Long-term climate impacts on breeding bird phenology in ... · To determine breeding season cutoff dates to use for all cap-ture metrics, capture frequency data for each species were

Long-term climate impacts on breeding bird phenology inPennsylvania, USAMOLLY E . MCDERMOTT and LUCAS W. DEGROOTE

Powdermill Nature Reserve, Carnegie Museum of Natural History, 1847 Route 381, Rector, PA, USA

Abstract

Climate change is influencing bird phenology worldwide, but we still lack information on how many species are

responding over long temporal periods. We assessed how climate affected passerine reproductive timing and pro-

ductivity at a constant effort mist-netting station in western Pennsylvania using a model comparison approach. Sev-

eral lines of evidence point to the sensitivity of 21 breeding passerines to climate change over five decades. The

trends for temperature and precipitation over 53 years were slightly positive due to intraseasonal variation, with the

greatest temperature increases and precipitation declines in early spring. Regardless of broodedness, migration dis-

tance, or breeding season, 13 species hatched young earlier over time with most advancing >3 days per decade.

Warm springs were associated with earlier captures of juveniles for 14 species, ranging from 1- to 3-day advancement

for every 1 °C increase. This timing was less likely to be influenced by spring precipitation; nevertheless, higher rain-

fall was usually associated with later appearance of juveniles and breeding condition in females. Temperature and

precipitation were positively related to productivity for seven and eleven species, respectively, with negative rela-

tions evident for six and eight species. We found that birds fledged young earlier with increasing spring tempera-

tures, potentially benefiting some multibrooded species. Indeed, some extended the duration of breeding in these

warm years. Yet, a few species fledged fewer juveniles in warmer and wetter seasons, indicating that expected future

increases could be detrimental to locally breeding populations. Although there were no clear relationships between

life history traits and breeding phenology, species-specific responses to climate found in our study provide novel

insights into phenological flexibility in songbirds. Our research underscores the value of long-term monitoring stud-

ies and the importance of continuing constant effort sampling in the face of climate change.

Keywords: breeding, climate change, migratory birds, mist-netting, phenology, precipitation, productivity, temperature

Received 6 October 2015; revised version received 14 April 2016 and accepted 13 May 2016

Introduction

As the mean global temperature continues to increase

(Pachauri et al., 2014), understanding ways in which

organisms are responding to climate change is crucial

to manage populations and predict future responses.

Documented responses by birds include shifts in latitu-

dinal and elevational distributions (Parmesan & Yohe,

2003; Wilson et al., 2005; Hitch & Leberg, 2007) as well

as changes in the timing and duration of migration and

breeding (Gordo, 2007; Lehikoinen & Sparks, 2010).

Migratory species are arriving on breeding grounds

earlier over time, and most are tracking spring temper-

atures such that in warmer springs, birds migrate even

earlier (Cotton, 2003; H€uppop & H€uppop, 2003; Marra

et al., 2005; Mills, 2005; Sparks et al., 2005). Some nest-

ing studies of single species show earlier egg laying

dates with increasing spring temperature (Laaksonen

et al., 2006; Møller et al., 2006; Charmantier et al., 2008;

Wiebe & Gerstmar, 2010; Matthysen et al., 2011), and

multiple species analyses have demonstrated that the

majority of species have advanced laying date in recent

decades (Crick et al., 1997; Dunn, 2004; Torti & Dunn,

2005; Dunn & Møller, 2014).

Phenological shifts can have fitness consequences,

especially for migratory species, and thus are important

to document to better understand the potential effects

of changing climate on bird populations (Smith &

Moore, 2005; Both et al., 2009; Bulluck et al., 2013;

Townsend et al., 2013; Lany et al., 2016; Velmala et al.,

2015). Arrival on the breeding grounds may advance in

warm springs as individuals attempt to maximize

potential reproductive success. Birds may begin nesting

earlier when emergence of vegetative and invertebrate

resources advances (Crick et al., 1997; Winkel & Hudde,

1997; McCleery & Perrins, 1998; Visser et al., 1998; Crick

& Sparks, 1999; Lany et al., 2016) resulting in earlier

hatching and fledging dates (Matthysen et al., 2011).

Although photoperiod is a primary cue for laying onset

in temperate breeding species (Dawson et al., 2001),

there is evidence that timing of laying and gonadal

Correspondence: Lucas W. DeGroote, Powdermill Nature Reserve,

1847 Route 381, Rector, PA 15677, USA, tel. +1 724 593 7521, fax +1

724 593 4554, e-mail: [email protected]

1© 2016 John Wiley & Sons Ltd

Global Change Biology (2016), doi: 10.1111/gcb.13363

development are affected by temperature and other cli-

mate cues (Dawson, 2008; Visser et al., 2009).

Spring temperature also has been shown to influence

the number of nesting attempts in multibrooded spe-

cies (Husby et al., 2009; Møller et al., 2010), with war-

mer springs leading to longer breeding seasons. Early

breeders tend to have larger clutches (Dunn & Møller,

2014) and are more likely to double brood resulting in

higher fecundity (Townsend et al., 2013). Thus, one

potential consequence of advanced breeding due to

warmer temperatures is improved productivity for

individuals that have an opportunity to renest after fail-

ure or raise an additional brood (Wilson & Arcese,

2003; Halupka et al., 2008; Møller et al., 2008). If indi-

viduals synchronize breeding with the timing of their

food supply, early breeders may have higher reproduc-

tive success under favorable climatic conditions (Visser

et al., 2009). Conversely, warm springs could contribute

to a decline in multiple broods or reduced reproductive

success if mismatches in timing of peak resource abun-

dance occur, as some single-species studies have shown

(Visser et al., 2006; Reed et al., 2013). However, this

trophic mismatch hypothesis does not take into account

overall levels of resource abundance during the nesting

season. There is evidence that steady seasonal food

abundance (e.g., insect biomass), which greatly limits

recruitment, can offset the timing mismatch with peak

resources (Durant et al., 2005; Dunn et al., 2011). Thus,

if warm springs positively affect overall food abun-

dance, local productivity and recruitment could be

favored with increasing temperatures (Townsend et al.,

2016).

Precipitation is another important climatic factor

that may influence avian demography by altering

recruitment and survival of young and adults (€Oberg

et al., 2015). Both rainfall and temperature can influ-

ence clutch size, nest survival (directly, or indirectly

through predator impacts), or number of broods in a

given breeding season (Collister & Wilson, 2007; Ska-

gen & Adams, 2012; Cox et al., 2013; Bordjan & Tome,

2014).

In addition to broodedness, another life history trait

that can lead to differential climate responses is migra-

tory distance. Long-distance migrants may be more

vulnerable to climate change because the geographi-

cally separated areas they use are prone to different

effects of climate change (Both et al., 2006; Jonz�en et al.,

2006; Sanderson et al., 2006). Furthermore, long-dis-

tance migrants are unable to utilize climatic cues to

time departure from wintering grounds and instead

rely on photoperiod to time migration (Coppack &

Pulido, 2004). Thus, short-distance migrants tend to

show a greater phenological response than species that

migrate farther (Gienapp et al., 2007). Different species,

or even populations within species, may respond

differentially to changing climate (Visser, 2008). Species

unable to track spring temperature increases may be

more vulnerable and show population declines (Møller

et al., 2008); however, migration distance may be a poor

predictor of phenological responses to climate com-

pared to number of broods, habitat, and diet (Dunn &

Møller, 2014).

