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Learning capabilities enhanced in harsh
environments: a common garden approach
Timothy C. Roth*, Lara D. LaDage and Vladimir V. Pravosudov
Department of Biology, University of Nevada, 1664 North Virginia Street, MS 314,
Reno, NV 89557, USA
Previous studies have suggested that the ability to inhabit harsh environments may be linked to advanced
learning traits. However, it is not clear if individuals express such traits as a consequence of experiencing
challenging environments or if these traits are inherited. To assess the influence of differential selection
pressures on variation in aspects of cognition, we used a common garden approach to examine the
response to novelty and problem-solving abilities of two populations of black-capped chickadees (Poecile
atricapillus). These populations originated from the latitudinal extremes of the species’s range, where we
had previously demonstrated significant differences in memory and brain morphology in a multi-
population study. We found that birds from the harsh northern population, where selection for cognitive
abilities is expected to be high, significantly outperformed conspecifics from the mild southern popu-
lation. Our results imply differences in cognitive abilities that may be inherited, as individuals from
both populations were raised in and had experienced identical environmental conditions from 10 days
of age. Although our data suggest an effect independent of experience, we cannot rule out maternal
effects or experiences within the nest prior to day 10 with our design. Nevertheless, our results support
the idea that environmental severity may be an important factor in shaping certain aspects of cognition.
Keywords: behavioural flexibility; environmental harshness; learning; neophobia;
natural selection; problem-solving
1. INTRODUCTION
Animals living in energetically challenging (e.g. unpre-
dictable and/or harsh) environments should benefit from
advanced cognitive abilities (Dukas 1998; Shettleworth
1998,2009). One aspect of advanced cognition often
examined is behavioural flexibility or learning (Reader
2003), also termed plasticity or innovation (sensu Lefebvre
et al. 1997). Rather than a fixed response to a given stimu-
lus, behavioural flexibility allows for the expression of a
variety of different behavioural outcomes under different
contexts based on previous experiences (Dukas 1998;
Reader 2003). Such flexibility seems to be adaptive and
therefore has strong ecological and evolutionary relevance
(Price et al. 2003;Biernaskie et al. 2009). For example,
various aspects of learning or behavioural flexibility may
play key roles in the success of biological invasions (e.g.
Sol et al.2002,2005a;Martin & Fitzgerald 2005), the
occupation of anthropogenic environments (e.g.
Echeverria & Vassallo 2008), as well as in some of the
basic ecological differences between populations and
species (e.g. Greenberg 1983,1984,1990;Liker &
Bokony 2009). However, the ultimate source of the
production of these cognitive differences is poorly
understood. Can all individuals of the species express
advanced learning traits simply as a consequence of
experiencing a challenging environment, or are these
traits an inherited product of differential selection
pressures in these environments?
Evidence for the relationship between increased
learning capabilities and harsh environments has been
observed in numerous taxa. For example, in an intra-
specific comparison, Martin & Fitzgerald (2005) found
that an actively invading population of house sparrows
(Passer domesticus) had reduced levels of neophobia when
compared with a long-established population. Moreover,
several large-scale comparative analyses have shown posi-
tive relationships between living in anthropogenic habitats
(i.e. those characterized by novelty and complexity) and
feeding innovation in birds (Lefebvre et al. 1997;Sol
et al. 2005a) as well as mammals (Lefebvre et al. 2004).
Thus, taxa that more often show innovative foraging tac-
tics appear to be those that are more successful at
invading novel environments. These patterns have also
been shown to correlate with brain size (or forebrain
size), suggesting a morphological basis to the variance
in cognition (Lefebvre et al. 1997;Solet al.2005a). Unfor-
tunately, the comparative studies that make up the bulk of
the large-scale evidence for the environmental complexity–
cognition relationship (e.g. Lefebvre et al.1997,2004;Sol
et al.2002,2005a) only present a ‘snapshot’ of traits influ-
enced heavily by selection pressures in evolutionary history,
which are generally unknown. These comparisons cannot
be used to address the influence of specific experiences
and population-level selection pressures. To address the
effects of selection pressures on cognition, it may be
more effective to examine differences in populations that
are currently experiencing potentially different selection
regimes.
