Download Temporal changes in a Chihuahuan Desert rodent community

OIKOS 59: 290-302. Copenhagen 1990
Temporal changes in a Chihuahuan Desert rodent community
James H. Brown and Edward J. Heske
Brown, J. H. and Heske, E. J . 1990. Temporal changes in a Chihuahuan Desert
rodent community. - Oikos 59: 290-302.
We used time series analysis of ten years of monthly census data to assess the
responses of both individual species and an entire community of rodents to a fluctuating desert environment. Autocorrelation analysis revealed different patterns of
intra-annual fluctuation among the It species: Dipodomys spectabilis and Perognathus Jarus had pronounced annual cycles: D. ordii, D. merriaini, Chaetodipus
penicillutu~,Onvchomys torridus, 0 . leucogusrer, and Neotoma alhigula exhibited
annual cycles modified by interannual variation; and Feromyscus eremicus, Pm.
maniculatus, and Reithrodontornys megalofis showed little evidence of annual periodicity. The timing af annual cycles and the pattern of inter-annual fluctuations also
differed among species. However. two results suggest that several species responded
similarly to long-term environmental variation: 1)population densities of four species
and total rodent biomass and numbers were positively correlated with the densities of
annual plants; and 2) many pairs of species exhibited positively correlated population
dynamics over the ten years,
Clustering of painvise cross-correlation coefficients was used to identify sets of
species with similar population dynamics. These clusters did not necessarily contain
closely related or ecologically similar species. Detrended Correspondence Analysis
identified three independent patterns of variation in species composition: a long-term
trend; a four- to five-year repeated pattern that appeared to correspond to the
climatic effects of the El Nifio Southern Oscillation: and an annual cycle. In general.
species appeared to respond individualistically to environmental variation. There was
no evidence of an equilibrium community composition or of alternative stable configurations, Some competing species had negatively correlated population dynamics,
but the majority of competitors exhibited positive correlations that apparently reflected similar responses to fluctuating resources.
J . H. Brown and E. J Heske, Dept of Bicdogy, Urrlv. of New Mextco, Albuquerque,
NM 87131, C'SA
Introduction
The nature of communities has been a central issue in
ecology since at least the times of GIeason (1917, 1926)
and Clements (1916). Despite this attention, the processes that determine the identity. abundance, and distribution of coexisting species remain poorly understood
and much debated. The questions of today, while
phrased somewhat differently than by Gleason and
Clements, remain essentially the same. How does the
composition of assemblages vary in space and time?
what are the roles of abiotic factors and interspecific
interactions in limiting abundance and distribution of
species? To what extent is community composition deAccepted 26 June 1990
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OJKOS
termined by processes operating within local areas, and
to what extent is it affected by physical variables and
movement of organisms occurring at larger spatial
scales? On what spatial and temporal scales and between what kinds of organisms do coevolutionary processes influence community organization?
The organization of a community and its patterns of
temporal change reflect the fluctuations of the local
species populations that comprise the community.
There is a rich theoretical literature on populations
dynamics. Many recent papers emphasize that wide,
seemingly chaotic fluctuations can be expected, not only
when populations are regulated primarily by densityindependent abiotic conditions, but also when they are
limited largely by density-dependent intra- and inter- techniques to ten years of monthly censuses of the 11
specific interactions (May 1976, 1980, Gilpin 1979, Kot most common rodent species inhabiting a 20-ha site of
and Schaffer 1984, Schaffer 1985, Schaffer et al. 1986). relatively homogeneous shrub habitat in the ChihuaThere is also a large literature of empirical studies of huan Desert of southeastern Arizona. Time series autopopulations dynamics, many of t h e m on small mam- correlation and spectral analyses of monthly census data
mals. Many different patterns have been documented, permit detection of seasonal and inter-annual fluctuincluding relatively stable populations, annual cycles, ations in each species. Cross-correlation analyses bemultiannual cycles, and irregular multiannual fluctu- tween abundances of species and densities of annual
ations (e.g., Krebs and Myers 1974, Hansson and Hent- plants indicate responses of populations to temporal
tonen 1985, Henttonen et al. 1985, Taitt and Krebs variation in productivity and food availability. Cross1985). Studies of the dynamics of multiple, coexisting correlations between pairs of species identify subsets of
species are fewer in number, confined primarily to mi- species whose temporal fluctuations are positively or
crotine rodents (but see O'Connell 1989), and have negatively correlated. Ordination on the basis of relausually emphasized that the multiannual cycles tend to tive abundances of species in each sample period ilbe synchronous (e.g., Henttonen et al. 1984, Henttonen lustrates how composition of the entire community
changes over time. Results suggest that this rodent com1985).