Although many have studied the changes in migra-

tion and breeding phenology associated with changing

climate, there has been less focus on phenological

events subsequent to laying date. We still lack informa-

tion on how many species are responding to climate

change during the breeding season, with comparatively

fewer studies conducted in North America than in Eur-

ope, and rarely for a suite of species. Recent efforts such

as the Monitoring Avian Productivity and Survivorship

(MAPS) program (Desante et al., 1995) have been used

to detect population trends and climate impacts on bird

productivity over a span of ~10 years (Nott et al., 2002;

Saracco et al., 2008), yet trends in productivity and

breeding advancement across several decades have not

been explored.

Given that by 2100 annual temperatures in temperate

North America are projected to increase by ≥2–3.5 °C,and both extreme and average precipitation will likely

increase (Pachauri et al., 2014), quantification of pheno-

logical responses to climate change with long-term data

is needed to better understand organismal responses.

Our objective was to assess whether historical climate

has affected the timing of passerine reproduction and

productivity at a constant effort mist-netting station in

western Pennsylvania. The data source is unique in

temporal length and precision, having been collected

year-round in a standardized effort across 53 years by

only a few highly skilled bird banders. We hypothe-

sized that warmer springs would be associated with (1)

earlier nesting and thus, earlier appearance of juveniles;

(2) earlier appearance of breeding condition in females

(brood patch or gravidity), and for multibrooded spe-

cies, an extended season with a longer duration of

breeding condition; and (3) an increased number of

young produced by local nesters for species that double

brood. We expected that spring and summer precipita-

tion would be positively correlated with productivity

as a result of either direct or indirect effects on nesting

success. Additionally, we explored the influence of life

history attributes on reproductive parameters, because

different avian strategies could limit or facilitate

responses to climate change. Specifically, we explored

how number of broods, timing of breeding season, and

migratory status (resident, short, or long distance)

affected responses in reproductive phenology and

productivity.

© 2016 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.13363

2 M. E. MCDERMOTT & L. W. DEGROOTE

Materials and methods

Breeding bird data

Bird banding data were collected from 1961 to 2014 at Pow-

dermill Nature Reserve (PNR; 400 m asl, 40°100N, 79°160W) in

the Laurel Highlands of Westmoreland Co., PA, USA. The 10-

ha banding area is comprised of scrub, wetland, and old field

habitats. The station is operated year-round, typically 3–4 days per week during the breeding season. Number of mist

nets (20–60) and number of hours opened (~6 h) were

recorded daily. Captured birds were fitted with individually

numbered USGS aluminum leg bands, and age, sex, wing

length, fat score, presence of brood patch, and presence of gra-

vidity (an egg clearly determined by feel and weight) were

recorded for each individual prior to release. Although

recorded, we did not utilize data for cloacal protuberance in

breeding males, because gonadal development in males usu-

ally precedes arrival on breeding grounds.

To determine breeding season cutoff dates to use for all cap-

ture metrics, capture frequency data for each species were

examined using Julian date in addition to records on timing of

reproductive events from the Second Pennsylvania Breeding

Bird Atlas (Wilson et al., 2012). The three reproductive timing

indicators were derived as follows. First, we calculated timing

(Julian date) for each species at which point 10% of juveniles

had been captured by year, using this margin to reduce the

effects of outliers. Temporal distributions such as quantiles

are preferable to distributional extremes (e.g., first appear-

ance/arrival) for reflecting phenology of populations, espe-

cially in cases of low sample size and imperfect detectability

(Mills, 2005; Moussus et al., 2010). Recaptures were omitted

from these calculations. Years with <10 captures of juveniles

were dropped, and species were only analyzed if >10 juveniles

were captured for at least 10 years. Second, for breeding con-

dition timing, we calculated the Julian date for each species at

which point 10% of the breeding females (gravid or with

brood patch) were captured by year. Only definitive brood

patch scores were used; that is, stages from the initiation (de-

feathering) of brood patch, complete vascularization, and sub-

sequent patch refeathering (Redfern, 2010). The stages of

brood patch and egg development are hormonally controlled

and highly linked to breeding cycles and thus represent excel-

lent indicators of breeding phenology. Third, breeding condi-

tion duration (range of days) was derived using the number of

days between first and last appearance of gravidity or brood

patch for a particular species For these two breeding condition

metrics, we used species having a minimum of 10 years of

data with >10 recorded individuals with presence of breeding

condition per season. Twenty-one species met minimum

requirements to analyze with juvenile capture date, and we

were able to analyze 18 species with breeding condition tim-

ing and range.

The index of breeding productivity was calculated on a

yearly basis as (juvenile capture rate)/(adult capture rate)

within the breeding window for 21 species. Number of cap-

tures per 100 mist-net hours (capture rate) was determined for

each species, age class, and year, with mist-net hours

calculated within the window used for each species. We used

conservative end dates to remove most autumn migrants from

analyses but still include locally hatched birds and dispersers.

Although not comparable among species due to interspecific

differences in capture probability, this ratio index has been

useful to track changes in breeding productivity, especially at

a single site or habitat type (Nur et al., 2000). We assumed that

our captures reflect timing of reproduction and that our pro-

ductivity index represents local recruitment only, which we

addressed by placing species-specific temporal restrictions on

breeding seasons in our data selection process.

Finally, life history characteristics were compiled for each

species including relative migration distance, broodedness,

diet, and breeding habitat guild (Poole, 2008; Table 1).

Climate data

Climate data were accumulated from February–August for the

study period from weather stations within 40 km of PNR

(N = 21) to account for climatic conditions near the breeding

grounds. We retrieved total daily precipitation and average

daily temperature from the National Oceanic and

Atmospheric Administration (NOAA) climate database

(http://www.ncdc.noaa.gov/cdo-web/search). Precipitation

was summed and temperature was averaged over all possible

three-week intervals.

A sliding window approach (e.g., Wiebe & Gerstmar, 2010;

Matthysen et al., 2011) was applied to delineate appropriate

temporal periods for the climate variables. We used a three-

week period (Ahola et al., 2004) for spring and summer cli-

mate with species-specific cutoff dates. For each species and

response variable combination, we determined the mean tem-

perature and total precipitation for the highest correlated per-

iod of all possible three-week combinations (Table 2). Spring

climate variables were chosen from months preceding and at

initiation of breeding season for the species studied, when

they would be most influenced by resource availability due to

plant and arthropod phenology. In addition, summer precipi-

tation and temperature were included for two of the response

variables, range of breeding condition and productivity index,

with the idea that temperature and precipitation patterns later

in the season influence these metrics, and that responses may

differ within season (€Oberg et al., 2015; Pearce-Higgins et al.,

2015).