The relationship between harsh environments and cog-
nitive abilities is not limited to learning. Theory suggests
that food-caching birds living in more harsh climates
should cache more and have better spatial memory than
those in more mild climates (Krebs et al. 1989;Sherry
*Author for correspondence (tcroth@unr.edu).
Proc. R. Soc. B (2010) 277, 3187–3193
doi:10.1098/rspb.2010.0630
Published online 2 June 2010
Received 23 March 2010
Accepted 10 May 2010 3187 This journal is q2010 The Royal Society
on September 10, 2010rspb.royalsocietypublishing.orgDownloaded from
et al. 1989;Pravosudov & Grubb 1997;Pravosudov &
Lucas 2001). Indeed, previous work supports the basis
for this relationship between environmental harshness
and memory, as birds from higher latitudes (i.e. more
harsh climates) had better spatial memory (Pravosudov &
Clayton 2002), probably owing to increased demands for
accurate cache retrieval. Moreover, these behavioural
differences seem to have a neurological basis. We have
previously demonstrated a gradation of hippocampal attri-
butes across populations, where hippocampal size and
neuron number decrease with declining latitude
(Pravosudov & Clayton 2002;Roth & Pravosudov 2009).
Overall, then, there is strong theoretical and empirical evi-
dence supporting a relationship between environmental
severity and one aspect of cognition—spatial memory—
along a latitudinal gradient in food-caching birds.
We extend this logic from memory for cache retrieval
to learning. Because of the drastically lower temperatures,
shorter winter day length and greater precipitation
(snow cover) in the northern when compared with the
southern parts of their range (see Roth & Pravosudov
2009), northern populations of food-caching birds must
eat more to fulfil their daily energy requirements
(Pravosudov & Grubb 1997;Pravosudov & Lucas
2001), but have less daylight in which to do it and
encounter more obstacles (snow) that conceal food.
Traits that increase food acquisition rates and increase
the accuracy and speed of decision-making should be
adaptive under these time-limited and energetically
demanding conditions (Lefebvre et al. 1997;Pravosudov &
Grubb 1997;McLean 2001;Pravosudov & Lucas 2001;
Hills 2006; see also Sol et al. 2005b). It would be naive
to presuppose that selection should work on a single
behavioural/morphological trait. Instead, selection may
result in the enhancement of numerous cognitive traits
produced in various ways depending upon the selection
pressures in the population. The ultimate effect would
be that individuals with such traits might have a higher
probability of recovering food, and hence of survival
during the winter. Indeed, birds from the more harsh
northern populations tend to have larger telencephalic
regions relative to body mass (a trait associated with
enhanced cognitive abilities; Lefebvre et al.1997,2004;
Sol et al.2002,2005a) than those in the south. For
example, based on our previous study, the telencephalon
volume of chickadees from northern populations are
larger than those from southern populations (ordered
heterogeneity test: r
s
p
c
¼0.774, p,0.010; methods
from Rice & Gaines 1994; based on data from Roth &
Pravosudov 2009). Interestingly, body mass showed the
opposite trend with birds from southern populations
being significantly heavier than those from the north
(ordered heterogeneity test: r
s
p
c
¼0.899, p,0.001;
methods from Rice & Gaines 1994; based on mass data
in Roth & Pravosudov 2009). Note that we do not
intend to make causal statements about the overall or rela-
tive size of the brain and cognitive ability, but only point
out their association. See Roth et al.(2010)for a detailed
discussion of these topics.
The goal of this study was to examine the environ-
mental complexity– cognition relationship using a
common garden approach in a food-caching model
system. To avoid the problems associated with comparing
different species (see Macphail 1996), yet to assess two
aspects of the cognitive abilities of different populations
experiencing differential selection pressures, we examined
the response to novel object and problem-solving tasks of
hand-raised black-capped chickadees (Poecile atricapillus).
