A half-century of changes in migratory landbird numbers along coastal Massachusetts.

We analyzed data from across five decades of passerine bird banding at Manomet in Plymouth, Massachusetts, USA. This included 172,609 captures during spring migration and 253,265 during fall migration, from 1969 to 2015. Migration counts are prone to large interannual variation and trends are often difficult to interpret, but have the advantage of sampling many breeding populations in a single locale. We employed a Bayesian state-space modeling approach to estimate patterns in abundance over time while accounting for observation error, and a hierarchical clustering method to identify species groups with similar trends over time. Although continent-wide there has been an overall decrease in landbird populations over the past 40 years, we found a variety of patterns in abundance over time. Consistent with other studies, we found an overall decline in numbers of birds in the aggregate, with most species showing significant net declines in migratory cohort size in spring, fall, or both (49/73 species evaluated). Other species, however, exhibited different patterns, including abundance increases (10 species). Even among increasing and declining species, the specific trends varied in shape over time, forming seven distinct clusters in fall and ten in spring. The remaining species followed largely independent and irregular pathways. Overall, life-history traits (dependence on open habitat, nesting on or near the ground, migratory strategy, human commensal, spruce budworm specialists) did a poor job of predicting species groupings of abundance patterns in both spring and fall, but median date of passage was a good predictor of abundance trends during spring (but not fall) migration. This suggests that some species with very similar patterns of abundance were unlikely to be responding to the same environmental forces. Changes in abundance at this banding station were generally consistent with BBS trend data for the same geographic region.

Introduction

Long-term data sets in ecology lead to discoveries often missed in shorter-term studies [1,2], and they are critical for establishing baselines and tracking changes in the natural world [3]. Because birds are widely surveyed by professional and amateur observers alike, and their natural histories are often well-understood, wild bird populations can be useful sentinels of environmental change and ecosystem condition [4,5] For example, during the 1980s and 1990s, wide-spread surveys were used to identify large-scale declines of birds across the continental U.S. and Canada [6-8]. Contemporary interests include documenting species’ range shifts due to climate change [9-11], and modeling the spread of exotic, invasive species like Eurasian Collared-Doves Streptopelia decaocto [12]. Prominent long-term bird monitoring data in North America are available for breeding birds in the USGS Breeding Bird Survey (BBS) [13] and the Christmas Bird Count (CBC) [14]. These surveys amass a wealth of valuable data on bird abundance, but there are biases and gaps in survey coverage that necessitate the integration of other data sources. For example, BBS data are biased in space because they are roadside surveys [15,16], and by being of short count duration [17,18]. Survey gaps can be temporal (e.g., during migration) or spatial (e.g., off-shore rocky islands), which in turn makes certain taxa much less likely to be detected. These gaps are filled by other monitoring programs. For example, eBird is allowing large-scale identification of migratory stopover and wintering areas [19], as are targeted taxon surveys such as the International Shorebird Survey (ISS) [20] and various hawk migration watches [21].

Migratory bird banding operations represent an underutilized source of data about the stages of avian life that connect breeding and wintering: migration [22]. These sites often have long-term datasets collected by highly-trained observers, coupled with detailed data on capture effort and local conditions. Although migratory bird data from a single banding station should be interpreted with care because of yearly stochasticity introduced by fluctuations in weather conditions [23,24], banding stations identified the previously-unknown breeding grounds of wintering sparrows in California [25], and demonstrated differences in stopover energetics between hatch-year and adult birds in southern Canada [26]. In addition to answering basic questions of natural history, banding station data have recently been used to describe and assess the way migrating birds are responding to climate change, both in Europe [27] and the Americas [28]. Because banding stations are typically situated in areas such as mountain gaps, desert oases, and coastal points where birds from many breeding populations naturally aggregate during migration, they can evaluate changes in population size across a much larger region from a typical single point in space [22].

Our goal was to analyze fall and spring migration banding data for >50 species of landbirds across almost half a century from Manomet, a banding station in eastern North America, along the Atlantic Flyway. There has been a series of important studies looking at population changes of breeding and migrating birds in this region of North America, mostly focusing on population declines and changing migration phenology associated with global climate change, including extensive work at Manomet [6,7, 29–32]. Often ignored, however, is the presence of stable and increasing species (e.g., Blue-headed Vireo Vireo solitarius, Carolina Wren Thryothorus ludovicianus) [13,33], perhaps because the stories are less dramatic. Previous analyses of Manomet migration data have focused on attempting to identify common trends among Neotropical migrant species [30,31]. We aim to take these analyses a step further, first by expanding the database with over a decade of new data, and then by examining the ways in which migratory bird abundances cluster over time. We then attempt to quantitatively evaluate what life-history factors best predict these groupings. By using a Bayesian state-space approach to analyzing migration count data, we use more accurate estimates of real trends in migratory cohort size to reduce the uncertainty in identifying species clusters. Generalizations are often made about the particular population vulnerability of, for example, birds of grassland and agricultural habitats [34] or birds that winter in the Neotropics [6]. If these life-history traits and their associated risks are driving the declines of avian guilds, then the shapes of population trends within that guild should be generally similar. This approach allows us to examine whether life-history traits predict observed trends in migratory cohort size, and to better identify species that are doing especially well or especially poorly along with mechanisms for the different patterns of change.

Methods

Manomet’s banding lab has operated mist nets at their coastal site in Plymouth, Massachusetts, USA (41° 50’ N, 70° 30’ W) every spring and fall since 1966. The site is dominated by second-growth hardwoods, but also borders swampy areas, old cleared fields, and a seaside coastal bluff. Migrating birds were captured using a system of 45–50 (depending on year) nylon mist nets (12 m long, 2.6 m high, 36 mm extended mesh) set at fixed spots along a series of trails covering part of the Manomet property. Opening and closing times for all nets were recorded and used to create a standard effort measure of net-hours. Nets were typically open from a half-hour before sunrise to a half-hour after sunset, 5–7 days per week depending on weather, in spring (15 April– 15 June) and fall (15 August– 15 November). Nets were occasionally closed due to weather conditions that might endanger birds; all such closures were recorded and factored into effort calculations.

All banding activity at Manomet was performed by trained personnel under the supervision of a master bander with an active Federal Bird Banding and Marking Permit from the USGS Bird Banding Lab, and MassWildlife bird banding and salvage permits from the Massachusetts Division of Fisheries and Wildlife. Lloyd-Evans is also a bird banding trainer certified by the North American Banding Council (1998).

Four-letter abbreviation codes and scientific names for all species analyzed appear in Table 1. Relevant wing formula data for the separation of Alder Flycatcher and Willow Flycatcher were not collected for more than half the study period, thus we have adopted the parsimonious strategy of not separating these two species trends and both are presented as “Traill’s Flycatcher.”‥ Subspecies of Palm Warbler were recorded as Yellow Palm Warblers (Setophaga palmarum hypochrysea) and Western Palm Warbler (S. p. palmarum). Hybrid Blue-winged x Golden-winged Warblers were recorded as Blue-winged Warblers. For a handful of species frequently caught in ground traps (White-throated Sparrow, Red-winged Blackbird, and Brown-headed Cowbird), we included hours of ground trap deployment in calculations of total effort-hours.

Table 1

Complete summary of all species analyzed.