Statistical analyses

We constructed linear regression models for comparison with

an Akaike’s information criterion (AIC) selection approach to

examine which climate variables best explained yearly varia-

tion in reproductive timing and productivity (Burnham &

Anderson, 2002). We selected a set of eight candidate models

to evaluate effects on juvenile capture date and breeding con-

dition date. The models we tested included a null model (no

effects), single variable models (year, spring temperature, and

spring precipitation), year + spring temperature, year

+ spring precipitation, spring temperature + spring precipita-

tion, and the global model (all effects). The impact of capture


BREEDING BIRD PHENOLOGY SHIFTS WITH CLIMATE 3

http://www.ncdc.noaa.gov/cdo-web/search

rate on these two metrics was evaluated a priori, and this vari-

able was included in all but the null models when a significant

factor (P < 0.05) for each variable.

Comparison sets of models evaluating length of breeding

season and productivity included several additional models

incorporating summer climate as well. The 15 models we

tested were a null model, single variable models (year, spring

temperature, spring precipitation, summer temperature, and

summer precipitation), year + each climate variable (four

models), spring temperature + spring precipitation, summer

temperature + summer precipitation, year + spring climate,

year + summer climate, and the global model.

We selected the most parsimonious model using AIC

adjusted for small sample sizes (AICc), ranked according to

DAICc, and we evaluated models based on Akaike weight. All

model statistics are included in Appendices S1–4 and those

with a DAICc < 2 were considered equally plausible

(Burnham & Anderson, 2002). All statistical analyses were

performed using R version 3.0.3 (R Core Team, 2015), and

model-averaged parameter estimates were obtained for each

candidate set (weighted averages of all competing models)

using the ‘AICcmodavg’ package (Mazerolle, 2013). This

package computes unconditional standard errors and 95%

confidence intervals, which we used to evaluate relationships

between variables.

Results

Mean annual temperature trends, although positive

over the 53-year study period, were nonsignificant in

our study area (Fig. 1a; F = 2.68, P = 0.11). However,

certain 3-week intervals, particularly in early spring,

showed 1–2 °C temperature increases over time

(Fig. 1b). In general, species showing earlier breeding

over time were responding phenologically to spring

temperature despite high annual variation and only

gradual increases since 1961 (Fig. 1a). Precipitation

relationships also demonstrated a positive but non-

significant temporal trend in our study area (Fig. 2a;

F = 1.32, P = 0.25). Late spring (May and June) saw the

greatest precipitation increases over time, while March

precipitation decreased since 1961 (Fig. 2b). High

Table 1 Species included in climate analyses and their life history and ecological traits. Bird data were collected from a constant

effort mist-netting station during 1961–2014 at Powdermill Nature Reserve, Pennsylvania, USA

Species Season* Mig† Hab‡

Diet§

Mass¶ Broods**F G I

Ruby-throated Hummingbird Archilochus colubris Middle Long Forest . . X 3.3 2

Eastern Phoebe Sayornis phoebe Early Short Open X . X 21.6 2

Red-eyed Vireo Vireo olivaceus Late Long Forest X . X 20.3 2

Black-capped Chickadee Poecile atricapillus Early Short Open . X X 11.0 2

House Wren Troglodytes aedon Middle Short Open . . X 11.0 2

Wood Thrush Hylocichla mustelina Middle Long Forest X . X 48.8 2

American Robin Turdus migratorius Early Short Open X . X 77.3 3

Gray Catbird Dumetella carolinensis Middle Long Open X . X 39.9 3

Cedar Waxwing Bombycilla cedrorum Late Short Forest X . X 33.7 2

Ovenbird Seiurus aurocapilla Middle Long Forest . . X 22.1 2

Common Yellowthroat Geothlypis trichas Late Long Open . . X 10.1 2

Hooded Warbler Setophaga citrina Late Long Forest . . X 10.5 2

American Redstart Setophaga ruticilla Middle Long Forest . . X 7.8 1

Yellow Warbler Setophaga petechia Middle Long Open . . X 9.8 1

Field Sparrow Spizella pusilla Middle Short Open . X X 13.6 2

Song Sparrow Melospiza melodia Early Short Open X X X 24 3

Scarlet Tanager Piranga olivacea Late Long Forest X . X 28.3 1

Northern Cardinal Cardinalis cardinalis Early Resident Open X X X 44.1 2

Rose-breasted Grosbeak Pheucticus ludovicianus Middle Long Forest X X X 45.9 1

Indigo Bunting Passerina cyanea Middle Long Open X X X 14.7 2

American Goldfinch Carduelis tristis Late Short Open . X X 13.6 1

*The breeding season defined by months prior to and at initiation of reproduction. Early = March–April, Middle = April–May,

Late = May–June.†General migration distance.

‡Breeding habitat guild. Open = generalist and early-successional. Forest = mid- to late-successional forest breeders.

§Foraging strategy present during breeding season: F = frugivory, G = granivory, I = insectivory.

¶Average mass in grams.

**Number of broods that are typically raised in a breeding season for the study area.



Table

2Beg

inningdateof3-weekperiodusedforclim

atevariablesbased

onhighestcorrelated

3-weekinterval

within

thebreed

ingwindowsforeach

species.Tem

p/Spring

Tem

p=meantemperature

atbreed

inginitiation;Precip/SpringPrecip=totalprecipitationat

breed

inginitiation;Summer

Tem

p=meantemperature

atpeakoren

dof

breed

ingseason;Summer

Precip=totalprecipitationat

peakoren

dofbreed

ingseason

Species

Juven

ilecapture

date

Breed

ing

condition

date

Len

gth

ofbreed

ingseason

Productivity

Tem

pPrecip

Tem

pPrecip

Spring

Tem

p

Spring

Precip

Summer

Tem

p

Summer

Precip

Spring

Tem

p

Spring

Precip

Summer

Tem

p

Summer

Precip

Ruby-throated

Hummingbird

9May

15April

..

..

..

10April

2April

6August

22July

Eastern

Phoeb

e14

Feb

ruary

9March

..

..

..

14May

10March

22June

10July

Red

-eyed

Vireo

16April

13May

20April

31May

4April

27April

6August

20July

18May

1June

4July

12August

Black-cap

ped

Chickad

ee

7March

28March

6April

16March

6April

15March

10June

7July

4April

22April

18June

11June

House

Wren

25April

22April

21March

20April

1May

28March

22June

19June

24April

12May

24June

16June

WoodThrush

19April

12April

25April

16May

13April

2May

10July

13June

18May

25May

25June

3July

American

Robin

22Feb

ruary

9May

..

..

..