Insights into the relationship between changes in munity has a "Gleasonian" nature; although our analycommunity composition and the dynamics of the com- ses reveal sets of species that exhibit generally similar
ponent species populations must come from long-term temporal fluctuations and previous work has demonstudies. The responses of individual species and the strated strong interspecific competition among some
community as a whole to fluctuations in abiotic condi- species, the overall picture is one of individualistic,
tions, availability of resources, and abundances of other species-specific responses to the pronounced temporal
species can provide information on the factors affecting variation in this desert environment.
the local distribution and abundance of each species, on
the nature of interactions among species, and on the
extent to which the species respond to environmental
variation individualistically or as an integrated unit.
Methods
Desert rodents have served as a model system for
The system
studying community organization (e.g., ~ o s e n z w e i ~
and Winakur 1969, Rosenzweig and Sterner 1970, Ro- We analyze ten years of rodent censuses from Brown's
senzweig 1973, Rosenzweig et al. 1975, Brown 1975, 20-ha experimental study site 6.5 km east and 2 km
1984, 1987, 1989, Brown et al. 1979, 1986, Price and north of Portal. Cochise Countv. Arizona. For detailed
Brown 1983, Price 1986, Kotler and Brown 1988, description of the site, experimental manipulations, and
Brown and Harney, in press). Experimental and com- rodent trapping protocol see Brown and Munger
parative studies have repeatedly demonstrated the im- (1985). With a few exceptions (see below) rodents have
portance of food limitation and interspecific competi- been trapped since July 1977 at monthly intervals corretion in determining the abundance of species and their spondingly approximately to the time of the new moon.
foraging behaviors (Brown 1973, 1989, Reichman 1975, Within each of the 24 experimental plots one trap was
1977, Munger and Brown 1981, Brown and Munger set for one night at each of 49 permanent grid stakes
1985, Frye 1983, Bowers et al. 1987). Since plant growth placed 6.25 m apart. The rodents captured were individand seed production in desert environments depend on ually marked, identified to species, weighed and sexed,
precipitation (reviewed in Brown et al. 1979), popula- and released.
tion fluctuations in desert rodents should be affected by
The present analyses are based on the total number of
seasonal and year-to-year variation in climate, primary individuals of each of the 11 most common species
production, and other rodent species. O n the one hand, captured in each monthly census on all experimental
because rodent populations appear to be limited by plots during the 10.5-yr period, July 1977 to January
food availability, which depends primarily on a single 1988. By combining the data for all plots we ignore the
variable, precipitation, all coexisting species might be differential responses of species to the experimental
expected to respond similarly to temporal variation in treatments, which include removal of different combithe environment. O n the other hand, because coexisting nations of rodent and ant species, addition of supplespecies differ in taxonomic relatedness, body size, food mental millet seeds, and an attempt to remove annual
habits, and life history, they might be expected to exhib- plants (see Brown and Munger 1985, Brown 1987). On
it different patterns of fluctuation in the same tempo- the other hand, by combining data in this way we
rally variable environment.
greatly increase the number of rodents captured, and
Time series analysis and multivariate ordination tech- hence the power of our statistical analyses. We feel
niques provide potentially powerful methods for assess- justified in summing captures over all plots, because the
ing the patterns of temporal variation within species same rodent species had access to the same plots and
populations and entire communities. We apply-these most of the experimental treatments were continued for
the entire 10-yr period. The only exceptions were one
Dipodomys spectabilis removal plot that was added in
February 1980 and eight plots where annual millet seed
addition treatments were terminated in July 1985 and
replaced by ineffective attempts to remove annual
plants through local applications of herbicide. Since the
addition of millet seed affected only a single rodent
species (causing an increse in D. spectabilis; Brown and
Munger 1985) and the attempt to remove annual plants
had no apparent effects o n rodent populations, we are
confident that most of the population dynamics reported here reflect responses of species to temporal
environmental variation over the entire study site,
rather than differential responses of species to experimental perturbations on certain plots. We will return to
this point in the discussion.
Annual plant densities were censused twice each year
at the end of the winter and summer growing seasons.
O n each of the 24 experimental plots, plants were
counted by species on 8 (1978-1982) or 16 (1983-present) permanent subplots, each measuring 0.5x0.5 m
(see Davidson et al. 1985 for details). There was a
weather station on the study site, and analysis of the
relationship between climatic variables and annual plant
density, biomass, and species composition will be reported elsewhere.
Analysis
Single species patterns: To detect patterns of intra-annual variation in rodent abundance, we calculated autocorrelations for time series of monthly censuses from
July 1977 through January 1988 for each of the 11 most
common rodent species. Autocorrelation analysis calculates a parametric correlation coefficient for all pairs of
data points separated by a given time lag (Davis 1976).