Species	Code	Cluster+		Number Caught		Overall Trends		BBS Trends+
		Fall	Spr	Fall	Spr	Fall	Spr	NEMA	ANF	BSS
Ruby-throated Hummingbird (Archilochus colubris)	RTHU	−	−	−	837	NA	Increase	Increase	Increase	−
Downy Woodpecker (Dryobates pubescens)	DOWO	−	−	844	−	Stable	NA	Increase	Increase	Stable
Northern (Yellow-shafted) Flicker (Colaptes a. auratus)	YSFL	−	−	607	−	Decline	Decline	Decline	Decline	Decline
Eastern Wood-Pewee (Contopus virens)	EAWP	−	6	129	264	NA	Decline	Stable	Decline	−
Yellow-bellied Flycatcher (Empidonax flaviventris)	YBFL	1	−	261	526	Decline	Decline	−	Stable	Increase
Alder & Willow (Traill’s) Flycatcher (E. alnorum & traillii)	TRFL	−	1	445	1442	Decline	Decline	Increase	Stable	Stable
Least Flycatcher (E. minimus)	LEFL	−	2	395	419	Decline	Decline	Decline	Decline	Decline
Eastern Phoebe (Sayornis phoebe)	EAPH	4	−	735	217	Increase	Stable	Stable	Decline	Stable
Great Crested Flycatcher (Myiarchus crinitus)	GCFL	−	−	−	422	NA	Increase	Increase	Decline	−
Blue-headed Vireo (Vireo solitarius)	BHVI	4	−	484	−	Increase	NA	Stable	Increase	Increase
Philadelphia Vireo (V. philadelphicus)	PHVI	−	−	244	−	Stable	NA	−	Increase	Increase
Red-eyed Vireo (V. olivaceus)	REVI	5	−	3627	464	Decline	Decline	Decline	Increase	Increase
Blue Jay (Cyanocitta cristata)	BLJA	6	10	2352	3211	Decline	Decline	Decline	Increase	Increase
Black-capped Chickadee (Poecile atricapillus)	BCCH	2		29064	1255	Decline	Decline	Stable	Increase	Increase
Tufted Titmouse (Baeolophus bicolor)	TUTI	−	5	5814	399	Increase	Increase	Increase	Increase	−
Red-breasted Nuthatch (Sitta canadensis)	RBNU	3	−	230	−	Decline	NA	Stable	Increase	Stable
White-breasted Nuthatch (S. carolinensis)	WBNU	−	−	292	−	Increase	NA	Increase	Increase	−
Brown Creeper (Certhia americana)	BRCR	1	6	1677	192	Decline	Decline	Stable	Increase	Increase
Carolina Wren (Thryothorus ludovicianus)	CARW	−	−	599	−	Increase	NA	Increase	−	−
Winter Wren (Troglodytes hiemalis)	WIWR	1	−	206	−	Decline	NA	−	Stable	Stable
Golden-crowned Kinglet (Regulus satrapa)	GCKI	1	−	3090	−	Decline	NA	−	Increase	Stable
Ruby-crowned Kinglet (R. calendula)	RCKI	5	9	1921	1245	Decline	Decline	−	Decline	Stable
Blue-gray Gnatcatcher (Polioptila caerulea)	BGGN	7	−	341	−	Stable	NA	Increase	−	−
Veery (Catharus fuscescens)	VEER	−	−	561	612	Decline	Stable	Decline	Decline	Increase
Swainson’s Thrush (C. ustulatus)	SWTH	−	−	1255	1517	Stable	Decline	−	Decline	Stable
Hermit Thrush (C. guttatus)	HETH	−	−	2009	1553	Stable	Stable	Decline	Stable	Stable
Wood Thrush (Hylocichla mustelina)	WOTH	−	10	207	385	Decline	Decline	Decline	Decline	−
American Robin (Turdus migratorius)	AMRO	−	−	7262	1179	Decline	Decline	Decline	Decline	Stable
Gray Catbird (Dumetella carolinensis)	GRCA	−	1	22923	17533	Stable	Decline	Increase	Decline	−
Northern Mockingbird (Mimus polyglottos)	NOMO	−	−	512	−	Decline	NA	Decline	−	−
Brown Thrasher (Toxostoma rufum)	BRTH	1	−	313	429	Decline	Decline	Decline	Decline	−
Cedar Waxwing (Bombycilla cedrorum)	CEDW	−	−	507	471	Decline	Decline	Increase	Stable	Stable
Blue-winged Warbler (Vermivora cyanoptera)	BWWA	−	−	218	−	Decline	NA	Decline	−	−
Tennessee Warbler (Oreothlypis peregrina)	TEWA	−	−	214	125	Decline	Decline	−	Decline	Stable
Nashville Warbler (Oreothlypis ruficapillus)	NAWA	−	−	665	−	Stable	NA	Decline	Decline	Stable
Northern Parula (Setophaga americana)	NOPA	−	4	−	366	NA	Stable	Increase	Increase	Increase
Yellow Warbler (S. petechia)	YEWA	2	−	250	874	Decline	Stable	Stable	Decline	Decline
Magnolia Warbler (S. magnolia)	MAWA	5	−	814	3380	Decline	Increase	−	Stable	Increase
Cape May Warbler (S. tigrine)	CMWA	−	−	469	−	Decline	NA	−	Decline	Decline
Black-throated Blue Warbler (S. caerulescens)	BTBW	−	−	684	567	Stable	Stable	Stable	Increase	Increase
Yellow-rumped (Myrtle) Warbler (S. coronata coronate)	MYWA	1	−	21014	754	Decline	Stable	Stable	Stable	Stable
Black-throated Green Warbler (S. virens)	BTNW	−	4	353	219	Stable	Stable	Stable	Stable	Stable
Prairie Warbler (S. discolor)	PRAW	−	−	−	142	NA	Decline	Decline	−	−
Yellow Palm Warbler (S. palmarum hypochrysea)	YPWA	−	4	−	342	NA	Increase	−	Increase	Stable
Western Palm Warbler (S. palmarum palmarum)	WPWA	2	−	286	−	Decline	NA	−	−	−
Bay-breasted Warbler (S. castanea)	BBWA	−	−	726	−	Decline	NA	−	Decline	Stable
Blackpoll Warbler (S. striata)	BLPW	1	3	7718	1113	Decline	Decline	−	Decline	−
Black-and-white Warbler (Mniotilta varia)	BAWW	−	1	1129	2442	Decline	Decline	Decline	Decline	Stable
American Redstart (S. ruticilla)	AMRE	−	−	3943	2965	Decline	Decline	Stable	Decline	Stable
Ovenbird (Seiurus aurocapilla)	OVEN	7	−	700	1512	Stable	Stable	Decline	Increase	Stable
Northern Waterthrush (Parkesia noveboracensis)	NOWA	−	3	922	1330	Decline	Decline	Stable	Decline	Increase
Mourning Warbler (Geothlypis philadelphia)	MOWA	5	2	366	535	Stable	Decline	−	Decline	Stable
Common Yellowthroat (G. trichas)	COYE	1	1	2125	4658	Decline	Decline	Decline	Decline	Decline
Wilson’s Warbler (Cardellina pusilla)	WIWA	6	−	880	822	Decline	Decline	−	Decline	Stable
Canada Warbler (C. canadensis)	CAWA	−	−	496	1466	Decline	Decline	Decline	Decline	Stable
Yellow-breasted Chat (Icteria virens)	YBCH	6	−	1121	−	Decline	NA	Decline	−	−
Eastern Towhee (Pipilo erythrophthalmus)	EATO	2	6	893	1525	Decline	Decline	Decline	Decline	−
Field Sparrow (Spizella pusilla)	FISP	2	−	275	−	Decline	NA	Decline	Decline	−
Song Sparrow (Melospiza melodia)	SOSP	5	7	3107	877	Decline	Stable	Decline	Decline	Decline
Lincoln’s Sparrow (M. lincolnii)	LISP	3	−	216	324	Decline	Stable	−	Decline	Stable
Swamp Sparrow (M. georgiana)	SWSP	−	7	1160	1341	Stable	Stable	Decline	Increase	Stable
White-throated Sparrow (Zonotrichia albicollis)	WTSP	−	1	8563	7038	Decline	Decline	Decline	Decline	Decline
Dark-eyed (Slate-colored) Junco (Junco hyemalis hyemalis)	SCJU	1	8	2237	241	Decline	Stable	Stable	Decline	Stable
Northern Cardinal (Cardinalis cardinalis)	NOCA	−	5	1892	747	Increase	Increase	Increase	Increase	−
Red-winged Blackbird (Agelaius phoeniceus)	RWBL	−	−	−	818	NA	Decline	Decline	Decline	Decline
Common Grackle (Quiscalus quiscula)	COGR	−	−	−	1543	NA	Stable	Decline	Decline	Stable
Brown-headed Cowbird (Molothrus ater)	BHCO	−	8	−	393	NA	Decline	Stable	Decline	Decline
Baltimore Oriole (Icterus galbula)	BAOR	2	6	774	1033	Stable	Decline	Decline	Decline	−
American Goldfinch (Spinus tristis)	AMGO	−	−	455	848	Stable	Decline	Increase	Stable	Stable
Scarlet Tanager (Piranga olivacea)	SCTA	−	−	231	−	Stable	NA	Decline	Decline	Stable
Purple Finch (Haemorhous purpureus)	PUFI	2	−	718	−	Decline	NA	Decline	Decline	Stable

