of Habitat Structure and the Distribution Small

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Habitat Structure and the
Distribution of Small
Mammals in a Northern
Hardwoods Forest1
In northern temperate forests small
mammals are distributed unevenly
across available habitat, even within
a single forest type or age class
(Dueser and Shugart 1978, Vickery
1981, Parren 1981, Seagle 1985a). Differential use of certain segments or
microhabitats within a broader habitat type has most often been reported
for sympatric species of small mammals, but intraspecific variation in
microhabitat use has also been noted
(Kitchingsand Levy 1981, Vickery
1981, Seagle 1985a).
Differential use of microhabitats
by small mammals may be a consequence of the ecological requirements of each species (i.e. habitat selection) or it may be the result of partitioning of habitat by competing species (Crowell and Pimm 1976, Porter
and Dueser 1982). Another hypothesis is that the observed use of microhabitats by small mammals is primarily a function of the density of
small mammal populations.
Under this hypothesis, use of a
certain microhabitat is determined
more by the availability of animals to
occupy the area than by structural
'Paper presented at symposium, Management of Amphibians, reptiles, and Small
Mammals in North America. (Flagstaff,AZ,
July 19-21, 1988.)
2JefferyA. Gore, formerly a graduate
student, University of Massachueits, Bepartment of Forestry and Wildlife Managemen t is currently Nongame WildlifeBiologist, Florida Game and Fresh Water fish
Commission, Rt. 4, Box 759, Panama City,
Florida 32405.
Abstract.-The influence of habitat structure on
the distribution of small mammals was studied in an
old-growth northern hardwoods forest in New
Hampshire. Logistic regression equations developed
with data from three live-trappinggrids were able to
classify locations of just three of eight small mammal
species better than expected by chance. For all
species the regression models failed to correctly
predict presence in an independent grid. At the
scale tested, habitat structure had little effect on the
distribution of small mammals within this forest type.
characteristics of the microhabitat.
Extrinsic factors, such as food availability, disease, or predation, could
alter population levels and thus indirectly influence the distribution of
small mammals among microhabitats. Observed microhabitat use
might also be a function of some
combination of habitat selection,
competitive partitioning, and factors
affecting population density.
Thequestion of which mechanism
most influences the distribution of
small mammals is of more than academic importance. If small mammals
select among microhabitats based on
structural features, then disturbance
such as timber harvesting may have
a considerable impact on population
density or species composition. Conversely, if the distribution of small
mammals is primarily a function of
population density, then habitat disturbance is likely to-have less effect,
or at least a less direct effect, on local
populations. Furthermore, if microhabitat requirements are known,
it might be possible to manipulate
population levels by altering structural components of the habitat.
I measured use of microhabitat by
small mammals in an old-growth
northern hardwoods (Acer-Fagus- Betula) forest, a habitat that contains a
variety of microhabitats (Bormann
and Likens 1979)and supports several species of small mammals (Lovejoy 1970).In this paper I identify the
small mammal-microhabitat associations observed, compare them to re-
sul ts of previous studies, and suggest
that the distribution of small mammals among microhabitats in the
northern hardwoods forest is influenced little by structural features of
the forest.
The study area was located in the
White Mountain National Forest,
New Hampshire in a topographically
isolated site known as the Bowl
(Martin 1977). All fieldwork was confined to the uncut, old-growth northern hardwoods forest that comprises
about 210 ha in the lower (600-750 m)
elevations of the Bowl. The oldgrowth forest is structurally heterogeneous; numerous treefalls and
gaps in the canopy are present and
the portion of the forest floor covered by rock, soil, water, or vegetation varies greatly across the stand
(Gore 1986).
In 1983, small mammals were livetrapped on three 60 x 105-m grids,
each consisting of 40 trapping stations spaced at 15-m intervals along
five rows. Two stations were added
to each grid in 1984 to increase sampling at seeps and along streams. A
fourth grid of 42 trapping stations
was also established in 1984. This
grid was used to evaluate the robust-
ness of models of microhabitat use
that were developed with data from
the initial three grids.
One Sherman (5 x 6 x 17 cm),
Pymatuning (Tyron and Snyder
1973), and pitfall trap were located at
each trapping station (Gore 1986).
