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e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 110–120
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journal homepage: www.elsevier.com/locate/ecolmodel
Modeling tradeoffs in avian life history traits and
consequences for population growth
M.E. Clark a,∗ , T.E. Martin b
a
Department of Biological Sciences, North Dakota State University, Fargo, ND 58105-5517, United States
USGS Biological Resources Discipline, Montana Cooperative Wildlife Research Unit,
University of Montana, Missoula, MT 59812, United States
b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Variation in population dynamics is inherently related to life history characteristics of
Received 26 January 2007
species, which vary markedly even within phylogenetic groups such as passerine birds.
Received in revised form 1 June 2007
We computed the finite rate of population change () from a matrix projection model and
Accepted 12 June 2007
from mark-recapture observations for 23 bird species breeding in northern Arizona. We
Published on line 20 July 2007
used sensitivity analyses and a simulation model to separate contributions of different life
history traits to population growth rate. In particular we focused on contrasting effects of
Keywords:
components of reproduction (nest success, clutch size, number of clutches, and juvenile
Birds
survival) versus adult survival on . We explored how changes in nest success or adult sur-
Life history traits
vival coupled to costs in other life history parameters affected over a life history gradient
Population dynamics
provided by our 23 Arizona species, as well as a broader sample of 121 North American
Simulation model
passerine species. We further examined these effects for more than 200 passeriform and
Elasticity
piciform populations breeding across North America. Model simulations indicate nest success and juvenile survival exert the largest effects on population growth in species with
moderate to high reproductive output, whereas adult survival contributed more to population growth in long-lived species. Our simulations suggest that monitoring breeding success
in populations across a broad geographic area provides an important index for identifying
neotropical migratory populations at risk of serious population declines and a potential
method for identifying large-scale mechanisms regulating population dynamics.
© 2007 Elsevier B.V. All rights reserved.
1.
Introduction
Population dynamics are in part a consequence of the collective life history traits of individuals within populations.
Understanding constraints among those life history traits is
critical to assessing population viability and the fitness of
individuals within the populations (Caughley, 1994). Tradeoffs
in life history traits, such as fecundity and survival (Sæther,
1988; Martin, 1995, 2002), could set constraints on turnover
rates (i.e., generation times) in populations. Determining the
∗
Corresponding author. Tel.: +1 701 231 8246; fax: +1 701 231 7149.
E-mail address: m.e.clark@ndsu.edu (M.E. Clark).
0304-3800/$ – see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolmodel.2007.06.008
optimal balance between these constraints may be a key to
resolving whether changes in reproductive success or annual
adult survival limit populations (via limitations in breeding
habitat or migration and winter conditions).
The need for an empirically based framework for population growth and life history traits is especially critical in
avian ecology. Debate continues on the relative importance
of breeding versus non-breeding habitat in controlling trends
in neotropical migratory bird populations (O’Connor, 1989;
Robbins et al., 1989; Sillett and Holmes, 2002). Given that avian
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e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 110–120
species vary along a gradient in fecundity and adult survival
(i.e., Sæther, 1988; Martin, 1995, 2002; Sæther and Bakke, 2000),
then the relative importance of reproductive success versus
adult survival to population growth may be expected to differ
as well. Thus, the role and importance of life history tradeoffs
are potentially critical to understanding avian demography
and population trends.
We develop a simple matrix population projection model
for general passerine species from data on populations
monitored in northern Arizona to address the influence
of reproductive success versus adult survival on passerine population growth while considering tradeoffs amongst
demographic parameters. By focusing on populations in the
same location during the same time period, we reduce spatial
and temporal variation that often hinders model corroboration and obscures patterns among life history parameters
(Sæther, 1997). We take advantage of the range in life history traits among closely related passerines to delineate
robust, clear patterns among life history parameters. We then
examine reproductive life history traits of numerous North
American passerines within our model framework to project
the level of influence of reproductive success on populations
at a more general, ubiquitous scale.
2.
Methods
2.1.
Population modeling
We developed a three-stage, post-fledging matrix population
projection model to represent the life cycle of passerine and
picid birds. Model stages are juveniles, age-1 adults, and age2+ adults connected in the life cycle as shown in Fig. 1.
We separated the adult stages into age-1 and age-2+ groups
because clutch size and nestling survival can be significantly
lower in first-time versus experienced adults (Sæther, 1990),
and assume that all female birds reach maturity and will breed
at age-1. In matrix form, the model is
⎡
⎤
⎡
⎢
⎥
⎣ N1 ⎦
=⎣
NJ
N2+
⎢
t+1
C1
S1
2
J
J A1
⎡
0
NJ
⎤
⎢
⎥
×⎣ N1 ⎦ ,
N2+
C2+
S2
2
0
1 A2+
1
⎤
C2+
S2+
2
⎥
⎦
0
2+ A2+
2+
(1)
t
where Ci , Ai and Si represent the clutch size, nesting attempts,
and nest success of each age-class i; J , 1 and 2+ represent
survival (i.e., transition probability) of fledglings, age-1 adults
and age-2+ adults, respectively from fledging time in year t to
fledging time in year t + 1; and NJ , N1 , and N2+ represent the
number of fledglings, age-1 adults, and age-2+ adults, respectively. Division of clutch size by two is used because we assume
a 50:50 sex ratio in the populations. The asymptotic finite rate
of change is set by the dominant eigenvalue () of the system
in Eq. (1) (Caswell, 2001a).
We calculated matrix elasticities to quantify functional
contributions of life history parameters to . Elasticity is the
measure of proportional change in with respect to proportional change in matrix elements. For matrix element aij (i.e.,
the element in the ith row and jth column of the 3 × 3 matrix
in Eq. (1)), the elasticity (eij ) is (Caswell, 2001a):
eij =
aij ∂
∂(log )
.