It is a powerful method of detecting the extent of cyclical variation. A n annual cycle is indicated in our analyses by a sinusoidal autocorrelogram with significant positive values at a time lag of 12 months.
During the 127-month study period, censuses were
missed on 9 months due to extended periods of bad
weather. Because autocorrelation analysis requires a
complete set of equally spaced data points, we.filled in
missing values by interpolation between the preceding
and following censuses. Because there was a high degree of temporal autocorrelation in censuses of all species for the time lags of one month, and we never missed
more than one month in succession, the error introduced by this interpolation procedure was very small.
For proper analysis of intra-annual patterns, there is the
additional requirement that the data be stationary; that
is, there must be no significant inter-annual trends in
numbers. To remove the effects of inter-annual density
variation on the autocorrelation analysis, we first normalized the data within each year separately so that all
years had the same mean and variance, and then performed the autocorrelation analysis.
Spectral analysis using the Fast Fourier Transform
(FFT) procedure was conducted on the time series of
monthly censuses to examine the population dynamics
of each species for multi-annual patterns. This technique quantifies the relative contributions of sine waves
of different frequencies and amplitudes to explaining
the pattern of temporal variation (Bloomfield 1976). It
identifies the dominant periodicities that are present in
the dynamics. We also inspected the time series of each
species for long-term trends in abundance.
The relationship between variation in rodent numbers and fluctuations in food availability was investigated by cross-correlating the time series of rodent censuses and annual plant censuses. This technique computes parametric correlations between two dependent
variables (in this case the abundance of plants and of a
rodent species) separated by time lags of varying duration. Significant values in the correlogram indicate the
magnitude and time lags of covariation between two
time series.
This analysis required two manipulations of the annual plant data. First, since we know that some of the
experimental treatments (rodent and ant removals) produced changes in vegetation on some plots (Davidson et
al. 1985, D . Samson, D . Davidson, and T. Phillipi, pers.
comm.), we used only data from the unmanipulated
control plots as an index of inter-annual variation in
plant production. Second, because winter and summer
annual plants differed greatly in absolute abundance,
plant size, and other factors affecting seed production,
we normalized censuses for winter and summer annuals
separately so that both time series had the same mean
and variance, and then combined them. The resulting
time series thus represents an index of relative goodness
or badness of each season in terms of mean number of
annual plants per m2 over the 10-yr period. No winter
annual census was made in 1980. We replaced this missing value with the mean value for the winter censuses
rather than using interpolation, because the plant censuses showed no evidence of being positively correlated
from year to year.
Because annual plant censuses were conducted twice
each year, we had to construct a comparable semiannual time series of rodent abundances in order to
calculate the cross-correlations between the two. Since
plant censuses were made at the end of the growing
seasons, usually in March and September of each year,
we made comparable winter (October- arch). and
summer (April-September) estimates of rodent abundance by calculating the mean number of each species
captured per census for each 6-month interval
Between-species comparisons: Comparisons between
the time series for pairs of rodent species were made in
two ways. First, cross-correlations were calculated for
all pairs of species (N = 55) using the time series of
LAG
Fig. 1. Autocorrelograms of normalized time series (see text)
for the 11 common rodent species at our Chihuahuan Desert
study site. Lags are in periods of months. Correlation coefficients greater than 0.18 (or less than -0.18) are significantly
different from zero at P < 0.05. Low correlations at six-month
lag and high values at 12 months indicate annual cycles. Species
are indicated by first letter of genus and species; see text for
full species names.
monthly censuses. Because these data include both intra- and inter-annual density variation, we additionally
made pairwise cross-correlations between all species
pairs using time series of the mean annual abundances
of each species calculated for each of the 10 yr. This
second analysis allowed us to focus on how similarly
rodent populations of the different species fluctuate
from year to year, independent of any seasonal patterns
of abundance within species. The onset of the monsoon
rains in early July after a late spring - early summer
drought is the most predictable seasonal event at our
study site. Therefore, we based our analyses on a biological year from July through June instead of a standard calendar year beginning in January.
Cross-correlation coefficients at 0 lag for both sets of
painvise analyses (monthly and annual averages) were
clustered using a standard UPGMA (unweighted pairgroup method using arithmetic averages) algorithm to
group species according to their similarity in population
dynamics. This UPGMA algorithm clusters values sequentially, beginning with low ones. For this reason and
also because some coefficients were negative, and clustering algorithms cannot accept negative distance values, we used the absolute value of 1 minus the coeffi-
WSWsWSWSWSWSWSWSWSWsW
1977
1988
YEAR
YEAR
Fig. 2. Population dynamics of the 11 common rodent species
at our Chihuahuan Desert study site over 10.5 yr. Numbers are
given as 6 month averages (see text) rather than monthly
census values for clarity in presentation. Above: Heteromyid
rodents. Below: Murid rodents. See text for full species names.
