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Forest Ecology and Management 208 (2005) 153–175
www.elsevier.com/locate/foreco

Small mammals as bioindicators of sustainable
boreal forest management
Jennie Pearce*, Lisa Venier
Great Lakes Forestry Centre, Canadian Forest Service, 1219 Queen St East, Sault Ste Marie, Ont., Canada P6A 2E5
Received 17 September 2004; received in revised form 26 November 2004; accepted 26 November 2004

Abstract
Small mammals such as mice and voles have potential as indicators of sustainable forest management. They have an
important functional role in forests, they are economically important as prey for furbearer populations, and they respond to
disturbance in a characteristic manner. In Ontario, Canada, several small mammal species have been suggested as bioindicators.
However, strong year-to-year variation in population levels independent of forest disturbance means that very long time frames
would be required to detect trends. Models of habitat supply have been suggested as a method of monitoring small mammals. We
explore the feasibility of monitoring structural measurements and habitat supply for small mammal species using an area near
White River, Ontario, Canada, as a case study. Small mammals were surveyed in the region for 3 years, and associations with
mapped and stand level habitat attributes examined. Thirteen species were recorded, but only five species were recorded in
sufficient numbers for habitat associations to be examined. The deer mouse and red-backed vole were recorded from all mature
forest habitats, although both were more prevalent in mixedwood stands. Red-backed vole abundance was linearly related to
stand age and the volume of downed logs. Deer mice were most abundant in recently clearcut stands, with abundance declining
sharply in 5–15-year-old stands. They were also abundant in mature forest, where they were significantly associated with
downed wood volume. Vegetation complexity was also significant for both species. Habitat supply maps for both species could
be readily developed, and structural attributes modified by forest practices were important. However, strong year-to-year
variation in the abundance of both species in mature forest prevented carrying capacities from being reliably assigned to habitat
supply maps. Thus, while relative changes in the availability of high, medium and low quality habitat are identifiable, expected
changes in minimum population size cannot be inferred. The effect of cumulative disturbances on the quality of available habitat
is also unknown. Without this information, change in habitat supply cannot be used to assess the sustainability of forest
management actions. We suggest that dynamic landscape meta-population (DLMP) models may provide one solution, and
require further exploration as a sustainability assessment tool.
# 2004 Elsevier B.V. All rights reserved.
Keywords: Biological indicators; Habitat modeling; Habitat supply models; Population modeling; Trend; Voles; Mice; Shrews; Mammalia

* Corresponding author. Tel.: +1 705 541 5603; fax: +1 705 541 5700.
E-mail address: jpearce@nrcan.gc.ca (J. Pearce).
0378-1127/$ – see front matter # 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2004.11.024

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J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

1. Introduction
Sustainable forest management is a widely held
international goal and in many cases a legislated
mandate (Mulder et al., 1999; Montreal Process, 2000;
UNCSD, 2001; Statutes of Ontario, 2001; Commonwealth of Australia, 2001). Reliable, practical and
affordable means of assessing the sustainability of
forest management remains elusive. Monitoring of
biological indicators is an important element (Montreal Process, 2000), but sufficiently powerful monitoring strategies are expensive and monitoring alone
may not provide answers in time to avoid irreversible
environmental or ecological damage (Green and
Hirons, 1991; Mulder et al., 1999; Schalk et al.,
2002; Venier and Pearce, 2004).
Monitoring has generally meant surveying populations over time for trend assessment. However, trend
monitoring faces a number of drawbacks. The long
time frames required to detect trends means that the
results are difficult to incorporate effectively into an
adaptive management program (Green and Hirons,
1991; Schalk et al., 2002). Population trends also do
not identify the cause of change, and so cannot inform
management or assist in the identification of management options (Ralph et al., 1995). Even given long
time frames, monitoring has often been unable to
establish that real and important changes are occurring
in populations (Morrison, 1986; Temple and Wiens,
1989; Ralph et al., 1995; Venier and Pearce, 2004).
Monitoring of bioindicators has been adopted in
Ontario, Canada, as one component of a wildlife
assessment program to assess sustainable forest
management (McLaren et al., 1998b). Small mammals, defined here as mice, voles, lemmings and
shrews, have potential as bioindicators of sustainable
forest management. They have an important functional role in forests, as they disseminate seeds, spores
and propagules of vascular plants, bryophytes, fungi,
and lichens, they mix the soil, decompose organic
matter and litter, they regulate invertebrate populations and they provide prey for terrestrial and avian
predators (Carey and Harrington, 2001). They also
have an important economic function in forests as
important prey items for furbearing animals, such as
martin (Martes americana) (Thompson, 1988). Small
mammals are dependent on forest elements such as
snags, fallen trees, canopy gaps, understory plant

structure, decayed wood, litter and humus, that may be
altered by forest management (Bowman et al., 2000;
Carey and Harrington, 2001).
Wildlife species or groups, such as small mammal
species, have the potential to tell us about the
functional effects of forest change. While landscape
condition is much cheaper and easier to measure, a
change in the structure or condition of the forest is not
important unless that change affects the functioning of
the forest system. Indicator species need to provide
information on ecosystem change under forest
management that is in excess of the changes
experienced under a regime of natural succession.
Species or species groups that have this role are called
ecological indicators. Many small mammal species
have the potential to function as ecological indicators.
Seven small mammal species have been suggested
as indicators of forest management in Ontario and are
being monitored for trend as part of the Ontario
Ministry of Natural Resources (OMNR) Wildlife
Assessment Program (Table 1; McLaren et al.,
1998a,b). However, a characteristic of many small
mammal species is strong year-to-year population
fluctuations, many of which appear to be cyclic (de
Vos, 1957; Terman, 1966; Reich, 1981; Henttonen
et al., 1985; George et al., 1986; Getz, 1989). Large
spatial and temporal variability in population counts
reduce the feasibility of trend monitoring, as larger
sample sizes and longer time frames are required to
detect declines under these circumstances. Trend
monitoring is therefore not expected to be feasible for
sustainability assessment for most small mammal
species in Ontario. Alternative indices of sustainability such as vegetation structure have been
proposed (Lindenmayer et al., 2000) and are used
in Ontario (OMNR, 1996). Structural indicators such
as amount of coarse woody debris or canopy cover are
appealing because they are easy to measure, although
not necessarily to map. However, the link between
species persistence and structural metrics is seldom
clear and structural indices may not be good indicators
of species persistence.
Recently there has been increasing emphasis on
exploring indicator responses through habitat supply
models (Mulder et al., 1999; Voigt et al., 2000;
Rieman et al., 2001). Predictions of the future
distribution of habitat provide an approximation of
the potential impact to a species under alternative

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175
Table 1
Small mammal species present within boreal Ontario, and the
habitats for which McLaren et al. (1998b) identified them as
potential indicators
Species

Boreal forest biome

Arctic shrew Sorex arcticus Kerr

Pole lowland conifer

Masked shrew Sorex cinereus Kerr

Young jackpine
Young lowland conifer
Young upland conifer
Pole upland conifer
Young deciduous

Southern bog lemming Synaptomys
cooperi Baird

Pole lowland conifer
Mature lowland conifer

Deermouse Peromyscus
maniculatus (Wagner)

Young deciduous

Heather vole Phenacomys
ungava Merriam

Pole jackpine
Mature jackpine
Mature lowland conifer

Red-backed vole Clethrionomys
gapperi (Vigors)

Mature mixedwood
Mature lowland conifer
Mature mixedwood

Rock vole Microtus
chrotorrhinus (Miler)

Young mixedwood
Young deciduous

management approaches (OMNR, 1996; Holloway
et al., 2004). Habitat supply models are more
appealing than simple vegetation structural measures
or trend measurements because they allow for
predictions of habitat availability into the future.
Here we explore the feasibility of monitoring
structural measurements and using habitat supply
models for small mammals as indicators of sustainable
forest management. We use a case study in the boreal
forests of Northern Ontario to propose indices for this
region, and explore the utility of this approach.