=
∂aij
∂(log aij )
(2)
Lower level elasticities are obtained by differentiation of the
matrix element with respect to the parameter of interest and
application of the chain rule (Caswell, 2001a).
We also performed simulation analyses to further quantify life history contributions to . Our simulation analyses
followed the life-stage simulation analysis (LSA) approach
outlined by Wisdom et al. (2000) (i.e., retrospective analysis,
Caswell, 2001b). For each species, we calculated and elasticities for 10,000 replicate matrices with life history parameters
randomly selected within a range of specified values that were
not constrained by covariance among life history parameters. We used frequency distributions of elasticity rankings
and coefficients of determination (r2 ) from linear regressions of from matrix values (i.e., life history parameters)
to assess contribution of life history traits to population
growth.
2.2.
Fig. 1 – Life cycle diagram for the matrix model given in Eq.
(1) for typical passerine species. Solid arrows indicate stage
transitions and dashed lines represent connections
between stages through reproduction.
Parameter estimation
Estimates of parameter values for the life cycle model were
determined for 23 species from populations studied on the
Mogollon Rim in northern Arizona from 1994 to 2000 (Martin,
2001). The list of species includes 18 passerines (songbirds)
and five picids (woodpeckers) (Table 1). The Mogollon Rim
study site consists of a collection of 22 forested-drainages
that include a total of approximately 265 ha of habitat within
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e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 110–120
Table 1 – Mean and standard error for adult body mass (M in g from Dunning, 1993), clutch size (C), incubation time (Ti ),
nestling stage duration (Tn ), nest success (S) and adult female survival () observed in northern Arizona populations on
the Mogollon Rim for 1994–1999
Species
American robin, AMRO Turdus migratorius
Audubon’s warbler, AUWA Dendroica coronata
Black-headed grosbeak, BHGR Pheucticus
melanocephalus
Brown creeper, BRCR Certhia americana
Cordilleran flycatcher, COFL Empidonax difficilis
Downy woodpecker, DOWO Picoides pubescens
Gray-headed junco, GHJU Junco hyemalis
Green-tailed towhee, GTTO Pipilo chlorurus
Hairy woodpecker, HAWO Picoides villosus
Hermit thrush, HETH Catharus guttatus
House wren, HOWR Troglodytes aedon
Mountain chickadee, MOCH Parus gambeli
Orange-crowned warbler, OCWA Vermivora
celata
Pygmy nuthatch, PYNU Sitta pygmaea
Red-breasted nuthatch, RBNU Sitta canadensis
Red-faced warbler, RFWA Cardellina rubrifrons
Red-naped sapsucker, RNSA Sphyrapicus varius
Northern flicker, RSFL Colaptes auratus
Virginia’s warbler, VIWA Vermivora virginiae
Warbling vireo, WAVI Vireo gilvus
White-breasted nuthatch, WBNU Sitta
carolinensis
Western tanager, WETA Piranga ludoviciana
Williamson’s sapsucker, WISA Sphyrapicus
thyroideus
a
M
C
Ti
Tn
S
77.30
12.20
42.20
3.46 ± 0.051b
3.83 ± 0.044b
3.29 ± 0.216b
12.97 ± 0.054
13.17 ± 0.232
12.90 ± 0.355
14.72 ± 0.276
12.35 ± 0.587
12.50 ± 0.555
0.212 ± 0.004
0.149 ± 0.011
0.378 ± 0.007
8.40
10.00
27.00
19.80
29.40
62.50
31.00
10.90
10.10
9.00
5.55
3.76
4.81
3.94
3.81
3.25
3.83
5.89
7.50
4.53
±
±
±
±
±
±
±
±
±
±
1.275c
0.026
0.600c
0.026
0.041
0.125
0.039
0.287b
1.500b
0.039
14.72
14.54
11.92
12.29
12.06
14.00
12.65
14.03
13.97
12.92
±
±
±
±
±
±
±
±
±
±
0.105
0.085
0.083
0.111
0.148
0.001
0.131
0.016
0.033
0.110
14.51
15.87
22.07
11.31
11.60
28.89
12.46
15.29
19.93
11.95
±
±
±
±
±
±
±
±
±
±
0.224
0.185
0.400
0.157
0.499
0.107
0.433
0.103
0.143
0.125
0.702
0.179
0.925
0.288
0.190
0.832
0.031
0.816
0.729
0.364
±
±
±
±
±
±
±
±
±
±
0.002
0.004
0.002
0.003
0.013
0.002
0.011
0.001
0.001
0.003
0.336
0.551
0.580
0.585
0.611
0.714
0.543
0.371
0.552
0.544
±
±
±
±
±
±
±
±
±
±
0.123d
0.114
0.094
0.147
0.133d
0.085
0.032
0.073
0.110
0.118
10.60
9.80
9.80
50.30
139.17
7.80
14.80
21.10
6.92
5.13
4.13
4.80
6.31
3.48
3.30
6.29
±
±
±
±
±
±
±
±
0.339
0.329
0.039
0.081
0.157
0.042
0.285b
0.468b
15.30
12.11
12.86
13.07
11.68
12.50
12.89
12.57
±
±
±
±
±
±
±
±
0.185
0.046
0.090
0.050
0.076
0.118
0.093
0.169
22.47
19.68
11.58
27.07
26.78
11.06
13.47
18.65
±
±
±
±
±
±
±
±
0.176
0.123
0.138
0.137
0.097
0.138
0.234
0.765
0.822
0.742
0.385
0.908
0.852
0.370
0.420
0.828
±
±
±
±
±
±
±
±
0.001
0.001
0.004
0.001
0.001
0.004
0.004
0.002
0.557
0.386
0.552
0.569
0.524
0.538
0.578
0.445
±
±
±
±
±
±
±
±
0.103
0.071
0.104d
0.031
0.049
0.099
0.095
0.089
28.10
47.60
3.61 ± 0.305c
4.92 ± 0.182
13.17 ± 0.083
13.00 ± 0.033
10.00 ± 0.087
30.76 ± 0.167
0.336 ± 0.007
0.852 ± 0.001
0.532 ± 0.101
0.563 ± 0.113
0.637 ± 0.107
0.614 ± 0.108
0.504 ± 0.048
Common and scientific names for species are listed along with a four-letter code used in the figures.