+Cluster indicates which abundance trend cluster the species was sorted into by hierarchical clustering, if any. BBS Trends include the trend from three Breeding Bird Survey Regions: New England / Mid-Atlantic (NEMA), Atlantic Northern Forests (ANF), and Boreal Softwood Shield (BSS) for all birds with medium or high survey confidence in the region in question.

Data processing

Records used in this analysis were from 1969–2015, and excluded repeat captures within the same season as well as local breeders (distinguished by physiological signs of breeding readiness, or local fledgling birds caught during spring migration). Although Manomet personnel banded birds from over 150 species during the target years, many of these occurred only a handful of times. Within fall and spring data, we removed from analysis all species not caught in at least 15 different years, and then examined the total birds caught for each of the remaining species. For the 108 remaining species, we removed those that had insufficient data to determine a significant trend, which we defined as a capture rate <5 individuals/year. This left us with 62 species with sufficient data to be analyzed for fall migration trends, and 52 species for spring migration.

Because sampling effort varies through time, and different species have their migratory peaks at different parts of each season, we calculated a peak migration window for each species according to the methods used in Lloyd-Evans and Atwood [31]. Briefly, we identified the dates that accounted for 98% of all captures—thereby excluding the first and last 1% of captures—for each species across all years, and excluded sampling effort outside this period when calculating effort-hours for a given species. For example, 98% of all spring Ovenbird captures across all years occurred between May 1 and June 5; therefore, all sampling efforts from outside this period were excluded when calculating the total spring effort-hours for Ovenbirds.

Once we had calculated the effort windows for each species, we converted the number of individuals caught in each year to the number of individuals caught / 10,000 effort-hours, in order to control for differing numbers of net-hours across years.

State-space modeling

Because migration counts from a single site only sample a small portion of the population, and such counts are susceptible to the effects of weather [35], we used a state-space modeling approach to estimate the underlying trends in the actual size of each species’ migratory cohort at Manomet. A state-space modeling approach allows us to separate the process variation (differing numbers of birds migrating through each year) from the observation variation (different proportions of those birds being caught each year) [36]. Our model was adapted from the one used by Kéry and Schaub [37], with the effort-adjusted number of birds caught in the first year of reliable survey data (1969 for fall, 1970 for spring) as the prior for initial size of the migratory cohort. All models were run 200,000 times, with the first 100,000 runs discarded as a burn-in, using WinBUGS through R and the R2WinBUGS package [38, 39].

Once the models were complete, we made a coarse assessment of each species’ net change in migratory cohort size across the study period by comparing the bird’s estimated abundance in 2015 with the 95% confidence interval around its abundance in the first year of data (1969 for fall, 1970 for spring). Birds whose 2015 abundance exceeded the first year’s upper 95% CI were classified as having significantly increased since the first year, while birds whose 2015 abundance was less than the first year’s lower 95% CI were classified as having significantly declined. We then compared these qualitative trends to the trend estimates provided by the USGS Breeding Bird Survey for the New England Mid-Atlantic Region (BCR 30), the Atlantic Northern Forests Region (BCR 14), and the Boreal Softwood Shield Region (BCR 8) [40], since these regions are the likeliest breeding grounds of birds caught at Manomet. We only used BBS trend estimates for species that had Medium or High Regional Credibility in a given region.

Cluster analysis of population patterns

With state-space model patterns in migratory cohort size over time already calculated, we were interested to determine if species could be grouped by their patterns of abundance over time. Accordingly, we standardized each species’ time series to its own maximum value, thus preserving the shape of the overall trend and allowing us to compare species on the basis of trend shape alone. We clustered our species within each season (fall and spring) using the hierarchical Ward’s method [41] as implemented in the R package pvclust [42]. Each point in a species’ standardized time series was compared against equivalent points in each of the other time series, and the algorithm minimizes the Euclidean distances between time series to form clusters of similar trends. The pvclust packages identifies clusters that are statistically supported at the p < 0.05 level and creates a dendrogram.

In order to determine whether these clusters aligned with life-history traits among species, we classified all species according to several life-history traits that are frequently cited as being of conservation relevance [34,43]: dependence on open habitats (e.g., grassland and shrubland), nesting on or very near the ground, human commensals (frequently visit bird feeders and/or especially thrive in human-altered habitats), and being a spruce budworm (Choristoneura sp.) specialist. We also included different migratory strategies, since several studies have suggested that birds with longer migrations may be adjusting their migratory behavior less, and may fare especially poorly in response to climate change [44, 45]. Migratory strategies included: Resident (non-migratory), Facultative migrant (individuals within the same population may or may not migrate), and likely predominant wintering location: Southeastern United States, Caribbean, Central America, and South America. Many species belonged to more than one category of migratory strategy, but species were assigned to categories judged to be most representative of the migratory behavior of birds caught at Manomet. For a complete list of species life-history traits by species, see the Supplemental Material (S1 Table). We also calculated the median date of passage for each species in each season, with the assumption that migration timing might be a surrogate for a suite of possible ecological factors not covered by the other traits (e.g., distance migrated, which might be associated with the potential for phenological disruption [46-48]).