2. Method

155

management area, which has been actively managed
for timber production for approximately 35 years, and
the northeast corner of Pukaskwa National Park,
designated in 1975. The area is characterized by
undulating hills of low to moderate relief, with
elevations ranging between 300 and 600 m. The
terrain is dominated by bedrock, often with a thin layer
of glacial till less than 1 m thick; the till layer may
reach 5 m thick on the sides of bedrock hills. Sand/silt
soil mixtures are also common. The area receives
approximately 850 mm of annual precipitation, with
half of this value received in winter as snow. The mean
annual temperature is approximately 1 8C, ranging
from a mean of 10.8 8C in winter and 13.2 8C in
summer. There are approximately 167 days above
5 8C.
As of 1972, approximately 83% of the land portion
of the research area was covered with mature closedcanopy forest, of which 43% was conifer dominated,
and 33% was hardwood and 25% dominated by mixed
forest. The main tree species in the study area are jack
pine (Pinus banksiana Lamb.), black spruce (Picea
mariana Mill.), trembling aspen (Populus tremuloides
Michx.), balsam fir (Abies balsamifera (L.) Mill.), and
white birch (Betula papyrifera Marsh.), with lesser
amounts of white spruce (Picea glauca (Moench) A.
Voss), eastern white cedar (Thuja occidentalis L.), and
tamarack (Larix laricina Koch).
Clearcut harvesting activities within the White
River management area have concentrated on harvesting mature jack pine or jack pine mixedwood stands.
As of 1998, approximately 21% of the land portion of
the research area had been harvested and replanted
principally to jack pine (occasionally to black spruce,
or natural regeneration). Within clearcut stands,
residual forest may have been left in narrow strips
along waterways and ridgelines, and within scattered,
small patches consisting of several trees. The
remaining mature, unharvested stands are of a similar
composition to that recorded in 1975, although the
relative proportion of conifer-dominated mature
stands has been reduced overall.

2.1. Study area
2.2. Study design
The research area is located at White River in north
central Ontario (858470 N, 488310 W, Fig. 1), and
encompasses approximately 187,800 ha. This area
includes approximately 23% of the White River

Thirty-seven plots were established in mature
forest, 17 of these within Pukaskwa National Park
and 19 of these within the White River management

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J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

Fig. 1. Location of the study area.

area. Mature forest was defined as stands greater than
50 years of age in which overstory trees had reached
their full development potential and were sexually
mature. Forested stands were randomly selected to be
large enough to contain a plot, allowing a 100 m buffer
from a clearcut edge around the plot, be separated
from another plot by at least 500 m and to be
accessible from a road. Jack pine dominated stands
were selected preferentially. However, insufficient
jack pine-dominated stands meeting these criteria
were available in the White River management area,
and so mixedwood stands were also selected (Fig. 2).
All available forested stands meeting these criteria
were sampled. These stands were representative of
forest within the south-central and south-western
boreal area of Ontario.
Forty-seven plots were established in regenerating
forest in the White River management area, 10 plots
each in 0–5, 5–10 and 10–15-year-old regeneration, 9
plots in 15–20-year-old regeneration and 8 plots in 20–
25-year-old regeneration. Regeneration stands were
randomly selected to be at least 500 m apart, allowing
a 100 m buffer around each plot within the stand type,

and to be accessible from a road. All available stands
that met these criteria within the 15–20 and 20–25year-old age classes were sampled (Fig. 2).
2.3. Mammal sampling
Sherman live traps (dimensions 7.5 cm 7.5 cm
30 cm) were established along a 90 m transect
consisting of 10 stations located 10 m apart. Transects
were located along the longest axis of the stand. Each
station contained two live traps (i.e. 20 traps/line in
total), with traps placed upon the ground adjacent to
suitable cover (e.g. stump or fallen log) within 1 m of
the station. A 0.032 gauge aluminum cover was placed
over exposed traps to limit temperature increases on
warm days, and shelter traps from precipitation. Each
trap was baited with a mixture of peanut butter, rolled
oats and sunflower seeds and a 1 cm3 slice of potato
provided as a source of water. A fist-sized wad of
cotton quilt batting was added to each trap to provide
nesting material. This protocol was chosen to conform
to that used by the OMNR Wildlife Assessment
Program (Sugar et al., 2003).

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

157

Fig. 2. Location of survey plots within the study area.

Small mammal trapping took place between July
and September 2001–2003. Each plot was surveyed
for three consecutive nights, twice a year. In 2003,
small mammals were only sampled once at regeneration stands, due to a lack of resources. Small mammals
were identified to species. To identify recaptures, the
fur on the dorsal side of each animal was clipped
before release. At the end of each trapping session,
traps were cleaned with a 10% solution of chlorine
bleach to prevent the spread of disease.
Small mammals were also captured incidentally
within small pitfall traps established to sample
ground-active invertebrates. The results are included
here, as pitfall traps often sample the shrew population
better than live traps (Handley and Kalko, 1993;
Kirkland and Sheppard, 1994; McCay et al., 1998).
Nine pitfall traps were established at each plot along a
single transect, with traps located 20 m apart. The
pitfall transect was parallel to the small mammal livetrap transect and separated from it by 20–30 m. Each
pitfall trap consisted of a 1 L clear plastic cup
(diameter 10.5 cm) inserted within a 20 cm section of
PVC pipe so that the top of the trap was flush with the
soil surface. Each trap was covered by a 20 cm
diameter plastic dinner plate suspended approximately
1 cm above the trap, to prevent rainwater entry. Traps
were filled to a depth of 2.5 cm with a 50:50 mixture of
propylene glycol and water to prevent invertebrate

escape and to preserve the invertebrate sample. Traps
were emptied every two weeks from 6 June to 28
August 2001 and 7 May to 9 July 2002. Small
mammals were identified to species.
The number of small mammals captured over the
study period in both pitfall traps and live traps was
standardized to number of mammals per 60 trap days.
This standardization accounted for unequal sampling
effort among plots due to trap disturbance.
2.4. Habitat and environmental assessment
In July–August 2003, at each forested and
regeneration plot, the structure and dominant floristic
composition of the stand was assessed at five stations:
one station at the center of the stand, and one station
50 m to the north, south, east and west of the center
station. At each station, tree density and overstory
composition were assessed. Tree density was recorded
by counting the number of trees present within a 5 m
radius of each station (with a diameter at breast height
(DBH) greater than 10 cm in mature forest stands); the
average of the five stations was taken to describe tree
density for the plot. In regenerating stands, the
dominant tree species were counted, irrespective of
tree age. Understory composition was described by
recording the dominant (by cover) three species at
each station. To describe overstory composition, the