Table 1. Frequency in months per cycle and relative heights of
the major peaks (all those peaks > 25% of the height of the
major peak) indicated by Fast Fourier Transform analysis of
monthly censuses.
Species
Months per cycle
D. spectabilis
D. ordii
D. merriami
C . penicillatus
Pg. Jlavus
Pm. eremicus
Pm. maniculatus
R. megalotis
N . albigula
0. leucogaster
0. torridus
'Includes peaks at 21, 25, or 31 months
cient value instead of the actual cross-correlation coefficients in this analysis. Thus, highly positive coefficients
received a low value, highly negative coefficients received a high value, and near zero coefficients received
an intermediate value by this adjustment.
Community-level patterns: To assess temporal variation
in community composition, we analyzed the proportional abundances of all 11 species in each monthly
census using detrended correspondence analysis
(DCA). D C A and related multivariate ordination techniques (e.g., Principal Components Analysis) decompose complex patterns of variation into a hierarchical
set of independent axes; each successive axis represents
a combination of variables that account for progressively smaller proportions of the total variation. D C A was
chosen as the appropriate ordination technique because
we expected nonlinear relationships among species. We
examined values of the first three orthogonal axes for
stability or a repeating pattern using time series autocorrelations with lags of 1 to 24 months. Spectral analysis (FFT) was also used to examine the first three D C A
axes for repeating patterns in community composition.
less distinct peaks at 6 and 12 mo indicated considerable
variation in the timing of the cycles. Three species, R.
megalotis, P. eremicus, and P. maniculatus, showed little
or no evidence of annual periodicity.
Long-term patterns of population dynamics also revealed great variation among the 11 species (Fig. 2).
Some species, C . penicillatus, Onychomys spp., and N.
albigula, maintained relatively constant populations
throughout the 10-yr study period. Others, such as D.
ordii, R. megalotis, and Peromyscus spp., which were
relatively rare at the start of the study, increased substantially over the decade. In contrast, D. spectabilis
and Pg. flavus, which were relatively common at the
beginning of the study, decreased dramatically in 1984
and never recovered to their former densities. D. merriami remained common throughout the study, but fluctuated approximately 5-fold over the 10-yr period. Several species attained peak densities in 1982-83, 1987-88,
or at both times.
Spectral analysis of monthly census data using Fast
Fourier Transformation gave little evidence of multiannual cycles (Table 1). Strong peaks for most species
corresponded to either annual cycles or a nonrepeating
DO-DM
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Single species patterns
Autocorrelations of the normalized (adjusted year-byyear to remove long-term trends) time series revealed
very different degrees of annual periodicity in the population dynamics of different species (Fig. 1). D. spectabilis and Pg. flavus exhibited regular annual cycles with
strong negative autocorrelations at a lag of 6 mo and
positive values at a lag of 12 mo. Several other species,
C. penicillatus, N. albigula, 0 . torridus, 0. leucogaster,
D. merriami, and D. ordii, had annual patterns, but the
LAG
Fig. 3. Selected examples of cross-correlograms between the 11
common rodent species at our Chihuahuan Desert study site.
Lags are in months. Correlation coefficients greater than 0.15
(or less than -0.15) are significantly different from zero at P <
0.05. See text for interpretation and full species names.
Table 2. Cross-correlation coefficients at lag 0 for the time series of monthly census data (N = 127) for pairwise comparisons
among the 11 most common rodent species (above diagonal). Cross-correlation coefficients for the same species pairs calculated
using 10 yr of annual averages (below diagonal).
D. spectabilis
D. ordii
D. merriami
C. penicullarus
Pg. flavus
Pm, eremicus
C. maniculatus
R. megalotis
N . albigula
0. leucogaster
0. rorridus
Fig. 4. Dendrograms
showing the 11 common
rodent species at our
Chihuahuan Desert study
site clustered according to
similarity in their population
dynamics. A: Clusters based
on cross-correlation
coefficients at lag 0 using
monthly censuses (N =
127). B: Clusters based on
cross-correlation coefficients
at lag 0 using annual
averages (N = 10). Species
are identified by the first
letter of genus and species.
B.
W S W S W S W S W S w S W S W S W S v ~ S W
1988
1977
YEAR
Fig. 5. TMal numbers and biomass of the 11 common species of
rodents at our Chihuahuan Desert study site over 10.5 yr. Both
axes are plotted as 6 month averages rather than monthly
census values for clarity in presentation. Note peaks in biomass
following the El Niiio years of 1977-78, 1982-83, and 198M7.