2April

13May

24June

1July

GrayCatbird

25April

14April

4May

5April

5May

5April

6August

29June

13May

13May

30July

28June

Ced

arW

axwing

28April

26May

28April

11May

29April

25April

26June

11August

14April

20April

17June

3July

Oven

bird

5April

15April

13May

27April

9April

8May

2July

7July

3April

7May

4June

6August

Common

Yellowthroat

25May

23May

2May

28April

3April

21May

29June

22June

1April

16April

28June

11June

Hooded

Warbler

26April

14April

17May

17April

31May

1April

11August

9July

2April

2May

17June

2August

American

Red

start

27April

25March

12April

23May

12April

16April

10June

19July

2April

12April

13July

9June

Yellow

Warbler

27April

23May

12May

5April

18April

19April

20June

27July

11May

25May

10June

1July

Field

Sparrow

21April

2May

20March

3April

27April

26May

20July

22July

13April

16March

9July

11June

SongSparrow

3April

8May

16April

4May

13April

2May

28June

28July

4April

11April

26July

25July

Scarlet

Tan

ager

24April

15April

22April

2June

9April

30May

4July

9July

20May

26April

27June

30July

Northern

Cardinal

11March

4April

13May

29May

11May

6May

6July

27June

23April

14April

10June

2June

Rose-breasted

Grosbeak

25April

9April

19May

4June

19April

14April

29June

28June

25April

23April

14July

17July

IndigoBunting

8May

4May

1May

8May

1May

3April

27June

14June

17May

11April

5July

28June

American

Goldfinch

31May

21May

27April

12June

15May

25May

26August

26July

14April

9June

29June

23July



annual variability may be obscuring the trends in both

climate variables, thus accounting for lack of signifi-

cance for the entire season.

Year effects

Year explained significant variation in breeding phenol-

ogy for many species, particularly juvenile capture date

and productivity. Juvenile capture date advanced over

time for 13 of 21 species (Table 3), and advancement

averaged more than 3 days per decade for eight spe-

cies. Earlier appearance of young occurred for both

short- and long-distance migrants regardless of breed-

ing initiation season. Year appeared in models with

DAICc < 2 for early breeders: American Robin (scien-

tific names in Table 1), Black-capped Chickadee, Song

Sparrow; mid-season breeders: American Redstart,

Gray Catbird, Ovenbird, Wood Thrush, and Yellow

Warbler; and late-season breeders: American Goldfinch

(the only species with a positive relationship), Cedar

Waxwing, Common Yellowthroat, Hooded Warbler,

and Scarlet Tanager (Table 3; Fig. 3). Appearance of

young advanced by an average of 22 days for early

breeders over the course of the study, which amounted

to 4 days per decade.

Pooling all species, mean date for breeding condi-

tion advanced 31 days over the study period, or

6 days earlier per decade. We observed advanced tim-

ing of brood patch in Black-capped Chickadees

(Table 4). The American Goldfinch showed later

breeding over time, in terms of both later initial juve-

nile captures and sign of breeding condition in females

(Tables 3 and 4; Fig. 3). We also found evidence of a

longer breeding season over time, as measured by an

increasing range of breeding condition days, for House

Wrens and American Goldfinches (~1 week per dec-

ade; Table 5).

Increased productivity was observed for two species

over the five decades studied: Gray Catbird and Wood

Thrush (by 1.59 and 39, respectively; Table 6).

Decreased productivity over time was observed for

four species: Ruby-throated Hummingbird, Black-

capped Chickadee, House Wren, and Field Sparrow

(Table 6).

(a)

(b)

Fig. 1 Mean temperature trend since 1961 between February

and September (a) and change in temperature from 1961 to 2004

for 3-week intervals (b). The bars represent the onset of each 3-

week interval.

(a)

(b)

Fig. 2 Mean monthly precipitation trend since 1961 between

February and September (a) and change in total precipitation

from 1961 to 2004 for 3-week intervals (b). The bars represent

the onset of each 3-week interval.



Table

3Can

didatemodel

weights

(model

probab

ilities)

comparingeightmodelsforjuven

ilecapture

date(atwhich10

%ofindividualswerecapturedeach

year),when

DAIC

c<2.

Model-averag

edparam

eter

estimates

andunconditional

stan

darderrors

(SE)arepresentedexceptwhen

the95

%confiden

ceinterval

overlapped

zero

(termed

NS=nonsignificant).n=years

with>10

captures.xaresp

eciesforwhichcapture

rate

was

notincluded

inan

ymodel

Species

n

Can

didatemodel

Akaikeweights

Model-averag

edparam

eter

estimates

(SE)

Null

Year

Tem

pPrecip

T+P

Y+T

Y+P

Global

Year

Cap

ture

rate

Tem

pPrecip

Ruby-throated

Hummingbird

45.

..

.0.71

..

.NS

x�1

.54(0.38)

0.00

8(0.003

)

Eastern

Phoeb

e36

..

0.20

.0.52

..

.NS

x1.41

(0.52)

�0.014

(0.007

)

Red

-eyed

Vireo

49.

.0.21

.0.50

..

.NS

x�1

.47(0.53)

0.00

6(0.003

)

Black-cap

ped

Chickad

ee49

..

..

..

.0.75

�0.38(0.12)

x�1

.89(0.72)

0.01

4(0.006

)

House

Wren

44.

.0.48

.0.22

..

.NS

x�3

.02(1.12)

NS

WoodThrush

33.

..

..

0.76

..

�0.45(0.08)

x�1

.67(0.53)

NS

American

Robin

33.

..

..

.0.63

0.35

�0.61(0.17)

xNS

0.02

4(0.006

)

GrayCatbird

53.

..

..

..

0.72

�0.19(0.06)

x�1

.08(0.47)

�0.009

(0.003

)

Ced

arW

axwing

52.

..

..

0.44

.0.55

�0.31(0.07)

x�2

.0(0.56)

NS

Oven

bird

31.

0.27

..

.0.20

0.19

0.24

�0.36(0.13)

xNS

NS

CommonYellowthroat

53.

..

..

0.66

.0.28

�0.24(0.05)

NS

�1.69(0.60)

NS

Hooded

Warbler

36.

..

..

0.78

..

�0.37(0.08)

NS

�1.45(0.36)

NS

American

Red

start

44.

..

..

..

0.60

�0.18(0.08)

x�1

.5(0.61)

0.01

(0.004

)

Yellow

Warbler

24.

0.42

..

.0.33

..

�0.46(0.14)

xNS

NS

Field

Sparrow

220.12

.0.27

0.17

.0.19

..

NS

x�1

.78(0.79)

NS

SongSparrow

53.

..

..

0.58

.0.40

�0.28(0.08)

x�2

.24(0.65)

NS

Scarlet

Tan

ager

37.

..

..

0.63

..

�0.47(0.10)

xNS

NS

NorthernCardinal

53.

..

..

.0.48

.�0

.25(0.12)

xNS

0.01

6(0.006

)

Rose-breastedGrosbeak

400.15

.0.19

0.12

0.27

..

.NS

xNS

NS

IndigoBunting

38.

.0.24

.0.40

..

.NS

x�1

.78(0.76)

NS

American

Goldfinch

52.

..

..

