Behavioral
Ecology
The official journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2015), 26(2), 601–608. doi:10.1093/beheco/aru237
Original Article
Avoiding predators in a fluctuating
environment: responses of the wood warbler
to pulsed resources
Jakub Szymkowiak and Lechosław Kuczyński
Department of Avian Biology and Ecology, Institute of Environmental Biology, Faculty of Biology, Adam
Mickiewicz University, Umultowska 89, 61–614 Poznań, Poland
Received 1 February 2014; revised 8 December 2014; accepted 14 December 2014; Advance Access publication 29 January 2015.
Deciduous forests are characterized by the production of seed crops that may vary dramatically among years. In response to these
pulsed resources, rodent populations grow rapidly, which may have crucial consequences for entire forest communities, including
songbirds. It has been hypothesized that in response to these rodent outbreaks, the wood warbler Phylloscopus sibilatrix may exhibit
nomadic behavior to avoid nest predation. We used data from the Polish Common Breeding Bird Survey and search query time series
from Google Trends as indices of rodent numbers to investigate whether wood warbler respond to rodent outbreaks at a broad spatial
scale. Additionally, we investigated whether population fluctuations of Eurasian Jay Garrulus glandarius, the important predator of
wood warbler nests, negatively correlated with wood warbler densities and how rodent outbreaks may have affected the outcome
of interactions between those species. Results suggested that in years with low rodent abundance, wood warblers avoided settling
in areas with high densities of jays. However, when rodent abundance increased in response to masting, wood warblers switched
settling strategy and exhibited nomadic behavior. Moreover, this phenomenon acts at a broad geographical scale, which is a unique
feature for European forest-dwelling insectivorous. We proposed the hypothesis that wood warblers perceive different predators as
unequal and exhibit a risk sensitive antipredator behavior in their habitat selection process. Such a sophisticated mechanism of avoiding predators would suggest that wood warblers are able to acquire information about predation risk, for example, by assessing rodent
abundance and use this information to adjust settlement decisions appropriately.
Key words: antipredator behavior, masting, nomadic behavior, risk-sensitive habitat selection, rodent outbreaks, songbirds.
Introduction
Deciduous forests are frequently characterized by dramatic interannual fluctuations in the production of seed crops. These “resource
pulses” initiate cascades of both direct and indirect effects that
have crucial consequences for entire forest communities. In years
with an extremely high production of acorns, generalist consumers such as rodents are temporally released from food limitation
and their populations grow rapidly the following year (Pucek et al.
1993; Ostfeld et al. 1996; Ostfeld and Keesing 2000). Such rodent
outbreaks in response to mast peaks of pedunculate oaks Quercus
robur and hornbeams Carpinus betulus were found for populations of
bank voles Myodes glareolus and yellow-necked mice Apodemus flavicollis in Białowieża Forest (eastern Poland) (Pucek et al. 1993). In
oak forests of Virginia (United States), a similar relationship was
found between acorn production and populations of white-footed
mice Peromyscus leucopus, eastern chipmunks Tamias striatus, and
Address correspondence to J. Szymkowiak. E-mail: [email protected].
© The Author 2015. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved. For
permissions, please e-mail: [email protected]
gray squirrels Sciurus carolinensis (Wolff 1996; McShea 2000). Pulsed
resources, however, not only affect mast-consumer populations but
also have wide effects for entire forest communities including songbirds (Jędrzejewska and Jędrzejewski 1998; Ostfeld and Keesing
2000; Schmidt and Ostfeld 2003; Clotfelter et al. 2007).
The wood warbler Phylloscopus sibilatrix is a small, forest-dwelling songbird that has a single subspecies across the entire breeding range, contrary to some other Phylloscopus sp. warblers (Baker
1997). Wood warbler populations show features that are typical for nomadic species, that is, little site tenacity and high interannual amplitudes of numerical fluctuations (Wesołowski et al.
2009). Wesołowski and Tomiałojć (1997) reported that year-to-year
changes of wood warbler numbers in Białowieża Forest were more
than 11-fold in some breeding seasons, whereas other warblers
did not show such a strong variation. Although nomadic behavior is well known and widespread in some birds of prey and owls
(Korpimäki and Norrdahl 1991; Korpimäki 1994; Newton 1998), it
is unique for European forest-dwelling insectivorous. To our knowledge, only Brambling Fringilla montifringilla exhibits such behavior in
Behavioral Ecology
602
response to the cyclic fluctuations of autumnal moth Epirrita autumnata and winter moth Operophtera brumata abundance (Enemar et al.
