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The Role of Habitat Structure
in Organizing Small Mammal
Populations and
Communities1
Gregory H. Adler2
Abstract.-Microha bitat structure influences
population density more than other demographic
variables such as age and sex cornposition
Microhabitat heterogeneity, or quantitative
variation in microhabitat structure, apparently has
little influence on phenomena such as population
stability. Scale mediates effects of habitat structure
and heterogeneity on population and community
organization. I suggest that microhabitat structure
influences density more than other aspects of
demography, whereas macrohabitat structure and
heterogeneity are more important in influencing
population stability, demography, and community
structure.
Environmental heterogeneity has
maintained a position of prominence
in theoretical population and community ecology (reviewed by Levin
1976, Wiens 1976, and Wiens et al.
1986).Heterogeneity allows organisms to select different habitats,
which subsequently can have profound consequences for the organization of populations and communities.
Environmental heterogeneity can be
studied, both theoretically and empirically, at different scales. Conclusions based on the study of habitat
structure may differ widely depending upon the scale of structure examined. The scale of environmental subdivision can be viewed as occurring
along various continua, e.g., from the
area occupied by a single individual
to a biogeographic or continental
area (Wiens et al. 1986), or from microhabitat to macrohabitat.
In this paper, I concentrate on the
microhabitat to macrohabitat scale. I
define microhabitat as physical habitat characteristics likely to vary over
the home range of a single individual
(e.g., the number of herbaceous
stems within a circumscribed area)
and macrohabitat as the major habi-
tat type where an entire population
may be found (e.g., grassy field or
deciduous woodland in the case of
small mammals). Microhabitat structure therefore can vary substantially
within a single macrohabitat.
I summarize results from a series
of long-term studies on the role of
habitat structure in organizing small
mammal populations and c o k u n i ties that I conducted in eastern Massachusetts. These studies were designed to examine (1) habitat associations and habitat selection and the
roles of intra- and interspecific interactions in affecting habitat utilization, and (2) the influence of habitat
structure on density and demography. In these studies, I focus primarily on microhabitat structure, and I
develop a conceptual scheme which
shows how microhabitat and macrohabitat structure organize small
mammal populations and communities.
'Paper presented at symposium, Management of Amphibians, Reptiles, and
Small Mammals in North America. (Flagstaff. AZ,July 19-21 , 1988.)
2GregoryH. Adler is Research Fellow in
Population Sciences, Department of Population Sciences,Schod of Public Health,
Harvard University, 665 Huntington Avenue,
Boston, MA 02 1 15.
The long-term studies were conducted at three sites in eastern Massachusetts: Broadmoor/Little Pond
Audubon Sanctuary, South Natick;
Great Island, near West Yarrnouth;
and the University of Massachusetts
Nantucket Field Station, Nantucket.
Sampling areas within each study
STUDY SITES AND GENERAL
METHODS
Study Sites
site were confined to a 300-ha area
and were exposed to the same climate and the same predators, competi tors, and parasites.
Broadmoor consists of a mosaic of
grassy fields separated by mixed deciduous-coniferous woodland. Sampling at Broadmoor was confined to
the fields, which were dominated by
the grasses Agropyron regens and Poa
pratensis. Other herbaceous and
woody plants, including goldenrod
(Solidago spp.), milkweed (Asclepias
syriaca), poison ivy (Rhus radicand,
and several species of deciduous tree
saplings, were much less prevalent.
Great Island is a 240-ha island
connected to mainland Cape Cod by
a causeway. The island is dominated
by deciduous and coniferous woodland but has structurally simpler
habitat along the shore. This shoreline habitat consists primarily of
beach grass (Ammophila breviligulata),
with patches of poison ivy, Virginia
creeper (Parfhenocissus quinquefolia),
bayberry (Myrica pensylvanica), rose
(Rosa carolina), and juniper (Juniperus
virginiana).
Nantucket Island (ca. 12,300 ha
and lying approximately 30 km off
the coast of Cape Cod) has large areas of low, dense woody growth
(heath) where small mammals were
sampled. Heath at the study site was
composed primarily of rose and bayberry, with patches of goldenrod and
other herbaceous plants and grasses
interspersed within the brush. Scattered juniper trees also were present.