10-yr trend. Some species, however, showed a 5-yr peak
that probably reflects peak densities in 1982-83 and
1987-88.
The fact that several species peaked at similar times
suggests that populations fluctuations may vary with
food availability. Cross-correlations between annual
plant population densities and the number of rodents
were significant (P<0.05) and positive at zero time lag
for four species of murids, Pm. rnaniculatus (r = 0.74),
N. albigula (r = 0.59), Pm,eremictcs (r = 0.49), and R.
rnegalotrs (r = 0.47). These were the same rodent species that showed strong 5-yr peaks in the spectral analysis. The heteromyids all showed positive but not statistically significant cross correlations with the peak often
occurring at a lag of 6 or 12 mo. Total rodent biomass
was also s~gnificantlycorrelated (r = 0.48; P<0.05) with
annual plant densities at zero time lag; total rodent
abundance was marginally significantly correlated (r =
0.41: 0.1>P>0.05) with the same variable.
Cross-correlations using the monthly census data revealed 26 significant positive and 5 significant negative
painvise associations at zero time lag (Table 2). There
were an additional 7 species pairs that had significant
positive cross-correlations and 2 that had negative crosscorrelations at time lags of 3 to 7 mo. Similar analysis
using annual averages gave 10 significant positive and
one significant negative cross-correlations at lag zero.
This, together with the predominance of positive but
nonsignificant values in the pairwise species-by-species
cross-correlation matrices (Table 2) suggests that most,
but not all. species have population dynamics influenced by the same environmental conditions.
Cluster analysis revealed sets of species that exhibited
similar population dynamics (Fig. 4). Dendrograms
based on both the monthly censuses and the yearly
averages were very similar, suggesting that species that
cluster together exhibit both similar seasonal fluctuations and similar year-to-year dynamics. The only
qualitative difference was in the linking of C. peniculla-
roo
8G (r,
20
Between-species patterns
Cross-correlograms showed a variety of relationships
between the population dynamics of different pairs of
species. Since there are 55 pairs of species, we only
illustrate a few examples of the patterns in Fig. 3. Some
pairs of species maintained nearly constant positive,
negative, or zero correlations regardless of time lag
(Fig. 3, top). These were species pairs that maintained
trends for several successive years. For other pairs, the
value of the cross-correlations showed an annual cycle
with significant peaks at time lags different from zero
(Q. 3, center). indicating positive year-to-year covariation but different dynamics within years. Still other
pairs exhibited cross-correlations which were significantly positive at zero time lag but declined with increasing time lags (Fig. 3, bottonl). These were species
pairs that fluctuated synchronously. but s h ~ w e dhigh
variation between successive seasons and years.
6
40
W S W S W S W S W S W ~ W S W S W S W S W
1988
1977
YEAR
Fig. 6. Proportional contribution of four functional groups
(based on cluster analysis; see text) to total rodent numbers
and biomass over 10.5 yr. Functional groups are: 1) Vertical
stripes: D. spectabilis and Pg, flavus, 2) White: D. merriami
and D.ordii, 3) Black: Pm. eremicus, Pm. municulutus, and R.
megalotis, 4 ) Gray: remaining species; C. penicillatu, N. albigula, 0. leucogaster, and 0. torridus. Numbers and biomass
given as 6 month averages rather than monthly census values
for clarity in presentation.
AXlS 1
20000
175-
1%.
AXlS 2
AXIS 2
A
175
150
AXlS 3
AXlS 3
1 B77
MONTHS
I
Ism
Fig. 7. Time series of scores on the first three DCA axes (see
text) for 127 monthly rodent censues at our Chihuahuan Desert
study site. Note the long-term trend in Axis 1, the peaks
corresponding to El Nido years in Axis 2, and the annual
pattern in Axis 3.
tus and N. albigula only when the monthly values were
used, which probably reflects the fact that both species
peak in summer (in part because the former species is
an obligate hibernator) but their inter-annual fluctuations are not well correlated. Although the species in
the clusters were sometimes similar in taxonomy, morphology, and life history (e.g. the medium-sized kangaroo rats, D. ordii and D. merriami, and the small murids, Pm. eremicus, Pm. maniculatus, and R. megalotis),
this was not always the case (e.g., D. spectabilis clustered with Pg. flavus, and the medium-sized kangaroo
rats and the small murids were more similar to each
other that either was to more closely related species).