2004; Hogstad 2005).
The nomadic behavior of wood warblers may be a direct
response to rodent outbreaks. Support for this hypothesis was provided in Białowieża Forest, where the numbers of wood warbler
were negatively correlated with rodent numbers (Jędrzejewska and
Jędrzejewski 1998; Wesołowski et al. 2009). Nomadic behavior is
here defined as shifts in breeding site selection decisions leading to
dramatic numerical fluctuations between years. Because rodents are
important nest predators of some songbirds (Bureš 1997; Schmidt
et al. 2001; Walankiewicz 2002), the nomadic behavior of the wood
warbler was explained as an attempt to minimize nest predation
risk (Jędrzejewska and Jędrzejewski 1998; Wesołowski et al. 2009).
However, although similar fluctuations of wood warbler numbers
were found in other parts of their breeding range (e.g., Herremans
1993), it is unknown whether this phenomenon results from fluctuating rodent numbers also at wider spatial scale. The Białowieża
Forest is a natural forest ecosystem that has retained its unique
ecological features (Tomiałojć and Wesołowski 2005; Wesołowski
2007). These features, such as an extremely high predator diversity,
predator pressure and avian nest mortality rates, clearly distinguish
it from other European woodland areas. Therefore, some patterns
and processes observed in Białowieża Forest may vanish when considered at a wider spatial scale or may be less pronounced in temperate, human-transformed forests.
Recent work using miniature nest cameras identified the
Eurasian Jay Garrulus glandarius as an important wood warbler nest
predator (Mallord et al. 2012). Therefore, it would be profitable for
wood warblers to avoid settling in areas with high densities of jays
with assumed high nest predation risk. It is not known, however,
whether Eurasian Jays respond to mast peaks in a similar manner
as rodents. Acorns serve as a primary food during autumn and winter for Eurasian Jays (Bossema 1979), and thus, it is possible that an
enormous production of acorns may increase numbers of jays in
the following spring, for example, due to increased overwinter survival. If this was true, the nomadic behavior of wood warblers may
actually be driven by fluctuations in Eurasian Jay numbers that
only coincides with rodent outbreaks during the same mast peaks.
The main goals of this study were 1) to investigate whether wood
warbler response to rodent outbreaks is a general rule and occurs at
a broad geographical scale, 2) to investigate whether wood warblers
respond to population fluctuations of Eurasian Jay, and 3) to investigate whether wood warbler’s response to Eurasian Jay (if any)
depends on the abundance of rodents.
Materials and Methods
Bird data
We used data collected from 2007 to 2012 under the Common
Breeding Bird Survey scheme in Poland (Monitoring Pospolitych
Ptaków Lęgowych, MPPL). MPPL is a scheme for monitoring the
population changes for the most widespread and common breeding
bird species. Methodology of the project was replicated from the
British Breeding Bird Survey, carried out in the United Kingdom
by the British Trust for Ornithology (http://www.bto.org/volunteer-surveys/bbs). Birds were counted by skilled volunteers on
1 × 1 km survey plots selected at random. In consecutive years, field
work was carried out on 454, 547, 560, 569, 664 and 696 survey
plots, respectively. Distance sampling method was used. Observers
walked along 2 parallel transects (each 1-km long) situated approximately 500 m apart and noted all birds seen or heard in 3 distance
categories from the transect line (<25, 25–100, and >100 m). Each
plot was surveyed twice a year. The first survey took place between
10 April and 15 May, whereas the second between 16 May and 30
June. Both visits were separated by at least 4 weeks. Each survey
started in the morning hours, usually between 06:00 and 07:00 AM
local time (Chylarecki and Jawińska 2007).
Density estimates for breeding wood warblers and Eurasian
Jays were calculated for each 1 × 1-km survey plot using the software DISTANCE 6.0 release 2 (Thomas et al. 2010). Only plots
where the wood warbler was observed at least once during the
study period were used for density estimation. A half-normal model
with no adjustments was selected. Data outside the range of 100
m contain observations of unknown distance (furthest distance
band, extending beyond 100 m, has no upper distance bound for
registered individuals) and were excluded from density estimation.