Traps were baited with sunflower
seeds and set simultaneously for four
consecutive days each month from
August to October, 1983 and June to
October, 1984. Each trapping period
began 3-5 days after a new moon (to
minimize variation in ambient light)
and continued regardless of weather
conditions. Captured animals were
marked and released at the capture
site. In 1983,4,320 trap-nights (12
nights x 120 stations x 3 traps) were
recorded on three grids. In 1984 four
grids provided 10,080 trap-nights (20
nights x 168 stations x 3 traps).
At each trapping station microhabitat
was quantified by measures from
within the 15 x 15-m plot in which
the station was centered. The variables used to quantify the habitat are
defined in table 1 and the methods
for measuring them are described in
detail by Gore (1986).
The 26 habitat variables selected
for analysis were a subset of a larger
group of variables on which measures were made. The number was
reduced in order to facilitate interpreta tion of results and to increase
the ratio of sample cases to variables
(Morrison 1985).The initial group
was condensed by deleting one of
each pair of highly correlated
(n0.50) variables that were similar in
ecological form or function. Some
highly correlated variables, such as
the number of dead trees and the
number of logs, were retained because I felt they represented distinct
ecological features. Variables were
included regardless of whether their
means or distributions varied between capture and no-capture stations. This allowed significant linear
combinations of variables to be incorporated, but at the risk of complicating the interpretation of results.
Data Analysis
Chi-square tests (Snedecor and Cochran 1980) were used to identify statistically significant associations between capture locations of all possible pairs of species. Within species,
differences between capture locations
in different years and seasons were
analyzed. The relative strength of associations was measured via the contingency correlation coefficient, Phi
(Brown 1983).
Habitat variables from plots with
and without captures in 1984 were
compared for each small mammal
species. Capture locations from the
entire trapping period were grouped
even though some values, such as for
vegetative cover, varied between seasons. If the habitat values within the
two groups were normally distributed and had equal variances, significant differences between groups
were determined via t-tests; if not,
Mann-Whitney tests were used
(Snedecor and Cochran 1980).
Logistic regression (Bishop et a'l.
1975, Engelman 1983) was used to
identify, for each mammal species,
the microhabitat variables that accounted for statistically significant
portions of the variation in capture
success. The product of the analysis
is a set of regression equations for
predicting presence of each species at
a station based upon quantitative
measures of the station's habitat
characteristics. Logistic regression
was used instead of discriminant
analysis because it does not assume
that independent or explanatory
variables are normally distributed or
have homogeneous variances (Press
and Wilson 1978).
I used the BMDP-LR computer
program (Engelman 1983) to construct regression equations, or models, for predicting microhabitat use
as defined by captures. The program
selected, in a stepwise manner, habitat variables that distinguished stations where animals were captured
from those where they were not.
Variables were entered into an equation if their F value was significant at
P<0.10 and removed if P subsequen tly exceeded 0.15.
Initial regression models were
formed using data obtained in 1984
from Grids 1-3. The regression models for each mammal species were
used to classify stations within the
three grids as locations where the
species was either present or absent.
The models were then used to predict the presence of each species at
stations in Grid 4. Finally, new regression models were developed for
each species using data from all four
grids. These were compared to the
models from the initial three grids in
order to assess the effect of different
sites on the models. The Kappa sta-
tistic (Fleiss 1973, Engelman 1983)
determined the significanceof agreement between locations where a species was observed and the speciespresent locations predicted by the
regression models.
Thirteen species of small mammals
were captured during the study, but
only those captured more than ten
times are considered. The number of
captures varied widely among species and, for some species, between
years (table 2). The smokey shrew
(Sorex fumeus), pygmy shrew (S.
hoyi), and eastern chipmunk (Tamias
striatus) were captured infrequently
in both 1983 and 1984, while the
masked shrew (S. cinereus) and the
southern red-backed vole (Clethrionomys gapped were common in both
years. The northern short-tailed
shrew (Blarina brevicauda), deer
mouse (Peromyscus maniculatus) and
woodland jumping mouse (Napaeozapus insignis) were captured
more often in 1984 than in 1983.
Captures from August through
October at the 120 stations trapped in
both years were compared for each
species to determine whether 1983
and 1984 capture locations were associated. For all but two species, individuals were captured in both
years at few (0-8 percent) of the stations with captures. No species
showed a significant association in
capture locations between years (X2
tests, P~0.05).Even for deer mice and
jumping mice, which were abundant
in both years, stations with captures
in both years comprised only 32-41
percent of all stations with captures
of these species.