We have chosen to work with the two populations at the
latitudinal extremes of the species’s range—Alaska (AK)
and Kansas (KS)—as they showed the largest morpho-
logical differences (in both relative hippocampal volume
and in telencephalon volume) along the previously
demonstrated multi-population gradient of environ-
mental harshness (Roth & Pravosudov 2009). Thus, it is
from these most geographically and climatically distinct
populations that we would expect to see the largest differ-
ences in learning, should they exist.
Our prediction was that birds from the climatically
harsh northern population (AK), which had the larger
brains in our previous work, would outperform conspeci-
fics from the more mild southern population (KS), which
had smaller brains. Given that these individuals were
raised in the laboratory and had experienced identical
environmental conditions (at least since day 10 after
hatching), difference between these populations may
suggest a component to learning that may potentially be
the result of differential selection pressures within their
respective populations.
2. MATERIAL AND METHODS
(a)Collection sites
Black-capped chickadees (Poecile atricapillus) were collected
during late May and early June 2009 from nests at the latitu-
dinal extremes of their range (Anchorage, AK: 618100N,
1498530W; Manhattan, KS: 398080N, 968370W). Two
chicks were taken from each nest, with siblings used in the
two different tests (see below). At both sites, we collected
chicks from both natural nests and those in nest boxes (con-
structed from wood and PVC) in a wide range of habitats
from anthropogenic to very ‘natural’. Average temperatures
during the respective collection periods were as follows:
AK: max ¼18.3 +1.18C, min ¼7.2 +0.48C; KS: max ¼
24.8 +1.48C, min ¼8.7 +1.18C. The average day length
during collection was 1149 min for AK and 856 min for KS.
Chicks were approximately 10 days old at the time of col-
lection and were hand-raised indoors on site until they were
approximately 18 days old. To retain consistency in the
hand-raising environment between the two sites, T.C.R.,
assisted by the same technician, worked at both locations.
In addition, the indoor environment was as similar as pos-
sible. Temperature was maintained between 21 and 238C
and lighting conditions were similar and on the same sche-
dule (15 : 9, L : D), beginning the first day of collection.
Chicks were transported to the University of Nevada,
Reno, via ground (from KS) and air (from AK), in the same
containers (wood nesting boxes; see below). We attempted
to transport the chicks as rapidly as possible and control the
environment as much as possible during transportation.
(b)Hand-rearing and housing
All chicks were fed a diet of: Orlux Handmix formula
(Versele-Laga, Deinze, Belgium); wax worms (Pyralidae sp.);
meal worms (Tenebrio molitor); phoenix worms (Hermetia
illucens); crickets (Acheta domesticus); a slurry consisting of
dog food (Canidae, San Luis Obispo, CA), cat food (Natura
EVO, Santa Clara, CA), Orlux Insect Patee Premium and
3188 T. C. Roth et al. Learning in harsh environments
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Orlux Handmix; and nut powder pellets consisting of
pulverized pine nuts (Pinus koraiensis), peanuts (Arachis hypo-
gaea), sunflower seeds (Helianthus sp.) and Insect Patee. Food
types were systematically cycled throughout the day and were
offered every 20 min during daylight hours, so the chicks from
both locations ate at approximately the same frequency and
for the same time period during the day. Food (same diet as
above less Handmix, all in whole form, plus Roudybush
Crumbles and Purina Game Starter) and water were provided
ad libitum after birds reached independence (approx. 30–35
days after hatch).
During hand-rearing, chicks were housed in groups of
four to six individuals in 17 17 24 cm wooden boxes
filled with sawdust to simulate nest cavities. At the fledgling
stage (approx. 18–20 days after hatch), chicks were housed
as sibling pairs in 120 42 60 cm wire cages. At the dis-
persal stage (approx. 60 days after hatching), all birds were
moved into a solitary, permanent arrangement in 60
42 60 cm wire cages. Sex was estimated via wing chord
measurements. To reduce aggression between males, birds
were placed in an M/F/F/M arrangement within a row of
four cages (all within visual contact). The populations were
systematically partitioned as AK/KS/AK/KS within these
rows, with siblings located in different rooms.