a
b
c
d
Determined from the model = e−0.299+0.242ln(M)−0.0057AC /(1 + e−0.299+0.242ln(M)−0.0057AC ) where A is attempts (see Table 2).
Estimated from observations from 1984 to 1999 to increase sample size.
Martin (1995).
The species was omitted from mark-recapture analysis due to insufficient captures.
study plots distributed across 10 km. Ponderosa pine (Pinus
ponderosa), Douglas-fir (Pseudotsuga menziesii) and a variety of
smaller deciduous trees (e.g., Populus tremuloides, Quercus gambellii, Acer grandidentatum, Robinia neomexicana) dominate the
forest at the Mogollon Rim study site (see Martin, 2001 for
details).
Information from monitored nests provided estimates for
clutch size, incubation time, nestling stage duration, and nest
success. Nesting birds were monitored approximately every
other day from early May to August during the study period.
We computed mean values for clutch size, incubation time,
and nestling stage duration (Table 1) from observations on
monitored nests. For seven species, we estimated clutch sizes
from observations from 1984 to 1999 (Table 1) because sample sizes from 1994 to 1999 were small (<10). We did not
include observations from years prior to 1994 for the other
species because monitoring effort was standardized from
1994 to 1999 as was mark-recapture monitoring. We used
estimates of clutch size reported in Martin (1995) for three
species for which we did not have data (Table 1). We estimated
nest success (Table 1) from calculations of daily nest survival
(Table 2) raised to the power of the mean nest duration period.
Daily nest survival was estimated using the Mayfield method
(Mayfield, 1975; Johnson, 1979). We also computed annual esti-
mates of nest daily survival rate using the Mayfield method.
Mean nest duration was the sum of incubation and nestling
periods.
We estimated the number of nesting attempts using an
individual-based simulation model. We simulated each day in
the nesting season for individual nesting females, and computed the mean number of nesting attempts made for 7000
females of each species. In this model, a female initiates a first
nest within the first 2 weeks of the nesting season. Her nest
survives each day if a random deviate is less than the daily
nest survival rate (Table 2), and the nest is successful if it lasts
for the duration of both the incubation and nestling stages
(listed in Table 1). If a nest fails or succeeds, the female waits
for a period, which depends on the outcome of the previous
nest, before renesting. If her previous attempt was successful,
the waiting period is determined by a random deviate and a
mean interval of 8.2 days (Ricklefs, 1969) and a standard error
(±4.0 days) that corresponded well with the range (1–20 days)
reported by Ricklefs (1969). If her previous attempt was unsuccessful the waiting period is determined by a random deviate
and a shorter mean interval (7.8 ± 2.3 days; Ricklefs, 1969).
Nests may be initiated until the last day of the nesting season.
Duration of the nesting season was estimated using observed
nest initiation dates across all years for each species and then
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Table 2 – Estimated number of equally good weeks
(EGW) in the nesting season and daily nest survival rate
(with standard error) (Sd ) observed in populations at the
Mogollon Rim study site and used to simulate mean
(and standard error) nest attempts (A) during the
breeding period
Species
EGW
American robin
Audubon’s warbler
Black-headed grosbeak
Brown creeper
Cordilleran flycatcher
Downy woodpecker
Gray-headed junco
Green-tailed towhee
Hairy woodpecker
Hermit thrush
House wren
Mountain chickadee
Orange-crowned warbler
Pygmy nuthatch
Red-breasted nuthatch
Red-faced warbler
Red-naped sapsucker
Northern flicker
Virginia’s warbler
Warbling vireo
White-breasted nuthatch
Western tanager
Williamson’s sapsucker
10.00
5.43
6.43
5.43
6.57
5.14
10.00
7.57
4.57
8.14
5.57
4.86
5.86
5.71
7.00
5.00
4.00
5.00
5.57
5.43
5.14
5.57
3.86
Sd
0.946
0.928
0.962
0.988
0.945
0.998
0.949
0.932
0.996
0.871
0.993
0.991
0.960
0.995
0.991
0.962
0.998
0.996
0.959
0.968
0.994
0.954
0.996
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
0.0040
0.0114
0.0071
0.0023
0.0038
0.0016
0.0034
0.0133
0.0017
0.0108
0.0011
0.0013
0.0033
0.0013
0.0014
0.0037
0.0007
0.0007
0.0039
0.0041
0.0021
0.0072
0.0008
EGW = exp
52
−
3.81
2.58
2.46
1.74
2.67
1.15
3.85
3.33
1.10
4.40
1.73
1.22
2.36
1.25
2.07
2.09
1.05
1.11
2.32
2.10
1.37
2.38
1.07
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
0.79
0.49
0.37
0.26
0.56
0.13
0.65
0.64
0.09
0.82
0.22
0.19
0.33
0.20
0.09
0.31
0.05
0.10
0.31
0.28
0.25
0.34
0.07
pi ln(pi )
logit() = −0.299 + 0.173
1 for males
0 for females
+0.242 ln(M) − 0.0057AC
(4)
A
computing the number of equally good weeks (Table 2) following MacArthur (1964). The number of equally good nesting
weeks (EGW) for a species is given by
apparent survival, i.e.:
(3)
i=1
where pi is the proportion of nests observed to be initiated in
week i (pooled across years). The total number of nest attempts
simulated for each female was then used to compute mean
and standard errors (Table 2) of nest attempts for each species
used in the population projection model.