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J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

species of the five closest trees to each station was
determined, and the proportion of the 25 selected trees
representing each dominant species determined. Stand
age and height was determined by measuring three
trees of each dominant overstory species (each
representing greater than 20% canopy cover of the
stand). To measure tree age a core was taken at breast
height from each tree and the number of rings counted.
Each ring was assumed to represent 1 year of growth.
Tree height was measured using a clinometer. Stand
age was determined as the age of the oldest tree, and
stand height as the average of all tree height
measurements.
The dominant vegetation of the stand was
determined within the 100 m 100 m area bounded
by the four outer vegetation stations using the
Northwest ecosite classification (Racey et al.,
1996). Ecosites represent mapping units within a
region and are described in terms of abiotic (soil
depth, texture, moisture regime, hydrology and
nutrient regime) and biotic (plant community structure
and composition) variables. There are 28 forested
ecosites in the Northwest ecosite classification. The
study area falls just to the west of the Northwest
region, and closely resembles the vegetation within the
Northwest region. However, because the study area
falls within the Boreal East region, the Boreal East
classification is used for forest management purposes
there. There are 21 ecosites in the Boreal East
classification. We used stand data to also classify plots
according to the Boreal East ecosite classification
(Taylor et al., 2000).
Coarse woody debris was measured in each stand
using three 50 m transects that formed the sides of an
equilateral triangle, with one side being centered on
the small mammal transect. The diameter and decay
stage of all downed logs of at least 10 cm diameter was
recorded at the point that they intersected each
transect. Decay stage was measured on a three point
scale: (1) >75% wood still hard and most bark intact,
(2) 25–75% of wood soft, shape still intact, (3) >75%
of wood soft, shape lost. The volume of downed
wood per hectare was calculated using Van Wagner
(1968).
At each individual live-trap and pitfall trap, the
dominant cover type at ground level (0–10 cm), depth
of litter (mm) and amount of vegetative cover was
determined. Dominant ground cover categories were

bare soil, rock, grass, leaf litter, moss, fine woody
debris, and herbs. The percentage composition of the
ground cover per plot was determined from the
number of traps where each category was dominant.
Percentage cover of vegetation was visually estimated
in seven height categories (0–0.1, 0.1–0.5, 0.5–2.5,
2.5–5, 5–10, 10–20, >20 m) within a 1 m radius of
each trap. Cover was recorded using the 5-point Braun
Blanquet scale (0: 0%, 1: 1–15%, 2: 16–40%, 3: 41–
65%, 4: 66–90%, 5: >90%, Kent and Coker, 1992).
Broad environmental and vegetation information
was also available for each plot from climate and
terrain maps of Ontario (100 m grid cell resolution,
McKenney, 2003). Elevation (m), aspect (8), mean
annual rainfall and wetness index (Moore et al., 1991)
were selected as being potential predictor variables. In
addition, broad vegetation information was available
in the form of Forest Resource Inventory (FRI) maps.
These maps are derived from the interpretation of
aerial photographs flown in the White River management area in 1998 and Pukaskwa Park in 1994. FRI
maps delineate homogenous forest stands and provide
a brief description of the relative composition of the
overstory, stand age, height and stocking rate, as well
as an assessment of site quality. FRI maps were used to
classify stands into standardised forest units according
to Holloway et al. (2004). Standardised forest units
(SFU’s) are aggregates of forest stands which
normally have similar species composition, develop
in a similar manner and are managed under the same
silvicultural system. There are 16 SFU’s in the Boreal
East forest, and they are generally defined using
mapped attributes such as FRI.
2.5. Data analysis
The distribution of small mammals within the study
area was examined at two scales. First, the association
between the presence and abundance of mammals and
mapped forest and climatic attributes was considered.
This level of analysis provides a broad description of
distribution. Secondly, the association between stand
level forest attributes and mammals was examined, to
further refine our understanding of small mammal
distribution.
Floristic analysis: Using the relative Sorenson
dissimilarity metric (McCune and Mefford, 1999)
and isotonic non-metric multidimensional scaling

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

(Venables and Ripley, 2002), we summarised the
overstory and understory stand floristic composition
into three ordination axes using PRIMER (Clarke and
Gorley, 2001). We examined small mammal association with vegetation floristics by overlaying mammal
presence or abundance onto these ordination axes.
Using the mature forest stands only, we examined the
relationship between mammals and three different
vegetation classification systems, the Northwest
ecosite classification (Racey et al., 1996), the Boreal
East ecosite classification (Taylor et al., 2000), and the
Boreal East standardised forest unit (SFU) classification (Holloway et al., 2004), by plotting the mean
abundance of mammals within each ecosite or SFU
class. Stands were assigned to the Boreal East ecosite
classification using attributes measured at each plot
and to the SFU classification using FRI maps. Results
for abundant species were compared to the OMNR
habitat suitability models based on Boreal East
ecosites and SFU’s provided within Holloway et al.
(2004). These models are described by an index of
suitability on a 4-point scale: 0 = not used (the species
rarely uses these habitats), 1 = used (the species
commonly uses these habitats but either density is low,
survival is low, or productivity is low relative to other
habitats), 2 = preferred (species always found in these
habitats with generally high density, high survival or
high productivity relative to other habitats), and
3 = optimal (species always found in these habitats
and exhibits exceptionally high density, survival or
productivity relative to other habitats).
Structural analysis: We initially explored the
relationship between small mammal relative abundance and each habitat variable using generalized
additive models (Hastie and Tibshirani, 1990) with 4
degrees of freedom to determine the shape of the
response function. Using polynomial terms where
indicated, we then used generalized linear Poisson and
logistic models (McCullagh and Nelder, 1989) to
describe the full model. Using stepAIC in S-Plus
(Venables and Ripley, 2002), we selected the most
parsimonious set of predictors from this larger set. The
out-of sample classification accuracy was assessed
using a leave-one-out jack-knife procedure to develop
a validation data set (Hastie et al., 2001); the
predictive performance of abundance models was
evaluated using spearman rank correlation and
presence-absence models using the area under the

159

ROC curve (Pearce and Ferrier, 2000). S-Plus was
used for all these analyses.

3. Results
3.1. Small mammal captures
Sixteen species were recorded using live traps for a
total of 5323 small mammals captured over 25,580
live-trap nights (Table 2). An additional two species
were recorded within pitfall traps, which captured
2517 mammals of 13 species over 47,227 pitfall-trap
nights (Table 3). Other species captured, but not
targeted by the trapping methods are listed in
Appendix A.
The red-backed vole and deer mouse were the most
abundant species captured in live-traps (44% and 39%
of the sample, respectively) and the red-backed vole
and masked shrew were the most abundant species
captured in pitfall traps (42% and 33% of the sample,
respectively). Using only the 2001 data for which livetrapping and pitfall trapping were conducted concurrently, significantly more red-backed voles (paired
t-test t = 8.26, n = 80, P < 0.01) and deer mice (paired
t-test t = 11.29, n = 80, P < 0.01), and significantly
fewer shrews (paired t-test t = 9.63, n = 80,
P < 0.01) were captured in live traps than pitfall
traps per trap day. Only the captures for the red-backed
vole in pitfall and live traps were significantly
correlated (red-backed vole R2 = 0.317, P < 0.01,
deer mouse R2 = 0.317, P = 0.68, shrews R2 = 0.135,
P = 0.23, n = 80).
Red-backed voles were recorded at all mature
forest plots and all except two regenerating plots, with
capture rates at mature forest plots varying significantly among years (F2,68 = 42.07, P < 0.001), and
survey plots (F35,68 = 2.30, P = 0.002). Red-backed
voles were more abundant in 2002. Coefficients of
variation for red-backed voles among years and
among replicates at mature forest plots were 0.67 and
0.45, respectively. At regenerating stands, the number
of red-backed voles varied significantly among years
(F2,138 = 21.07, P < 0.001), with a mean coefficient of
variation among years of 1.04.
Deer mice were recorded at all mature forest plots
and all but three of the regenerating plots, although not
all plots were occupied every year. At mature forest

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J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