0.66

.0.28

0.21

(0.04)

x�1

.65(0.54)

NS

Cap

ture

rate

=number

ofjuven

ilebirdscapturedper

100net

hours

within

thebreed

ingwindow

foreach

species;Tem

p=meantemperature

atbreed

inginitiation;Precip=to-

talprecipitationat

breed

inginitiation.Meantemperature

andtotalprecipitationforeach

specieswerecalculatedfrom

themost

correlated

3-weektimeinterval

(Tab

le2).T+P

aremodelswithtemperature

andprecipitation.Y

+Taremodelswithyearan

dtemperature.Y

+Paremodelswithyearan

dprecipitation.



Climate effects

The majority of species that we analyzed displayed sen-

sitivity to changes in temperature and precipitation.

Spring temperature was negatively associated with cap-

ture date of young for 14 species, with 1 to >3 days

advancement for every 1 °C increase, and positively

associated with capture date for Eastern Phoebes

(Table 3, Fig. 4). Appearance of brood patch/gravidity

was later in warmer springs for American Redstart, but

earlier in warmer springs for seven species (Wood

Thrush, Gray Catbird, Cedar Waxwing, Common Yel-

lowthroat, Hooded Warbler, Northern Cardinal, and

Indigo Bunting; Table 4), representing three migration

distances and three seasonal breeding categories. Warm

breeding season temperatures (either spring or sum-

mer) appeared to influence the length of the breeding

season for House Wren, Gray Catbird, Cedar Waxwing,

Common Yellowthroat, American Redstart, Indigo

Bunting, and Northern Cardinal, but may have had a

negative effect for Red-eyed Vireo, Black-capped Chick-

adee, Wood Thrush, Hooded Warbler, Field Sparrow,

Rose-breasted Grosbeak, and American Goldfinch

(Table 5). We observed increased productivity in war-

mer springs for seven species, ranked by decreasing

strength of the association: Eastern Phoebe, Black-

capped Chickadee, Rose-breasted Grosbeak, Gray Cat-

bird, Indigo Bunting, Red-eyed Vireo, and Northern

Cardinal. Five species (Cedar Waxwing, Hooded War-

bler, American Redstart, Ovenbird, and American

Goldfinch) showed evidence of decreased productivity

in warmer springs. Furthermore, three species showed

evidence of decreased productivity in warmer

summers: Cedar Waxwing, Rose-breasted Grosbeak,

and American Goldfinch (Table 6).

Increased total precipitation in spring was signifi-

cantly related to later juvenile captures of six species

(Ruby-throated Hummingbird, Red-eyed Vireo,

Black-capped Chickadee, American Robin, American

Redstart, Northern Cardinal, and Indigo Bunting) and

earlier juvenile captures for two species (Eastern

Phoebe and Gray Catbird; Table 3). Precipitation was

also positively related to the later appearance of breed-

ing condition in House Wren, Gray Catbird, Ovenbird,

Yellow Warbler, Song Sparrow, Rose-breasted Gros-

beak, and American Goldfinch, and negatively related

for Red-eyed Vireo (Table 4). Increased spring precipi-

tation resulted in a longer breeding season for two

species and a shorter season for eight, while summer

precipitation positively influenced season length of

seven species and negatively for two (Table 5).

In models where DAICc < 2, summer precipitation

was positively related to productivity for eight species:

House Wren, American Robin, Cedar Waxwing, Com-

mon Yellowthroat, American Redstart, Yellow Warbler,

Field Sparrow, and Northern Cardinal. Spring precipi-

tation was positively related to productivity for six spe-

cies: American Robin, House Wren, Wood Thrush,

Hooded Warbler, American Redstart, and American

Goldfinch (Table 6). Total precipitation was negatively

associated with annual productivity for Eastern Phoebe

(spring and summer), Ovenbird (spring), Hooded War-

bler (summer), Yellow Warbler (spring), Song Sparrow

(summer), Rose-breasted Grosbeak (spring), Indigo

Bunting (spring), and American Goldfinch (summer;

Table 6).

Capture rate effects

Capture rate was included in models for two species

for juvenile appearance candidate sets (Common Yel-

lowthroat and Hooded Warbler), but the model-aver-

aged parameter estimate was not significant for either

(Table 3). Capture rate was included in models for one

species for breeding condition date, having a positive

influence on breeding condition date (later sign of

breeding with increased captures) for Rose-breasted

Grosbeak.

Discussion

Given high yearly variation typical of ecological stud-

ies, long-term research is critical for capturing patterns

and responses to global change (Lindenmayer et al.,

2012). Our study is novel in that we were able to utilize

five decades of breeding data for many species col-

lected in a standardized, constant effort approach to

Fig. 3 Seasonal timing of juvenile captures (the Julian day at

which 10% of young were captured) as a function of year for

one early breeder: Song Sparrow, one mid-season breeder:

Wood Thrush, and two late-season breeders: American Gold-

finch and Cedar Waxwing. Birds were captured over five dec-

ades at a constant effort mist-netting station in Pennsylvania.



Table

4Can

didatemodel

weights

(model

probab

ilities)

comparingeightmodelsforbreed

ingconditiondate(atwhich10

%ofindividualswerecapturedeach

year),when

DAIC

c<2.

Model-averag

edparam

eter

estimates

andunconditional

stan

darderrors

(SE)arepresentedexceptwhen

the95

%confiden

ceinterval

overlapped

zero

(termed

NS=nonsignificant).n=years

with>10

captures.xaresp

eciesforwhichcapture

rate

was

notincluded

inan

ymodel

Species

n

Can

didatemodel

Akaikeweights

Model-averag

edparam

eter

estimates

(SE)

Null

Year

Tem

pPrecip

T+P

Y+T

Y+P

Global

Year

Cap

ture

rate

Tem

pPrecip

Red

-eyed

Vireo

24.

..

0.29

0.18

.0.19

0.25

NS

xNS

�0.026

(0.009

)

Black-cap

ped

Chickad

ee13

.0.49

..

..

..

�1.25(0.50)

xNS

NS

House

Wren

150.13

.0.11

0.30

0.28

..

.NS

xNS

0.02

4(0.010

)

WoodThrush

14.

.0.35

.0.26

..

.NS

x�2

.87(1.22)

NS

GrayCatbird

25.

..

.0.72

..

.NS

x�2

.79(0.78)

0.01

3(0.004

)

Ced

arW

axwing

34.

.0.27

.0.13

0.26

.0.12

NS

x�3

.84(1.66)

NS

Oven

bird

12.

..

0.54

..

..

NS

xNS

0.03

7(0.014

)

CommonYellowthroat

30.

.0.42

..

..

.NS

x�3

.09(1.45)

NS

Hooded

Warbler

10.

.0.68

..

..

.NS

x�6

.82(2.34)

NS

American

Red

start

22.

0.20

0.30

..

0.17

..

NS

x1.63

(0.88)

NS

Yellow

Warbler

160.14

.0.15

0.20

0.36

..

.NS

xNS

0.01

0(0.005

)

Field

Sparrow

130.40

.0.23

0.19

..

..

NS

xNS

NS

SongSparrow

20.

..

0.67

..

..

NS

xNS

0.03

2(0.009

)

Scarlet

Tan

ager

130.15

.0.20

0.19

..

..

NS

xNS

NS

NorthernCardinal

10.

.0.77

..

..