We obtained estimates of annual adult survival for matrix
model projections from mist net recaptures and confirmed
resightings for 1994–2000 on the Mogollon Rim study site. We
analyzed this mark-recapture information via the CormackJolly-Seber model (Lebreton et al., 1992) using Program MARK
(White and Burnham, 1999) to estimate apparent survival,
which is the probability of annual survival and site fidelity,
and may be biased low with respect to true survival. Following Martin (1995), we included log-transformed body mass and
annual fecundity (the product of nesting attempts and clutch
size) of age-2+ females and sex as group covariates for modeling adult apparent survival. That is, species were pooled
initially in the model and separated based on the group-level
(species-level) covariates. The most parsimonious statistical
model (based on adjusted Akaike Information Criteria or AICc ,
Akaike, 1985) assumed additive effects of sex, log-transformed
body mass (M) and annual fecundity on the logit transform of
and an additive effect of sex on the logit transform of recapture probability. The next most parsimonious model (with
AICc = 1.39) assumed an identical formulation for apparent survival, but included additive effects of both sex and
body mass on recapture probability. Adequate goodness of fit
(observed deviance <75% of expected random deviance in at
least 100 bootstrap pseudoreplicates) was determined for the
global model, in which additive effects of sex, body mass and
annual fecundity were modeled for both apparent survival and
recapture probability. Insufficient captures of known-age individuals prevented modeling age effects on apparent survival,
hence we assumed apparent survival rates were the same for
all adults (and hence 1 = 2+ in the matrix projection model;
thus A and C are age-independent as well). Observed annual
apparent survival rates for age-1 and age-2+ females based
on Eq. (4) are shown in Table 1 and represent the values used
for true survival (1 and 2+ ) in the matrix projection model.
Three species were excluded from the mark-recapture analysis because capture rates were low, however, we used the
statistical model in Eq. (4) to estimate an apparent survival
rate for these species nonetheless (Table 1).
Low recapture rates hindered us from estimating juvenile survival, and this represents the largest uncertainty in
model parameters. We estimated juvenile survival as 40% of
adult annual survival, which was similar to empirical values observed in several passerine species (Ricklefs, 1973;
Krementz et al., 1989; Sullivan, 1989; Powell et al., 2000). It has
been assumed that juvenile survival rates in passerines are
40–50% of adult survival rates (Ricklefs, 1973), however, this
has not been adequately tested to date.
We also estimated differential clutch size between age-1
and age-2+ birds from a linear regression. First, we calculated mean clutch sizes for known age (either age-1 or age-2+)
females for four species and compared these observations
with a regression developed by Sæther (1990) for European
birds. Our observations fit this relationship (r2 = 0.94), in which
mean clutch size of age-1 females is linearly related to mean
clutch size of age-2+ females (C1 = 1.01C2+ − 0.515). We then
used the regression to estimate clutch size for age-1 females
from the observed mean clutch size of all females because
we assumed age-2+ females dominated the observed nesting
birds.
We also computed from count data and mark-recapture
observations at the Mogollon Rim area to compare with model
projections. We used annual Breeding Bird Survey (BBS) statewide mean counts from 1994 to 1999 from Arizona (or New
Mexico if Arizona counts were not available) (Sauer et al., 2001)
to estimate according to methods outlined by Dennis et al.
(1991). Furthermore, we used a reverse-time analysis of the
mark-recapture data from the Mogollon Rim populations to
directly estimate and recruitment (Pradel, 1996; Nichols et
al., 2000). Finally, we computed the proportional change in BBS
annual counts (i.e., BBS countt+1 /BBScountt ) for each species to
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compare with annual nest success estimates as an alternative
retrospective analysis of the rate of population change that is
not based on asymptotic projections.
2.3.
Simulation of tradeoffs
We used simulations to examine the relative importance of
nest success versus adult survival when these tradeoff with
costs in other life history traits on population dynamics. We
performed a baseline simulation and two sets of simulations in which we increased effective reproduction or adult
survival from the baseline observed values for each species
as follows. The baseline simulation was based on the mean
life history parameters (clutch size, nesting attempts, nest
success, and age-1 and age-2+ survival) from Arizona populations. In the increased nest success simulations, we increased
nest success of all species by 10%, 20%, 30%, 40% and 50%
and used the individual-based nest attempt simulation model
to compute the subsequent decrease in nesting attempts
based on the higher daily nest survival rate (i.e., greater nest
success comes at the cost of fewer nesting attempts). In
the increased adult survival simulations, we increased adult
survival by 10% and reduced nesting attempts by 10% (a conservative reduction based on Eq. (4), which would indicate
reductions greater than 50%) in one simulation but by 25%
in another simulation. Thus, increased adult survival comes
at the cost of fewer offspring produced per breeding season
(Martin, 1995). A no-cost increase in a demographic parameter essentially demonstrates expectations based on sensitivity
analysis from the baseline simulation. However, considering
increases alone ignores tradeoffs among life history traits
(e.g., longevity versus age at maturation). Hence, we used
the tradeoff simulations to compare changes in population
growth when an increase in nest success (or adult survival)
comes at cost to another life history trait. For survival probabilities in replicate matrices, normal deviates were generated
for logit-transformed survival (see Eq. (4)) before back transformation to survival, which ensured that survival values were
constrained between 0 and 1. Similarly, we generated normal
deviates from arcsine-square root transformed nest success
and back-transformed these to matrix values to constrain nest
success between 0 and 1.