We then used these life-history traits in a k-modes clustering approach to sort all species into an equal number of clusters as in the time-series analysis (seven clusters for fall, ten clusters for spring). If membership in a particular time-series cluster is driven primarily by life-history traits, then we would expect the life-history clusters and time-series clusters to show high concordance. For example, we might expect to see high concordance between a cluster of steadily declining time-series trends and a life-history cluster of open habitat specialists who migrate to South America. Concordance was assessed using multinomial logistic regression, with membership in a life-history cluster as a predictor variable and membership in a time-series cluster as a response variable. These results were compared to a null model (time-series cluster membership is random) and a model using median date of passage as a predictor variable.

Results

In total, we analyzed information from 253,265 birds caught across 1,487,999 net hours during fall migration, and 172,609 birds captured across 925,327.5 net hours during spring migration (Table 1). The average 98% migration window was longer in the fall (65.6 days, ± 16.8) than in the spring (40.5 days, ± 13.2) (Table 1), as might be expected based on selective pressures for early arrival for breeding [49,50]. For the 43 species in our samples that appeared in both the fall and spring counts, the differences in migration windows are likely driven by the larger sample size and longer banding season in the fall.

Each of the season-specific state-space model graphs for every species is found in the Supplemental Materials. State-space model trends removed much of the interannual variation of raw time-series data (see example in Fig 1), but trends over time were often nonlinear and occasionally complex, defying easy categorization. Of the 62 fall species evaluated, 30 sorted into seven statistically significant clusters based on their time-series trends (Fig 2, Table 1). Group sizes ranged from 2 to 9 species. Cluster significance was determined by an approximately unbiased (AU) p-value < 0.05 from multiscale bootstrap resampling [42]. The clusters show that some species declined sharply in the late 1970s and then persisted at lower abundance (such as Eastern Towhee, Field Sparrow, Purple Finch, and Baltimore Oriole in cluster 2) while others have declined steadily over time (e.g., Magnolia Warbler, Mourning Warbler, Red-eyed Vireo and Song Sparrow in cluster 6). Others have increased overall, such as Blue-headed Vireo and Eastern Phoebe in cluster 4. (Fig 3; S1 Table). Some species showed a fair amount of interannual variation, but overall had no distinct net change over time (e.g., Blue-gray Gnatcatcher and Ovenbird in cluster 7).

Fig 1

Example time-series graphs of bird captures at Manomet for four different species from four different trend clusters.

Grey lines indicate raw capture data, blue lines indicate state-space estimates of actual migratory cohort size, and the shaded area is the 95% confidence interval around the state-space model estimate. Some species are unambiguously increasing or declining, while others show more complicated patterns. Lincoln’s Sparrow data is from spring migration, all others are from fall.

Fig 2

Dendrogram of fall bird species, clustered via Ward’s hierarchical clustering with a Euclidean distance method based on time series trend shape.

Colored rectangles enclose clusters significant at the approximately unbiased (AU) p < 0.05 level. 4-letter species codes and cluster numbers at the right of the figure match those in Table 1.

Fig 3

Graphs of abundance trends over time of each significant fall species cluster.

Each colored line is a different species.

Of the 52 spring species evaluated, 26 sorted into ten statistically significant clusters based on their time-series trends, with 2–5 species within each cluster (Fig 4). As with the fall clusters, different clusters of declining species exhibit distinct timing in the onset of decline. Birds in cluster 2 (Mourning Warbler and Least Flycatcher) showed a steep crash in spring captures in the late 1990s, while spring cluster 7 (Song Sparrow and Swamp Sparrow) had their greatest declines before 1980, and have since been stable or even recovering (Fig 5). Spring cluster 4 (Black-throated Green Warbler, Northern Parula, and Yellow Palm Warbler) are primarily united by an incredibly high rate of spring captures in 2010. Concordance in cluster membership between spring and fall clusters was remarkably low (Table 1).

Fig 4

Dendrogram of spring bird species, clustered via Ward’s hierarchical clustering with a Euclidean distance method based on time series trend shape.

Colored rectangles enclose clusters significant at the approximately unbiased (AU) p < 0.05 level. 4-letter species codes and cluster numbers on the right of the figure match those in Table 1.

Fig 5

Graphs of abundance trends over time of each significant spring species cluster.

Each colored line is a different species.

For fall trends, 41 species (66%) showed significant declines in migratory cohort size between 1969 and 2015 (Table 1). Six species (10%) showed significant increases in migratory cohort size, and 15 species did not have a significantly different abundance in 2015 than in 1969. For spring migrants, 32 species (62%) showed a decline in abundance at Manomet since 1970, while six species (12%) increased significantly, and 14 species (27%) neither increased nor declined overall from 1970 to 2015. Several species demonstrated notable peaks and dips in abundance during the intervening years (see fall cluster 7 in Fig 4 for an example of this).

The group (cluster) affiliations of species based on life-history characteristics showed no concordance with cluster affiliations based on patterns of population size over time (Table 2). This was true for both fall and spring, where their models never had support over the null model. The same is true for median passage date for fall birds, but not for spring birds. In spring, median date of passage was the best-supported model (Table 2), explaining a moderate amount of the variation in species membership between the two cluster types (maximum likelihood pseudo-r2 = 0.56).

Table 2

Multinomial modeling results.

Model (fall)	k	ΔAICc	ω	Model (spring)	k	ΔAICc	ω
Null model	6	0.0	0.946	Median arrival date	18	0.0	0.70
Median arrival date	12	5.9	0.048	Null model	9	3.3	0.13
Life history	12	10.3	0.006	Life history + median arrival	27	3.4	0.13
Life history + median arrival	18	17.0	<0.001	Life history	18	5.6	0.04

Results of multinomial models relating species affiliations with clusters based on patterns of change with the same number of clusters based on life-history characteristics (dependence on open habitats, nesting on or very near the ground, human commensals, and whether or not the species was a spruce budworm specialist). Degrees of freedom (k), differences in Akaike Information Criterion corrected for small sample size (ΔAICc), and model weights (ω) are reported.

Our clustering analyses that evaluated species similarities in population size change over time left 47 fall species and 47 spring species not affiliated with any cluster. Of these, 27 species were found unclustered in both seasons.

Discussion

In general, the patterns of abundance observed at Manomet signal that many of our landbird species are in trouble. With more than 60% of all species in both fall and spring showing significant declining trends, and fewer than 15% apparently increasing, our data support the widespread conservation concern that has hovered around North American migratory songbirds for decades [7,51]. That said, the lack of association between our trend clusters and life history traits suggests that simple narratives about the species most vulnerable to decline might not suffice.

For example, aerial insectivores are frequently cited as an avian group particularly at risk for decline [52,53]. Indeed, our data and the Breeding Bird Survey agree that Least Flycatchers and Eastern Wood-Pewees are probably declining in northeastern North America, yet the signals for Yellow-bellied Flycatchers are decidedly mixed, and Eastern Phoebes and Great Crested Flycatchers appear to be stable or increasing. Eastern Phoebes are short-distance migrants, but Great Crested and Yellow-bellied Flycatchers are Neotropical wintering birds just as Least Flycatcher is, and all use a wide variety of forested and second-growth habitats on their shared Central American wintering grounds [54-56]. Least Flycatchers are certainly declining in the eastern portion of their range, but the performance of other flycatchers suggests that the explanation cannot be as simple as “loss of aerial insect food” or “loss of habitat in the Neotropics.”