Beginning in early August and until mid-October,
the light cycle gradually shifted (approx. 0.5 h per week) to
9 : 15 (L : D). All tests occurred on this light cycle. Tests
began when birds were approximately five months old.
However, because of the asynchronous breeding of the two
populations, the AK birds were on average slightly less than
three weeks younger than the KS individuals (average age
at testing: AK, 20.8 weeks; KS, 23.7 weeks).
(c)Learning tests
We focused on two aspects of learning: problem-solving and
the response to novelty. Innovative problem-solving is a goal-
oriented form of learning, whereby an animal encounters a
novel problem with a known goal or reward (often food)
and must perform a series of novel steps to achieve the goal
(Dukas 1998; e.g. Webster & Lefebvre 2001;Keagy et al.
2009). The speed required to solve the task is frequently
used as an indication of the animal’s ability to learn (e.g.
Carlier & Lefebvre 1996). Habituation to a non-threatening
novel object can also be viewed as a form of this type of learn-
ing. Although a novel object may initially be perceived as
risky, through the process of examination the animal learns
that the object is not a threat. As learning is inherent to
both of these processes, the performance on these tasks prob-
ably reflects selection on the ability to learn (sensu Dukas 1998).
(i) Problem-solving test
Problem-solving tests were conducted approximately 2 h
after lights-on from 4– 9 November 2009. This test was per-
formed with 24 birds (12 AK, 12 KS) from different nests.
The problem-solving test involved removing galvanized
steel washers (3.5 cm diameter, 1.5 cm diameter hole;
roughly equal to the mass of the birds, approx. 15 g) covered
with clear 3M acetate from a 3 5 grid of 1.5 cm wells
drilled into a wooden board (40 18 cm) containing wax
worms. All birds had been fully habituated to the boards
(total duration of prior exposure .30 h) in their home
cages. Birds were habituated to the washers for 8 h the day
prior to the test. During this habituation, washers were
secured to the boards (adjacent to, but not covering, the
wells) with double-sided tape so that they could be touched,
but not moved. Wax worms were offered in 8 of the 15 wells,
and habituation was considered successful if birds took all
wax worms (which occurred in all cases).
A pre-trial control occurred approximately 1 h before
lights-off the day prior to the test. During this control, one
wax worm was placed on each board, and we recorded the
latency to remove the worm (300 s max). The boards were
then removed. The following morning, birds were allowed
to feed for 1 h after lights-on, and then deprived of food
for 1 h before the problem-solving trial. The boards were
introduced into the cages with one wax worm in each of
the same eight wells as during the habituation period, but
now washers covered all 15 wells. The birds could see the
worms, but could only retrieve a worm by moving the
washer. The birds could not puncture the acetate.
The trials occurred in the home cages, with birds in the
cage row (i.e. two AK, two KS) tested simultaneously. All
trials were observed remotely with a live video feed to another
room and recorded using Sony DCR-SR300 and DCR-SR47
digital video cameras on tripods. We recorded the latency
(in seconds) to land on the board and the latency to take
the first worm (3600 s max). We considered the problem
solved when the bird had taken a worm. To control for
motivation, a post-trial control was performed. After
3600 s, one wax worm was placed on top of the board and
we recorded the latency to take the worm (300 s max).
(ii) Novelty test
Novelty tests were conducted 0.5 h after lights-on from 18 – 21
October 2009. This test was performed with 25 birds (12 AK,
13 KS) from different nests (siblings of the birds in the pro-
blem-solving test). All birds were deprived of food 0.5 h
prior to lights-off the evening before testing and until after
the test the following day (approx. 2 h after lights-on). Birds
were recorded using video as in the previous test. We recorded
the cumulative latency to approach and remove food (a single
wax worm) from a control (usual type 300 ml circular stainless
steel) and novel feeder (usual type feeder modified with paint
and protruding bolts) in an A : B : A (control : treatment : con-
trol) design. The feeders were placed in the centre of the home
cage floor and the birds were recorded for 300 s (during the
controls) and 1800 s (during the treatment). We recorded
the latency (in seconds) to touch the feeder, to sit on the
feeder and to take the worm from the feeder for both control
and treatment trials.