A similar comparison was made
between the capture locations of each
species in summer (June-August)and
fall (September-October)of 1984 on
all four grids. No species exhibited a
significant (P>0.05)association between summer and fall locations.
For all trapping periods and species combined, each trapping station
had at least two captures. In 1984, all
168 stations recorded at least one
capture and 85 percent had more
than four captures. The maximum
number of captures at a single station
was 22 for all species combined and
15 for a single species, the jumping
mouse. Only for deer mice and jumping mice did stations with multiple
captures outnumber stations with
single captures. All other species
were taken only once or not at all at
the majority of trapping stations.
The only pairs of species captured
at the same locations more often than
expected by chance (X2 tests, P<0.05)
were jumping mouse:red-backed
vole, short-tailed shrew:masked
shrew, and short-tailed
shrew:smokey shrew. The association between each pair was positive
and weak (0.15<Phi<0.30).
Some animals died after capture,
but the effect upon local populations
of each species was unknown. For
abundant species that experienced
low mortality, such as the deer
mouse and jumping mouse, the effect
was probably negligible. For the
shrews, which had high mortality
rates during capture, the effect may
have been substantial. However, cap-
ture rates indicated no adverse effects on shrew abundance. In 1984
more shrews of each species were
captured in September than in the
previous three months. Furthermore,
of the four shrew species, only captures of pygmy shrews declined between years (table 2).
Microhabitat Use
Differencesbetween habitat values
for the capture and no-capture stations of Grids 1-3 from 1984 were
compared. For each species, at least
one habitat variable differed significantly between stations with and
without captures (table 3).
Logistic regression is not useful if
the number of cases of either of the
dependent variable values (species
presence or absence) is less than five
percent of the total number of cases
(D. Hosmer, University of Massachusetts, personal communication). Because the pygmy shrew was found at
only two percent of the stations, it
was deleted from the analysis. Deer
mice and smokey shrews each also
had widely disparate group sizes, 95
percent present and 95 percent absent respectively; therefore, results
for these species should be considered cautiously.
The first set of logistic regression
models of microhabitat use were
based on captures in 1984 from the
126 stations in Grids 1-3. The number
of significant variables included in
each model ranged from one, when
presence of red-backed voles was the
dependent variable, to 12, when
presence of eastern chipmunks was
used (table 4). For most species the
variables included in the regression
models were not the same as those
whose means differed between capture and no-capture stations (table 3).
This suggests that some linear combination of habitat variables was important in defining the microhabitat
where a species was captured, even
though individual variables alone
were not.
The habitat variables included in
the logistic regression models (table
4) were selected because each was
associated with a significant (P<0.10)
portion of the variance in the capture
data of a species. However, if these
variables and their regression coefficients cannot be used to correctly
predict the capture success of a species at a station, they are of limited
practical value regardless of their statistical significance. To assess the
utility of regression models as descriptors of microhabitat, they were
independently used to classify each
trapping station, based on habitat
parameters, as one with the species
present or absent (table 5).
Tests of the agreement between
predicted and observed capture success were not possible for the
smokey shrew because no sites were
classified as having the species present. The regression model was not
able to identify, based upon the habitat variables measured, the eight sta-
tions that captured smokey shrews.
Conversely, nearly all stations were
predicted to capture deer mice and
jumping mice (table 5).The regression models for those two species
were unable to distinguish those stations where the animals were not
For the other four species the
numbers of stations with and without captures were more similar and,
consequently, so were the number of
species-present and species-absent
classifications. For red-backed voles,
however, only 47 percent of the classifications of present were correct
and this was not significantlybetter
than chance (table 5). Only for the
eastern chipmunk, short-tailed
shrew, and masked shrew were the
regression models able to classify
capture success at a level better than
chance agreement. For these three
species, the logistic regression models may be useful descriptors of the
microhabitat used within Grids 1-3.
Because of the large number of
variables included in the regression
model for the eastern chipmunk
(table 4), it was difficult to concisely
describe the microhabitat of this species. Briefly, the eastern chipmunk
was associated with large trees
(+BATWE),downed wood
+AVGVOLC), and dense vegetation
taller than 0.5 m (+VEG52, +VEGT2,
The microhabitat of the shorttailed shrew has fewer variables but
is also difficult to characterize. Captures were negatively associated with
numbers of medium-sized shrubs
and with vegetative cover between
0.5 and 2 m above ground. The
masked shrew was found at stations
with numerous logs and dense vegetation <0.5 m tall. The positive association with slightly decayed logs
and dense ground cover and the
negative association with standing
dead trees suggest that recent
treefalls may provide good habitat
for masked shrews.
