Relationship between the energetic cost of burrowing and genetic variability among populations of the pocket gopher, T. bottae: does physiological fitness correlate with genetic variability?
1 Department of Ecology and Evolutionary Biology, University of California
Santa Cruz, CA 95064, USA
2 Environmental Studies UCSC, PO Box Hotchkiss, CO 81419, USA
* Author for correspondence (e-mail: kelly.hildner{at}noaa.gov)
Accepted 14 April 2004
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Summary |
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Key words: metabolic efficiency, burrowing, genetic variability, fitness, inbreeding, genetic drift, Thomomys bottae
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Introduction |
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Pocket gophers in California exist in many isolated and semi-isolated
populations that, together, exhibit an extraordinary range of genetic
variation. Mean heterozygosity values among populations range from near zero
to almost 20% (Patton and Smith,
1990). We were therefore able to test whether genetic variability
in pocket gophers is related to burrowing efficiency. We used metabolic
efficiency, as determined by oxygen consumption during burrowing, as a
surrogate for physiological fitness, or vigor, because burrowing is (1) an
activity that is crucial to the survival of this highly fossorial species, (2)
has been shown to be energetically costly
(Vleck, 1979
) and (3) is
likely to be correlated with overall fitness in this species.
Although it is generally accepted that genetic variability in populations
is required for evolutionary adaptation to changing environments, the role of
heterozygosity in determining differences between individuals in physiological
fitness has been dismissed by a number of researchers
(Caro and Laurenson, 1994;
Caughley, 1994
;
Dawson et al., 1987
;
Lande, 1988
;
Ouborg and Groenendael, 1996
;
Pimm et al., 1988
,
1989
;
Schwartz et al., 1986
).
Conversely, there are many studies that demonstrate a significant relationship
between genetic variability and a wide range of fitness characters (for
reviews, see Britten, 1996
;
Mitton, 1997
;
Zouros, 1987
;
Zouros and Foltz, 1987
). Few
such studies, however, have been conducted on mammals, probably because of the
technical and logistical difficulties of acquiring sufficiently large samples
to detect correlations between levels of heterozygosity and phenotypic traits
(Britten, 1996
;
Zouros and Foltz, 1987
). In
the present study, we overcame the problem of small sample size by using a
statistically powerful paired design and testing whether individuals from
populations with low genetic variability were less efficient burrowers (had a
higher energetic cost of burrowing) than those from high variability
populations of the same subspecies. We measured genetic variation and cost of
burrowing on individuals from three pairs of T. bottae populations;
both populations in each pair were from the same subspecies but had
substantially different levels of genetic variability.
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Materials and methods |
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Cost of burrowing
Cost of burrowing was determined from the oxygen consumption per gram of
gopher per unit of work performed. Oxygen consumption during burrowing was
measured using an open-circuit respirometry system modified from Vleck
(1979), consisting of a 1
m-long Plexiglas tube filled to approximately 10 cm from the open end with a
constant density (1.63 g cm3) of sand (RMC Lonestar Lapis
Lustre 30 Mesh; Davenport, CA, USA). Three different diameter tubes were used;
the diameter of the tube was empirically determined and was dependent on the
mass of the gopher such that the gopher moved the entire volume of sand as it
burrowed (58120 g gophers were placed in a 5.72 cm tube, 120190
g gophers in a 6.35 cm tube, and >190 g gophers in a 6.99 cm tube). The
tube was connected via an airtight seal to a chamber where the gopher
could push the excavated sand. Wire mesh prevented the gopher from entering
the chamber. Airflow through the tube was kept constant at 1.4 l
min1 (Cole-Parmer N092-04 Flowmeter, Vernon Hills, IL, USA),
and the fractional oxygen concentration of air leaving the chamber was
determined using an Ametek S-3A oxygen analyzer connected to a computer for
data acquisition and analysis (Sable Systems, Salt Lake City, UT, USA). Carbon
dioxide and water were absorbed (baralyme and drierite, respectively) from air
samples prior to oxygen analysis, and water vapor was also absorbed prior to
air flow measurement (Fig.