Community-level patterns
Total numbers and biomass of rodents fluctuated approximately 3-fold and 2-fold, respectively, over the
course of the study (Fig. 5). Because the Dipodomys
spp. accounted for the vast majority of both individuals
and biomass, these patterns largely reflect the combined
influence of D. merriami and D. spectabilis. Peaks of
Fig. 8. Spectral analysis of the time series of monthly rodent
census scores on the first three DCA axes using the Fast
Fourier Transform procedure. Frequency of peaks is given as
months per cycle. Results are presented as a continuous function for clarity in presentation; however, actual values were
obtained only for frequencies corresponding to approximately
m, 126,63,42,31,25,21, 18,16,14, 13, 11, and 10 months per
cycle. Note the peaks corresponding to: the entire study period
for Axis 1; the entire study period, the four- to five-year El
Nido pattern, and the annual cycle for Axis 2; and the annual
cycle for Axis 3.
biomass tended to follow the El Nino years of higher
than normal winter precipitation in 1977-78, 1982-83,
Table 3. Species loading and eigenvalues (in parentheses) for
the first three DCA axes, calculated from monthly census data.
Species
D. spectabilis
D. ordii
D. merriami
C. penicullatus
Pg. flavus
Pm. eremicus
Pm. maniculatw
R. megalotis
N. albigula
0. leucogaster
0. torridus
Axis 1
Axis 2
Axis 3
(Eig=O.151) (Eig=0.050) (Eig=0.045)
and 198687. Equally interesting are the changes in the
proportional contribution of individual species and
functional groups (Fig. 6). These patterns were quite
similar for both numbers of individuals and biomass.
The cluster of D. spectabilis and Pg. flavus decreased,
the clusters of small murids and of D. merriami and D.
ordii increased, and the other rodents showed little
change except for a late increase in biomass owing to a
few individuals of N. albigula.
The plots of the D C A values as a function of time
(Fig. 7) and FFT analysis of these time series (Fig. 8)
showed that the composition of the community as a
whole neither remained constant over time, nor exhibited pronounced multi-annual cycles. The first three
D C A axes accounted for a substantial proportion of the
month-by-month variation in abundances of the 11 species and were readily interpretable (Table 3). Axis 1
loaded heavily on the small murids and D. ordii, and
negatively on D. spectabilis and Pg. flavus. This axis
corresponded primarily to directional changes in these
species over the decade as indicated by the major peak
at 126 mo in the FTT analysis. Axis 2 loaded positively
for Pg. flavus, the small murids, and D. spectabilis, and
negatively for C. penicillatus and Onychomys spp. The
FFT analysis indicated that this axis reflects fluctuations
both on the scale of the entire study and with a period of
approximately five years. It also indicated a substantial
annual cycle. Only an annual cycle was detected by FFT
analysis of Axis 3.
Results of the D C A gave no evidence of alternative
stable community structures (sensu MacArthur 1972). If
the community composition fluctuated between alternative stable configurations, we would expect the D C A
scores to shift rapidly and then to remain relatively
constant for long periods. Instead, the different components of the community identified by the D C A axes
appeared to vary continuously and independently over
the decade of the study. Similarly, there was continuous
variation over time in the extent to which the functional
groups identified in the cluster analyses contributed to
the total community composition (Fig. 6).
Discussion
Temporal variation in this rodent community
This study revealed a variety of patterns that characterize the dynamics of single species populations and the
community as a whole. Rodent populations in this desert habitat fluctuated considerably, but the magnitude
and periodicity of these fluctuations varied among species. The most dramatic fluctuations were exhibited by
species such as Pg. flavus and the small murids, which
varied from being virtually absent from the study site to
having substantial populations. Not mentioned here
were several rare species that were absent for most of
the study, but made irregular appearances. A t the other
extreme were species such as C. penicillatus and Onychomys spp. that maintained very constant populations
from year to year. The numerically dominant Dipodomys spp. all varied several-fold in density, but this was
substantially less than the orders-of-magnitude fluctuations of other rodents (e.g., some microtines and Australian murids) that are the dominant small mammals in
other habitats.
Despite the large responses of several rodent species
to experimental manipulations of food supply and presence of potentially competing rodent and ant species on
some of the experimental plots, there is little evidence
that these manipulations had significant effects on the
patterns of temporal variation reported here. For example, although D. spectabilis increased on plots where
millet seeds were added (Brown and Munger 1985), the
crash in the D. spectabilis population occurred in summer of 1984, a year before t h e seed addition treatments
were discontinued. D. spectabilis also declined synchronously and has subsequently failed to recover on a
nearby site studied by P. Waser (pers. comm.). Similarly, the long-term increase in the abundance of D.
ordii has been reported in nearby areas of similar habitat (J. Randall, pers. comm.). Finally, the densities of
the small granivorous murids, which were greatest on
Dipodomys removal plots (Brown and Munger 1985),
showed similar temporal trends on control plots. Therefore we are confident that the population and community dynamics that we report here reflect regional trends
rather than artifacts of our experimental manipulations.
