High-intensity flight feather molt and comparative molt ecology of warblers of eastern North America

Rapid high-intensity molt of flight feathers occurs in many bird species, and can have several detrimental consequences, including reductions in flight capabilities, foraging performance, parental care, and plumage quality. Many migratory New World warblers (family Parulidae) are known to have intense remigial molt, and recent work has suggested that simultaneous replacement of the rectrices may be widespread in the family as well. However, the phylogenetic distribution of simultaneous rectrix molt, and high-intensity flight feather molt more generally, has not been systematically investigated in warblers. We addressed this issue by examining flight feather molt in 13 species, representing 7 different warbler genera, at Powdermill Avian Research Center in southwestern Pennsylvania, USA. All 13 species replaced their 12 rectrices simultaneously, with the onset of rectrix molt occurring in the early-middle stages of high-intensity primary molt. As expected, single-brooded early migrants molted earlier than double-brooded species whose nesting activities extend into late summer. However, our finding that late-molting species replaced their primaries more slowly and less intensively than early-molting species was unexpected, as late-molting species are widely hypothesized to be under stronger migration-related time constraints. This surprising result appears to be at least partially explained by a positive association between pace of molt and daylength; shorter late-summer days may mandate reduced daily food intake, lower molt intensity, and a slower pace of molt. In comparison to other passerines, flight feather molt in warblers of eastern North America is extraordinarily intense; at its peak, individuals are simultaneously replacing 50-67% of their 48 flight feathers (all 12 rectrices and 6-10 remiges on each wing) for 2-3 weeks or more. Because molt of this intensity is likely to present numerous challenges for flight, avoiding predators, foraging, and parental care, the period of flight feather molt for warblers constitutes a highly demanding phase of their annual cycle.Most variable names in the data files should be self explanatory. Molt scores for primaries 1-9 are noted in the fields P1-P9, for secondaries 1-6 in S1-S6, for tertials 1-3 in S7-S9, and for rectrices 1-6 in R1-R6. The fields "Prefix" and "Suffix" refer to the bird band numbering system of U. S. Fish and Wildlife Service bands. The files included in the archive are the following: 1. AllRawData.csv. This file includes the raw molt data for all 1289 molt scores that comprise the entire data set. 2. Table2_PeakPrimaryMoltIntensity.csv. This file is a subset of 300 molt scores for calculating Peak Intensity of primary molt (Table 2), which we defined as the mean number of primary feathers on the right wing being replaced by individuals with total primary molt scores > 15 and ≤ 35; our estimate of peak intensity therefore excludes individuals in the initial or terminal stages of primary molt when few primaries are being molted simultaneously. "NPRIMmolt" (number of primaries in molt) is the key dependent variable in this file. 3. AllRecapsSameYear.csv. This file contains 309 molt records of 137 individuals recaptured and scored multiple times during the same molt year. This file was used to generate Figure S1 and also to generate the following file. The "Suffix" variable contains the indentifier for each individual. 4. Figure5_DaylengthIndividualPaceMolt.csv. This file includes estimates of the pace of primary molt for the 137 individual birds represented in AllRecapsSameYear.csv, and was used to generate Figure 5. 5. Figure7_RohwerRohwer2013RawData.csv. This file is the raw data for calculating Average Intensity of primary molt, following the method used by Rohwer and Rohwer (2013). It is a 645-record subset of the AllRawData.csv. This filed was used to generate the compiled data in the following file. 6. Figure7_RohwerRohwer2013Compiled.csv. This file is a summary and compilation of the raw data in the previous file. It includes four variables: Species, Outermost Primary in Molt, Mean N Primaries in Molt, and Sample Size. From this file, Average Intensity of primary molt could be calculated for primaries 2-8 for each of the 13 species in the dataset. 7. Figure7_RohwerRohwer2013Comparison.csv. This file shows the values plotted in Figure 7, and includes data for passerine presented by Rohwer and Rohwer 2013.Field methods Molt data were collected during summer and fall banding operations, 1986-2000, at Powdermill Avian Research Center (40.1637ºN, 79.2674ºW) in the Laurel Highlands of Westmoreland County, southwestern Pennsylvania, USA. Molt scores were obtained from 1289 individual captures of 13 different species representing 7 different parulid genera (Table 1). Because all 13 focal species nest regularly at Powdermill and surrounding areas of eastern Westmoreland County (Brauning 1992, Wilson et al. 2012), individuals captured during molt were likely local breeders on or near their nesting grounds (Pyle et al. 2018). In all 13 focal species, birds were captured at all stages of prebasic molt, and we found no evidence that individuals arrested or suspended molt prior to migration. All 18 remiges on the right wing (9 primaries, 6 secondaries, 3 tertials) and the 6 right rectrices were scored using the 0-5 molt scoring system of the British Trust for Ornithology: 0 = old feather, 1 = old feather missing or new pin feather, 2 = new feather emerging from sheath up to one third grown, 3 = new feather between one and two thirds grown, 4 = new feather more than two thirds grown but still sheathed at base, and 5 = fully grown new feather with no trace of sheath at its base (Ginn and Melville 1983). Data analysis and statistical methods For each individual we calculated a total primary molt score (0-45) and a total rectrix molt score (0-30) by summing the individual scores for each feather. To examine interspecific variation in the degree to which individuals molted all their rectrices simultaneously, we