Within each year and each study plot, the density estimates from
both early and late visits were averaged.
Rodent data
For logistic and economical reasons, it is almost impossible to collect quantitative data on rodent numbers at a national scale by
traditional methods, for example, calculating numbers of species/
individuals caught per a reasonable number of trap-nights (most
commonly 100) on permanent plots. Therefore, to obtain indices of
rodent numbers in each year of the study, we used the search query
series from the Google Trends Web site. This tool calculates the
search volume of particular keywords and tracks how this volume
changes over time. Moreover, because the raw data from Google
Trends are reported by both country and language, we were able to
obtain specific information about the search volume of particular
terms at a national scale. We assumed here that rodent outbreaks
are synchronized at broad spatial scale. This assumption seems to
be reasonable because the production of seeds is spatially autocorrelated over large areas, even between sites located thousands
of kilometers apart (Pucek et al. 1993; Koenig and Knops 1998,
2000). Recent studies suggest that Internet-based methods of data
collecting may be highly effective for predicting consumer behavior or tracking economic activity (Goel et al. 2010; Fondeur and
Karamé 2013; Kristoufek 2013; Preis et al. 2013). There is also a
growing body of examples in which this novel method improved
biological research efficiency such as detection and surveillance of
seasonal influenza epidemics (Polgreen et al. 2008; Ginsberg et al.
2009) or assessment of species distribution and habitat use (Olea
and Mateo-Tomás 2013; Rousselet et al. 2013).
Rodent outbreaks may have a wide impact on agriculture
by causing crop or cereal stock damage (Singleton et al. 2010).
Moreover, some rodent species are well known for their tendency
to enter into human settlements (e.g., Buckle and Smith 1994;
Langton et al. 2001; Pocock et al. 2004). During rodent outbreaks,
significantly more individuals may potentially enter houses, barns
and stalls, amongst other places frequented by humans. Therefore,
we assumed that superabundant rodent populations in years following mast peaks may be inconvenient for people who live near forest
and woodland areas, especially in the countryside or in small towns.
These people may use the Internet as a source of information
about methods to control rodents. Thus, we can expect that search
queries associated with rodents should be distinctly more frequent
during rodent outbreaks than in years with low rodent numbers.
Szymkowiak and Kuczyński • Habitat selection responses of the wood warbler to pulsed resources
We calculated search volumes for the term “na myszy” (which
in Polish means: something for/against mice) for each year of the
study period (Figure 1A). By this approach, we obtained data about
search volumes of this particular keyword, as well as derivative
phrases, which are constructed based on it, for example, “pułapka
na myszy” (mouse trap), “trutka na myszy” (rodenticide), “co na
myszy” (what to use against mice), “odstraszacz na myszy” (mouse
repellent), and so on. The annual numbers of rodents are highest
in autumn, both in years following mast peaks and when seed fall
is low (Pucek et al. 1993), thus cumulative search volumes from
August to December in each year were used.
An overall increasing trend of using the Internet as a source of
information is evident, which in Poland proceeds in a linear way
over recent years (Central Statistical Office 2012). This is due to
the more widespread availability of the Internet and an increase
of user awareness. To take this increase over time into account, we
fitted a linear regression trend to raw search volumes obtained from
Google Trends against year. Then, we calculated residual values
from this model, expressing the search volume not explained by
the temporal trend itself, which were used in further analysis and
served as annual indices of rodent numbers.