Table 2
Small mammal captures in live traps from 2001 to 2003 by forest age class, standardized to number of animals per 2000 trap nights
Mature forest
Pukaskwa
Deermouse Peromyscus
maniculatus (Wagner)
Heather vole Phenacomys
ungava Merriam
Masked shrew Sorex
cinereus Kerr
Meadow jumping mouse
Zapus hudsonius (Zimmerman)
Meadow vole Microtus
pennsylvanicus (Ord)
Northern short-tailed shrew
Blarina brevicauda (Say)
Red-backed vole Clethrionomys
gapperi (Vigors)
Rock vole Microtus
chrotorrhinus (Miller)
Southern bog lemming
Synaptomys cooperi Baird
Unidentifiable shrew
Water shrew Sorex palustris
Richardson
Woodland jumping mouse
Napaeozapus insignis (Miller)

Forest regeneration

Total

White
River

0–5
years

5–10
years

10–15
years

15–20
years

20–25
years

53.29

185.91

326.4

242.8

102.8

90.91

51.36

0.34

2.10

0.69

1.36

2.84

1.68

0

9.01

2.73

6.01

0

0

0.71

2.53

0

11.97

0

0.60

0.69

0

0

0

1.98

3.27

2.05

5.71

20.66

10.2

1.68

3.95

62.69

0.68

0.60

0

0.99

2.27

195.4

369.73

0

37.24

5.47

0

0

0

79.56

87.23

206.2

1.38

1.36

0.71

0

0

40.69

3.00

0.69

0.68

2.13

6.73

6.91

25.61

6.15
0

7.51
0.30

0
0

1.36
0

8.51
0

2.53
0

1.98
0

28.03
0.3

1.03

2.10

0

0

0.71

0

1.98

5.81

plots, capture rates varied significantly among years
(F2,68 = 11.47, P < 0.001) and among survey plots
(F35,68 = 4.02, P < 0.001), with a temporal coefficient
of variation of 0.87 and spatial coefficient of variation
of 0.71. However, at regenerating plots deer mouse
abundance was consistent among years (F2,138 = 1.38,
P = 0.26), with a mean coefficient of variation of
0.75.
The meadow vole was only recorded in live-traps in
2001–2002, most likely because of the single trapping
period conducted in regenerating plots late in 2003.
Meadow voles were consistently recorded at recently
harvested plots 0–5 years old, although single
individuals were recorded at older regeneration stands
and mature forest. Their presence and abundance in
plots was not consistent from year to year.
The rock vole was recorded consistently at seven
mature forest plots in the White River forest, and
infrequently at another six plots. Generally, only one
to three individuals were recorded at plots each year.
However, in 2003 up to 10 rock voles were recorded at

101.2

18.44

156

1054

1195

a given plot. Heather voles were also recorded in the
study area, although we had substantial difficulty
distinguishing live specimens from rock voles. Length
of tail was the primary distinguishing feature (Kurta,
1995); skull measurements of deceased individuals are
required to confirm identifications. Single individuals
were recorded from seven plots over the 3 years of the
study.
Masked shrews were recorded from 97% of mature
forest plots, 100% of 0–10-year-old regeneration plots
and 67% of 10–25-year-old regeneration plots using
pitfall traps. Pygmy shrews were recorded from 50%
of forest plots, 85% of 1–10-year-old plots and 44% of
10–25-year-old plots. We were not able to examine
variation in shrew abundance among years due to the
shorter pitfall-sampling time frame in 2002. Other
shrews recorded included the smoky shrew, recorded
from seven forest plots, and six regeneration plots, the
northern short-tailed shrew, recorded from six forest
plots and five regeneration plots and the water shrew,
recorded from five plots.

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161

Table 3
Small mammal captures in pitfall traps from 2001 to 2002 by forest age class, standardized to number of animals per 2000 trap nights
Mature forest
Pukaskwa
Deermouse Peromyscus
maniculatus (Wagner)
Heather vole Phenacomys
ungava Merriam
Masked shrew Sorex cinereus Kerr
Meadow jumping mouse
Zapus hudsonius (Zimmerman)
Meadow vole Microtus
pennsylvanicus (Ord)
Northern short-tailed shrew
Blarina brevicauda (Say)
Pygmy shrew Sorex hoyi Baird
Red-backed vole Clethrionomys gapperi (Vigors)
Rock vole Microtus chrotorrhinus (Miller)
Smoky shrew Sorex fumeus GM Miller
Southern bog lemming Synaptomys
cooperi Baird
Unidentifiable shrew
Unidentifiable vole
Water shrew Sorex palustris Richardson

Forest regeneration

Total

White
River

0–5
years

5–10
years

10–15
years

15–20
years

20–25
years

0.22

2.23

5.69

1.79

2.31

0.00

0.00

12.24

0.00

0.45

1.09

1.02

1.39

0.00

0.00

3.95

33.11
0.00

44.06
0.00

33.68
0.22

31.93
0.00

29.09
0.46

19.80
0.00

32.80
0.00

224.48
0.68

0.66

3.13

9.40

2.81

7.39

1.41

1.03

25.82

0.22

1.19

0.66

0.26

1.85

0.00

2.05

6.22

4.17
18.20
0.00
1.10
2.41

4.02
101.82
4.47
0.60
9.97

5.90
21.43
0.87
0.66
5.25

4.85
20.44
0.51
0.51
11.75

5.54
36.47
0.92
0.46
12.93

7.07
52.33
0.00
1.41
9.90

10.25
33.83
0.00
0.00
12.30

41.81
284.53
6.78
4.73
64.52

0.00
0.66
0.22

0.60
0.74
0.00

0.66
0.88
0.00

1.02
0.77
0.00

0.46
0.92
0.92

0.00
0.00
1.41

0.00
0.00
0.00

2.74
3.97
2.56

3.2. Relationship with mapped climate and
vegetation variables
Since survey year was found to be a significant
factor for the red-backed vole and the deer mouse, all
subsequent analysis using species abundance was
based on the residuals from a regression of abundance
against survey year for these species. The mean
standardised abundance per plot was then calculated
using these residuals.
Sufficient red-backed voles and deer mice were
recorded using live-traps at both regenerating and
mature forest plots to explore their distribution in
relation to mapped predictors. Generalised additive
models suggested linear response shapes for all
variables, except stand age, elevation and stand height
with the deer mouse model. Red-backed voles were
more abundant in older, but shorter stands where white
birch, trembling aspen and black spruce composition
was high (Table 4). Deer mice occupied both very
young and older stands where white birch, trembling
aspen and black spruce composition was high. Tall,
forested stands were preferred (Table 5). These
models had good rank predictive performance

(red-backed vole r = 0.813, deer mouse r = 0.758,
n = 83, P < 0.01).
The OMNR habitat suitability index calculated
using standardised forest units was not strongly related
to either red-backed vole or deer mouse abundance,
although few ‘optimal’ standardised forest unit values
Table 4
Generalised linear model describing the standardised relative abundance of red-backed voles in the study area using mapped variables
Coefficient

S.E.

t-value

Intercept
Elevation
Wetness index
Aspect
FRI stand age
White birch (Bw)
Jack pine (Pj)
Trembling aspen (Pot)
Black spruce (Sb)
FRI stand height

6.353
0.005
0.013
0.002
0.069
0.642
0.047
0.168
0.623
0.041

3.345
0.008
0.044
0.003
0.010
0.179
0.095
0.120
0.204
0.044

1.899
0.635
0.293
0.650
7.008
3.584
0.495
1.401
3.057
0.938

Final model

3.98 + 0.07 FRI
age + 0.68
Bw + 0.18 Pot + 0.63
Sb 0.06 FRI height

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Table 5
Generalised linear model describing the standardised relative abundance of deermice in the study area, using mapped variables