1).
|
Before introducing a gopher into the tube, air was allowed to flow through the tube until the system stabilized, and the oxygen analyzer was set to the baseline value of 20.94% (the percentage of oxygen in the compressed air tank). After removal of the gopher at the end of the trial, the system was again allowed to stabilize to ensure that the baseline oxygen concentration remained constant during the experiment. In cases where the baseline shifted (<0.3%), a baseline correction was performed on the data using the Sable Systems analysis software.
Individual gophers were weighed and placed in the open end of the tube, which was then connected to the respirometry chamber with an air-tight collar. Gophers typically began burrowing shortly after being introduced to the chamber and continued to burrow until they reached the end of the tube, achieving a steady-state rate of oxygen consumption for at least a 10-min period. Gophers failing to burrow continuously were removed from the chamber and re-tested later. Only gophers that burrowed consistently for two burrowing trials were used in the analyses.
Using these criteria, 81 gophers were measured. During each burrowing trial, the distance that the gopher burrowed (D) and the amount of the tube filled with sand (F), which the gopher did not push completely out of the tube, were recorded (Fig. 1). Additionally, using two stopwatches, we recorded to the nearest second the amount of time that each gopher spent digging, as well as the amount of time spent pushing sand. These amounts were summed to calculate the total amount of time each gopher spent working in minutes. The rate of soil displacement was calculated as [(g soil/cm)/1000](D min1)(60) to arrive at the kg of soil moved per hour. The average distance the sand was displaced in meters was estimated as [(D/2)+S(F/2)]/100 (for definitions of variables, see Fig. 1).
Rates of oxygen consumption were calculated according to equation 8 of
Depocas and Hart (1957), as
modified by Hill (1972
;
equation 2). The mean rate of oxygen consumption measured during the 10-min
steady state of burrowing (ml O2 g1
h1) was corrected for standard temperature and pressure
(STP) and then divided by the rate of the soil displacement (kg
h1) and the average distance the sand was moved (m) to
arrive at an estimate of cost of burrowing (ml O2
g1 kgm1). The cost of burrowing,
therefore, was estimated as oxygen consumption per gram of gopher per kilogram
meter of soil moved. Reported values are the means of the two burrowing trials
for each gopher.
Protein electrophoresis
Liver, kidney and heart samples were surveyed for variation in 17 enzymatic
and nonenzymatic proteins encoded by 28 presumptive gene loci using standard
electrophoresis procedures (Patton et al.,
1972; Patton and Yang,
1977
; Selander et al.,
1971
). For details of buffer systems, see Hildner et al.
(2003
). The 28 loci scored
were (Sdh), (Idh-1 and Idh-2), (Pgm), (Mdh-1
and Mdh-2), (Ipo-1 and Ipo-2), (Ldh-1 and Ldh-2),
(Pept-1 and Pept-2), (Got-2), (Pgi), (6Pgd),
(Me), (Est-2 and Est-3), (Adh-1 and Adh-2), (Xdh-1 and
Xdh-2), (Ck-1 and Ck-2), (Ak-1 and Ak-2), and (Gp-2 and
Gp-3), of which 12 (those underlined) were polymorphic in at least
one population. Protein electrophoresis was conducted on a total of 89
gophers; estimates of heterozygosity (H) were derived from actual counts of
presumed heterozygotic genotypes.
DNA fingerprints
DNA fingerprints were produced using MS1 and Jeffreys' 33.15 probes as
described in detail elsewhere (Hildner et
al., 2003). Briefly, for each gopher, DNA was extracted from tail
tissue, purified, digested with HaeIII, electrophoresed on 1% agarose
gels in TAE buffer until bromophenol blue dye had migrated 15 cm, and
transferred to Hybond N nylon membrane. Filters were hybridized with Jeffreys'
33.15 probe (Jeffreys et al.,
1985
) conjugated to alkaline phosphatase at 52°C for 25 min,
washed according to the probe manufacturer's instructions (Lifecodes,
Stamford, CT, USA) and subjected to autoradiography after applying CDP-star
substrate (Tropix, Foster City, CA, USA). Filters were then stripped of old
probe and hybridized with MS1 (Jeffreys et
al., 1988
) for 30 min at 52°C. Individuals from the same
subspecies were electrophoresed on the same gel, and band sharing was only
measured within gels. DNA fingerprints were successfully conducted on 84
gophers; the average level of genetic similarity in each population was
measured as the mean band similarity (S). Here, we present the average band
dissimilarity (D=1S) (Soulé
and Zegers, 1996
), a value that is significantly correlated with
average heterozygosity (Stephens et al.,
1992
).