examined rectrix molt data from 582 individuals of all 13 species captured during active tail molt (total rectrix molt score > 0 and < 30). For each individual we calculated two indices of synchrony in rectrix molt: (1) the maximum difference in score between any two rectrices of any molt score (MaxDiff; 0 = completely synchronous rectrix molt, 5 = completely staggered rectrix molt), and (2) the standard deviation of molt score for the 6 scored rectrices (SD; 0 = completely synchronous, 1.87 = completely staggered). Because neither MaxDiff nor SD was normally distributed, we made between-species comparisons using Kruskal-Wallis and Dunn's tests, nonparametric equivalents of ANOVA and post-hoc multiple comparisons. JMP Pro 12.2 (SAS Institute, Cary, North Carolina, USA) was used for all statistical calculations. To determine the status of primary molt at the onset of rectrix molt, we examined primary feather molt scores for 126 individuals of 10 species that were captured during the initial early stages of rectrix molt (total rectrix molt score > 0 and ≤ 6). We made between-species comparisons of total primary molt score for these 10 species using ANOVA and Tukey-Kramer multiple comparison tests, as examination of normal quantile plots of residuals indicated that total primary molt scores were approximately normally distributed. We explored interspecific variation in the timing and duration of molt by focusing on primary molt. Primary molt in parulid warblers follows the standard passerine pattern, with loss of the first (innermost) primary invariably indicating the start of flight feather molt, with sequential loss of the remaining primaries occurring in a regular proximal-to-distal pattern (Foster 1967, Nolan 1978, Rimmer 1988). Because of the regularity and predictability of primary molt, and because the period of primary replacement nearly always encompasses molt of the rectrices, secondaries, and tertials as well (e.g., Foster 1967), our focus on the timing and duration of primary molt is appropriate. We estimated the day of the year when the midpoint of primary molt was achieved by fitting 3-parameter logistic models to the total primary molt score data for each species, using the Nonlinear Platform of JMP Pro 12.2. We focused on estimating the midpoint (or halfway date; see Jackson 2017, Erni 2018) rather than the onset of primary molt because the estimated midpoint was much less sensitive to curve-fitting assumptions; a variety of linear and non-linear curve-fitting approaches — including Pimm regression (Pimm 1976), 3-parameter logistic models, cubic splines, and kernel smoothing — all produced similar estimates of the midpoint of primary molt. With the fitted logistic models, we then used JMP's Inverse Prediction tool to estimate the mean ± SE day of the year that the midpoint of primary molt (primary molt score = 22.5) was achieved for each species. We estimated the pace of primary molt for each species from total primary molt scores of individual birds captured two or more times during the same season while in primary molt (primary molt score > 0 and < 45). We favored this approach over alternatives such as Pimm regression (Pimm 1976) and Underhill-Zucchini maximum likelihood models (Underhill and Zucchini 1988, Erni et al. 2013) because it allowed us to examine the pace of molt for individual birds and also avoid problems of meeting assumptions of regression and maximum likelihood models (Newton 2009, Rohwer 2013). For each species, we fit a general linear model to total primary molt score with two fixed effects: day of the year as a continuous variable, and band identification number as a categorical variable. The coefficient (mean ± SE) for day of the year in these models represented an overall estimate of the slope of the relationship between primary molt score and day (change in molt score per day). All models were created using the Fit Model platform of JMP Pro 12.2. With estimates of both the midpoint and pace of primary molt, we were able to estimate the mean date of onset of primary molt and mean duration of primary molt for each species. A total of 309 molt records from 137 individual bird-seasons and 12 species were included in these analyses; recapture data for Blue-winged Warblers (BWWA) were too limited to use this approach, and this species was not included in the analysis of timing and pace of primary molt. Recapture data for Canada Warbler (CAWA) were also limited, but because recaptures spanned almost the entire range of total primary molt scores (Supplemental Material Figure S1), this species was included in the analysis. Our estimates of midpoint and pace of primary molt, however, should be viewed cautiously for this species. For all 13 species we calculated two species-level estimates of primary molt intensity: (1) Peak Intensity, which we defined as the mean number of primary feathers on the right wing being replaced by individuals with total primary molt scores > 15 and ≤ 35; our estimate of peak intensity therefore excludes individuals in the initial or terminal stages of primary molt when few primaries are being molted simultaneously. (2) Average Intensity (as described by Rohwer and Rohwer 2013), the mean number of primaries on the right wing growing simultaneously for each feather between the 2nd and 8th primary, inclusive; we used this somewhat more conservative estimate, which includes some individuals in the early stages of molt replacing only 2-3 primaries, to allow us to compare primary molt intensities of the 13 species of warblers with the values calculated for other passerines by Rohwer and Rohwer (2013). To examine the effects of daylength on the pace of primary molt, we used the online SolarTopo calculator (van der Staay 2020) to determine daylength on particular dates for the latitude (40.1637ºN) of Powdermill Avian Research Center. For some species-level comparisons we controlled for phylogenetic relationships via phylogenetic independent contrasts, using a recent phylogeny of the Parulidae (Lovette et al. 2010) and the package 'caper' (version 1.01; Orme 2018) of R version 3.6.2 (R Core Team 2019)