Sampling Procedures
I sampled small mammals at each
s b d y site by monthly Iive-trapping
with Longworth live-traps for approximately 4 to 5 years. At each
study site, I monitored two 0.4-ha
grids located in grassy or brushy
habitat. One grid served as a control
in which all small mammals were
individually marked by ear-tags (rodents) or toe-clips (insectivores).The
other grid, located 30.4 rn from the
conirol and situated in csaagigpous
habitat, served as an experimental
grid from which all small m a m a k
were removed permanently upon
first capture (Adler 1985). All small
mammals captured on this grid after
the initial removal period were considered colonists. 1also sampled
small mammals on 4 nearby trapping
plots which also were located in
similar macrohabitat but covered a
range of microhabitats. Each plot
consisted of two parallel traplines
located 30.4 m apart. Each trapline
was 15 stations long at Broadmoor
(except on one plot where both traplines were 12 stations long) and on
Nantucket and 20 stations long on
Great Island. These plots were
trapped on a rotation basis (AdPer
1987). On Great Island, an additional
4 control grids were monitored
monthly from April through September for five years (Adler and Wilson
1987). These grids were not confined
to structurally simple macrohabibts
but ranged from grassland to mature
woodland habitats.
Grid 1 was located at the edge of a
stand of pitch pine. (Pinus rigida),
white oak (Quercus alba), and black
oak ((2. velictina?. Dense brushy
understory covering a large portion
sf the grid consisted of bayberry,
huckleberry (Gaylussacia bcrccata), and
inkberry holly (Ilex glabra). Low-lying
areas sf the grid were damp and harbored large cranberry (Vaccinium
macrocarpon) and sundew (Drosera
spp.). Very little herbaceous vegetation was present. Grid 2 also was located at the edge of a pitch pine,
white oak, and black oak w d l a r r d
but was more elevated and ccpnsequently drier. A dense brushy understory cmsisl'ed of bayberry, p i s o n
ivy, and c o r n o n g ~ w ~ ~ b r!Snrilux
ier
rotundifilia). Crass was present in the
brushy, treeless portions of the grid.
Grid 3 was ~ocatedwithin a white
oak and black oak woodland. A
dense shrub cover of blueberry,
'marlberry (Arctostaphylos uvra-ursi),
common greenbrier, and bullbrier
greenbrier (S. born-nod was present,
along with bracken fern (Ptere'ditkm
aquiiiniurn). Grid 4 was located on
Pine Island, a 7-ha islet 37 rn from
Great Island and co~ulwtedto the
latter by a narrow sandy spit. White
oak and black oak formed a canopy
over much of the grid, and a dense
woody understory of bayberry and
other shrubs also was present. Densc
beach grass was preLsentin the beeless portions of the grid. Grid 5 was
the cornpanior, control for the experimental grid and was located in dense
beach grass containing scattered
patches of bay&~;y,juniper, and poison ivv.
F sampled vegetakon structure at
every trip station on all grids a d
plots by measuring 23 habitat variables related to plant structure and
species richness (table 1).Two additional habitat vari sbles describing
canopy structure were included in
the analysis on Great Island control
gids (table 1). Measurement procedures were given by Adler (1985)
and Adler and Wilson ( 1987).
d
Ba Analysis
I relied extensively upon principal
components analysis CPCA) and discriminant function analysis (DFA) in
order to uncover the structure of
complex and temporally variable
small mammal populations and their
relationships to habitat structure.
Specifically, my aims were to (1) reduce the number of habitat dimensions, (2) derive a quantitative measure of habitat heterogeneity, (3)
quantify patterns of habitat utilization, (4) combine covarying demographic traits into single variables,
and 353 derive indices of demographic variability.
In these studies, H recognized two
related descriptors of microhabitat
structure. H defined a microhabitat
structure-diversi t); variable or gradient as a characteristic that described
the physical structure of the microhabitat and that varied in magnitude along a continuum. 1 defined
microhabitat heterogeneity as a
quantitative measure of horizontal
variation in microhabitat ckaracteristics (August 1983, Adler 1987).