(d)Statistical analyses
Repeated-measures analysis of variance tests were used to
test the overall models for population (AK and KS) and
within-subject effects (controls and treatment). In addition,
we used planned comparisons to confirm that controls were
not significantly different within populations. We also com-
pared the habituation time in the novelty test (treatment
minus pre-control times) and the time to solve the problem
(latency to take the worm minus latency to land on the
board) with t-tests. Data were log-transformed for all ana-
lyses; raw data are presented in figures for clarity (means +
s.e. are reported;
a
¼0.05).
3. RESULTS
(a)Problem-solving
Problem-solving was assessed by the time required to
remove a transparent, weighted cover from a well
Learning in harsh environments T. C. Roth et al. 3189
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containing a food item. The motivation to land on the
testing apparatus was assessed with pre- and post-treat-
ment trials of a single wax worm placed on the board.
The individuals from AK landed on the testing board,
uncovered the well and removed the wax worm signifi-
cantly faster than those from KS ( population: F
1,22
¼
32.888, p,0.001; within-subject: F
2,46
¼181.825, p,
0.001; figure 1). There was no effect of motivation or
habituation to the experimental set-up, as there were no
differences between pre- and post-treatment times in
either population (AK: p¼0.092; KS: p¼0.320). The
time to solve the task (latency to eat minus latency to
land) was significantly longer for the KS population
(t
22
¼25.340, p,0.001).
(b)Response to novelty
The response to novelty was assessed by the latency to
approach, sit on and finally take a food item from a
novel feeder. Motivation was controlled with both pre-
and post-treatment exposures of the same food item in
a familiar feeder. There was a large and significant differ-
ence between the two populations in the latency to
approach (population: F
1,23
¼13.691, p¼0.001;
within-subject: F
2,46
¼92.724, p,0.001), sit on
(population: F
1,23
¼12.359, p¼0.002; within-subject:
F
2,46
¼104.262, p,0.001) and take the food item
from (population: F
1,23
¼11.528, p¼0.002; within-
subject: F
2,46
¼142.876, p,0.001; figure 2) the novel
feeder . The individuals from the KS population took sig-
nificantly longer than those from the AK population to
approach (t
23
¼23.349, p¼0.003), sit on
(t
23
¼22.972, p¼0.007) and take the wax worm from
(t
23
¼23.159, p¼0.004) the novel feeder relative to
the pre-treatment control. Comparisons of the pre- and
post-treatment controls showed that motivation, the ten-
dency to feed on the floor of the cage and/or the testing
set-up did not play a substantive role in the results
(touch: AK, p¼0.181; KS, p¼0.019; sit: AK, p¼
0.146; KS, p¼0.014; take: AK, p¼0.027; KS, p¼
0.005; figure 2). Although we did see small, yet
significant, differences in some pre/post comparisons,
the latencies were lower in the post-trials in all cases.
This suggests some habituation in both populations
within the study, but these differences are very minor
relative to the overall treatment effect (figure 2).
4. DISCUSSION
We found significant differences in problem-solving and
neophobia between two populations originating from
drastically different environmental conditions. Our results
suggest that selection has produced variance in the ability
to learn (sensu Dukas 1998), as the chickadee population
from the more harsh environment (AK) were faster in
problem-solving and less neophobic relative to their
southern conspecifics (KS) despite being raised in identi-
cal environments since age 10 days post-hatch. Thus,
there seems to be the possibility of an inherited com-
ponent (genetic and/or maternal effects) to the speed of
2000
1600
1200
latency (s)
800
400
0
pre land eat post
Figure 1. Latency to the completion of a problem-solving
task (removing a weighted, transparent cover from a well con-
taining a wax worm) in black-capped chickadees. Pre- and
post-treatment exposure of a wax worm on the testing appar-
atus controlled for motivation, the tendency to feed on the
floor of the cage and habituation to the testing set-up.
Filled circles, Kansas; open circles, Alaska.