3.
Results
Simulated population growth rates were similar to estimates
from BBS population trends and independent estimates from
the mark-recapture data. Baseline-simulated values of were
within 95% confidence limits of values estimated from BBS
counts for 15 of the 20 species for which BBS trends were
available (Fig. 2) and within 95% confidence limits of values
estimated from the mark-recapture data for 8 of the 20 species
for which Pradel estimates of were available (Fig. 2). Simulated rates were weakly correlated with BBS trends (r = 0.35,
p = 0.128) (although removal of a single outlier, Williamson’s
Sapsucker, which had crude adult survival estimates, yielded
a stronger correlation r = 0.46, p = 0.047) and with Pradel estimates (r = 0.36, p = 0.146) (again with large effects from two
species, Audubon’s Warbler and Hermit Thrush, that have
Fig. 2 – Mean finite population growth rate () by species as
determined from BBS annual indices (filled bars), Pradel
estimates from mark-recapture data (shaded bars) and
projected from the baseline simulation (open bars). Error
bars indicate maximum values from the simulations and
the upper 95% confidence limits on the BBS and
mark-recapture estimates.
extremely low reproductive success on the Mogollon site
(Martin and Roper, 1988) and when excluded yielded a strong
correlation (r = 0.45, p = 0.077)). The Pradel estimates were correlated (r = 0.62, p = 0.014) with BBS trends. Thus, even though
our data include local nuances, the simulations and Pradel
estimates are correlated with, and show similar variation to,
broader population projections from BBS data, and indicate
that our simulations are reasonable demographic projections
for exploring the relative importance of different life stages to
demography.
In general, juvenile survival made the strongest functional
contribution to population growth. Elasticity values for juvenile survival and nest success were similar and equal to or
greater than values for adult survival among approximately
80% of the species (Fig. 3a). Matrix model simulation results
agreed with results for elasticity as juvenile survival and nest
success were the life history parameters most highly correlated to (Fig. 3b).
The combined components of reproductive success were
important in predicting the magnitude of asymptotic population growth. Clutch size and nest success had reasonable
success for predicting (r = 0.59 and p = 0.003, and r = 0.70 and
p = 0.0002, respectively), whereas adult survival was a weak
indicator of the magnitude of (r = 0.21 and p = 0.346). The
strongest predictor of was annual effective reproductive success (i.e., the product of clutch size, nesting attempts, nest
success and juvenile survival) of age-2+ adults (Fig. 4a). Annual
nest success was not a good predictor of proportional change
in population size (Fig. 4b).
The relative effect of changes in reproduction versus adult
survival depended upon the life history strategy of the species.
Results from simulations where nest success was increased
10% (over baseline values) against the tradeoff of fewer nest-
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e c o l o g i c a l m o d e l l i n g 2 0 9 ( 2 0 0 7 ) 110–120
Fig. 3 – (a) Elasticities and (b) linear coefficients of
determination (from LSA) for age-2+ nest success (filled
circles), juvenile survival (filled squares) and age-2+
survival (open circles) based on the matrix model with
parameters from the 23 species monitored at the Mogollon
Rim site plotted with annual fledgling production (the
product of clutch size, nesting attempts and nest success).
Higher values of elasticity and coefficient of determination
indicate greater sensitivity of to the corresponding
demographic parameter. Note elasticities for nest success
and juvenile survival are identical.
ing attempts resulted in an increase in all species (change
in > 0; Fig. 5a, solid circles). The extent of the increase in
was greater for species with greater annual fecundity. In
contrast, a simulated 10% increase in adult survival against
the tradeoff of 10% decreased annual fecundity resulted in a
smaller increase in compared to the increase realized in simulations of the 10% increase in nest success for species with
annual fledgling production greater than about 3.5 (Fig. 5a,
open circles). In fact, the tradeoff between 10% increase in
adult survival against fewer nesting attempts even resulted in
a decrease in (change in < 0, Fig. 5a) in those species with
the highest reproductive effort (annual fledgling production
>5, Fig. 5a). Changes in adult survival have a greater impact
on than changes in nest success for species with low reproductive effort. However, changes in nest success lead to larger
increases in than similar changes in adult survival as annual
fledgling production increases above about 3.5 (intersection of
the lines in Fig. 5a), and the simulations with 10% incremen-
115
Fig. 4 – Relationships between effective reproductive
success (i.e., recruitment) and (a) simulated asymptotic
population growth rate and (b) the coefficient of
determination from linear regression of proportional,
transient population growth based on annual BBS counts
and nest success in the 23 species monitored at the
Mogollon Rim site.
tal increases in nest success indicate that larger increases in
would be expected at even lower levels of fledgling production (Fig. 5b). Moreover, if the 10% increase in adult survival
were accompanied by reductions in annual fecundity of more
than 20%, then greater increases in would be expected from
increases in nest success for almost all of the observed levels
of fledgling production (Fig. 5b).
These relationships between nesting success versus adult
survival on the change in were applied to a wider range
of passerines across North America to gain insight into their
relative importance. Estimates of fledgling production were
obtained from nest success values and clutch sizes reported in
Martin (1995) for 121 North American species (Table 3). These
estimates were plotted against the change in from the curves
fit to 10% increase simulation results. The results suggest that
a 10% increase in nest success causes a greater change in population growth than a 10% increase in adult survival in more
than 73% of the species (89 of 121 species, Fig. 6a).
