelated predictor variables and removing one variable at a time. We
 also conducted analyses at the individual nest level (i.e., individual
 nesting attempts 222 treated as replicates; n=232 nests for Oklahoma;
 n=612 nests for Arkansas) to assess effects of discrete weather events on
 nesting success and partial brood loss. For these analyses, we used
 generalized linear mixed models in the lme4 package, with year and cluster
 as random effects, and binomial and Poisson distributions for nest success
 and brood loss analyses, respectively. We treated cluster as a random
 effect. For nest success analyses, all active nests used in analyses were
 found within three days of the first egg being laid. We used initiation
 dates for individual nests to define unique, nest-specific time windows.
 We also defined 2 windows prior to nest initiation for each nest (7 and 14
 days). Three windows were also defined for after nest initiation,
 including the 11 day incubation period, the 7 day nestling period before
 nestlings were banded and brood reduction events were identified, and the
 19 day nestling period between banding age and fledging at day 26. For all
 nest-level analyses, we used the above-described GLMM model structure, and
 for fixed effects, we included nest initiation date and weather variables
 (absolute maximum and minimum temperature, and absolute maximum daily
 precipitation value for each time window). For the partial brood loss
 analysis, we included variables for the 11 day incubation window and 7 day
 window between hatching and nestling banding age. However, we used all 5
 time windows for this analysis. For all response variables assessed at the
 nest level, we used a similar approach to identify and exclude correlated
 predictor variables as described for population-level analyses. We used
 AIC to compare an a priori set of candidate models. We first constructed
 single variable models; based on rankings for these models, we then
 systematically built models containing more than one variable. We inferred
 that models were strongly supported when ∆AIC values were between 0 and 2
 and at least two (2) less than the null (i.e., intercept-only) model, and
 when they did not include uninformative parameters.