1200
1400
1600(a)
(b)
(c)
1000
latency to touch (s)
800
400
600
200
0
1200
1400
1600
1000
latency to sit (s)
800
400
600
200
0
1200
1400
1600
1000
latency to take worm (s)
800
400
600
200
p
re treatment
p
ost
0
Figure 2. The response to novelty of black-capped chickadees
as assessed by the latency to (a) approach, (b) sit on and
(c) take a wax worm from a novel feeder. Pre- and post-
treatment exposure of a wax worm in a familiar feeder
controlled for motivation and the tendency to feed on the
floor of the cage. Filled circles, Kansas; open circles, Alaska.
3190 T. C. Roth et al. Learning in harsh environments
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problem-solving and habituation to novelty within this
species, although we could not rule out any experiential
or environmental effects taking place prior to day 10,
when blind chicks were in a dark nest cavity. As both of
these traits are aspects of learning (Dukas 1998;Reader
2003;Lefebvre et al. 2004), our results suggest that selec-
tion may favour enhanced learning abilities in more
extreme climates, at least in this species.
We found differences in both of our measures of learn-
ing, suggesting that there may be a difference in the
selection pressures for these aspects of learning between
these populations from extremely different climates. This
does not imply, however, that all aspects of learning will
necessarily be different or that all aspects of cognition will
necessarily be superior in the more northern populations.
One very broad interpretation of our results could be that
selection might enhance all types of cognitive qualities in
more harsh climates. However, Pravosudov & Clayton
(2002) reported differences in spatial memory but not
colour memory in a two-population comparison in this
same species. This may suggest that the selection on cogni-
tion is quite complex, and differing selection pressures in
different populations must be considered thoroughly
(Dukas 1998;Shettleworth 1998). On the other hand,
the test for colour memory by Pravosudov & Clayton
(2002) was purposefully simplistic (a single colour) as the
goal of that study was to test for motivation to perform a
spatial task and not to test for differences in memory for
colour per se. Still, it is not to say that selection should
enhance all cognition. Rather, selection should enhance
cognitive abilities that may affect fitness under specific
environmental conditions. So, in the case of the food-
caching chickadee, selection for spatial memory (which is
important for successful cache recovery) has been shown
to be particularly important for the northern population,
but presumably under less selection in the southern popu-
lation (Pravosudov & Clayton 2002). Selection for colour
memory, however, may not be a function of climate in
this system as both populations seem to use it similarly
(Pravosudov & Clayton 2002). According to the adaptive
specialization hypothesis, specific differences observed
between the populations should be a function of the specific
selection regimes experienced by those populations.
Given that we have very specific predictions based on
our previous study of multiple populations along a latitu-
dinal gradient (Roth & Pravosudov 2009), we emphasize
that these results are not likely to be due to chance alone.
Our selection of the two populations in this study was
based on our previous multi-population studies of the
relationship between environmental harshness, memory
and brain morphology, as well as theoretical differences
(e.g. Pravosudov & Grubb 1997;Pravosudov & Lucas
2001;Pravosudov & Clayton 2002;Roth & Pravosudov
2009). As we had already shown the large-scale pattern
between the environment and the brain, our next objec-
tive was to examine the relevance of individual
experiences by comparing the differences in learning
capabilities between populations with maximal differ-
ences in brain morphology. Although the inclusion of
additional populations would have increased the scope
of our comparison, we were limited by logistical and
ethical constraints.