Magnolia Warbler is another interesting case. Declining at Manomet in the fall but increasing in the spring, the Breeding Bird Survey indicates that breeding populations north of Massachusetts are stable or increasing. It may be the case that Magnolia Warblers coming north in the spring represent a mixture of many breeding populations that separate somewhere north of Manomet. Fall birds may be primarily hatch-year birds from the breeding populations northeast of Manomet in New Brunswick and Nova Scotia, which are locally declining according to the Breeding Bird Survey. Magnolia Warblers are known to take more eastern routes in fall than in spring [57], and this serves as a good example of how migration capture data can supplement breeding-season surveys to complete an otherwise puzzling narrative. Such “loop migration” has also been demonstrated in Blackpoll Warblers. Via stable isotope analysis, northward spring migrant Blackpolls at Manomet have been linked to breeding populations east of Hudson Bay, while fall migrant Blackpolls that congregate at Manomet before crossing the Atlantic appear to be from western breeding populations [58].

Similarly, birds exhibiting similar trends over time are not necessarily responding to the same threats. Blue Jay and Wood Thrush, for example, show remarkably similar declines in spring abundance in cluster 10 (Fig 5). Aside from being broadly associated with “forests,” however, these birds have almost nothing in common. They have different diets, different migration routes, different wintering grounds, and different nesting habits [59,60]. Yet, both are significantly declining in the New England region in both Manomet captures and Breeding Bird Survey abundance (Table 1). Vulnerability to cowbird parasitism, complex edge dynamics, and loss of Neotropical wintering habitat have all been implicated in Wood Thrush declines [59], but these seem unlikely to be major factors in the decline of Blue Jays [61]. Finally, discrepancies between patterns of abundance at Manomet and apparent trends in the Breeding Bird Survey underscore the complexity of avian population dynamics. Black-capped Chickadees are non-migratory and one of the most frequently caught birds at Manomet. In both fall and spring, chickadee captures have declined considerably since banding started at Manomet. The Breeding Bird Survey, by contrast, shows chickadee populations are stable or increasing in all regions nearby. Closer examination of the Black-capped Chickadee fall trends (S2 Table) shows that the apparent decline is driven by several “spikes” in fall captures during 1971 and the 1980s. Breeding seasons with high fledging success followed by a poor autumn seed crop have been shown to lead to these irruptive movements in an otherwise non-migratory species [62]. An increase in winter bird feeding by humans and milder winters as a result of climate change may have resulted in fewer large-scale autumn movements of Black-capped Chickadees, but determining the true population trend of northeastern chickadees from these data is not a straightforward enterprise. Apparent fall declines at Manomet in other species with short or facultative migration, such as Yellow-rumped Warbler, may similarly reflect changes in migratory behavior rather than actual population declines [63], but more complex dynamics may also be at play.

Even so, for many species, trends at Manomet and those reported by the Breeding Bird Survey are in agreement, and the overall picture is a troubling one. Our findings on the prevalence of landbird declines are consistent with those of earlier analyses of Manomet data [31] as well as data from other northeastern migration sites [64,65]. Many migrant songbirds are showing significant long-term declines in migratory cohort size. This phenomenon is evident in Neotropical migrants, but also in many common and familiar species that migrate only short distances, such as American Robin and Blue Jay. Many of the species that show significant increasing trends in Manomet capture rates are resident human commensal species, such as Tufted Titmouse, Northern Cardinal, and White-breasted Nuthatch. Other increasing birds like Yellow Palm Warbler and Blue-headed Vireo are likely individuals wintering in the Gulf states of the USA rather than in the Neotropics.

Generally speaking, of the 28 species that did not fall into any significant clusters in either season, many had nearly horizontal abundance trendlines over time, either broadly stable (e.g., Hermit Thrush, S1 Table) or slowly declining (e.g., American Robin, Canada Warbler, S1 Table). Many were species that Manomet catches in relatively small numbers (e.g., Ruby-throated Hummingbird, Yellow-bellied Sapsucker, Great Crested Flycatcher). There were no obvious life history traits in common among these species, although smaller groups of birds with commonalities do exist within the group. A few (Bay-breasted Warbler, Cape May Warbler, and Tennessee Warbler) showed trends clearly driven by spruce budworm outbreaks in the 1970s [66], and a few exhibited strange patterns that defy easy description but may be related to unpredictable captures of wandering foraging flocks in late fall (e.g., American Goldfinch, Cedar Waxwing; see S1 Table).

Interestingly, the large proportion of bird trend clusters defy simple mechanistic categorization. Some life-history traits were consistent with time-series groupings; for example, the spring significant trend cluster of Northern Cardinal and Tufted Titmouse accurately reflects the similar life histories of these resident seed-eating backyard birds. However, there are no apparent connections between species in many of the other clusters. The poor ability of life-history traits to predict time-series trend cluster membership strongly suggests that there are many “paths” to the same apparent abundance trajectory, as shown by the example of Blue Jay and Wood Thrush, above.

Even the largest clusters of species show few (or no) commonalities in life history. Fall cluster 1 (Yellow-bellied Flycatcher, Brown Creeper, Winter Wren, Golden-crowned Kinglet, Brown Thrasher, “Myrtle” Warbler, Blackpoll Warbler, Common Yellowthroat, and Dark-eyed Junco) are united by the fact that they all declined sharply in abundance at Manomet through the mid-1980s, and then leveled off to slower declines or near-stability. The closest apparent unifying life-history trait among them is association with mature conifer-dominated forests for breeding. However, two of these species (Brown Thrasher and Common Yellowthroat) do not use mature conifer woods at all, instead preferring disturbed areas of brushy shrubland for breeding, and other species that are strongly associated with breeding in conifer woodlands (such as Blue-headed Vireo and Black-throated Green Warbler) did not associate with this cluster.

The predictive utility of median date of migration in predicting spring trends underscores a point made by previous researchers about the importance of weather in the movements of migrating birds [30,67]. Large movements can occur with little warning as conditions change, and birds with similar migratory timing may be caught in large numbers or missed altogether depending on whether nets are open during a major migratory fallout [68]. Spring migration, especially, is a time when migrating birds are attempting to return to the breeding grounds as quickly as possible in order to secure high-quality territories and mates [69]. For example, as previously mentioned, spring trends for Blue Jay and Wood Thrush align very neatly and clustered significantly in the hierarchical analysis (Fig 5), but in terms of life history, these two birds share very little ecologically beyond being “forest birds” in the broadest possible sense. However, their peak spring migration dates are the same, and so their annual spring capture rates were likely strongly influenced by weather during peak migration. That both birds are declining is supported by their Manomet abundance trends and by the Breeding Bird Survey [13], but again, it is likely that they are declining for different reasons.