Rodent numbers are largely determined by tree seed crop of the
previous year (Pucek et al. 1993). Therefore, to evaluate the accuracy of identification of rodent outbreaks and years with low or
moderate rodent abundance, we correlated indices of rodent numbers in year t with the oaks (pedunculate oak and sessile oak Quercus
petraea) seed crop in year t − 1 during the study period. For the
purpose of the Polish Forest Gene Bank, data on acorns production (kg/ha) were collected annually in September and October on
permanent forest plots (stands designated as the seed base) distributed across all Regional Directorates of the State Forests (RDSF)
in Poland (State Forests in Poland, unpublished data). Acorns were
collected directly from the forest floor and by using standard seed
traps. For each year, data about oak seed crop were averaged across
all RDSF, providing annual indices of acorns production. Moreover,
we correlated indices of rodent numbers with combined numbers
of bank voles and yellow-necked mice caught in September during
research conducted in Puszcza Gorzowska Forest (West Poland) in
years 2010–2012 (R. Zwolak and M. Bogdziewicz, unpublished
data). This research utilized small mammal trapping and provided
precise information about rodent population abundance in consecutive years. We found a strong, positive correlation between indices of rodent numbers in year t obtained based on Google Trends
search query series with the oaks seed crop in year t − 1 (r = 0.89,
t = 3.18, P = 0.025). Moreover, there was also a strong, positive
correlation between our indices and rodent numbers in Puszcza
Gorzowska Forest (r = 0.90, P = 0.147). This correlation coefficient
was, however, statistically insignificant, possibly due to the smaller
sample size. Nonetheless, qualitative validation suggested that our
results matched with data from Puszcza Gorzowska Forest. In summary, both quantitative and qualitative validation suggested that
our approach correctly described overall rodent population dynamics during the study period, in which rodent outbreaks occurred in
the years 2007 and 2012, low rodent numbers in years 2009 and
2011, whereas in years 2008 and 2010, rodents population showed
moderate abundance (Figure 1B).
Statistical analysis
To control for the habitat influence, we first built a predictive habitat model for the wood warbler. Then, we entered densities predicted from this model as an offset while fitting the generalized
additive mixed models (GAMM) concerning co-occurrence and
mutual relationships between both species. This approach is equivalent to calculating residuals from the habitat model (expressing the
wood warbler’s population density, which is not explained by the
habitat itself) and subsequently using those residuals as a response
variable in further analysis.
Habitat modeling
Data used for the habitat modeling are explained in detail in the
Supplementary Appendix A (Table S1). In general, a total of 70
environmental variables were used from the following sources:
1) Corine Land Cover database; 2) landscape characteristic derived
from Corine Land Cover using Fragstat software (McGarigal
A
200
1200
100
Rodent index
Google Trends search volume
1400
603
1000
800
0
-100
Year
2012
2011
2010
2009
2008
2007
2012
2011
2010
2009
2008
2007
B
Year
Figure 1
The raw search volumes for term “na myszy” (in Polish: something for/against mouse) from Google Trends (A) and the index of rodent population numbers
(B) expressed as residual values from linear regression trend in which search volumes were fitted against year.
Behavioral Ecology
604
and Marks 1995); 3) SRTM digital elevation; 4) bioclimatic variables; 5) vegetation phenology; 6) forest type maps; 7) forest pattern derived from forest type maps; and 8) DMSP-OLS night-time
lights.
Only sampling plots where wood warblers were present at least
once were used for fitting the model. We used a random forest (RF)
algorithm (Breiman 2001). The model was trained in regression setting, where log densities were treated as the model response. RF is
a machine learning method based on classification and regression
trees (CART). However, instead of constructing one, “the best” tree,
it constructs multiple trees, each fitted to randomly perturbed data.
Each tree is produced from random sample of cases and each split
from random sample of predictors. The final output is achieved by
averaging predictions. The bootstrap mechanism (sampling with
replacement) ensures that about one-third of the instances are left
out and not used in the fitting process. These out-of-bag (OOB)
samples are used to assess the prediction error of the procedure.
This overcomes the common problem of bias of prediction error.
The performance of the RF model was examined as percentage
of variance explained by computing the value: R2 = 1 – MSEOOB/
observed variance, where MSEOOB is the mean squared error between
observations and OOB predictions (Liaw and Wiener 2012).
Initially, the RF model was fitted with all available predictors,
using 1000 trees in the ensemble. Then, we ranked all predictors
accordingly to their importance and used a grid searching algorithm to find the number of predictors, which gave maximum fit
(measured by the value of R2). Afterwards, the RF model was refitted on the reduced data set.