Analysis
To test whether populations with low genetic variability have relatively
high burrowing costs, 3-way ANCOVAs were conducted using the program JMP (SAS,
Cary, NC, USA) with subspecies, genetic variability class (high/low) and sex
as main effects, and log(mass) as a covariate. All main effects were treated
as fixed. The dependent variables were cost of burrowing (log ml O2
g1 kgm1) and oxygen consumption during
burrowing (log ml O2 g1 h1).
Using the methodology described in Quinn and Keough
(2002), the full model was run
to test for heterogeneity of slopes, and the interactions between the
covariate and the main effects were removed because no evidence of
heterogeneity was found. Non-significant interactions among the main effects
were also removed using a conservative criterion of P>0.25. Only
results of the reduced model are presented here.
In the ANCOVA for cost of burrowing, error variances were heteroscedastic (Levene's test P=0.04). For this reason, we also ran the analysis using a reciprocal transformation of cost of burrowing (Y=cost1) as the dependent variable, which resulted in homoscedastic error variances. Results of this ANCOVA were qualitatively the same as those for the ANCOVA with log-transformed cost of burrowing as the dependent variable, but the P-values for genetic variability and the interaction between genetic variability and subspecies were smaller (P=0.004 and P=0.003, respectively). Because our results and interpretation are unaffected, we only present the results of the original analysis.
In order to extrapolate the results of the analyses of covariance to subspecies not included in our analyses, subspecies would need to be treated as a random effect. We considered subspecies as a fixed effect here, which should be accounted for in extrapolating from our results. Treating subspecies as a random effect drastically reduces the denominator degrees of freedom (for cost of burrowing from 67 to 2). Therefore, a random effect model would reduce the power so much that it would be impossible to detect an effect of genetic variability without much larger sample sizes (J. Estes, personal communication).
Although cost of burrowing was measured at the individual level, we
characterized genetic variability at the population level. Ideally,
individual-level statistical analyses would be performed, while including
terms for common population-level factors other than genetics. The small
sample sizes, however, combined with the strong differences between
populations in genetic variability (with some populations having almost no
genetic variability), precluded this approach. To address the concern that our
results may have arisen from population level effects unrelated to genetics,
we used the Akaike Information Criterion (corrected for small sample
size=AICc) and Akaike weights to compare the above ANCOVA model
with one in which population (nested within subspecies) replaces genetic
variability class (population model)
(Burnham and Anderson, 1998). A
total of four models were compared: (1) genetic model (ANCOVA described above)
with no interaction terms, (2) genetic model including interaction terms
between genetics and other factors, (3) population model with no interaction
terms and (4) population model with interaction terms. If the differences
among populations are due to factors other than genetic variability, the
population models should provide a better prediction of patterns in the
dependent variables than the models ascribing effects to genetic
differences.
Mass-adjusted values for cost of burrowing were calculated using the
following equation:
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Descriptive statistics, t-tests and correlations were performed using Statview 4.51 (Abacus Concepts, Inc., Berkeley, CA, USA). Means are reported as mean ± 1 S.D., unless otherwise noted.
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Results |
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Relationship between cost of burrowing and genetic variability
For each of the three subspecies, the three measures of genetic variability
consistently ranked one population as having low genetic variability relative
to its paired population. Genetic variability results are summarized in
Table 1 and are described in
Hildner et al. (2003). Cost of
burrowing was significantly greater in the populations with low genetic
variability than in those with high variability (ANCOVA P=0.015;
Table 2;
Fig. 2). Mass-adjusted cost of
burrowing in the low variability populations averaged 0.57±0.24 ml
O2 g1 kgm1 high variability and
that populations averaged 0.42±0.19 ml O2
g1 kgm1. 6.1% of the total sums of squares
was explained by genetic variability. As expected, there was a significant
negative relationship between log-transformed cost of burrowing and log(mass)
(ANCOVA P=0.001). There was no effect attributable to the sex or
subspecies of the gophers. The interaction between subspecies and genetic
variability class, however, approached significance (P=0.056) because
the magnitude of the effect of genetic variability on cost of burrowing
differed for different subspecies.