As with any migration study based on data from a single site, even if that site is drawing birds from larger breeding and wintering ranges, these results should be interpreted with care. Birds captured at Manomet do not compose a random sample of any species’ populations, but in many cases they integrate data from several breeding populations, as well as from birds whose breeding grounds are too remote for effective surveys by other methods [30]. Recent work from Long Point Bird Observatory and Powdermill Avian Research Center has confirmed that banding stations tend to capture fall juvenile birds out of proportion with their abundance in the population [70]. Manomet’s coastal location probably amplifies this effect, especially in the fall [23], since younger inexperienced birds are more likely to become disoriented or exhausted at coastal sites and land there [71]. Where fall and spring apparent trends differed, as in 13 of 73 (18%) of the species analyzed here, such differences are likely attributable to 1) differences in fecundity (fall) and overwinter survival (spring), 2) differences in the breeding populations being sampled in each season, as for Magnolia Warbler or Blackpoll Warbler, 3) species taking different migratory routes in each season, and 4) differences in age structure of migratory populations sampled at coastal sites. In this way, as long as the data are interpreted with a careful understanding of their limitations, insights can be drawn from migratory data that breeding and wintering surveys alone cannot provide.

In an era of rapid global change, studies using migration data can detect potential behavioral shifts such as those of Black-capped Chickadees and Yellow-rumped Warblers, above. Such population-wide changes in migratory behavior in wild birds have already been observed [72,73]. As winters in northern North America grow milder and storms become more unpredictable, it seems likely that short-distance and facultative migrant species will overwinter farther north than in the past, or in some cases, not migrate at all.

This table indicates which life history traits were assigned to each species, which cluster that species was sorted into in fall and spring based on life history traits (lifehist.num.f and lifehist.num.s), which statistically significant cluster that species was sorted into in fall and spring (trend.f and trend.s), the start date, end date, and length (in days) of each species’ window of passage in fall and spring, median date of passage (in ordinal day of year) in fall and spring, and an estimate of the species’ apparent trend in fall and spring abundance at Manomet over the full study period.

(XLSX)

Click here for additional data file.

The number of new fall captures of each species by Institute for Bird Populations (IBP) four-letter code for each year from 1969–2015.

NET.HOURS indicates the total number of effort-hours for that season.

(CSV)

Click here for additional data file.

The number of new spring captures of each species by Institute for Bird Populations (IBP) four-letter code for each year from 1970–2015.

NET.HOURS indicates the total number of effort-hours for that season.

(CSV)

Click here for additional data file.

8 Jul 2019

PONE-D-19-15287

A Half-century of Changes in Migratory Landbird Numbers along Coastal Massachusetts

PLOS ONE

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Additional Editor Comments (if provided):

Both reviewers recommend minor revision and enjoyed your study. I agree with them. However, please go through their comments and make the suggested changes, or explain why not for those that you do not change.

I have pasted the review letter from Dick Veit, which came directly to me rather than the PLOS ONE site. The other review letter is uploaded below.

In addition to the comments from the reviewers, I also have a few minor comments:

1) You may want to check that the analyses also work with 1970 spring data excluded, as for 1969.

2) Check the k values in Table 2. Why are they so different for similar variables between fall and spring?

3) Please stick to one format for the list of references. You have several varieties, some of which are not normally used in scientific writing (e.g. giving issue numbers, capitalizing all words in an article title, etc.).

Veit's review:

Detailed comments below. I found this to be a very interesting paper, well suited to publication in PLoS One. I think that the use of long-term migration data for estimating population trends has been an underutilized resource, but clearly has its strengths as pointed out by the authors. Migration data are not without limitations (also pointed out by the authors) but especially in combination with BBS and CBC data provide a powerful tool. I like the use of the clustering techniques to try to identify what species cluster together on the basis of population trend, and the results are interesting to a broad audience interested on climate change and its impacts on any organisms –these results suggest a diversity of species-specific responses ; this broad appeal makes the paper particularly suitable to PLoS ONE (as opposed to an ornithology journal). I am not an expert on state space models but know enough about the approach and feel that the techniques used were appropriate, and interesting in that I have never seen them used in quite this context.

Furthermore, the MBO database on migration is truly extraordinary for its persistence and continuity since 1966, and that in itself lends credibility to the results of this study.

1.) With most species showing significant net declines in at least one season (49/73 species evaluated). How can a species decline in one season?

2.) Because banding stations are typically situated at narrow points in migration Please use different wording here or explain more fully. I think what you mean is that, since, during migration, birds of a variety of species breeding in a variety of localities tend to co-occur at the same places during migration, counts of migrants integrate population trends over broader spatial scales than is possible with censuses of breeding birds. If that is indeed what you mean, then I would emphasize this point more strongly here and in the abstract. Ornithologists have a bias against counts of migrants because they think such counts are too “messy”, whereas in some sense the opposite is true.

3.) For a more thorough account of local conditions, see (delete this phrase, top of page 6)

4.) Approval of the work by an ethics committee is not required for catching and banding

birds in the United States. Such requirement is legally determined by the institution concerned; I would delete if I were you!

5.) Because Willow Flycatcher and Alder Flycatcher cannot be separated reliably in the hand. I disagree, and think the evidence is strong that the majority can be identified (Pyle, latest edition). Do you have guesses? If so it seems there are changes going on with both species, and it would be interesting to identify migrants (and dates of migration). As a contrast to your next statement, I suspect that Willow and Alder flycatchers are more reliably separated than are subspecies of Palm Warblers.

6.) (distinguished by physiological signs of breeding

readiness, or hatch-year birds caught on spring migration). What do you mean by a HY bird here - I guess a < 1 yr old bird in may would be SY rather than HY but in a sense it is a “hatching year bird”. So you are talking about baby chickadees and the like that you capture during May? My first reaction was that you excluded all SY birds in spring, which I am sure is not true. Maybe phrase a bit more clearly.

7.) Page 7, second to last paragraph. How does excluding captures from outside the peak migration periods affect your results for species whose peak migration periods have changed (some quite substantially, I think)?

8.) being a spruce budworm (Choristoneura sp.) specialist. I wouldn’t argue with any species previously determined to be a spruce budworm specialist, but what is the basis for saying any given species is not? Might not any passerine nesting in boreal forests benefit from outbreaks of these caterpillars?

9.) What is your basis for saying that any species lacks plasticity in migration strategy? MØller is a proven fraud; I haven’t read Both 2010. I am suspicious, and think that most species are fairly plastic, given the “incentive” to be.

10.) Page 20 typo Blackpool Warblers

Dick Veit

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: This is a well thought out paper that analyzes long-term banding data in an effort to determine trends in bird populations. The results and implications of the work are of great value, especially since there are many challenges in the interpretation of data from banding efforts during migration.

Complicating factors related to synthesizing data collected in this manner (e.g. mixing of populations of species from different regions with different population trends, weather patterns related to overall abundance observed as captures at a single location) are discussed sufficiently and effectively. And the fact that results of this study generally reflect population declines documented using different methodologies and study designs, lends support to the authors' conclusions and highlights the importance of publishing this paper in the peer reviewed literature.

A few comments below on how revising and expanding the interpretation of the data could provide additional insight and thoughts for follow up work:

1. A more expanded interpretation of each cluster would be useful. While some discussion of species in each cluster is provided, more detail on the species clustering can provide a better understanding for the reader about what the authors' thoughts are about species composition in each cluster.