Mixed effects modeling
To investigate how the population fluctuations of wood warbler
correlated with variation in both rodent and European Jay numbers, GAMM was used (Wood 2006). We fitted a set of 4 candidate
models (Table 1) and used an information-theoretic approach to
model selection and multimodel inference (Burnham and Anderson
2002). Each model was fitted by penalized quasi-likelihood method
with Gaussian distribution of errors, cubic smoothers (allowing for
the nonlinear relationship to be fitted), and the identity link function. Data were analyzed by taking log-transformed wood warbler
yearly population densities as a response and fitting a GAMM with
random intercept including plot ID as a random factor. This model
allows multiple density estimations from different years and the
same sampling plot to be correlated imposing a compound symmetric correlation structure (Zuur et al. 2009). The first model
contained 3 predictors (model M1 in Table 1): an offset from the
habitat model, the temporal trend modeled by adding a year as a
predictor, and the log-density of wood warbler population on the
same plot from the previous year (which is equivalent to include a
first-order autoregressive correlation structure as a separate error
term). The model was further extended by including the indices
of rodent numbers and the Eurasian Jay log densities as predictors
(models M2 and M3 in Table 1, respectively). Finally, interactions
were also included by allowing for independent fits of wood warbler density on the same plot from the previous year and Eurasian
Jay density (from the same year) for groups of years with different rodent numbers (model M4 in Table 1). Rodent abundance
was assigned according to Figure 1B as “high” for the years 2007
and 2012, “moderate” for 2008 and 2010, and “low” for 2009
and 2011.
The effect of all terms and their interactions on model fit was
assessed using Akaike Information Criterion (AIC), that is, an estimator of the expected Kullback–Leibler information lost (Akaike
1974; Wood 2006; Zuur et al. 2009); with the model having the
lowest AIC value being considered the best, given the data. Models
were compared based on ΔAIC values, calculated as a difference between the AIC of a particular model and the AIC of the
Kullback–Leibler best model in the candidate set. The value of
ΔAIC ≤ 2 was assumed as a threshold indicating models with substantial support to explain the dependent variable (Burnham and
Anderson 2002). Moreover, Akaike weights (wi) representing a normalized estimate of the relative likelihood of each model being best
in the set were calculated (Burnham and Anderson 2002).
Statistical analysis was performed in R version 3.0.2 (R
Development Core Team 2013) using the mgcv 1.7–27 package
(Wood 2006). Model validation by graphical exploratory inspection
of residual patterns indicated normality and homogeneity. Spatial
autocorrelation of residuals calculated for all candidate models
was explored using a smoothed nonparametric functions (‘spline.
correlog’ function from the package ‘ncf ’, Bjornstad and Falck
2001). They describe the variance of residuals between pairs of
locations as a function of the geographical distance between study
plots. Ninety-five percent confidence intervals around the estimated
functions were computed using a bootstrap resampling technique
with 1000 replications. Autocorrelation functions are presented in
Supplementary Appendix B (Figures S5–S8).
Results
Habitat modeling
An RF model with the 18 most contributing variables achieved
the highest fit. This model explained 94% of total variance
for the wood warbler density (estimated on training data). The
Table 1
A set of candidate GAMM models fitted and ranked according to their ΔAIC values
Model
Predictors
k
AIC
ΔAIC
wi
M4
M3
M2
M1
HABITATi + fm(WWij − 1) + fm(EJij) + f(RODENTSij) + f(YEARj)
HABITATi + f(WWij − 1) + f(EJij) + f(RODENTSij) + f(YEARj)
HABITATi + f(WWij − 1) + f(RODENTSij) + f(YEARj)
HABITATi + f(WWij − 1) + f(YEARj)
11
7
6
5
3883.16
3887.78
3914.53
3916.85
0.00
4.62
31.37
33.69
0.91
0.09
0.01 × 10–5
0.004 × 10–5
The k is the number of parameters in the model (including intercept and random effect); wi is the Akaike weight for each model. Predictors included as
explanatory variables: HABITATi is the offset from the habitat model for each plot; f(YEARj) is a smoothing function representing the general temporal trend;
f(WWij − 1) is the smoothing function of wood warbler log-density on the plot i during the year j − 1; f(RODENTSj) is the smoothing function of index of rodent
abundance in the year j; f(EJij) is the smoothing function of Eurasian Jay log-density on the plot i during the year j; and m is a variable denoting the class of
rodent abundance (i.e., “low,” “moderate,” or “high”). The fm terms refer to interaction effects and ensure that a separate smoother was fitted for each rodent
abundance class.
Szymkowiak and Kuczyński • Habitat selection responses of the wood warbler to pulsed resources
explained variance estimated on OOB samples was lower and
exceeded 42%. Because we were not interested in studying the
habitat use of the wood warbler, we do not explore this model in
detail here. Interpretation of it can be found in Kuczyński and
Chylarecki (2012). For the purpose of this study, predictions from
the habitat model were necessary as an explanatory variable to
account for the habitat effect when testing the influence of both
rodent and Eurasian Jay abundance on wood warbler population
densities.