.NS

x�8

.16(2.38)

NS

Rose-breastedGrosbeak

18.

..

0.57

..

..

NS

19.52(7.58)

NS

0.02

8(0.013

)

IndigoBunting

11.

.0.46

..

0.47

..

0.59

(0.27)

x�7

.54(1.77)

NS

American

Goldfinch

410.15

..

0.41

..

..

NS

xNS

0.00

7(0.004

)

Cap

ture

rate

=number

ofad

ultfemales

capturedper

100net

hours

within

thebreed

ingwindow

foreach

species;Tem

p=meantemperature

atbreed

inginitiation;Precip=to-

talprecipitationat

breed

inginitiation.Meantemperature

andtotalprecipitationforeach

specieswerecalculatedfrom

themost

correlated

3-weektimeinterval

(Tab

le2).T+P

aremodelswithtemperature

andprecipitation.Y

+Taremodelswithyearan

dtemperature.Y

+Paremodelswithyearan

dprecipitation.



show patterns in phenological shifts. Indeed, we found

compelling evidence that breeding passerines exhibited

phenological sensitivity to changing climate. As

hypothesized, the majority of species fledged young

earlier, and several exhibited greater productivity with

increasing temperatures. Our results indicate that the

breeding season was extended for some species that

raise more than one clutch, and as predicted, annual

variation in precipitation and temperature both influ-

enced number of young produced for many species.

However, we found no consistent response to climate

change among life history traits, such as diet, body size,

or breeding habitat type, of the most common breeding

birds. Species tended to have similar responses to cli-

matic and temporal variables regardless of migration

distance, breeding season, or broodedness.

Previous studies demonstrate that long-distance

migrants respond less rapidly to temperature increases

on breeding grounds compared to short-distance

migrants (Butler, 2003; Gienapp et al., 2007; MacMy-

nowski & Root, 2007; Miller-Rushing et al., 2008; Saino

et al., 2011; Bitterlin & Van Buskirk, 2014). Timing of

egg laying and gonadal development can be affected by

climate cues (i.e., temperature and rainfall) through

changes in resource phenology (Dawson, 2008; Visser

et al., 2009). Short-distance migrants and residents,

which have better access to information about exoge-

nous cues and rely less on photoperiod, should there-

fore suffer fewer fitness consequences from climate

change (Coppack & Pulido, 2004; Dawson, 2008; Visser,

2008; V�egv�ari et al., 2010). Contrary to these studies,

but consistent with a comparative analysis by Dunn &

Table 5 Candidate model weights (model probabilities) comparing 15 models for breeding condition range, when DAICc < 2.

Model-averaged parameter estimates and unconditional standard errors (SE) are presented except when the 95% confidence

interval overlapped zero (termed NS = nonsignificant). n = years with >10 captures

Species n

Candidate model Akaike weight

Null Year

Spring

Temp

Spring

Precip Y + SPT Y + SPP Y + Spring

Summer

Temp

Summer

Precip Y + SUT

Red-eyed Vireo 24 . . . . . . . 0.17 . 0.29

Black-capped

Chickadee

13 . . . . . . . 0.42 . .

House Wren 15 . . . . . . . . . .

Wood Thrush 14 . . 0.42 . 0.34 . . . . .

Gray Catbird 25 . . . . . . 0.39 . . .

Cedar Waxwing 34 . . 0.08 . . . . 0.17 0.08 .

Ovenbird 12 . . . 0.37 . . . . 0.18 .

Common

Yellowthroat

30 . . 0.16 . . . 0.19 . . .

Hooded Warbler 10 0.11 . 0.29 . . . . . 0.16 .

American Redstart 22 . . . . . . . . . .

Yellow Warbler 16 0.07 . . 0.09 . 0.11 0.09 0.10 0.15 0.10

Field Sparrow 13 . . . . . . . . 0.42 .

Song Sparrow 20 . . . . . . . . 0.26 .

Scarlet Tanager 13 0.14 . . 0.34 . . . . . .

Northern Cardinal 10 . . . . 0.76 . . . . .

Rose-breasted

Grosbeak

18 . . . . . . . . 0.46 .

Indigo Bunting 11 . . . 0.70 . . . . . .

American Goldfinch 41 . . . . . . 0.38 . . .

Spring Temp = mean temperature at breeding initiation; Spring Precip = total precipitation at breeding initiation; Summer

Temp = mean temperature at peak or end of breeding season; Summer Precip = total precipitation at peak or end of breeding

season. Mean temperature and total precipitation were calculated from the most correlated 3-week time interval (Table 2).

Y + SPT are models with year and spring temperature. Y + SPP are models with year and spring precipitation. Y + Spring are

models with year, spring precipitation, and spring temperature. Y + SUT are models with year and summer temperature.

Y + SUP are models with year and summer precipitation. Y + Summer are models with year, summer precipitation, and summer

temperature. Spring are models with spring temperature and precipitation. Summer are models with summer temperature and

precipitation.



Møller (2014), we did not find that residents or short-

distance migrants responded more rapidly to spring

temperature than their long-distance counterparts

whose reproductive timing may be constrained by arri-

val date. However, our finding of advanced breeding

phenology in warmer springs regardless of migratory

distance suggests at least two possibilities. First, long-

distance migrants may be able to compensate after

arriving on the Gulf Coast and advance migration more

quickly to arrive earlier on breeding grounds (Cohen

et al., 2015). Advanced arrival times by several long-

distance migrant species in our study area provide sup-

port for this theory (Van Buskirk et al., 2009). Secondly,

as arrival time is less plastic for long-distance migrants,

in warmer springs they may initiate breeding more

rapidly upon arrival or reduce the time to fledge due to

favorable climate conditions, adapting to a changing

climate in this manner.

We found strong evidence for temporal shifts in

reproductive timing in our 53-year study, especially for

appearance of juveniles, and across species with differ-

ing life history strategies. At PNR, we observed an

advancement of 0.2–0.5 days per year for juvenile cap-

tures, compared to an average of 0.16 days per year

advancement of laying for a suite of 71 species studied

by Dunn & Møller (2014). The impressive yearly

advancement we report tracked spring temperature

changes in eight of 13 species. The temporal shift mea-

sured for the remaining five species may have been

caused by an environmental or endogenous factor we

did not measure. For example, American Robin breed-

ing phenology may be more tied to climate on the

Model-averaged parameter estimates (SE)

Y +SUP

Y +Summer Spring Summer Global Year

Spring

Temp

Summer

Temp

Spring

Precip

Summer

Precip

. . . . . NS NS �6.88 (2.91) NS NS

. . 0.27 . . NS �7.21 (2.58) NS NS NS

0.16 . 0.40 . . 0.62 (0.31) 4.94 (1.89) NS �0.031 (0.011) �0.016 (0.008)

. . . . . NS �19.22 (5.86) NS NS NS

. . 0.48 . . NS 4.33 (1.48) NS �0.031 (0.009) NS

. 0.08 . 0.21 0.08 NS NS 6.30 (3.02) NS NS

. . . . . NS NS NS �0.040 (0.015) 0.023 (0.011)