2. In addition, are there any characteristics of the species that don't belong in any cluster?

3. There is no concordance in species clustering for spring and fall, and the authors explain that this as the result of potentially the result of sampling different breeding populations in different seasons or species using different migratory routes. While this may not be the broader purpose of the paper, looking at patterns more closely and providing a better in-depth understanding of the discrepancies would be useful.

4. Clusters consist of different number of species (2-9 species). It would interesting to understand if similarities in life history traits may be more strongly reflected in species belonging to larger clusters. In other words, while the lack of similarity in spring cluster 10 (Blue Jay and Wood Thrush) is discussed, are there stronger similarities in the larger groups(for example fall group 1). Do these greatly dissimilar clusters of 2 drive the lack of relation between species affiliation and cluster? That should be further explored.

Thanks for the opportunity to review a very nice project!

**********

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16 Aug 2019

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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Response: We have revised our formatting to comply with PLOS ONE’s style requirements.

1. Thank you for including the following funding information; "This work was internally funded."

a. Please provide an amended Funding Statement that declares *all* the funding or sources of support received during this specific study (whether external or internal to your organization) as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now.

b. Please state what role the funders took in the study. If any authors received a salary from any of your funders, please state which authors and which funder. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

Response: Our amended funding statement is as follows. This work was funded entirely by the generosity of Manomet’s friends, donors, and trustees. This funding paid the salaries of MK, MH, and TLE during their work on this project. The donors had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All data collection was done by Manomet employees or volunteers; design of this study, data analysis, decision to publish, and preparation of the manuscript was done by the authors with no input from any donor or trustee.

Additional Editor Comments (if provided):

Both reviewers recommend minor revision and enjoyed your study. I agree with them. However, please go through their comments and make the suggested changes, or explain why not for those that you do not change.

I have pasted the review letter from Dick Veit, which came directly to me rather than the PLOSONE site. The other review letter is uploaded below.

In addition to the comments from the reviewers, I also have a few minor comments:

1) You may want to check that the analyses also work with 1970 spring data excluded, as for 1969.

Response: We repeated the analyses with spring 1970 excluded, and found some changes in cluster membership. Some clusters remained the same, others gained a species, and some new significant clusters formed from species not previously included in any cluster. The same happened when we then excluded 1971, making spring 1972 the first year. Ultimately, the shape of the trend (which is the basis for the clusters) is undoubtedly sensitive to the starting values, and this will remain true regardless of which year is chosen as the first. We have re-checked the effort distribution and count data from 1970 and found the data quality to be consistent with that of the several following years. Therefore, in the interest of covering as many years as possible. we have elected to leave 1970 as the first year of spring coverage.

2) Check the k values in Table 2. Why are they so different for similar variables between fall and spring?

Response: We thank the editor for bringing this to our attention. This was due to an error in data structures, wherein median date of arrival in the spring was stored as a factor with 45 unique levels, rather than as a numeric quantity. This has been corrected, and the model ranks remained unchanged. Values in Table 2 have been updated accordingly.

3) Please stick to one format for the list of references. You have several varieties, some of which are not normally used in scientific writing (e.g. giving issue numbers, capitalizing all words in an article title, etc.).

Response: Revisions have been proofread and re-formatted to meet journal guidelines.

Veit's review:

Detailed comments below. I found this to be a very interesting paper, well suited to publication in PLoS One. I think that the use of long-term migration data for estimating population trends has been an underutilized resource, but clearly has its strengths as pointed out by the authors. Migration data are not without limitations (also pointed out by the authors) but especially in combination with BBS and CBC data provide a powerful tool. I like the use of the clustering techniques to try to identify what species cluster together on the basis of population trend, and the results are interesting to a broad audience interested on climate change and its impacts on any organisms –these results suggest a diversity of species-specific responses ; this broad appeal makes the paper particularly suitable to PLoS ONE (as opposed to an ornithology journal). I am not an expert on state space models but know enough about the approach and feel that the techniques used were appropriate, and interesting in that I have never seen them used in quite this context.

Furthermore, the MBO database on migration is truly extraordinary for its persistence and continuity since 1966, and that in itself lends credibility to the results of this study.

1.) With most species showing significant net declines in at least one season (49/73 species evaluated). How can a species decline in one season?

Revision made: With most species showing significant net declines in migratory cohort size in spring, fall or both (49/73 species evaluated).

2.) Because banding stations are typically situated at narrow points in migration. Please use different wording here or explain more fully. I think what you mean is that, since, during migration, birds of a variety of species breeding in a variety of localities tend to co-occur at the same places during migration, counts of migrants integrate population trends over broader spatial scales than is possible with censuses of breeding birds. If that is indeed what you mean, then I would emphasize this point more strongly here and in the abstract. Ornithologists have a bias against counts of migrants because they think such counts are too “messy”, whereas in some sense the opposite is true.

Revision made: Because banding stations are typically situated in areas such as mountain gaps, desert oases, and coastal points where birds from many breeding populations naturally aggregate during migration…

Revision made: Migration counts are prone to large interannual variation and trends are often difficult to interpret, but have the advantage of sampling many breeding populations in a single locale

3.) For a more thorough account of local conditions, see (delete this phrase, top of page 6)

Revision made.

4.) Approval of the work by an ethics committee is not required for catching and banding

birds in the United States. Such requirement is legally determined by the institution concerned; I would delete if I were you!

Revision made.

5.) Because Willow Flycatcher and Alder Flycatcher were not separated reliably in the hand for the first three decades of this study. I disagree, and think the evidence is strong that the majority can be identified (Pyle, latest edition). Do you have guesses? If so it seems there are changes going on with both species, and it would be interesting to identify migrants (and dates of migration). As a contrast to your next statement, I suspect that Willow and Alder flycatchers are more reliably separated than are subspecies of Palm Warblers.

Revision made: Relevant wing formula data for the separation of Alder Flycatcher and Willow Flycatcher were not collected for more than half of the study period, thus we have adopted the parsimonious strategy of not separating these two species trends and both are presented as “Traill’s Flycatcher.”

Response: Although many Traill’s complex Empidonax can be identified with modern methods, we lack the recorded wing data to do so with more than half of the Traill’s records in this dataset. It has not been our experience that Willow and Alder flycatcher are more reliably separated than the Palm Warbler subspecies.

6.) (distinguished by physiological signs of breeding

readiness, or hatch-year birds caught on spring migration). What do you mean by a HY bird here - I guess a < 1 yr old bird in may would be SY rather than HY but in a sense it is a “hatching year bird”. So you are talking about baby chickadees and the like that you capture during May? My first reaction was that you excluded all SY birds in spring, which I am sure is not true. Maybe phrase a bit more clearly.

Revision made: distinguished by physiological signs of breeding readiness, or local fledgling birds caught during spring migration…

7.) Page 7, second to last paragraph. How does excluding captures from outside the peak migration periods affect your results for species whose peak migration periods have changed (some quite substantially, I think)?

Response: Since only 2% of all captures are excluded and the peak window is calculated across all years, only the very earliest and very latest records are excluded; the use of the full dataset to calculate peak migration makes the method sensitive to shifts in peak migration.

8.) being a spruce budworm (Choristoneura sp.) specialist. I wouldn’t argue with any species previously determined to be a spruce budworm specialist, but what is the basis for saying any given species is not? Might not any passerine nesting in boreal forests benefit from outbreaks of these caterpillars?