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Individuals from populations with lower genetic variability had a higher cost of burrowing in all three subspecies, but the difference in mass-adjusted cost of burrowing (CAdj) was greatest in the two subspecies with the greatest difference in genetic variability, laticeps (t-test P=0.0265) and saxatilis (t-test P=0.0001), and was not significant in subspecies navus (t-test P=0.797). The CAdj of gophers from the low genetic variability population was 47% higher in laticeps, 79% higher in saxatilis and 6% higher in navus than that of gophers from the corresponding high variability population. Although not statistically significant, there was a trend among the three subspecies for the difference in CAdj to be positively correlated with the difference in genetic variability as a fraction of the pair's average allozyme heterozygosity (Kendall's Tau=1.0, P=0.12; Fig. 3). In other words, subspecies with greater difference in allozyme heterozygosity also had greater difference in cost of burrowing.
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Results of the AICc analysis (see Materials and methods) indicated that the genetic model with no interactions had the lowest AICc value and hence provided the best fit to the data. Akaike weights estimate a relative likelihood of 0.70 that the genetic model with no interactions is the best explanation of the data. The population model with no interactions provided the second best explanation of the data, with an Akaike weight of 0.26.
As with cost of burrowing, oxygen consumption during burrowing was significantly dependent on genetic variability. Individuals from low genetic variability populations had significantly higher oxygen consumption during burrowing than those from the paired high variability populations (ANCOVA P=0.02, N=81). As expected, there was a significant negative relationship between log-transformed burrowing oxygen consumption and log(body mass) (ANCOVA P=0.0009). None of the other factors or interactions had a significant effect on burrowing oxygen consumption.
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Discussion |
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Our results are consistent with those in a number of previous studies that
demonstrate fitness consequences of low genetic variability (for a review, see
Mitton, 1997). For example,
Mitton and co-workers have shown that within populations of the tiger
salamander (Ambystoma tigrinum), both growth rate and scope for
activity increase with allozyme heterozygosity
(Mitton et al., 1986
;
Pierce and Mitton, 1982
).
Also, in one of the few studies on a mammal, Teska et al.
(1990
) found that, on a low
quality diet, more heterozygous individuals of the old field mouse,
Peromyscus polionotus, had higher digestive efficiencies and
maintained body mass better than individuals with lower heterozygosity.
It should be noted that because we measure genetic variability at the
population level, there are two possible causes for the association between
genetic variability and burrowing efficiency found in the present study. One
possibility is that the differences in fitness are caused by individual-level
heterozygosity effects such as overdominance (heterozygote advantage) at
individual loci or associative overdominance effects (see, for example,
Pogson and Zouros, 1994). A
more probable explanation is that the low genetic variability of some gopher
populations is the result of inbreeding coupled with genetic drift in small,
isolated populations, and therefore the lower burrowing efficiency in these
populations is a reflection of homozygosity for deleterious alleles, causing
inbreeding depression (for a recent meta-analysis, see
Reed and Frankham, 2003
). The
present study does not provide sufficient information to distinguish between
these alternatives. Regardless of the specific mechanism, however, our results
demonstrate a significant association between genetic variability and
physiological efficiency among populations.
Although correlation does not prove causation, our results suggest that
differences in genetic variability influence burrowing efficiency. If the high
cost of burrowing of individuals in the low genetic variability populations is
indeed caused by their low genetic variability, one would expect to observe
effects in other characters as well. In fact, we do see such effects; gophers
from the low variability populations have both lower digestive efficiencies on
a low quality diet (Hildner,
2000) and lower growth rates
(Hildner et al., 2003
) than
gophers from the genetically more variable populations.