To be useful predictors of species
microhabitat, regression models
should be successful with data that
are independent of those from which
the models were formed. To test sitespecificity of regression models, they
were applied to data from 42 stations
in Grid 4. Unlike the other three
grids, Grid 4 had a perennial stream
running through it, two extensive
canopy gaps from recent treefalls,
and highly variable soil conditions.
Regression models from each of
the seven species were used to classify the stations in Grid 4 according
to capture success. None of the classifications, even those of the eastern
chipmunk, short-tailed shrew, and
masked shrew were correct more often than expected due to chance (for
all tests Kappa ~0.48,b0.05).
Because none of the regression
models were useful in predicting
capture locations in Grid 4, data
from all four grids were combined
and new logistic regression models
for predicting species presence were
developed to determine the influence
of data from Grid 4 (table 7). Deer
mice, jumping mice, and smokey
shrews again had widely disparate
group sizes and the models could not
correctly classify the stations in the
less common group (table 7).
Agreement be tween observed and
predicted locations was significant
for red-backed voles as well as eastern chipmunks, short-tailed shrews,
and masked shrews (table 71, which
had significant models earlier. Some
of the variables included in the models of each species (table 6 ) were different from those included when
only data from Grids 1-3 were used
(table 4). For the masked shrew, eastern chipmunk, and red-backed vole
the regression models created with
and without the data from Grid 4
were similar, even though the predictions from Grids 1-3 for the redbacked vole were not better than expected by chance. The coefficients
changed but most variables were the
same. This suggests that for these
three species the microhabitats in
Grid 4 were similar to those identified in the other three grids. The relationship of species to microhabitat
parameters may not be as sensitive
as the regression models suggest.
This would account for the poor performance of the models from Grids
1-3 in predicting captures on Grid 4.
For short-tailed shrews the regression model changed greatly when
data from Grid 4 were included.
Four new variables were added, and
the sign of the coefficient was reversed on the only variable, vegetation between 0.5 and 2 m, that was
retained. This suggests that captures
of short-tailed shrews or the measured habitat parameters poorly reflect the microhabitat requirements
of the species, or that short-tailed
shrews are not restricted by microhabitat within this forest.
In an environment of limited resources, sympatric species are expected to partition resources as a
means of coexisting, i.e. avoiding
competitive exclusion (Schoener
1974). Since Brown (1973) first suggested that temperate forest rodents
would be likely to partition habitat
rather than seasonally variable food
supplies, numerous studies in northern temperate forests have identified
statistically significant associations
between habitat structure and small
mammal distributions (Dueser and
Shugart 1978, Kitchings and Levy
1981, Parren 1981, Vickery 1981,
Schloyer 1983, Seagle 1985a).
Statistical significance, however,
does not necessarily impart biological meaning to observed patterns of
species distributions. Few authors
have tested the biological relevance
of their models of microhabitat use
by using them to predict microhabitat use at independent locations or
times. Parren and Capen (1985)
found that capture locations of deer
mice could not be accurately predicted using discriminant functions
of rnicrohabitat use developed with
data from similar habitats the previous year. Similarly, none of the logistic regression models I developed
were useful in predicting capture locations at stations other than those
from which the models were developed.
One reason for the poor predictive
capabilities of the multivariate models may be that trapping'does not accurately portray the relationship between species presence and habitat
requirements. In addition, the way in
which habitat features are measured
may not depict the variability perceived by small mammals or the
variation in microhabitat structure
may be small relative to the niche
breadth of each species. Unfortunately, these problems are not easily
identified or solved. Ideally, the activity of many individual animals
would be intensively monitored, but
that is very difficult to accomplish.
Another reason for the poor performance of the models is that problems in applying the mu1tivaria te
analyses, such as disparate sizes of
presence and absence groups and
mu1ticollinearity of variables, make it
difficult to interpret the results of
habitat models (Noon 1984). The
scale at which habitat and small
mammals are sampled also greatly
affects the relationship that can be
defined (Morris 1984).