Our data suggest the possibility of an inherited effect
on learning; however, there are two important caveats to
this interpretation. First, owing to the logistical difficulty
of hand-raising very small birds, we collected chicks from
the nest at approximately 10 days of age. Thus, it is pos-
sible that experiences during early development (from
hatching to day 10) could have produced the observed
results. We think that this is unlikely as it is around day
10 that black-capped chickadees’ eyes begin to open,
and any experiences would have occurred in a dark nest-
ing cavity. Thus, the visual conditions that the two
populations experienced were probably very similar. How-
ever, we cannot rule out the possibility that thermal
differences or differences in parental feeding had an
effect on our results. Second, we cannot rule out the
possibility of maternal effects. It is possible that our
observed differences could have been due to physiological
decisions made by the mother prior to egg laying. For
example, stressed mothers may deposit increased levels
of corticosterone into their eggs to ‘prepare’ the young
for a challenging environment (Chin et al. 2009). This
possibility, in particular, may explain the differences in
response to novelty. It is possible that exposure to corti-
costerone during development may produce a response
in specific brain regions such as the amygdala, which
may affect neophobia responses (Burns et al. 1996). How-
ever, we argue that these maternal effects, should they
exist, are likely to be the result of selection as well. Thus,
it is still not the individual chicks’ experiences that produce
such effects. Moreover, differences in corticosterone
would not clearly explain the differences in problem-
solving abilities between the populations. Still, as a
consequence of these caveats, we interpret our results as
evidence of a possible inherited effect, since maternal
effects are an aspect of inheritance, but suggest that
future studies consider breeding experiments to fully dis-
sociate these factors.
Although one possible explanation of our results is a
heritable component to cognition, we acknowledge that
complex behaviours are probably the result of both inheri-
tance and experience. For example, Greenberg (1983,
1984) supports an experiential explanation for specific
responses such as neophobia. These studies suggest that
differences in foraging niches themselves may be the pro-
duct of ontogenetic experiences produced in part by
neophobia. Experiences may still be important and may
produce variation in addition to that generated through
inheritance. It is possible that the ultimate differences in
foraging niches created by experience as realized in
Greenberg’s (1984) study may be the result of genetic
differences in response to novelty between different
species. In other words, using Greenberg’s approach,
some of the ecological differences between generalists
and specialists may be due to genetic difference in
response to novelty. Ontogenetic experience may then
be the mechanism by which a particular species is ‘intro-
duced’ to (and maintained in) its habitat. This will
require further study.
Overall, our data suggest a large difference in some
aspects of the cognitive abilities of black-capped chicka-
dees that may be due in part to differential selection
pressures within different environments. Based on
theory and our previous work comparing brains of chick-
adees from multiple populations across a gradient of
environmental harshness, we suggest that these results
are probably due to the climatic severity of the
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environments. A complementary explanation is that the
observed differences are not the result of climatic harsh-
ness per se, but of range expansion. Several studies
suggest an important role of behavioural flexibility in
the success of biological invasions (Martin & Fitzgerald
2005). It is possible that the AK population has more
recently (on an evolutionary scale) been involved in
range expansion, at least since the retreat of the glaciers
during the last Ice Age (Harrap & Quinn 1995). Thus,
rather than an effect of current climatic conditions, the
AK population may possess faster learning skills owing
to their ancestors’ recent range expansion. It is important
to point out that the range expansion and environmental
harshness hypotheses are not mutually exclusive; both
could be relevant to our study system. Both of these
hypotheses, nevertheless, imply a selective component
to the differences between the populations, suggesting
that these cognitive traits are important, adaptive and
probably the product of natural selection rather than
individual experiences alone.
We are grateful to E. Horne, B. Van Slyke, K. Hampton and
Kansas State University’s Konza Prairie Biological Station
for their assistance at our Kansas site. We are also indebted
to C. Handel, V. Jorgensen, M. Pajot and the United States
Geological Survey’s Alaska Science Center for their
assistance at our Alaska site. C. Freas and J. Ream assisted
in nestling collection. C. Freas and G. Hanson assisted in
animal care and maintenance. We are also grateful to
K. Otter for logistical advice, and to our many colleagues
(too numerous to mention) for advice on hand-rearing
chickadees. This research was funded in part by grants
from the National Science Foundation (IOB-0615021) and
the National Institutes of Health (MH079892 and
MH076797). Birds were collected under United States
Fish and Wildlife (MB022532), Alaska (09-020), Kansas
(SC-039-2009) and Nevada (S30942) permits. This
research was supervised by the University of Nevada,
Reno, IACUC (protocol no. A05/06-35), and followed
all federal and local guidelines for the use of animals in
research.
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