In the strict sense, fitness is defined as "the average ability
of organisms with a given genotype to survive and reproduce"
(Snyder et al., 1985). In
practice, however, many surrogates of fitness have been used in studying the
relationship between genetic variability and fitness, including such
characters as developmental stability
(Mitton, 1997
) and growth rate
(Hildner et al., 2003
;
Mitton, 1997
). The relevance
of a particular physiological trait to an individual's fitness is not always
apparent, but burrowing efficiency is clearly important to the survival and
reproduction of pocket gophers. Pocket gophers spend most of their time
underground and they rarely venture more than a few body lengths from their
burrow openings (Howard and Childs,
1959
). In addition, survivorship appears related to burrowing
efficiency. In a study by Sanjayan
(1997
) using T.
bottae from a single population, individuals with lower cost of burrowing
were more likely to survive between their release in the spring and the
following winter.
Gophers are a favored prey of many avian and mammalian carnivores and,
because they rarely venture far from their burrow openings, the extensiveness
of the burrow system is correlated with a gopher's access to food. Indirect
evidence that gophers try to limit their exposure to above-ground predators
comes from a study in which the above-ground movements of gophers (T.
talpoides) in an alfalfa field appeared to be tied to the height of the
surrounding vegetation (Proulx et al.,
1995); in addition, above-ground movements were less frequent and
less extensive when vegetation was shorter, possibly because of the increased
risk of predation. We have shown that gophers from low variability populations
have higher metabolic costs of burrowing than gophers from populations with
higher genetic variability and will therefore need to spend more time digging,
on average, in order to obtain the same net energy gain as gophers from high
variability populations or will need to spend more time foraging above ground,
increasing their risk of predation.
Burrowing is an energetically costly activity for gophers. It has been
estimated that the energy expended while burrowing is 3603400 times
that of moving the same distance over the surface
(Vleck, 1979). The amount of
burrowing necessary to meet a gopher's energy demand on a particular day
varies with habitat, season and forage quantity and quality
(Andersen and MacMahon, 1981
;
Loeb, 1987
). For T.
talpoides, a Rocky Mountain species, the average daily energy needs are
between 2 and 10 h of burrowing per day, and the most common cause of death is
thought to be lack of food caused by stochastic weather events that affect the
rate of burrowing, thus altering energy acquisition rates
(Andersen and MacMahon, 1981
).
Thus, everything else being equal, a reduced cost of burrowing translates into
more energy available for growth and reproduction.
Previous studies have shown that gopher fitness is associated with the
ability to acquire adequate forage. For example, Loeb
(1981) showed that T.
bottae in an irrigated alfalfa field had significantly larger body sizes
and nearly twice the reproductive rates of those in a non-irrigated field.
These differences in size and reproductive rates were probably due to
year-round availability of high quality forage in the irrigated habitat
(Loeb, 1981
).
Based on our results, gophers from high variability populations are likely
to have a foraging advantage over those from low variability populations. The
cost of burrowing of gophers from populations with low genetic variability was
676% higher than that of gophers from high variability populations, and
the two subspecies with the largest difference in genetic variability,
laticeps and saxatilis, also had the largest difference in
cost when comparing high and low variability populations (47 and 79%,
respectively). These values, however, are almost certainly an underestimate of
the energetic advantage of gophers from the more heterozygous populations
because they do not take into account that gophers from high variability
populations also have significantly higher digestive efficiencies
(Hildner, 2000).
Finally, are such differences in vigor likely to translate into differences
in population persistence? Other studies suggest that genetically less
variable populations do indeed have a lower probability of persistence
(Frankham, 1995;
Saccheri et al., 1998
;
Westemeier et al., 1998
),
especially during periods of environmental stress
(Bijlsma et al., 2000
). Any
loss of genetic variability that results in decreased physiological vigor or
efficiency can hasten extinction because it can decrease survival and
reproduction and lead to further decreases in population size and hence to
more severe inbreeding and genetic drift the extinction vortex
(Gilpin and Soulé,
1986
). We predict, therefore, that low variability gopher
populations will have a significantly higher extinction risk than their more
genetically variable counterparts. Further studies are needed to test this
prediction and the generality of our results to other populations and
species.
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Acknowledgments |
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