Response: The reviewer makes a good point; our definition is particular to birds that breed mostly or only in the boreal forest and whose abundance patterns at Manomet seem to be primarily determined by the abundance of spruce budworm in the preceding breeding season.

9.) What is your basis for saying that any species lacks plasticity in migration strategy? MØller is a proven fraud; I haven’t read Both 2010. I am suspicious, and think that most species are fairly plastic, given the “incentive” to be.

Revision made: …have suggested that birds with longer migrations may be adjusting their migratory behavior less, and may fare especially poorly in response to climate change (Butler 2003, Both et al. 2010)

Response: It is true that it is difficult to separate a lack of response from a lack of ability to respond; we have adjusted our language to reflect the widely-documented disparity in adjustment of migratory timing between short- and long-distance migrants. We have replaced the reference to Møller et al.

10.) Page 20 typo Blackpool Warblers

Revision made: Blackpoll Warblers

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a well thought out paper that analyzes long-term banding data in an effort to determine trends in bird populations. The results and implications of the work are of great value, especially since there are many challenges in the interpretation of data from banding efforts during migration.

Complicating factors related to synthesizing data collected in this manner (e.g. mixing of populations of species from different regions with different population trends, weather patterns related to overall abundance observed as captures at a single location) are discussed sufficiently and effectively. And the fact that results of this study generally reflect population declines documented using different methodologies and study designs, lends support to the authors' conclusions and highlights the importance of publishing this paper in the peer reviewed literature.

A few comments below on how revising and expanding the interpretation of the data could provide additional insight and thoughts for follow up work:

1. A more expanded interpretation of each cluster would be useful. While some discussion of species in each cluster is provided, more detail on the species clustering can provide a better understanding for the reader about what the authors' thoughts are about species composition in each cluster.

Response: See response to point 4.

2. In addition, are there any characteristics of the species that don't belong in any cluster?

Revision made: Generally speaking, of the 28 species that did not fall into any significant clusters in either season, many had nearly horizontal abundance trendlines over time, either broadly stable (e.g., Hermit Thrush, Supplemental Material) or slowly declining (e.g., American Robin, Canada Warbler, Supplemental Material). Many were species that Manomet catches in relatively small numbers (e.g., Ruby-throated Hummingbird, Yellow-bellied Sapsucker, Great Crested Flycatcher). There were no obvious life history traits in common among these species, although smaller groups of birds with commonalities do exist within the group.

Response: We have expanded our discussion of this group of species.

3. There is no concordance in species clustering for spring and fall, and the authors explain that this as the result of potentially the result of sampling different breeding populations in different seasons or species using different migratory routes. While this may not be the broader purpose of the paper, looking at patterns more closely and providing a better in-depth understanding of the discrepancies would be useful.

4. Clusters consist of different number of species (2-9 species). It would interesting to understand if similarities in life history traits may be more strongly reflected in species belonging to larger clusters. In other words, while the lack of similarity in spring cluster 10 (Blue Jay and Wood Thrush) is discussed, are there stronger similarities in the larger groups(for example fall group 1). Do these greatly dissimilar clusters of 2 drive the lack of relation between species affiliation and cluster? That should be further explored.

Revision made: Even the largest clusters of species show few (or no) commonalities in life history. Fall cluster 1 (Yellow-bellied Flycatcher, Brown Creeper, Winter Wren, Golden-crowned Kinglet, Brown Thrasher, “Myrtle” Warbler, Blackpoll Warbler, Common Yellowthroat, and Dark-eyed Junco) are united by the fact that they all declined sharply in abundance at Manomet through the mid-1980s, and then leveled off to slower declines or near-stability. The closest apparent unifying life-history trait among them is association with mature conifer-dominated forests for breeding. However, two of these species (Brown Thrasher and Common Yellowthroat) do not use mature conifer woods at all, instead preferring disturbed areas of brushy shrubland for breeding, and other species that are strongly associated with breeding in conifer woodlands (such as Blue-headed Vireo and Black-throated Green Warbler) did not associate with this cluster.

Response: We appreciate these comments, and agree that there is much of interest in delving into the specific details of each group. However, it is our opinion that adding individual analysis for each of the seventeen significant clusters would add considerably to the length of the paper without adding significantly to the paper’s specific narrative; no groups had strong life history connections beyond those already mentioned in the text. We have added the above analysis of fall cluster 1 as an illustrative example; it is possible to propose explanations for cluster membership in each case, but every cluster includes exceptions to these explanations and lacks species that would be expected if the explanations were primary driving forces.

22 Aug 2019

PONE-D-19-15287R1

A half-century of changes in migratory landbird numbers along coastal Massachusetts

PLOS ONE

Dear Mr. Kamm,

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Additional Editor Comments (if provided):

Thank you very much for the detailed responses to the questions from the reviewers and editors, and for the changes made to the manuscript.

Before accepting the article for publication, there are two questions I have that should be addressed:

1) I had asked "Check the k values in Table 2. Why are they so different for similar variables between fall and spring?" and your response was "We thank the editor for bringing this to our attention. This was due to an error in data structures, wherein median date of arrival in the spring was stored as a factor with 45 unique levels, rather than as a numeric quantity. This has been corrected, and the model ranks remained

unchanged. Values in Table 2 have been updated accordingly."

When I look at Table 2, I see only changes to the delta-AICc values and model weights but not to k. Is this correct? If not, then please correct the values. If yes, then please add a sentence to the caption explaining why.

2) The paper cited by Dorian et al. is not published (listed as "in review"). Is there a pre-print or something else you can cite? If you don't have more information, then this reference should be removed from your manuscript. If there is something more specific to cite, then the numbering for citations would also need to change.

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Reviewers' comments:

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22 Aug 2019

Dear Handling Editors,

Thank you very much for the opportunity to revise our manuscript, “A half-century of changes in migratory landbird numbers along coastal Massachusetts. We have made both of the requested revisions. Our responses and revisions are detailed in the letter below, along with the feedback that prompted them.

1) I had asked "Check the k values in Table 2. Why are they so different for similar variables between fall and spring?" and your response was "We thank the editor for bringing this to our attention. This was due to an error in data structures, wherein median date of arrival in the spring was stored as a factor with 45 unique levels, rather than as a numeric quantity. This has been corrected, and the model ranks remained

unchanged. Values in Table 2 have been updated accordingly."

When I look at Table 2, I see only changes to the delta-AICc values and model weights but not to k. Is this correct? If not, then please correct the values. If yes, then please add a sentence to the caption explaining why.

Revision made: We have corrected the k values; this was missed in the last round of corrections.

2) The paper cited by Dorian et al. is not published (listed as "in review"). Is there a pre-print or something else you can cite? If you don't have more information, then this reference should be removed from your manuscript. If there is something more specific to cite, then the numbering for citations would also need to change.

Revision made: We have removed this reference.

26 Aug 2019

A half-century of changes in migratory landbird numbers along coastal Massachusetts

PONE-D-19-15287R2

Dear Dr. Kamm,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Brian G. Palestis, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for making these changes.

Reviewers' comments:

29 Aug 2019

PONE-D-19-15287R2

A half-century of changes in migratory landbird numbers along coastal Massachusetts

Dear Dr. Kamm:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE.

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on behalf of

Dr. Brian G. Palestis

Academic Editor

PLOS ONE