I subjected the habitat data rneasured at each trap station to PCA to
reduce the number of habitat variables. At each site, I conducted two
PCks of the 25 variables, one with
control and experimental grids combined and one with the 4 trapping
plots cornbind. I also conducted a
PCA of 24 habitat variables for all
five control. grids on Great Island
combined. HBl(dO was eliminated
from this analysis because only one
nonzero value was recorded on the
five grids.
Each principal component (PC)
with an eigenvalue greater than 1.0
was retained for further analysis as a
new habitat variable. Principal components derived h m PCAs of grid
and plot data were quite similar
within each site, based upon factor
loadings on the original habitat variables (Adler 1985,1987). At Broadmoor, five PCs were retained for
analysis from both grid and plot
data, whereas six were retained from
analysis of grid data; four PCs were
interpreted similarly in both data
sets. PCAs of Nantucket grid and
plot data both yielded seven retainable PCs, three of which could be interpreted similarly between the two
data sets. The PCA of habitat data
from the five control grids on Great
Island yielded seven PCs.
I computed a microhabitat heterogeneity index for each of the four
trapping plots at the three study sites
and for each of the five control grids
on Great Island (Adler 1987, Adler
and Wilson 1987).This index was
based on the supposition that the
standard deviation of the within-plot
or within-grid mean vector of a PC
described the variability of a microhabitat gradient on a given plot or
grid. Since each successive PC contributed less to the total variance in
habitat data, I adjusted for each PC's
contribution to the total variance by
multiplying the factor scores by the
square root of that PC's eigenvalue.
I examined capture data in relation to habitat structure at both the
level of individual trap stations
(habitat association and selection)
and at the level of a grid or plot (demography). I used mu1tiple linear
regression and residuals analysis to
relate these small mammal (dependent) variables to habitat (independent) variables. More complete descriptions of analytical techniques are
given in each section below, and a
brief outline of the sampling design
is given in table 2.
SPECIES COMPOSITION
I recorded 9,170 captures of 10 small
mammal species in 42,773 trapnights
at the 3 study sites (table 3). Each
study site generally had an abundant
herbivore (Microtus pennsylvanicus),
an abundant granivore (Peromyscus
leucopus, except at Broadmoor where
it was rare in the grassland trapping
areas), a common insectivore (Blarina
brevicauda or Sorex cinereus), and any
of several rarer granivores, omnivores, or insectivores.
HABITAT STRUCTURE AND
POPULATION STATISTICS
Habitat Associations and
Selection
Study Purpose
I examined both small mammal microhabitat associations and selection
at all three study sites (Adler 1985).
Density-dependent effects of conspecifics and other species may restrict access to certain habitat types,
thereby resulting in different patterns
of habitat utilization. I therefore reserved the term habitat selection for
situations where individuals had
more or less unrestricted access to a
variety of habitat types.
Analytical Approach
Inferences
I defined an association as a statistical relationship between the numbers
of captures of a species at trap stations and a quantitative measure of
microhabitat structure. To determine
these relationships, I regressed the
total number of captures of a species
at each control grid trap station on
factor scores of each PC. The experimental grid represented an area
where densities were continually
being reduced and vacant microhabitats were more often available to
colonizing individuals.
To determine differences in microhabitat associations between control and experimental grids, I included a dummy variable coding for
grid (control or experimental) and
habitat variable x grid interaction
terms (Adler 1985).
Most small mammals (8 of I I populations examined) demonstrated affinities for specific microhabitat types
on either control or experimental
grids (table 4). These affinities generally were consistent with other published reports of habitat associations
of these species. For instance, P. leucopus generally were associated positively with woody microhabitats or
negatively associated with herbaceous microhabitats. M.pennsylwanicus generally showed the opposite
associations. Microhabitats selected
by small mammals, as determined
from capture data on experimental
removal grids, sometimes differed
from associations determined from
capture data on the adjacent control
grids (table 4). Differences in habitat
selection and association were attrib-
utable to opportunistic responses of
small mammals to between-grid differences in microhabitat structure
and to differences in the level of intraspecific interactions brought about
through density reductions on the
experimental grids (Adler 1985).
Temporal Patterns of Habitat Use
Study Purpose
I examined temporal patterns of microhabitat use by M. pennsylwanicus
at the three study sites and by P. leucopus on Great Island and Nantucket.