However, estimates of fledgling production for some of
the 121 species in Table 3 reported from 205 populations
(representing 94 species) monitored in the Breeding Biology
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Fig. 5 – Change in population growth rate from the baseline
simulation of the 23 species monitored at the Mogollon Rim
site in (a) simulations with a 10% increase in nest success
and correspondingly reduced nesting attempts (filled
circles) vs. a 10% increase in adult survival and equivalent
(10%) reduction in annual fecundity (the product of nesting
attempts and clutch size) (open circles), with regression
curves fit to the results. (b) Regression curves fit to results
from the incremental nest success simulations (solid lines)
compared to regression curves from the 10% increase in
adult survival with 10% and 25% reductions in fecundity
(dotted lines). Note that as nest success incrementally
increases, the point of intersection corresponds to lower
annual fecundity. Data points from the regressions in (b)
are not shown for clarity, but r2 > 0.57 for all curves.
Fig. 6 – Change in population growth predicted for fledgling
production in (a) 121 species (listed in Table 3; from Martin,
1995) and (b) 205 populations (94 species) monitored in the
BBIRD network assuming increases in nest success (solid
line) vs. adult survival (dotted line) as described by the
curves in Fig. 5a.
iteroparity through increased adult survival can increase population growth (Fig. 6b) given that greater iteroparity is still
possible.
4.
Research & Monitoring Database (BBIRD) program (Martin,
2003) indicated that a change in adult survival could affect
more than reproductive output in a substantial proportion of the populations (72 of 205 populations, Fig. 6b), but
that reproductive success was the most important in the
majority (65%) of populations. Annual fledgling production
in these 205 populations is lower than four fledglings for a
large proportion of populations, especially in Eastern North
America because nest success was low for many of these
populations. For example, nest success in Red-eyed Vireo
populations decreased from Montana to Ohio (Fig. 6b). As a
result, small increases (10%) in nest success are insufficient
to increase population growth very much, whereas greater
Discussion
Our results build on previous studies demonstrating a slowfast continuum in avian life histories based on tradeoffs
between fecundity and survival (Sæther, 1988; Martin, 1995,
2002; Sæther and Bakke, 2000; Ghalambor and Martin, 2001).
In those studies, evidence suggested tradeoffs exist between
adult survival and clutch size (or total egg production) from
populations widely separated by phylogeny or geography. We
observed a similar pattern for traits in closely related species
from populations in the same geographic location, within the
same ecological community, and over the same time period
(also see Martin, 2002). Projected population growth rates
from a model based on these observations were within estimates derived independently from mark-recapture data and
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Table 3 – Clutch size (C), incubation time (Ti ), nestling stage duration (Tn ), and nest success (S) reported in Martin (1995)
for 121 North American species, and used to estimate annual fledgling production and projected changes in population
growth associated with increases in nest success and adult survival
Species
Red-headed woodpecker
Acorn woodpecker
Red-bellied woodpecker
Red-naped sapsucker
Williamson’s sapsucker
Downy woodpecker
Hairy woodpecker
Red-cockaded woodpecker
Pileated woodpecker
Red-breasted nuthatch
Pygmy nuthatch
Tree swallow
Purple martin
Cliff swallow
Barn swallow
Eastern phoebe
Black-capped chickadee
Carolina chickadee
Mountain chickadee
Plain titmouse (Oak titmouse)
Tufted titmouse (Eastern titmouse)
White-breasted nuthatch
Brown creeper
House wren
American dipper
Eastern bluebird
Western bluebird
European starling
Prothonotary warbler
Horned lark
American pipit (Water pipit)
Orange-crowned warbler
Virginia’s warbler
Kirtland’s warbler
Black-and-white warbler
Worm-eating warbler
Ovenbird
Louisiana waterthrush
Kentucky warbler
Wilson’s warbler
Red-faced warbler
Bachman’s sparrow
Vesper sparrow
Lark sparrow
Savannah sparrow
Grasshopper sparrow
White-throated sparrow
Harris’ sparrow
Grey-headed junco
Yellow-eyed junco
Lapland longspur
Snow bunting
Bobolink
Eastern meadowlark
Western meadowlark
Acadian flycatcher
Willow flycatcher
Least flycatcher
Dusky flycatcher