Correlations of rodent densities with annual plant
densities, annual population cycles in some species, and
the tendency for many species to show positively correlated inter-annual fluctuations all suggest that population dynamics respond to environmental variation (see
also Kenagy 1973, O'Farrell 1974, Kenagy and Bartholomew 1985). O n the other hand, the fact that none of
these patterns were consistent across all species has two
implications. First, no one aspect of environmental variation has a pervasive coordinating effect on rodent
population dynamics and community composition. This
is true even though infrequent, unpredictable precipitation events determine the amount and timing of primary production in desert ecosystems (Went 1949, Tevis
1958, Hillel and Tadmor 1962, Rosenzweig 1968, Brown
et al. 1979), and the reproduction of 5everal rodent
species apparently is stimulated by the appearance of
newly-germinated annual plants (Beatley 1969, 1974,
1976, Van D e Graff and Balda 1973, Reichman and Van
D e Graff 1975). Second, the rodent community is comprised of species that respond to temporal variation in
the same environment in very different ways. Some
species differed strikingly in the degree and timing of
their annual cycles and in their pattern of inter-annual
variation.
The rodent community could be clustered into functional groups of species on the basis of the extent to
which their population dynamics were correlated. These
clusters did not consistently correspond to taxonomic
relationships, nor did they consistently reflect similarities in morphology, behavior, and life history. Instead
they appeared to reflect the way that the population
dynamics respond to environmental variation. For example, the two medium-sized kangaroo rats appear so
similar in many aspects of their ecology that they act
almost like a single species (Schroder 1987). They exhibited moderate year-to-year variation; their fluctuations were also negatively correlated with those of D.
spectabilis (Table 2), and both species increased on plots
where D. spectabilis had been removed (Brown and
Munger 1985). The small granivorous murids seem to
be opportunists that are able to colonize and increase
rapidly in density in response to temporarily favorable
conditions (Whitford 1976, Conley et al. 1977, Munger
et al. 1983). The almost equally tight clustering of Pg.
flavus with D. spectabilis is more difficult to explain
because these species are different in so many respects.
This may reflect response to some common environmental factor, perhaps summer precipitation. Both of
these species have biogeographical affinities with the
Chihuahuan Desert, which is characterized by summer
precipitation, and both begin reproducing in late winter, presumably using the productivity of the previous
season to support their initial reproductive effort. Another possibility is that D. spectabilis facilitates Pg. fluvus, either by 1) reducing the local density of the other
kangaroo rat species which may interfere with Pg. fluvus (Brown and Munger 1985, Bowers et al. 1987), o r 2)
maintaining burrow systems and food stores that Pg.
flavus exploits. The remaining species all have relatively
stable population densities from year to year, and are
linked at lower levels in the dendrograms. These last
species include a granivore, a folivore, and two insectivores, and a wide range of body sizes as well as the
only obligate hibernator. This implies that stable population dynamics cannot be attributed to any single
combination of traits (see also Kenagy and Bartholomew 1985, Brown and Zeng 1989).
D C A provided a method of characterizing each census in terms of the composition and relative abundances
of species present. The D C A quantified the contributions of the different species to eigenvectors corresponding to patterns of community composition that
varied independently over time. Three major axes were
identified. Fast Fourier Transform analysis of values for
these axes for each census identified three patterns:
annual cycles, a non-repeating, long-term change in the
relative proportion of species, and an approximately
5-yr repeated pattern that may correspond to rodent
responses to climatic changes induced by the El Niiio
Southern Oscillation. There was a strong El Nifio event
in 1982-83, and there were moderate ones in 1977-78
and 1987-88 (Quinn et al. 1987, Molles and Dahm
1990). El Niiio years are characterized by unusually
heavy winter precipitation in the desert region of the
southwestern United States, so it is not surprising that
the resulting productivity and food availability stimulated increases in many rodent populations.
Our community-level analyses provide no evidence
either for an equilibrium community composition that
remained constant or was restored after perturbation,
or for shifts in community composition between alternative steady states. Rather, community composition varied continously over time, reflecting the different responses of species and groups of species to different
environmental variables. The community was comprised of species whose responses to environmental variation were neither completely independent nor highly
coordinated. Instead, there were functional groups of
species that responded similarly, and other sets of species that showed independent or opposite trends.
Species that are known or suspected to compete did
not necessarily exhibit negatively correlated population
fluctuations. Variations in D. ordii and D. merriami
were negatively correlated with those in D. spectabilis, a
known competitor (Frye 1983, Brown and Munger
1985, Bowers et al. 1987). The proportion of total rodent numbers and biomass comprising the two former
species also increased when D. spectabilis declined (Fig.