AnalyticaP Approach
Monthly trapping periods were
grouped into winter (1Dec.-Feb.),
spring (Mar.-May),summer gun.Aug.), and fall (Sep.-Nov.) seasons
each year. I divided trap stations on
control grids into favorable and unfavorable microhabitats each season
depending upon whether the total
number of captures in a season was
above (favorable) or below (unfavorable) the seasonal mean (Van Home
1982; Adler 1985). 1 then used a twogroup DFA, with favorable and unfavorable trap stations defining the
two groups, to develop a discrirnination index of habitat use (Rice et al.
1983; Adler 1985).This index was the
percentage of trap stations classified
correctly as either favorable or unfavorable. High discrimination values
indicated a sharp distinction between
favorable and unfavorable microhabitats; low values indicated little
difference between favorable and unfavorable areas.
To determine the importance of
intra- and interspecific population
densities on temporal pa ttems of
habitat discrimination by P. leucopus
and M. pennsylwanicus, I regressed
the seasonal discrimination values on
the mean seasonal densities of each
of the major small mammal species
present at each study site.
inferences
Analytical Approach
In the case of M. pennsylvanicus, density and discrimination were negatively related at Broadmoor and
positively related on Great Island.
The unexpected positive relationship
on Great Island could be explained
by the distribution of captures over
the grid; 17 capture stations had less
than two captures during the entire
study and were in a sparsely vegetated area. As density increased, the
remaining 32 trap stations became
increasingly utilized. The distinction
between favorable and unfavorable
microhabitats increasingly became a
distinction between unoccupied,
sparsely vegetated stations and occupied, densely vegetated stations.
On Nantucket, discrimination followed a pattern similar to density
but was not linearly related to the
latter. For P. lsucopus on both Great
Island and Nantucket, habitat discrimination was related negatively to
density (fig. I), indicating that the
distinction between favorable and
unfavorable microhabitats decreased
with increasing density. Densities of
other species were not related to
temporal variation in habitat use
(Adler 1985).
Therefore, intraspecific competition appeared to be more important
than interspecific interactions in determining microhabitat use by the
species I examined. As intraspecific
density increased, the range of microhabitat types utilized also increased, as predicted by early t h e
ries of habitat selection (e.g.,
Svardson 1949).
I calculated density (log,, number
per 100 trapnights), sex composition
(proportion males, arcsin square root
transformed), age structure (proportion of adults captured during sampling periods from April through
September, arcsin square root transformed), and breeding intensity (proportion of adults in breeding condition captured in sampling periods
from April through September,
arcsin square root transformed) each
trapping period.
I also computed variability measures for each of these demographic
variables as squared distances from
plot means. I divided the estimates
for density variability on each plot by
the mean density of the respective
plot in order to adjust for population
size.
I regressed the estimates for each
of the eight demographic variables
separately on plot means for each
microhabitat variable derived from
PCA and the index of heterogeneity.
I then regressed the unstandardized
residuals from each of these regressions separately on each habitat PC
and the heterogeneity index to search
for nonlineari ties and missing variables (Framstad et al. 1985).
GREAT ISLAND
DENSITY
NANTUCKET
Microhabitat Structure and
Demography
Study Purpose
I examined the relationship between
demography of M. pennsylvanicus
and microhabitat structure from data
collected on the four trapping plots
at each study site (Adler 1987).
0
5
10
15
20
25
DENSITY
Figure 1 .-Relationships between seasonal habitat discrimination and population density in
Peromyxus leucopus at two study sites in eastern Massachusetts.
Inferences
Densities of M .pennsylvanicus and P.
leucupus were ordered linearly along
microhabitat gradients (Adler 1987),
consistent with patterns of microhabitat associations and selection
in these two species (table 5). In
general, M. pennsylvanicus densities
were higher on plots with more herbaceous and grassy cover or less
woody cover. Nantucket was exceptional, however, with M.pennsylvanicus densities increasing along gradients of increasing woody growth and
shrub species richness. I captured
large numbers of this vole in dense
heath with little or no herbaceous
vegetation.