Cordilleran flycatcher
Sedge wren
Marsh wren
C
Ti
Tn
S
A
4.82
4.36
4.31
4.93
4.38
4.81
3.93
3.27
3.80
5.80
6.50
4.70
4.93
3.60
4.49
4.74
6.82
6.50
7.06
6.75
6.00
7.30
5.55
6.50
4.30
4.42
4.82
5.36
4.87
3.36
4.60
4.46
3.60
4.63
4.76
4.76
4.70
5.80
4.62
4.18
4.53
4.00
3.75
3.61
4.04
4.39
4.27
4.26
3.94
3.40
5.06
5.23
5.12
4.49
4.83
2.93
3.63
3.95
3.60
3.30
6.26
4.91
13.5
11.5
11.5
13.0
13.0
12.0
14.0
11.5
18.0
12.0
16.0
14.5
15.5
13.0
15.0
16.0
12.0
12.0
14.0
15.0
13.5
12.0
15.0
14.0
16.0
14.1
13.8
12.0
12.5
11.7
14.4
14.0
12.7
14.1
11.0
13.0
12.0
12.5
12.5
12.8
13.0
13.5
12.0
11.5
11.8
11.5
13.0
12.8
11.8
13.0
12.0
12.0
11.5
14.1
14.8
14.2
13.9
14.0
13.5
16.0
14.0
13.1
26.0
31.0
25.0
27.0
31.5
22.5
29.0
26.0
26.0
19.5
22.0
20.0
28.0
23.0
20.5
16.0
16.0
16.4
20.0
18.5
17.5
15.0
15.5
15.0
25.0
17.5
21.0
21.0
12.0
9.5
14.4
11.0
11.4
11.4
11.0
11.0
9.0
10.0
9.0
9.7
12.0
9.0
9.0
9.5
9.0
9.0
9.0
9.3
10.0
10.5
7.0
13.5
12.0
11.0
11.0
13.7
13.5
14.8
18.0
16.3
13.0
13.0
0.780
0.929
0.820
0.966
0.923
1.000
0.875
0.727
0.830
0.689
0.868
0.458
0.572
0.648
0.432
0.700
0.663
0.760
0.572
0.600
0.900
0.602
0.647
0.715
0.245
0.482
0.887
0.650
0.690
0.560
0.585
0.500
0.420
0.300
0.737
0.727
0.452
0.700
0.700
0.603
0.520
0.357
0.314
0.452
0.408
0.392
0.449
0.477
0.599
0.481
0.494
0.560
0.628
0.375
0.423
0.574
0.493
0.474
0.380
0.179
0.682
0.394
1.34
1.15
1.31
1.12
1.16
1.08
1.21
1.43
1.26
1.56
1.24
1.95
1.60
1.57
1.99
1.53
1.67
1.48
1.73
1.69
1.22
1.82
1.65
1.55
2.33
1.97
1.22
1.61
1.69
2.11
1.81
2.12
2.38
2.70
1.64
1.62
2.44
1.71
1.74
1.95
2.07
2.66
2.90
2.44
2.59
2.66
2.39
2.31
1.98
2.23
2.42
1.96
1.85
2.47
2.30
1.86
2.06
2.06
2.22
2.86
1.65
2.37
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Table 3 – (Continued)
Species
Cactus wren
Blue-gray gnatcatcher
Hermit thrush
Wood thrush
American robin
Wrentit
Gray catbird
Northern mockingbird
Brown thrasher
Sage thrasher
Curve-billed thrasher
Loggerhead shrike
Bell’s vireo
Black-capped vireo
Black-throated blue warbler
Prairie warbler
Swainson’s warbler
MacGillivray’s warbler
Common yellowthroat
Hooded warbler
Yellow-breasted chat
Northern cardinal
Indigo bunting
Painted bunting
Dickcissel
Green-tailed towhee
Eastern towhee (Rufous-sided towhee)
Abert’s towhee
Chipping sparrow
Clay-colored sparrow
Brewer’s sparrow
Field sparrow
Sage sparrow
Sharp-tailed sparrow
Seaside sparrow
Song sparrow
White-crowned sparrow
Red-winged blackbird
Yellow-headed blackbird
Brewer’s blackbird
American goldfinch
Cassin’s kingbird
Western kingbird
Eastern kingbird
Blue jay
Florida scrub jay
Pinyon jay
American crow
Solitary vireo
Warbling vireo
Red-eyed vireo
Yellow warbler
Audubon’s warbler (Yellow-rumped warbler)
American redstart
Scarlet tanager
Western tanager
Rose-breasted grosbeak
Black-headed grosbeak
House finch
C
Ti
Tn
S
A
3.43
4.25
3.82
3.29
3.36
3.74
3.75
3.91
3.72
3.50
3.80
5.85
3.39
3.63
3.91
3.89
3.33
3.83
3.90
3.58
3.54
3.12
3.23
3.75
3.95
3.82
3.75
2.96
4.00
4.11
3.04
3.56
2.93
3.86
3.81
3.60
3.56
3.49
3.18
4.87
4.88
3.40
3.81
3.56
4.70
4.80
3.94
4.00
3.74
3.57
3.18
4.30
4.00
3.20
4.00
3.61
4.00
3.16
4.40
16.0
15.0
13.0
13.5
13.0
15.5
13.1
12.2
13.1
15.0
14.0
16.6
14.0
15.5
12.0
11.9
14.5
13.0
12.0
12.0
11.0
12.5
12.5
11.5
12.0
12.0
12.5
14.0
11.5
11.0
11.0
11.2
14.2
11.8
12.4
12.6
12.6
12.6
13.1
13.0
12.3
18.5
14.0
14.7
17.0
18.2
16.5
18.3
15.0
13.0
13.0
11.0
13.0
11.0
13.5
13.0
13.5
12.7
13.3
20.9
12.5
13.0
12.0
13.6
15.5
10.9
12.0
11.3
12.3
14.0
17.6
11.0
12.4
9.0
9.3
11.0
10.0
8.3
8.5
8.0
9.5
9.5
13.0
9.0
12.0
11.0
12.5
9.0
9.0
8.5
7.5
10.0
10.0
9.6
10.0
10.0
12.1
12.8
13.5
13.5
16.5
16.5
16.4
19.0
20.0
21.0
28.8
13.0
13.0
10.5
9.5
13.0
9.0
12.0
13.0
10.5
12.1
15.1
0.688
0.244
0.060
0.333
0.488
0.504
0.532
0.497
0.435
0.450
0.438
0.617
0.114
0.183
0.609
0.223
0.333
0.507
0.444
0.415
0.197
0.364
0.364
0.588
0.339
0.220
0.481
0.275
0.588
0.427
0.795
0.351
0.564
0.243
0.319
0.428
0.374
0.307
0.300
0.394
0.450
0.287
0.202
0.470
0.520
0.540
0.305
0.328
0.453
0.550
0.506
0.498
0.470
0.552
0.674
0.538
0.500
0.661
0.449
1.50
2.81
3.93
2.59
2.10
1.93
2.08
2.16
2.33
2.17
2.18
1.65
3.58
3.04
1.99
3.26
2.59
2.18
2.50
2.58
3.55
2.67
2.67
1.92
2.81
3.09
2.23
2.74
2.06
2.57
1.56
2.93
1.99
3.13
2.82
2.43
2.60
2.72
2.68
2.36
2.23
2.37
2.84
2.01
1.80
1.72
2.25
1.99
2.14
1.97
2.16
2.32
2.17
2.19
1.70
1.99
2.16
1.74
2.14
Number of nesting attempts (A) was estimated by a regression of daily nest mortality rate (Mn , computed from Sn , Ti and Tn ) and simulated
nesting attempts for the 23 species in Table 2 where A = 1.08 + 4.6(1 − e−9.43 Mn ), r2 = 0.82.