Intercept
Elevation (elevation2)
Wetness index
Aspect
FRI stand age (age2)
White birch (Bw)
Jack pine (Pj)
Trembling aspen (Pot)
Black spruce (Sb)
FRI stand height (height2)
Final model

Coefficient

S.E.

t-value

61.337
0.283
(0.0003)
0.024
0.003
0.131
(0.0008)
0.518
0.123
0.354
0.656
0.070
(0.007)

35.034
0.180
(0.0002)
0.049
0.003
0.035
(0.0002)
0.217
0.125
0.128
0.248
0.227
(0.009)

1.750
1.576
(1.425)
0.484
0.821
3.74 3.387)
2.387
0.979
2.764
2.646
0.309
(0.691)

56.12 0.262 elevation + 0.0003
elevation2 0.139 age + 0.0009
age2 + 0.619 Bw + 0.353
Pot + 0.688 Sb 0.152
height + 0.010 height2

were sampled for the red-backed vole (Fig. 3). Jack
pine-dominated units (PJ1, PJ2) and spruce-fir mixedwood (MW2) recorded few red-backed voles and deer
mice, and jack pine mixedwood (MW1) recorded few
deer mice.
Masked shrews were recorded from pitfall traps
evenly across all standardised forest units, although
they may have been slightly less prevalent within jack
pine mixedwood (MW1) and black spruce lowland
(SB1). There appeared to be little agreement with the
OMNR habitat suitability index based on standardized
forest units (Fig. 3). In contrast, pygmy shrews and
short-tailed shrews were more abundant within stands
dominated by trembling aspen (PO1). This appeared
to agree well with the OMNR habitat suitability index
for the short-tailed shrew (Fig. 3). No index was
available for the pygmy shrew.
3.3. Relationships with stand level vegetation
Red-backed voles were recorded abundantly from
all mature forest habitats, less abundantly from those
dominated by jack pine (Figs. 4a and 5a). This agrees
well with the OMNR Habitat Suitability Index based
on Boreal East ecosite types (Fig. 5a). Red-backed
voles were also recorded from all regenerating forest

age classes, with abundance increasing linearly with
stand age (Fig. 6a, F1,81 = 106.72, P = 0.001). Redbacked voles were most abundant at older forest plots
with high volumes of downed wood of low decay, and
with high structural complexity in the shrub layer
(Table 6). This model had high relative predictive
ability (Spearman correlation r = 0.780, n = 83,
P < 0.01).
In mature forest, deer mice were most abundant
within the mixed deciduous habitats dominated by
white birch, and with a dense understory of beaked
hazel Corylus cornuta or mountain maple Acer
spicatum (Fig. 4b). They were recorded less abundantly in mixedwood forests dominated by trembling
aspen, and were sparse in habitats dominated by jack
pine. This agreed well with the OMNR Habitat
Suitability Model based on Boreal East ecosite types
(Fig. 7).
The relationship between the abundance of deer
mice and stand age was complex (Fig. 6b). In
regenerating forest, deer mouse abundance decreased
linearly with stand age (F1,44 = 49.28, P < 0.001).
Within mature forest, deer mouse abundance
increased from 50 to 100 years of age and then began
to gradually decline in older age classes (F1,34 = 6.29,
P = 0.02). After controlling for stand age, the volume
of downed wood was a strong predictor of deer mouse
abundance within mature forest plots (Fig. 7,
F1,33 = 7.65, P = 0.009) but not regenerating forest
plots (F1,43 = 2.59, P = 0.11). Deer mouse abundance
was highest in both very young and older forest stands
with a high volume of downed wood of lower decay
classes, low shrub cover below 50 cm high, high shrub
cover in the 50–250 cm range and, in forests, high
shrub cover in the 2.5–5 m range (Table 7). This model
had good relative predictive ability (Spearman
correlation r = 0.693, n = 83, P < 0.01).
Masked shrews were recorded by pitfall trap from
almost all mature forest plots, irrespective of ecosite
type (Fig. 7). Their capture in pitfall traps appeared
weakly related to the volume of coarse woody debris
and more strongly related to the amount of shrub cover
in the 50 cm–2.5 m strata (Table 8, Spearman
correlation, r = 0.369, n = 83, P < 0.01). There
appeared little relationship between the capture rate
of masked shrews and the OMNR habitat suitability
index (Fig. 7). In contrast, the OMNR habitat
suitability ranking for the short-tailed shrew explained

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163

Fig. 3. The mean number of (a) red-backed voles, (b) deermice (c) masked shrews, (d) short-tailed shrews and (e) pygmy shrews recorded within
each standardised forest unit. The numbers above each bar refer to the number of sites sampled within each forest unit, and the lines represent
standard errors. The shading of bars refers to the habitat suitability index of Holloway et al. (2004), based on the standardised forest units, where
white: not used, grey: used, and black: preferred. Standardised forest units are: BW1, birch-poplar; LC1, lowland conifer; MW1, jack pine
mixedwood; MW2, spruce-fir mixedwood; PJ1, jack pine; PJ2, pine-spruce; PO1, poplar; SB1, black spruce lowland; SF1, spruce-fir; SP1,
spruce-pine.

the capture rate of short-tailed shrews at mature forest
sites well (Fig. 7). These shrews were more abundant
in aspen dominated habitats, at sites in which a welldeveloped shrub layer existed (Table 9, ROC
area = 0.653).

Pygmy shrews were associated with mixedwood
ecosites (Fig. 7) with a high volume of coarse woody
debris and shrub cover in the ground layer (0–10 cm
strata). Shrub cover within the 50–250 cm strata was
low (Table 10, ROC area = 0.700).

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J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

Fig. 4. NMDS ordination of floristic data, with (a) the first column of graphs showing red-backed vole abundance at each site, with abundance
proportional to the diameter of the circle, and (b) the second column showing deermouse abundance at each site. Plant species are: Pj, jack pine;
Bw, white birch; Bf, balsam fir; Pot, trembling aspen; Sbund, black spruce seedlings and saplings; Vspp, Vaccinium species; Ccor, Corylus
cornuta; Aspi, Acer spicatum.

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

165

Fig. 5. The mean number of red-backed voles recorded within each (a) northwestern ecosite type standardized to remove the effect of yearly
variation. The mean volume of coarse woody debris within each ecosite is overlain on each graph. The shading of bars refers to the habitat
suitability index of Holloway et al. (2004), based on the Boreal East ecosite types, where grey: 1 and black: 2. The conversion between NW and
Boreal East ecosite types is shown in (b).

4. Discussion
4.1. Relative abundance of small mammals in the
study area
The small mammal fauna recorded here was typical
of mature boreal forest, and forest regenerating
following clearcutting. Red-backed voles and deer
mice were ubiquitous throughout the study area, and
showed strong year-to-year variation in abundance.
Red-backed voles are typically the most commonly
recorded species in boreal forest (Merritt, 1981;
Nagorsen and Petersen, 1981; Naylor and Bendell,
1983). They have been found to reach their greatest
abundance in older mature forest elsewhere, as they
did here (Ramirez and Hornocker, 1981; Martell,
1983a; Probst and Rakstad, 1987; Sullivan et al.,

2000). Following clearcutting, the red-backed vole has
been observed to decline immediately (Spires and
Bendell, 1983; Martell, 1984; Creˆ te et al., 1995) or
initially increase and then decline (Martell and
Radvanyi, 1977; Martell, 1983a,b; Clough, 1987;
Sekgororoane and Dilworth, 1995; Sullivan et al.,
1999; Moses and Boutin, 2001, however, see Kirkland,
1977, 1990; Kirkland et al., 1985; Parker, 1989 for a
different response). In the study area, red-backed voles
reached their greatest abundance in older forest, and
their lowest abundance in recent clearcuts (although
slightly more red-backed voles were recorded in 0–5year-old regeneration than 5–10-year-old regeneration). They increased steadily in abundance with stand
age. Following severe wildfire red-backed voles have
also been reported to decline (Spires and Bendell,
1983; Martell, 1984; Creˆ te et al., 1995; Sullivan and

166

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

Fig. 6. Relationship between standardized mammal abundance (corrected for annual differences) and stand age for (a) the red-backed vole, and
(b) the deermouse. The dotted lines represent 95% confidence intervals.