. . 0.39 . . NS 3.86 (1.26) 4.58 (2.15) �0.020 (0.010) NS

. . . 0.14 . NS �12.08 (4.83) �11.72 (5.55) NS 0.043 (0.018)

. . . 0.48 . NS NS 5.89 (2.23) NS 0.024 (0.009)

. . . 0.10 . NS NS NS �0.020 (0.009) NS

. . . . . NS �4.69 (1.81) NS �0.021 (0.010) 0.026 (0.008)

. . . 0.29 . NS NS NS �0.051 (0.026) 0.076 (0.027)

. . . . . NS NS NS 0.059 (0.026) NS

. . . . . �1.93 (0.59) 16.0 (3.63) NS NS NS

0.18 . . . . NS �6.26 (2.80) NS NS 0.053 (0.017)

. . . . . NS NS 9.45 (4.45) �0.062 (0.012) �0.041 (0.021)

. . . . 0.40 0.87 (0.30) �4.05 (1.76) 0.03 (0.011) NS 0.022 (0.01)



wintering grounds (Zuckerberg et al., 2015). Alterna-

tively, advancement of breeding across our study per-

iod may be the result of the ability to maximize

breeding potential in warm springs without incurring

reduced fitness in years with cold spring temperatures.

American Goldfinch was the only species to exhibit

signs of breeding later over time, in terms of both

female breeding condition and appearance of young.

This species offers particular insight and a valuable

comparison to the others. Because they utilize late-sea-

son resources, the relationship in Fig. 3 may be pre-

dicted when climate change extends the breeding

season later into the year. However, the situation is

complex because, like the other species analyzed,

appearance of juvenile goldfinches advanced in warm

springs. This unique pattern in timing may be related

to specific resource cues such as flower initiation by

thistle, a climate-, and CO2-sensitive plant used by

goldfinches to initiate breeding (Stokes, 1950).

Our findings provide limited evidence that future

warming could potentially benefit multibrooded spe-

cies. House Wrens, Gray Catbirds, Common Yel-

lowthroats, and Northern Cardinals showed an

increase in breeding season duration in warm springs,

but only the Gray Catbird and Northern Cardinal pro-

duced more young with increasing spring

Table 6 Candidate model weights (model probabilities) comparing 15 models for productivity index, when DAICc < 2.

Model-averaged parameter estimates and unconditional standard errors (SE) are presented except when the 95% confidence

interval overlapped zero (termed NS = nonsignificant). n = years with >10 captures

Species n

Candidate model Akaike weight

Null Year

Spring

Temp

Spring

Precip Y + SPT Y + SPP Y + Spring

Summer

Temp

Summer

Precip Y + SUT

Ruby-throated

Hummingbird

47 . 0.18 . . 0.11 0.26 0.12 . . 0.11

Eastern Phoebe 44 . . . . . . . . . .

Red-eyed Vireo 53 . . . . 0.18 . . . . .

Black-capped

Chickadee

51 . . . . 0.30 . 0.32 . . .

House Wren 51 . . . . . . . . . .

Wood Thrush 47 . . . . . 0.43 0.18 . . .

American Robin 46 . . . . . . . . . .

Gray Catbird 53 . . . . . . 0.27 . . .

Cedar Waxwing 53 . . . . . . . . . .

Ovenbird 41 . . . . . . . . . .

Common

Yellowthroat

53 . . . . . . . . 0.09 0.08

Hooded Warbler 40 . . . . . . . . . .

American Redstart 48 . . . . . . . . 0.16 .

Yellow Warbler 45 . . . . . . . . 0.17 .

Field Sparrow 43 . . , . . . . . . .

Song Sparrow 53 . . . . . . . . 0.26 .

Scarlet Tanager 42 . 0.06 0.07 0.06 0.14 0.06 0.11 . 0.07 .

Northern Cardinal 53 . . . . . . . . 0.28 .

Rose-breasted

Grosbeak

47 . . . . . . . . . .

Indigo Bunting 53 . . . . . . . . . .

American

Goldfinch

53 . . . . . . . . . .

Spring Temp = mean temperature at breeding initiation; Spring Precip = total precipitation at breeding initiation; Summer

Temp = mean temperature at peak or end of breeding season; Summer Precip = total precipitation at peak or end of breeding

season. Mean temperature and total precipitation were calculated from the most correlated 3-week time interval (Table 2).

Y + SPT are models with year and spring temperature. Y + SPP are models with year and spring precipitation. Y + Spring are

models with year, spring precipitation, and spring temperature. Y + SUT are models with year and summer temperature.

Y + SUP are models with year and summer precipitation. Y + Summer are models with year, summer precipitation, and summer

temperature. Spring are models with spring temperature and precipitation. Summer are models with summer temperature and

precipitation.



temperatures. Overall, we observed that the species

whose productivity responded positively to climate

variables were a mix of single- and multi-brooded spe-

cies. We expected that multibrooded species would

produce more clutches as birds breed earlier (Brinkhof

et al., 2002; Møller et al., 2008; but see Visser et al., 2003)

and increase breeding duration compared to single-

brooded species (Halupka et al., 2008; Møller et al.,

2010; Dunn & Møller, 2014). In our study, all species

showing positive temporal relationships with local pro-

ductivity were multiple brooders, but so were the spe-

cies with declining productivity. Although we expected

single-brooded species to have a shortened breeding

season or lower productivity associated with warm

springs (Jiguet et al., 2007; Dunn & Møller, 2014), we

found no evidence of either in our small sample of sin-

gle brood breeders.

Fitness consequences of changing breeding phenolo-

gies can be complicated by potential mismatches in tim-

ing of resource abundance with warming temperatures.

Phenological asynchrony may occur in bird species

most sensitive to changes in peak resource abundance

(Visser et al., 2006; Jiguet et al., 2007; Reed et al., 2013),

affecting productivity, especially for insectivorous

migrants (Both & Visser, 2005). Some species at PNR

that were hatching young earlier in warmer springs

Model-averaged parameter estimates (SE)

Y +SUP

Y +Summer Spring Summer Global Year

Spring

Temp

Summer

Temp

Spring

Precip

Summer

Precip

. . . . . �0.20 (0.06) NS NS NS NS

. . . . 0.57 NS 0.99 (0.44) NS �0.008 (0.004) �0.007 (0.003)

. . . . 0.49 NS 0.14 (0.05) 0.15 (0.07) NS NS

. . . . 0.14 �0.08 (0.02) 0.42 (0.18) NS NS NS

0.30 0.19 . . 0.36 �0.033 (0.012) NS NS 0.001 (0.0006) 0.001 (0.0004)

. . . . 0.24 0.06 (0.02) NS NS 0.003 (0.001) NS

. . . . 0.78 NS NS NS 0.001 (0.0004) 0.001 (0.0004)

. . . . 0.35 0.023 (0.009) 0.17 (0.07) NS NS NS

. 0.25 . 0.16 0.39 NS �1.57 (0.74) �2.35 (1.04) NS 0.01 (0.004)