Mixed effects modeling
1.0
Following model selection and multimodel inference based on
information-theoretic approach, the best model, that is, ranked
with the lowest AIC, was model M4 (Table 1). Among the range
of candidate models that we fitted, the second best model was
model M3. However, this model received considerably less level of
empirical support, as revealed by its ΔAIC value (Table 1), which
was more than twice the threshold value of ΔAIC ≤ 2. The Akaike
weight (wi) for model M4 was relatively high (Table 1), suggesting
low model selection uncertainty. Moreover, the evidence ratio, that
is, the ratio between weights of the best and second best model,
suggested that the model M4 was 10 times more likely to be the
Kullback–Leibler best model in the candidate set than model M3.
Therefore, the final inference was made based only on model M4
results.
We found that there was a strong negative relationship between
wood warbler densities and rodent index values (effective degrees
of freedom [edf] = 1, F = 15.88, P < 0.001; Figure 2A). Eurasian
Jay densities negatively correlated with densities of wood warbler,
but only in years with low rodent numbers (edf = 1, F = 3.93,
P = 0.048; Figure 3D). When considering years with high (edf = 1,
F = 0.11, P = 0.738) or moderate (edf = 1, F = 0.57, P = 0.443)
rodent abundance, the negative correlation between Eurasian Jay
and wood warbler densities vanished (Figure 3E,F). At the same
time, Eurasian Jay densities did not vary between years with different rodent abundance (Kruskal–Wallis test, χ22 = 0.06, P = 0.973).
We found a positive relationship between densities of wood warbler in years t and t − 1 (Figure 3A–C), irrespectively of whether
rodent numbers were high (edf = 2, F = 18.50, P < 0.001), moderate (edf = 2, F = 19.26, P < 0.001) or low (edf = 2, F = 41.76,
P < 0.001). Moreover, the temporal trend was also significant
(edf = 1, F = 8.76, P = 0.003, Figure 2B).
Discussion
Our results suggest that the nomadic behavior of the wood warbler is a response to rodent outbreaks. Moreover, we found that this
behavior is not restricted to the natural conditions of Białowieża
Forest, but occurs on a large spatial scale. This phenomenon constitutes a unique response of a forest-dwelling songbird to pulsed
resources and fluctuating rodent numbers because at least to our
knowledge such behavior, that is, nomadism in years following
mast peaks, has so far not been described for any other European
passerine.
We argue that the nomadic behavior of the wood warbler during
rodent outbreaks may serve as an antipredator strategy and is an
attempt to increase chances of successful breeding, following suggestions of Jędrzejewska and Jędrzejewski (1998) and Wesołowski
et al. (2009). Rodents, especially Apodemus sp. mice, were found
to depredate songbird nests (Walankiewicz 2002; Schaefer 2004)
and are considered important nest predators for wood warblers
(Wesołowski 1985; Jędrzejewska and Jędrzejewski 1998; Wesołowski
and Maziarz 2009; Wesołowski et al. 2009), but without direct
evidence. In fact, we are aware of only 2 studies that investigated
predators of wood warbler nests using video cameras, both did not
record any rodent species depredating warbler nests (Grendelmeier
2011; Mallord et al. 2012). However, mast peaks generate cascade
effects that greatly permeate entire forest communities and affect
populations of both seed consumers and generalist predators. In
response to rodent outbreaks, mammalian and avian predators,
for example, pine martens Martes martes, weasels Mustela nivalis,
tawny owls Strix aluco, and buzzards Buteo buteo, exhibit their own
numerical or functional response (Korpimäki and Norrdahl 1989,
1991; Korpimäki et al. 1991; Korpimäki 1994; Jędrzejewska and
Jędrzejewski 1998; Ostfeld and Keesing 2000; Reif et al. 2004).
Also populations of wild boar Sus scrofa grow rapidly in response
to a high production of acorns, due to increased reproduction and
survival of piglets (Jędrzejewska and Jędrzejewski 1998; Cutini
et al. 2013). Areas with superabundant rodent populations may
also attract opportunistic predators, for example, red foxes Vulpes
vulpes (Angelstam et al. 1984; Jędrzejewski and Jędrzejewska 1992,
1993). All these species constitute a serious threat for nesting songbirds and were recognized as important predators of wood warbler nests (Wesołowski 1985; Grendelmeier 2011; Mallord et al.