Boateng, 1996), suggesting that the lack of cover may
be the cause of decline, rather than specific changes
associated with clearcut harvesting.
The deer mouse is also abundant and widespread in
mature deciduous boreal forests (Martell and Radvanyi, 1977; Nagorsen and Petersen, 1981; Martell,

1983a), although they tend to reach their greatest
abundance in open habitats as was observed here
(Ramirez and Hornocker, 1981; Monthey and Soutiere, 1985). Deer mice have good dispersal ability.
This may explain their abundance in recently
disturbed habitats, where deer mice have been

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

167

Table 6
Generalised linear model describing the standardised relative abundance of red-backed voles in the study area using site variablesa
Coefficient

S.E.

t-value

Intercept
Age
CWD volume total
CWD volume medium decay
CWD volume high decay
Shrub 0–10 cm (shrub 0–10)2
Shrub 10–50 cm
Shrub 50–250 cm
Shrub 2.5–5 m
Shrub 5–10 m
Shrub 10–20 m

5.041
0.065
0.002
0.012
0.004
2.144 (0.633)
0.106
1.154
0.172
2.454
0.247

2.778
0.011
0.013
0.013
0.009
2.321 (0.505)
0.487
0.505
0.614
0.688
0.514

1.815
6.149
0.113
0.872
0.512
0.924 (1.254)
0.217
2.287
0.280
3.566
0.479

Final model

7.970 + 0.064 age 0.017 CWD high decay + 0.009 CWD total + 0.769 shrub
0–10 + 1.060 shrub 50–250 cm + 2.428 shrub 5–10 m

a

Null deviance 1448.921 (82 df), full model deviance 448.233 (71 df), final model deviance 460.064 (76 df).

Fig. 7. The mean number of (a) deermice, (b) masked shrews, (c) short-tailed shrews and (d) pygmy shrews recorded within each northwestern
ecosite type. The mean volume of coarse woody debris within each ecosite is overlain on each graph. The shading refers to the habitat suitability
index of Holloway et al. (2004) according to the Boreal East ecosite types (described in Fig. 5) where white: 0, grey: 1 and black: 2. No ratings are
available for the pygmy shrew.

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J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

Table 7
Generalised linear model describing the standardised relative abundance of deermice in the study area using site variablesa
Coefficient

S.E.

t-value

Intercept
Age (age2)
CWD volume medium decay
CWD volume high decay
CWD total volume
Shrub 0–10 cm
Shrub 10–50 cm
Shrub 50–250 cm (shrub 50–2502)
Shrub 2.5–5 m (shrub 2.5–5 m2)
Shrub 5–10 m
Shrub 10–20 m

1.265
0.113 (0.001)
0.011
0.038
0.033
0.165
1.356
3.835 ( 0.430)
6.947 (2.019)
0.909
0.659

2.176
0.032 (0.0002)
0.013
0.012
0.008
0.471
0.470
1.817 (0.412)
1.357 (0.381)
0.662
0.502

0.581
3.531 (2.975)
0.851
3.161
4.288
0.349
2.887
2.110 ( 1.043)
5.119 (2.299)
1.372
1.312

Final model

3.212 0.073 age + 0.0004 age2 0.029 CWD high decay + 0.023 CWD
volume 1.182 shrub 10–50 + 1.728 shrub 50–250 m 6.383 shrub
2.5–5 m + 1.941 shrub 2.5–5 m2

a

Null deviance 998.20 (82 df), full model deviance 375.483 (69 df), final model deviance 418.076 (74 df).

observed to increase rapidly following wildfire
(Krefting and Ahlgren, 1974; Creˆ te et al., 1995)
and clearcutting (Hooven and Black, 1976; Martell
and Radvanyi, 1977; Ramirez and Hornocker, 1981;
Martell, 1983b; Monthey and Soutiere, 1985; Probst
and Rakstad, 1987; Parker, 1989; Sullivan et al., 1999;
Moses and Boutin, 2001). Their abundance has been
observed to decline sharply as the regeneration
matures (de Bellefeuille et al., 2001). This response
may be different in mixedwood regeneration (Sullivan
and Boateng, 1996), although we did not study this
Table 8
Generalised linear model describing the relative abundance of
masked shrews in the study area using site variablesa
Coefficient

S.E.

t-value

Intercept
Age
CWD volume total
CWD volume medium decay
CWD volume high decay
Shrub 0–10 cm
Shrub 10–50 cm
Shrub 50–250 cm
Shrub 2.5–5 m
Shrub 5–10 m
Shrub 10–20 m

0.113
0.001
0.004
0.005
1.458 10-5
0.006
0.021
0.341
-0.004
0.175
0.113

0.421
0.003
0.002
0.004
0.004
0.140
0.142
0.147
0.179
0.200
0.149

0.268
0.360
2.013
1.442
0.004
0.040
0.145
2.325
0.024
0.872
0.759

Final model

0.128 + 0.002 CWD total + 0.364
shrub 50–250 cm

a
Null deviance 52.285 (82 df), full model deviance 38.717 (72
df), final model deviance 41.329 (80 df).

here. In the study area deer mice were most prevalent
in young forest regeneration, their abundance declining sharply within 0- to 25-year-old regeneration, and
then increasing again in mature forest with a high
deciduous component.
Meadow voles were recorded primarily from 0- to
15-year-old regeneration. It has been found elsewhere
that they increase on clearcut stands only after two to
three growing seasons (Clough, 1987), and that this
Table 9
Generalised linear model describing the probability of occurrence of
short-tailed shrews in the study area using site variablesa
Coefficient

S.E.

t-value

Intercept
Age
CWD volume total
CWD volume medium decay
CWD volume high decay
Shrub 0–10 cm (shrub 0–10)2
Shrub 10–50 cm
Shrub 50–250 cm
Shrub 2.5–5 m
Shrub 5–10 m
Shrub 10–20 m

3.201
0.007
0.002
0.003
0.003
1.394
1.114
0.293
1.377
2.125
0.307

2.091
0.012
0.009
0.016
0.015
0.759
0.716
0.228
0.797
1.174
0.657

1.531
0.596
0.167
0.162
0.242
1.836
1.556
0.526
1.727
1.810
0.468

Final model

1.833 1.470 shrub
0–10 + 0.892 shrub
10–50 + 1.365 shrub
250–500 cm 1.686 shrub
5–10 m

a
Null deviance 68.589 (82 df), full model deviance 55.130 (72
df), final model deviance 57.164 (78 df).