Despite these potential limitations,
I believe the inability of my models
to predict species presence on a independent grid in the same forest stand
suggests that structural features
alone, at least at the microhabitat
level, are not important to the distribution of small mammals. Comparisons of capture locations and review
of habitat requirements for each species supports my argument.
The locations where species were
captured suggest that no interspecific
segregation of microhabitats occurred. Overlap in capture sites was
high among species and no inverse
relationships were observed, even
when data were examined by season.
This suggests that habitat partitioning or microhabita t selection is absent or operating at a finer scale than
my trap stations.
The weak association I found
among capture locations of each species between years and seasons suggests that individual species were not
selecting particular trapping stations.
It is possible that subtle shifts in the
microhabitat used would not be per-
ceived by examination of trapping
locations alone. The microhabitat occupied by small mammals has been
reported to shift with season (Kitchings and Levy 1981, Vickery 19811,
population density (M'Closkey 1981,
Adler 19851, and species composition
(Seagle 1985b).This suggests that microhabita t use is dynamic, regardless
of whether the shifting is deterministic or stochastic.
Another argument against differential use of microhabitats by the
small mammals I observed is the variety of habitats they occupy. The
eight species 1found in the oldgrowth northern hardwoods forest
have been found in other age- and
size-classes of northern hardwoods
forests as well as in other forest types
(Lovejoy 1970, Richens 1974,
Kirkland 1977, Miller and Getz 1977,
Hill 1981, and others). Except for
smokey shrews and pygmy shrews,
the species are common in a variety
of habitats comprising a wide range
of structural characteristics. In fact,
descriptions of the important habitat
features associated with each species
d o not always agree [e.g. see Hamil-
ton (1941), Brower and Cade (1966),
Lovejoy (1970), Vickery (19811, and
Parren (1981).fordescriptions of
jumping mouse habitat]. If each species is common under a wide range
of habitat conditions, it seems unlikely that they would partition or
select habitat based on the advantages of structural features alone.
Fine discrimination of the forest
habitat seems more improbable when
the temporal variability of microhabitats is considered. Within the
northern hardwoods forest of New
Hampshire microhabitats are greatly
modified in winter by deep snow
cover, in summer by closed canopies
and sparse ground cover, and in fall
by deep leaf litter. Therefore, resident species must accommodate seasonally variable microhabitats as well
as seasonally variable food supplies.
The reasoning Brown (1973) used to
suggest that temperate forest rodents
could not specialize on seasonally
variable food resources seems applicable also to seasonally variable microhabitats.
In the forest I sampled, presence
of most species at individual trapping stations could not be accurately
predicted based on structural features of the habitat. If microhabitat
structure does not greatly influence
the distribution of small mammals
within this forest type, disturbance of
the habitat should not directly affect
population levels. However, the scale
at which the disturbance occurs may
determine to what extent local populations are affected. Small scale disturbance of the habitat, such as harvesting by single-tree or small-group
selection, would likely not affect species composition or density of the
resident small mammals. More wide
scale disturbance, such as clear-cutting of the entire forest stand, might
alter the habitat so greatly that species abundance and distribution is
affected (Kirkland 1977).Given the
apparent wide range of habitat conditions in which these small mammals occur, even a large scale disturbance of the northern hardwoods
forest would likely cause only temporary changes in species composition or population levels of small
The relationship between small
mammals and habitat structure
within the northern hardwoods forest remains poorly understood.
However, the data presented here, as
well as comparisons at different
scales (Morris 1984,1987),suggest
that microhabitat features play only a
minor role in the distribution of
small mammals within the forest. A
more important determinant of small
mammal distribution may be population size and the factors that affect it,
such as food, weather, and predators. Consequently, models for predicting the distribution of small
mammals within the northern hardwoods forest will likely remain unsuccessful until factors that affect
population size are included. The
temporal and spatial scales at which
these factors influence distribution
must also be addressed.
I thank C. D. Warren, J. M. Prince,
and S. L. Crane for assistance with
fieldwork. Staff of the U.S. Department of Agriculture, Forest Service
(USE) facilitated work in the White
Mountain National Forest. W. A. Patterson 111, R. M. DeGraaf, S. L. Garman, S. W. Seagle, M. W. Sayre, D. I?
Snyder, and an anonymous reviewer
provided helpful comments on earlier drafts of the manuscript. This research was supported by grants from
the USFS and the McIntire-Stennis
Cooperative Forestry Research Program (Grant No. MS-47).
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American Midland Naturalist