2012). Moreover, because wood warbler is a ground-nester, they
may destroy warbler nests even unintentionally, when hunting for
A
B
-0.15
-1.0
Residuals
-0.5
0.0
Residuals
-0.05
0.05
0.5
605
-100
0
100
Rodent abundance index
200
2007
2008
2009 2010
Year
2011
2012
Figure 2
Generalized additive mixed effects model smoothing curves representing the modeled effect of an index of rodent abundance (A) and temporal trend (B).
Shaded regions represent standard errors for smoothers.
Behavioral Ecology
606
Rodent index: LOW
Rodent index: HIGH
0.0
Residuals
0.5
1.0
1.5
Rodent index: MEDIUM
A
0
1
2
3
B
1
4
5
0
1
2
3
4
5
0
Wood Warbler log-density from the previous breeding season
Residuals
-0.3 -0.2 -0.1 0.0 0.1
0.2
Rodent index: LOW
Rodent index: MEDIUM
D
0
C
2
3
0
3
4
5
Rodent index: HIGH
E
1
2
F
1
2
3
Eurasian Jay log-density
0
1
2
3
Figure 3
Generalized additive mixed effects model smoothing curves representing the modeled effect of wood warbler density from the previous breeding season
(A–C) and Eurasian Jay densities (D–F). All effects were modeled by separate fits for years with high, moderate, and low rodent population numbers. Shaded
regions represent standard errors for smoothers.
rodents or searching for acorns on the forest floor. Therefore, the
risk of a nest being predated or destroyed likely increases dramatically in years following mast peaks and nomadic behavior may be
profitable.
We did not find support for the hypothesis that the nomadic
behavior of the wood warbler correlated with fluctuations in
Eurasian Jay numbers. In fact, there were no significant differences in Eurasian Jay densities between years with low, moderate,
and high rodent abundance. This may suggest that jays respond to
mast peaks in a different manner than rodents or do not exhibit a
numerical response at all. However, Eurasian Jay constitutes a serious threat for nests of many forest-dwelling songbirds, including
the wood warbler (Wesołowski 1985; Schaefer 2004; Stevens et al.
2008; Weidinger 2009; Mallord et al. 2012). Therefore, it would
be profitable for wood warblers to avoid settling in areas with high
densities of jays. In fact, we found that wood warbler densities were
negatively correlated with densities of Eurasian Jay, however, only
in years with low rodent numbers, and this relationship vanished in
years when rodents were more abundant.
We propose the hypothesis that when making settlement decisions, wood warblers perceive the different risk posed by predators
(predators are not equal) and exhibit a risk-sensitive antipredator
behavior in their habitat selection process. When rodent abundance
is low, wood warblers avoid nesting in areas with high densities of
Eurasian Jay. However, in years following mast peaks, when overall
predation risk on the forest floor extremely increases, wood warblers switch their strategy, exhibit nomadic behavior, and attempt
to search for safer breeding grounds. Such risk-sensitive antipredator behavior during habitat selection would not be a unique feature
of wood warbler only. It has been demonstrated, for example,
that Pied flycatchers Ficedula hypoleuca adjust territory selection and
reproductive investment decisions according to perceived risk in a
multipredator environment (Morosinotto et al. 2010).
If true, such a sophisticated mechanism of avoiding predators
in the habitat selection process would suggest that wood warblers
are able to acquire accurate information about predation risk when
making settlement decisions. Reliance on Eurasian Jay density in
years with low rodent abundance should not be difficult for wood
warblers. Many studies have reported that songbirds are able to
acquire precise information about the location of avian predators (reviewed in Lima 2009), particularly their nest sites, which
are often initiated prior to migrant arrival (Thomson et al. 2006).
In the case of outbreak years, we agree with Jędrzejewska and
Jędrzejewski (1998) and Wesołowski et al. (2009) that wood warblers may use rodent abundance as a proxy to assess predation risk
when making settlement decisions. Although monitoring of wood
warbler nests revealed that there were no records of predation by
rodents (Grendelmeier 2011; Mallord et al. 2012), the fluctuating
numbers of rodents may serve as valuable indices about the overall predation pressure, generated by the entire predators assembly
(not necessarily by rodents per se). We propose 2 possible sources
of information that wood warblers may use to assess rodent abundance. First, wood warblers may rely on rodents’ excrements (feces
and urine) because as recent studies suggest, songbirds are able to
detect and use this type of information during the habitat selection process (Forsman et al. 2013). Second, wood warblers may rely
on acoustic cues and acquire information about rodent abundance
by eavesdropping on rodent vocalizations. Such a mechanism of
Szymkowiak and Kuczyński • Habitat selection responses of the wood warbler to pulsed resources
assessing the risk of predation was described for ovenbirds Seiurus
aurocapilla and veery Catharus fuscescens, which eavesdrop on eastern
chipmunks (Emmering and Schmidt 2011).