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175
Table 10
Generalised linear model describing the probability of occurrence of
pygmy shrews in the study area using site variablesa
Coefficient

S.E.

t-value

Intercept
Age
CWD volume total
CWD volume medium decay
CWD volume high decay
Shrub 0–10 cm (shrub 0–10)2
Shrub 10–50 cm
Shrub 50–250 cm
Shrub 2.5–5 m
Shrub 5–10 m
Shrub 10–20 m

0.824
0.004
0.020
0.003
0.007
0.914
0.353
1.263
0.112
0.497
0.210

1.590
0.010
0.009
0.013
0.012
0.601
0.464
0.548
0.533
0.606
0.440

0.518
0.354
2.247
0.243
0.574
1.519
0.760
2.304
0.209
0.820
0.477

Final model

0.680 + 0.018 CWD
total + 0.998 shrub
0–10 1.137 shrub 50–250

a
Null deviance 113.018 (82 df), full model deviance 90.760 (72
df), final model deviance 92.635 (79 df).

increase may be short-lived (Sullivan and Boateng,
1996). Numerous studies have found the meadow vole
to occur in primarily grassy conditions (Getz, 1961a;
Nagorsen and Petersen, 1981; Naylor and Bendell,
1983; Parker, 1989; Simon et al., 1998), and their
association with young regenerating stands may be an
association with this feature.
Other species recorded in low numbers, with the
exception of the jumping mice, were typical of boreal
forests. The heather vole and rock vole were recorded
in low numbers, but were difficult to distinguish in the
field, making identifications unreliable. Habitat
associations were not examined further for these
species because of this uncertainty. However, although
little is known about these species, habitat differences
have been noted in the literature. In Ontario, heather
voles have primarily been recorded in pure jack pine
forest (Foster, 1961; Naylor and Bendell, 1983; Naylor
et al., 1985), where a dense, relatively continuous
understory of ericaceous shrubs exists. Naylor et al.
(1985) found that cover of sheep laurel and blueberry
was the habitat factor most correlated with relative
abundance of this species in coniferous forests, with
caches of leaves being found at the burrow entrance.
Rock voles seem to prefer cool, moist rocky woodlands (Martell and Radvanyi, 1977; Burt and
Grossenheider, 1980), and the transition zone of open
rock and mature forest (Timm et al., 1977). Both
habitat types were present within the study area.

169

Five species of shrew were recorded here, with the
masked shrew the most abundantly recorded. Masked
shrews were associated with all mature and regenerating forest habitats, as has been found elsewhere
(Getz, 1961b; Naylor and Bendell, 1983; Pagels et al.,
1994, but see Probst and Rakstad, 1987 for a stronger
association with pole-age stands). The two other
dominant shrew species, the pygmy shrew and the
northern short-tailed shrew, were more often associated with deciduous forest. Stand age did not appear
important to any of these species. The pygmy shrew
and arctic shrew have been reported to be more often
associated with marshy areas (Long, 1972; Nagorsen
and Petersen, 1981; Probst and Rakstad, 1987), which
we did not examine here although the deciduous forest
conditions in the study area would be expected to be
cool and moist relative to the more open coniferous
stands. The short-tailed shrew has been associated
with mesic habitats (Getz, 1994), and more generally
hardwoods (Getz, 1961b; DeGraaf et al., 1991). All
species are associated with high herbaceous cover
(this study, Getz, 1961b; Miller and Getz, 1977;
Nagorsen and Petersen, 1981; Ford et al., 2002).
4.2. Monitoring for trend
Only the deer mouse, red-backed vole and masked
shrew were captured in sufficient numbers using live
trap and pitfall trap methods for population trends to
be realistically monitored. However, the amount of
temporal variability described by the temporal
coefficient of variation in capture rates combined
with annual population cycles (e.g. de Vos, 1957;
Terman, 1966; Reich, 1981; Henttonen et al., 1985;
George et al., 1986; Getz, 1989, 1994) suggests that
there are few statistical advantages to monitoring these
species for trend, as very long time frames would be
required to determine the existence of a decline with
confidence. This time frame would be even longer if
population fluctuations are cyclical, as has been
observed elsewhere (Terman, 1966). Changes in small
mammal abundance unrelated to forest management,
such as the positive influence of stand age, would also
need to be considered. Given these limitations, the cost
of conducting a broad-scale, long-term monitoring
study based on small mammals to assess the
sustainability of forest management cannot be justified
on financial grounds.

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J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

However, this does not preclude the use of small
mammals to inform forest management planning and
policy development within a research framework.
Targeted surveys in conjunction with modelling could
provide significant information to guide forest
management. Bekessey et al. (in preparation) provide
an example of how models may be used to explore the
impact of forest management activities on the redbacked vole.
4.3. Developing habitat supply models
The red-backed vole was associated with all
vegetation types, although abundance was higher in
mixed-deciduous stands. Because FRI maps frequently did not describe stand overstory composition
measured at plots, stand-level attributes were required
to adequately describe the distribution of voles. Stand
age was a good predictor of vole abundance, as was
understory cover. Elsewhere, red-backed voles have
been found associated with high understory vegetative
cover (Van Horne, 1982; Nordyke and Buskirk, 1991),
and with the abundance of trees >2 m high and
broadleaved shrubs <0.5m high (Simon et al., 1998).
Red-backed voles are associated with moderately
decayed coarse woody debris (Gunderson, 1959; Van
Horne, 1982; Ucitel et al., 2003; this study) although
this relationship is not consistent across studies (Hayes
and Cross, 1987; Bowman et al., 2000).
These habitat associations describe an association
by the red-backed vole with older moister forest
stands, which typically have tall trees, suppressing
understory vegetation to less than 0.5 m. As well, the
high canopy would reduce sunlight, providing a cooler
and moister environment, stimulating decay in
downed woody debris, and increasing the amount of
cover and fungi food resources. The importance of
moisture and forest cover may explain the less
dramatic reductions in red-backed vole numbers
observed following strip cutting, selective cutting,
clearcut with even distribution of slash, and clearcutting with protection of regeneration (Martell and
Radvanyi, 1977; Verme and Ozoga, 1981; Martell,
1983a; Potvin et al., 1999; Moses and Boutin, 2001),
and the almost complete removal of vole populations
following post-harvest treatments such as burning or
scarification (Ahlgren, 1966) and competing vegetation control (D’Anieri et al., 1987; Lautenschlager,

1993; Lautenschlager et al., 1997; Gagne´ et al., 1999;
cf. Runciman and Sullivan, 1996).
The greatest numbers of deer mice were found in
recently clearcut habitats, as has been observed
elsewhere (Ramirez and Hornocker, 1981; Monthey
and Soutiere, 1985). Further disturbance to the site
post-harvest that reduces the amount of coarse woody
debris or vegetative cover, such as by burning or
scarification, is expected to further enhance deer
mouse abundance (Sullivan et al., 1999). Disturbing
the soil layer during these practices may increase seed
availability, providing food (Ahlgren, 1966; Sullivan,
1979) and may reduce competition from other small
mammal species by reducing vegetative cover. Deer
mice populations either do not respond to vegetation
control practices (Sullivan and Boateng, 1996; Runciman and Sullivan, 1996; Lautenschlager et al., 1997;
Gagne´ et al., 1999), or increase following vegetation
control (Lautenschlager, 1993).
We found that deer mice were unaffected by coarse
woody debris levels in regenerating forest, but were
strongly associated with downed wood volume in
mature forest stands. Van Horne (1982) also noted the
importance of downed wood for deer mice. In this
study, downed wood volume was also strongly
correlated with ecosite type, and so it is difficult to
determine whether deer mice are responding to the
vegetation characteristics of the stand, coarse woody
debris or both. Certainly for shrew species, both coarse
woody debris volume and understorey vegetation
characteristics appear important here. Pagels et al.
(1994) and Getz (1961b) suggest that amount of cover
is more important than the type of cover, and that
coarse woody debris and vegetative cover may interact
in supplying suitable cover, particularly for the
masked shrew. This may explain the variable response
to coarse woody debris by many small mammal
species in the literature (e.g. Bowman et al., 2000).
The distribution of both deer mice and red-backed
voles were well described by the OMNR habitat
suitability models based on ecosite but not standardised forest units. The distribution of the short-tailed
shrew was described by both classifications, although
the model predicted this species to occur less often
within the jack pine (PJ1) standardised forest unit than
was observed. Ecosite classification was based on sitelevel measurements of vegetation, but standardised
forest units were derived from mapped vegetation