. . 0.21 . 0.48 NS �0.37 (0.15) NS �0.002 (0.0007) NS

0.21 0.14 . 0.08 . NS NS NS NS 0.003 (0.001)

. . 0.20 . 0.53 NS �0.80 (0.31) NS 0.003 (0.001) �0.003 (0.001)

. . 0.26 0.15 . NS �0.56 (0.22) NS 0.003 (0.001) 0.003 (0.001)

0.22 0.12 . 0.12 . NS NS NS �0.001 (0.0004) 0.001 (0.0004)

0.21 0.39 . . . �0.03 (0.01) NS NS NS 0.0009 (0.0004)

0.14 0.14 . 0.34 . NS NS NS NS �0.002 (0.0006)

0.11 . 0.08 . 0.07 NS NS NS NS NS

. . . 0.32 0.20 NS 0.13 (0.06) NS NS 0.001 (0.0004)

. . . . 0.79 NS 0.29 (0.13) �0.53 (0.17) �0.001 (0.0008) NS

. . 0.45 . . NS 0.16 (0.06) NS �0.001 (0.0004) NS

. . . . 0.75 NS �0.14 (0.05) �0.20 (0.08) 0.001 (0.0002) �0.0007 (0.0003)



showed negative relationships with productivity over

the course of our study (e.g., Ruby-throated Humming-

birds and House Wrens) but no associations with tem-

perature. On the other hand, Black-capped Chickadees

(which in our study area are a mix of residents and

short-distance migrants) had earlier appearance of

juveniles and increased productivity in warm springs,

suggesting a more successful response to climate

change. Indeed, climate changes that affect overall food

abundance positively can be more important than peak

resource timing in influencing productivity and recruit-

ment for some species (Durant et al., 2005; Dunn et al.,

2011; Townsend et al., 2016).

Although we found a tendency toward delayed

breeding with increased spring precipitation in most

species studied, climate effects on productivity were

mixed, possibly resulting from within-season climate

variability (Figs 1 and 2). Extreme rain and heat can

directly reduce nest survival (Skagen & Adams, 2012;

Cox et al., 2013; Bordjan & Tome, 2014; €Oberg et al.,

2015). Climate model predictions of increased precipita-

tion (Pachauri et al., 2014) could hamper productivity of

Eastern Phoebe, Ovenbird, Hooded Warbler, Song Spar-

row, Rose-breasted Grosbeak, and Indigo Bunting. Simi-

larly, predicted increases in temperature would

negatively impact reproductive success for Cedar

Waxwing, Ovenbird, Hooded Warbler, American Red-

start, and American Goldfinch. Conversely, increased

precipitation in both spring and summer may augment

plant or arthropod abundance, thereby increasing the

number of young birds successfully fledged and

promoting adult survival. Increased temperatures and

precipitation during breeding can reduce thermoregula-

tory costs and increase resource abundance, thus

improving survival of young and increasing productiv-

ity. Indeed, earlier broods may have higher recruitment

in warm springs resulting in selection for early laying

(Visser et al., 2015). To that end, we found that several

species produced more young in warm (Eastern Phoebe,

Red-eyed Vireo, Black-capped Chickadee, Gray Catbird,

Northern Cardinal, and Indigo Bunting) and/or wet

(House Wren, Wood Thrush, American Robin, Cedar

Waxwing, Common Yellowthroat, American Redstart,

Field Sparrow and Northern Cardinal) seasons. The

simultaneous positive and negative effects of climate

change our study demonstrates are not unexpected as

productivity responses are likely species specific.

Climate change likely impacts other stages of the

avian lifecycle, with evidence that some migrants are

experiencing opposite climate pressures at geographi-

cally disparate stages; that is, on breeding grounds,

wintering grounds, and migratory stopovers (Calvert

et al., 2009; Cohen et al., 2015). Our study did not

account for climate variation on wintering grounds

because the study species use broad wintering ranges,

and population connectivity for many eastern popula-

tions is unknown. For example, a population of local

breeders of one species may use geographically distinct

wintering areas that experience different climate condi-

tions in a given year.

Some bird species may benefit from climate change,

as our study has shown potential for favorable timing,

longer breeding season, and increased productivity with

warmer and/or wetter seasons. In fact, the breeding spe-

cies we analyzed were mostly generalists, which possess

greater flexibility in resource use, thereby buffering

them against negative effects of climate change (Salido

et al., 2012). However, the apparent short-term benefits

some species may be experiencing could result in even-

tual mismatches in timing of peak resource availability

that could reverse these trends in our study area. Antici-

pated changes in forest composition, for which there is a

time lag due to the long-lived nature of trees, will fur-

ther affect future bird assemblages at PNR as habitat

quality is altered, potentially counteracting any short-

term benefits of warming (Matthews et al., 2011).

Our research is one of few studies to analyze pheno-

logical trends over many decades and contributes impor-

tant knowledge about breeding phenology changes in

North America. Despite substantial yearly variation, the

continuity and consistency of this large data set show

strong adaptive responses to climate. Specifically, our

findings demonstrate that many bird species have phe-

nological flexibility, advancing breeding as a response to

increasing spring temperatures by >15 days over five

Fig. 4 Seasonal timing of juvenile captures (the Julian day at

which 10% of young were captured) as a function of spring tem-

perature for one early breeder: Song Sparrow, one mid-season

breeder: Wood Thrush, and two late-season breeders: American

Goldfinch and Cedar Waxwing. Birds were captured over five

decades at a constant effort mist-netting station in Pennsylvania.

Average spring temperature was calculated from the most cor-

related 3-week time interval for each species (Table 2).



decades. Our discovery of significant patterns of repro-

ductive timing and productivity underscores the value

of long-term monitoring studies and the importance of

continuing constant effort monitoring programs in the

face of climate change. Given that annual temperatures

in the study area are projected to increase by 2–3.5 °C by

2100 under the lowest emissions scenario (Pachauri et al.,

2014), and increases in precipitation are likely, long-term

monitoring will continue to prove vital to enhancing our

knowledge of global climate change impacts on wildlife

and improving predictions on future responses to

change.

Acknowledgements

We are grateful to R. Leberman, R. Mulvihill, A. Leppold, A.Vitz, M. Shidel, and countless seasonal technicians and volun-teers for collecting field data, and to M. Niedermeier for main-taining the database for many years. Thanks to J. Wenzel andthree anonymous reviewers for providing constructive com-ments on earlier drafts of the manuscript. Funding for thisresearch was generously provided by the Colcom Foundation,Laurel Foundation, and numerous private donors who havesupported the bird banding laboratory since its inception.

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Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Appendix S1. Complete model comparison results for juve-nile capture date (at which 10% of individuals were cap-tured each year), ranked from lowest to highest DAICc.Appendix S2. Complete model comparison results forbreeding condition date (at which 10% of individuals werecaptured each year), ranked from lowest to highest DAICc.Appendix S3. Complete model comparison results forbreeding condition range, ranked from lowest to highestDAICc.Appendix S4. Complete model comparison results for pro-ductivity index, ranked from lowest to highest DAICc.



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