Perumyscus leucopus densities on
Great Island could not be related to
microhabi tat structure, probably because of the generalist nature of this
mouse relative to the breadth of microhabitats sampled. Indeed, when
sampling areas included other microhabitats, density could be related
to overall microhabitat structure (see
below). P. leucopus densities on Nantucket increased with increasing
shrub species richness (table 5). Densities of both species were more vari-
able in poorer habitats. Microhabitat
structure was a poor predictor of
other aspects of demography such as
age and sex composition. However,
variability in demographic structure
often was greater in low-density
habitats. While some of the variability in density and demography may
have been due to statistical dependence on population size (i.e., greater
sampling error at small population
sizes), biological effects
(e.g.,response to environmental fluctuations) also must have been important. More favorable microhabitats
should have maintained a more
stable composition over time due to
greater intraspecific interactions,
whereas poorer microhabitats should
have contained a more unstable assemblage of predominantly transient
and subordinate individuals due to
spillover during periods of high density (Adler 1987).In contrast to the
importance of microhabita t gradients, the quantitative measure of microhabitat heterogeneity generally
was unrelated to demographic phenomena. In only one case did microhabitat heterogeneity explain variation in demography better than any
structure-diversity variable.
Macrohabitat Structure and
Demography
Study Purpose
I further examined the relationship
between demography of P. leucopus
and microhabitat structure across
macrohabitats. P. leucopus is a habitat
generalist which occurs in habitats
ranging from grassland to mature
deciduous and coniferous forests in
southeastern Massachusetts.
Analytical Approach
For this purpose, data from the five
control grids on Great Island were
analyzed (Adler and Wilson 1987).
Monthly trapping data were analyzed with respect to 10 demographic variables. Grid means of
density (log,, minimum number
known alive), adult male body mass,
and observed range length (ORL, the
maximum linear distance between
capture points of an individual,
Stickel 1954) were compared using
Tukey's multiple comparisons test.
Mean male and female ORLs were
compared on each grid using t-tests.
Contingency table analysis was
used to compare age structure (proportion adult), adult survival (standardized 14-day rates), sex composition (proportion of mice tagged that
were males), adult residence rates
(proportions of adults captured in at
least two trapping periods), overwinter residence (proportions of mice
present during Sep. and surviving to
the subsequent Apr.), the p r o p r tions of adults that were reproductively active, and the proportions of
young mice (mice with some grey
pelage remaining) that were reproductively active. These 10 variables
were examined for intersex differences within a grid (except sex composition) and for intergrid differences.
To examine temporal dynamics of
demography, monthly trapping data
were grouped into early summer
(Apr.-Jun.)and late sum~ner(Jul.Sep.) seasons. The following demographic variables were estimated on
each grid during each season: density
(mean log,, minimum number
known alive), proportions of males
and of females that were adults, proportion of males, mean adult male
body mass, proportions of adult
males and of adult females breeding,
and survival rates of adult males and
of adult females (weighted mean 14day rates). Variables expressed as
proportions were arcsin q u a r e root
transformed.
Many rodent population parameters are known to covary (e.g., Schadfer and Tamarin 1973). Accordingly,
a PCA of the eight variables was executed in order to include covarying
parameters as single demographic
variables; four PCs with eigenvalues
greater than 1.0 were retained for
further analysis.
These PCs were correlated with
(Udensity and adult survival,
(2)adult female breeding activity,
(3)adult male breeding activity, and
(4)the proportion of males. Variability indices of each of these PCs were
calculated each season for each grid
as squared distances from grid
means (Adler and Wilson 1987).A
measure of overall demographic
variability was calculated for each
grid each season as squared distances of the factor scores from the
mean factor score, summed over the
four PCs.
Factor scores within each PC were
multiplied by the square root of that
PC's eigenvalue in order to account
for the unequal contributions to
overall variance of each PC (Adler
and Wilson 1987).This method allowed variables with different scales
of measurement to be included together without further scaling or
weighting. Seasonal estimates of each
of the PCA-derived demographic
variables and their variability estimates were regressed separately on
each of the PCB-derived microhabitat variables and the index of heterogeneity.