Wood warblers exhibit nomadic behavior in response to rodent
outbreaks although some individuals take the risk and settle even
in years following mast peaks (Wesołowski et al. 2009). Our study
suggested that there is a positive, nonlinear relationship between
wood warbler densities from the previous breeding season and densities in the following year. This may suggest that on average, more
birds return to areas in which high densities of wood warbler were
observed in previous seasons, perhaps due to high habitat quality
of those sites. However, currently very little is known about how
individuals that decide to settle during rodent outbreaks adjust territory selection and breeding investment decisions according to the
perceived predation risk. There are only 2 studies from Białowieża
Forest; both revealed that wood warbler clutch size decreased
when rodents were more abundant (Wesołowski 1985; Wesołowski
and Maziarz 2009). Other leaf warblers Phylloscopus spp. also show
behavioral plasticity in nest placement as a response to rodent
outbreaks (Forstmeier and Weiss 2004; Larson 2012). However, it
remains unknown whether wood warblers show similar plasticity
in response to extremely increased predation risk in years following
mast peaks.
In conclusion, we propose the hypothesis that wood warblers
show risk-sensitive antipredator behavior in their habitat selection
process. We suggest that when rodent abundance is low, wood warblers avoid settling in areas with high densities of Eurasian Jays.
However, during rodent outbreaks, they switch strategy and exhibit
nomadic behavior, which can be observed at a broad geographical scale. There is always uncertainty when making inference about
animal behavior based on correlative results. Hence, we strongly
encourage future studies testing this hypothesis using field experiments. Considering that the fluctuating numbers of rodents may
serve as valuable indices about overall predation pressure, we carefully propose 2 sources of information that wood warblers may
use when assessing rodent abundance, that is, rodent excrements
or eavesdropped vocalizations. These mechanisms of acquiring
information about predation risk may be also relatively easily tested
using field experiments. Moreover, we suggest that future studies
on wood warbler nest predators should take into account temporal variation of rodent densities to avoid potentially biased results.
In years following mast peaks, when rodents are enormously abundant, the risk of a nest being predated by rodents may increase,
especially in case of ground-nesting passerines such as the wood
warbler (Jędrzejewska and Jędrzejewski 1998). However, when
rodent populations collapse, their role as wood warbler nest predators may vanish. Moreover, our study revealed that by tracking
human behavior, researchers may gather information about ecological phenomena that might be difficult to obtain in a large spatial
scale. Hence, novel, Internet-based methods of data collection may
potentially improve ecological research efficiency.
Supplementary Material
Supplementary material can be found at http://www.beheco.
oxfordjournals.org/
Funding
This work was supported by the National Science Centre in Poland
(DEC-2012/07/N/NZ8/00129).
607
We express our gratitude to Dr Robert L. Thomson for his helpful comments and linguistic corrections. We are grateful to the editor and 2 anonymous reviewers for their insightful comments and suggestions. We thank
all volunteers who have contributed to the Polish Common Breeding Bird
Survey (MPPL). CORINE Land Cover 2006 database was provided by the
General Inspectorate of Environment Conservation in Poland. We thank
Tomasz Chodkiewicz (OTOP) for providing the bird data. We are grateful
to Michał Bogdziewicz and Rafał Zwolak for providing unpublished results
about mast years in Puszcza Gorzowska Forest and Jerzyna Przypaśniak
from the State Forests in Poland for sharing the data about oaks seed crop.
The authors declare that they have no competing interests. J. Szymkowiak
and L. Kuczyński conceived and designed the study. L. Kuczyński processed
the data, L. Kuczyński and J. Szymkowiak analyzed it, and J. Szymkowiak
wrote the first draft of the manuscript. Both authors read, critically revised,
and approved the final manuscript.
Handling Editor: Johanna Mappes
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