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from annual abundance estimates for other populations in
the region (Fig. 2). Therefore the model offers a useful tool for
exploring relationships among life history traits for passerine
birds.
Our results from the model simulations show that juvenile
survival had the highest elasticity (and thus changes in juvenile survival result in the highest proportional change in ), but
reproductive success determined the magnitude of . Elasticity
and LSA measures were typically highest for juvenile survival
(Fig. 3), which is disconcerting given that this is among the
least understood components of avian demography. Effective
reproductive output ultimately determined in simulations,
but reproductive success may not be indicative of short-term
changes in avian populations (Fig. 4). Effective reproductive
success (i.e., recruitment) is the product of two components:
fledgling production and juvenile survival. Therefore, the sensitivity of to juvenile survival suggests that small changes in
juvenile survival can lead to large dividends in future reproductive success (Gotelli, 1998), assuming fertility does not
decrease significantly with age.
Fledgling production provides a gradient for understanding
constraints on population growth as a function of evolved life
history traits. Species with relatively high adult survival and
low reproductive output obtained greater gains in through
increases in adult survival than in increased nesting success when these increases were accompanied by a tradeoff
(decrease) in nesting attempts. However, gains in declined for
species with increased reproductive output to the point that
species with high reproduction actually obtained a decrease in
with increased adult survival, because of the cost of reduced
clutch sizes (Fig. 5). In contrast, a very different result was
produced by simulated increases in nest success (at a cost of
fewer nesting attempts); always increased with an increase
in nest success, but gains in from nesting success exceeded
potential benefits of increased adult survival in species with
moderate to high reproductive output (Fig. 5). Thus, evolved
life history traits of species and their resulting position on
the slow-fast continuum strongly influenced whether adult
survival or nesting success has the biggest effects on and
fitness.
These relationships yield interesting perspectives when
considered in the context of variation in fledgling production
among populations of North American passerines. Observed
reproductive output in 205 populations breeding across the
United States was relatively low for many populations, particularly in the Eastern US, because of low nest success (Fig. 6b).
We suspect that lower reproductive success in Eastern populations is the result of human impact on breeding areas.
For example, rates of cowbird parasitism (and lower nest
success) increase with forest fragmentation (Robinson et al.,
1995; Tewksbury et al., 1998), which was characteristic of
many forested habitats in the eastern United States. As a
result, these species can obtain greater gains in population
growth through increased adult survival (Fig. 6b), which follows from life history theory: selection will favor increased
iteroparity (through increased adult survival) through reduced
reproductive effort when survival is relatively high or reproductive output is low or unpredictable (Roff, 1992). Yet, therein
lies a potentially significant problem for North American
passerines: selection will favor reduced reproductive effort if
119
that reduction yields increased adult survival. But, adult survival may be limited by environmental constraints; migratory
passerines suffer mortality during migration (see Sillett and
Holmes, 2002), such that gains in from reduced reproduction
and increased survival can be severely limited for most species
and, ultimately, emphasis on improving breeding success will
be more important. Of course, these results also indicate that
if adult survival is impacted, for example from habitat loss on
the wintering grounds, then increases in adult survival from
habitat enhancements on the wintering grounds can pay big
dividends.
The simulations also point to the potential importance of
juvenile survival to demography. Juvenile survival remains a
‘black box’ and perhaps the area of greatest ambiguity in the
passerine life cycle, yet it exerts considerable influence on
population growth (Figs. 3 and 4) and it may play an integral
role in evolution of life history strategies. Juvenile survival historically has been considered as a function of adult survival
(Ricklefs, 1973), but it may be related to reproductive traits
(Russell, 2000) and population density (Rodenhouse et al.,
2003). Greater reproductive effort (either from larger clutches
or additional nesting attempts) diminishes parental care after
fledging, and therefore likely reduces post-fledging survival
(Russell, 2000). If juvenile survival covaries with reproductive effort, this underscores the importance of variation in
breeding habitat and nesting mortality in limiting population growth. For example, simulations indicate brown creepers
would only require a 3% increase in juvenile survival to maintain a viable population and offset a 25% decrease in nest
success, but it would take a 34% increase in juvenile survival
for Virginia’s warblers and almost 60% for western tanagers.
Straightforward monitoring of nest success and clutch size in
a variety of species at sites located throughout North America
(as in the BBIRD network) provides an efficient mechanism for
quantifying reproductive output among neotropical migrants
and thereby identifying populations at risk of problems (i.e.,
declines). Linking landscape patterns, human disturbance,
meteorological conditions or predation rates to the variation in breeding success could then provide an important
means for identifying the factors driving avian population
dynamics.
Acknowledgements
This research was supported by a grant from the Environmental Protection Agency Science to Achieve Results (STAR)
Program and the USFS to the BBIRD program. R.L. Redmond
provided helpful comments on an earlier version of this
manuscript.
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