Inferences
Statistical tests which were significant at H34I.05 are qualitatively summarized in table 6. Grid means of the
first three demographic PCs revealed
t h e e demographic groups. Grids 1
and 5 were located farthest from any
adjacent grid in three-dimensional
space, whereas grids 2,3, and 4 were
clustered more tightly together with
respect to demographic structure
(table 7). Grid 1 was characterized by
low density and survival, a Iow proportion of females, low breeding intensity, and high demographic variability. Grid 5 was characterized by
low density and survival, a high proportion of females, moderate breeding intensity, and high demographic
variability. Grids 2,3, and 4 were
characterized by high density and
survival, low lo m erate proportion
of females, moderate to high breeding intensity, and low demographic
variability. Two low-density groups
(represented by grid 1 and grid 5)
and one high-density group (represented by grids 1,2, and 3) therefore
were evident. The low-density
groups were more variable in terms
of each of the demographic PCs and
in overall demographic structure. In
general, density, survival, and b r e d ing activity increased along gradients
of increasing woodiness or decreasing herbaceousness, whereas demographic variability decreased along
these gradients (table 8).
SYNTHESIS
I found microhabitat structure to be a
potentially important force in organizing small mammal populations,
particularly in reiation to associations and densities. Small mammals
generally were associated with particular rnicrohabitats, as revealed by
analysis of single trap stations. However, associations often differed between control and experimental
grids. I suggest that the small mammals I studied selected specific microhabitats and were opportunistic in
their responses to habitat not occupied by other individuals (as on the
experimental grids). Since most small
mammals that I studied were microhabitat selectors, microhabitat
structure therefore was a crucial determinant of local community composition. Furthermore, microhabitat
st~ucturealso should have affected
temporal variability of community
structure since populations in lowdensity areas were more variable.
Affinities of each small mammal
species for particular microhabitats
resulted in density-habitat relationships when averaged over a larger
sampling area (grids or plots). Thus,
small mammal densities generally
could be related to microhabitat
structure. Survival and breeding activity, which generally covary with
density, also could be related to microhabitat structure when sampling
areas spanned macrohabitat boundaries. The importance of microhabitat
structure in affecting other demo-
graphic characteristics such as sex
composition and age structure was
not as pronounced. Gradients of microhabitat structure can be envisioned as comprising an environmental suitability gradient, with the
endpoints being uninhabitable and
optimal (where individual fitness is
highest). Demographic characteristics then vary along this gradient of
suitability and along other gradients.
The gradient of suitability is cornposed of factors related not only to
habitat structure but also to focd resources and release from predation,
competition, and parasitism. Density
alone may not be a strong correlate
of suitability (Van Home 19831, but
density in concert with survival and
breeding activity should increase
along the gradient of suitability. By
contrast, demographic variability
should decrease along this gradient.
Several habitat types may represent
similar csndi tiom of environmental
suitability, particuIarEy for habitat
gerwralists such as P~omyscusleucopus. Therefore, it may be difficult to
relate demography to microhabitat
structure because similar demographic structure may be found in
different habitats (AdPes and Wilson
1987).
CQuantitaiivemeasures of habitat
hc iurrpgemeity generally were unrela kcd to dern13g~aphicvariables, in
cun~trastto the mass of theory predicting that heterogeneity promotes
population stability (eg., den Bser
1968, Levins a969, Smith 1972, Maynard Sn~'e?h
2974, Steek 1974, Tanner
1975, Siesrseth 1977,1980, Lomnicki
1978,1980, de Jong 1979, Hassell
11981)). The contrast between my reslalts and theoretical predictions may
be reconciled by introducing scale.
My measures of heterogeneity were
at the microhabitat level, whereas
many models have implied macrohabi tat heterogeneity so that organisms may disperse into a patch
and establish a resident population
(e.g., Levins 1969). Increasing the
number of such patches increases the
spatial heterogeneity sf an area,
which then promotes population stability. I suggest that microhabitat
structure will affect density more
than it will other demographic characteristics, whereas macrohabitat
structure and heterogeneity will be
more important in stabilizing populations and in influencing demographic structure (e.g., sex composition and age structure).
My conclusions concerning the
importance of habitat structure in
organizing small mammal populations and communities can be shown
schematically (fig. 2). According to
this scheme, microhabitat structure
primarily affects habitat selection,
density, and density variability (since
density generally is related inversely
to variability). Macrohabitat shucture primarily affects population stability (stability being enhanced by
macrohabitat heterogeneity) and
demographic structure. Habitat se-
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