6). However, D. ordii, which was most negatively correlated with D. spectabilis, had been increasing since the
beginning of the study so this should be interpreted with
caution. The small granivorous murids, which increased
dramatically on plots where kangaroo rats were experimentally removed, had positively correlated dynamics
and clustered with D. merriami and D. ordii. Although
the appropriate competition experiments have not been
performed, there is much circumstantial evidence to
suggest that there should be substantial competition
between D. ordii and D. merriami and also among the
three species of small murids; the species within each of
these two clusters (Fig. 4) exhibited strongly positively
correlated population fluctuations (Table 2).
General implications
The results of this study imply that there is a great deal
of temporal variation, both in the composition of the
community and in the dynamics of the individual species
populations. This is consistent with empirical observations and theoretical predictions that "stochastic" variation in the abiotic environment can result in wide
fluctuations in population density (Leigh 1975, Beddington et al. 1976, Goh 1980, Grossman et al. 1982,
Strong 1986). The fluctuations seen here, ranging
through several orders of magnitude, are comparable to
those reported for invertebrates and some vertebrates
(e.g., Connell and Sousa 1983, Ostfeld 1988). The local
extinctions and colonizations are also consistent with
both other empirical observations (e.g., Whitford 1976,
Pimm et al. 1988) and with "source-sink" models of
local population dynamics (Hanski 1982, Holt 1983,
Stenseth 1983, Wiens 1986, Pulliam 1988).
The characteristics of temporal variation in community composition seen here are reminiscent of the temporal changes in small mammal and plant communities
over the last 10,000 yr (e.g., Cole 1982, Huntley and
Birks 1983, Davis 1986, Graham 1986, Van Devender
1986). The great spatial variation in the species composition of North American desert rodent assemblages
(Brown and Kurzius 1987, 1989) complements the results of this study to give a relatively "Gleasonian"
interpretation of the structure and dynamics of these
communities. The component species have different
biotic and abiotic requirements, and their abundances
vary in space and time in response to variation in these
environmental factors.
This individualistic or Gleasonian concept of community organization must be reconciled with the abundant
evidence of strong interspecific interactions. For example, for desert rodents both competition (Rosenzweig
1973, Price 1978, Munger and Brown 1981, Frye 1983,
Brown and Munger 1985, Bowers et al. 1987, Brown
1987) and predation (Thompson 1982a,b, Kotler
1984a,b, 1985) have been shown to influence the combinations of species that coexist in local habiatats, and the
abundance, distribution, and resource use of these species.
The kinds of techniques employed here should be
useful for investigating the relationships between population- and community-level dynamics. Time series
analysis has often been used to examine the population
dynamics of individual species, and ordination and clustering techniques have been used to characterize community structure, but rarely have the two been used in
combination. Together, they can offer insights into how
population- and community-level processes are coupled. However, at our present level of understanding, it
is difficult to make and test unambigous a priori predictions. O n the one hand, to the extent that species
require similar resources or respond similarly to abiotic
conditions, their population densities will tend to covary
positively. O n the other hand, direct and indirect negative interactions between species, such as competition
and "apparent competition" (Holt 1977), should result
in negative covariation, at least on some scale. Since
both of these phenomena are known to occur, how do
they interact to influence the dynamics?
Results of the present study suggest that analyses that
regress the abundance of one species against another
(e.g., Crowell and Pimm 1976, Hallett and Pimm 1979,
Dueser and Hallett 1980, Emlen 1981, Pimm 1981, Hallett 1982, Hallett et al. 1983, but see Gilpin et al. 1982,
Rosenzweig et al. 1985, Schoener 1985) will often fail to
detect interspecific competition when it occurs and may
suggest negative interactions when they d o not exist. In
a fluctuating environment, there will be periods when
resources are temporarily abundant, and competing
species that overlap in their requirements for these limiting resources will tend to increase synchronously. But
this need not always occur. Species with quite different
patterns of population dynamics (e.g., a species that is
active all year and an obligate hibernator) could still
interact strongly if they overlap sufficiently in their use
of limiting resources during critical time periods. Moreover, species that do not interact can have positively or
negatively correlated dynamics, depending on whether
they respond similarly or differently to variation in
abiotic conditions (e.g., climate).
A combination of nonmanipulative studies of temporal and spatial variation, and experimental manipulations to test specific mechanistic hypotheses, seems to
offer the best method of clarifying the complex relationships between community organization and the abundance and distribution of the component species.
Acknowledgements - We thank all of those individuals who
have helped over the years to conduct the experiments and to
census the rodents at Portal. P. Nicoletto helped with the data
analysis and figure preparation, J. Rotenberry and B. Milne
contributed statistical advice, and L. Hawkins, B. Kotler, D.
Morris, R. Pierotti, and S. Pimm made valuable suggestions
for improving the manuscript.
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