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

composition from FRI. The poor performance of
habitat suitability models based on standardised forest
units is assumed to be because of poor FRI mapping.
We found little agreement between the overstory
species composition recorded within the 1ha plot and
that recorded on FRI maps. FRI maps tended to be
optimistic regarding the prevalence of jack pine and
black spruce, with several stands described as being
pure jack pine on FRI maps, when they were
predominantly aspen with little to no jack pine
present. There are several explanations for this
mismatch. Firstly, the field plot may be at the
boundary of two FRI polygons, with disagreement
due to a mismatch between the mapped location and
the ground location, although plots were chosen to be
at least 100 m from the edges of stand polygons.
Secondly, FRI interpreters delineate relatively homogenous stands based on canopy composition and
structure, and describe the stand by its average
condition. Lack of agreement between site measurements and FRI may be due to heterogeneity within the
FRI stand not being captured by the average condition.
Thirdly, the interpretation may be poor. Any
combination of these may also be true. Without
extensive field work to verify the accuracy of the FRI
interpretation within polygons, the choice amongst
these explanations is unclear.
There are clear implications for the use of FRI in
sustainability assessment if the second and third
explanations for lack of agreement are correct.
Incorrect FRI interpretation or too much homogenisation of the landscape by the interpreter will result in
incorrect estimates of habitat supply using this
approach. If the errors are random, then this problem
may not be significant. However, because our results
suggest that FRI describes more coniferous forest
types on the landscape than actually exist, errors
appear biased in favour of coniferous stand types. It is
recommended that, if possible, habitat models be
based on ecosite mapping rather than FRI, although
ecosite maps are not currently available in most
locations.
4.4. Value as bioindicators
Strong habitat relationships were recorded for both
the red-backed vole and the deer mouse. The
development of models for these species to describe

171

relative habitat quality across a landscape is therefore
feasible. Research into improving our understanding
of the habitat of other species, in particular the heather
vole and rock vole, also appears warranted. The
specific association of these species with mature forest
types of high economic value (such as jack pine)
makes them particularly good indicators of the
functional consequences of landscape change. Habitat
supply for these species may undergo significant
fluctuations over time with forest management.
Targeted monitoring activities would be required to
sample these species in sufficient numbers to allow
statistical analysis.
The red-backed vole, deer mouse and meadow vole
have been shown to respond to forest management
activities at the local level in predictable and
consistent ways. Therefore, we may reliably predict
changes in relative habitat quality over time for these
species. Both clearcutting and fire shifts the dominance of the small mammal community from redbacked voles to deer mice in the short term, with
meadow voles becoming abundant for 5–10 years
post-disturbance. Gradually, red-backed voles regain
dominance as the vegetation matures, reaching their
highest levels in old mature forest. Post-harvest
treatments such as scarification, and vegetation
control, further enhance the habitat for deer mice.
Although relative changes in the amount of suitable
habitat across the landscape may be predicted with
some reliability for the red-backed vole and deer
mouse, it is not possible to reliably assign carrying
capacities to these habitat classes because of the large
temporal fluctuations in population size that do not
appear to be directly related to structural attributes of
habitat availability and quality. Forest planners may
have to infer changes in relative minimum population
size from estimates of change in the availability of
high, medium and low quality habitat. Spatial
arrangement of habitat may also influence population
size such as through dispersal dynamics, which would
require more complicated dynamic modelling.
The lack of information on the cumulative effects
of repeated forest harvesting activities such as
clearcutting, scarification, planting, herbicide application, and short rotation time, on small mammal
populations complicates our understanding of habitat
availability through time. Successive harvesting
rotations may have a greater impact on small mammal

172

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

populations than is evident by studies examining a
single harvesting event. Coarse woody debris amount,
type and quality are expected to decline under current
management practices over several harvesting rotations. Although the response to coarse woody debris
volume has been variable in small mammal studies,
small mammals such as the red-backed vole use coarse
woody debris for denning, hunting and movement
(Nordyke and Buskirk, 1991; Bowman et al., 2000).
Thompson et al. (2003) suggest that forest harvesting
results in changes to the vegetation structure and
composition of the forest understory which may also
have consequences for small mammals such as the
red-backed vole, heather vole and masked shrews
(Martell, 1981; Naylor and Bendell, 1983; Innes et al.,
1990). Thompson et al. (2003) hypothesised that redbacked vole density would decline by 20% within 80
years under all forest management scenarios examined
over a 200-year time frame, due to a reduction in oldgrowth forest area and associated loss of important
structural elements within regenerating forest. The
implications of such a decline are unknown, but may
be important when coupled with population fluctuations, and cumulative disturbance effects over time.
Given these limitations, criticisms of the trend
analysis approach to monitoring can also be applied to
the habitat modelling approach. Habitat models can
provide information on relative habitat availability
before and after forest management actions, but they
cannot provide an assessment of the sustainability of
forest management actions without an examination of
the spatial and temporal impacts of these actions on

the indicator population itself. One solution would be
to link dynamic landscape models describing habitat
availability through time with spatially explicit
models of population viability over time. The resulting
dynamic landscape meta-population (DLMP) model
would provide us with a more holistic examination of
management impacts on indicator species. Bekessey
et al. (in preparation) provide an example of this type
of model using the red-backed vole within the study
area as a case study.
Acknowledgements
Funding for this study was provided by the Living
Legacy Trust as part of a collaborative study by the
Canadian Forest Service, Parks Canada, the Ontario
Ministry of Natural Resources, Domtar, and the Upper
Lakes Environmental Research Network. We would
like to thank Dan Schuurman, Elisa Sturgeon, Pat
Siegwart, Tanya Hunter, Darlena Tousignant, Scott
Rocks, Vern Bastable, Andrew Davis, Keith Wade,
Cheryl Widdifield, Jamie Broad, Christine Kormos
and Daryl Edwards for assistance with the collection
of small mammal and vegetation data. Live trapping
was conducted under OMNR Animal Care permit
#01-49, #02-49 and #03-49. Pitfall trapping was
conducted under permit #01-66 and #02-66.
Appendix A
See Table A1.

Table A1
Incidental captures of non-target small mammal species during live trapping and pitfall trapping
Mature forest

Forest regeneration

Total

Pukaskwa

White
River

0–5
years

5–10
years

10–15
years

15–20
years

20–25
years

Live traps
Eastern chipmunk Tamius striatus (Linnaeus)
Least chipmunk Tamius minimus Bachman
Snowshoe hare Lepus americanus Erxleben
Red squirrel Tamiasciurus hudsonicus (Erxleben)
Short-tailed weasel Mustela erminea Linnaeus
Northern flying squirrel Glaucomys sabrinus (Shaw)
Unidentified weasel

7.17
0.00
0.00
1.71
1.03
1.03
0.34

18.02
1.20
0.00
2.10
0.00
0.60
0.00

16.53
11.02
0.69
0.69
4.13
0.00
0.69

2.04
32.64
0.00
2.04
1.36
0.00
0.00

15.6
9.93
6.38
0.71
4.26
0.00
0.71

50.51
10.10
10.10
3.37
3.37
0.00
1.68

12.84
20.74
4.94
6.91
0.99
1.98
0.00

122.7
85.63
22.11
17.53
15.13
3.60
3.42

Pitfall traps
Least chipmunk Tamius minimus Bachman

0.00

0.00

0.22

0.00

0.00

0.00

0.00

0.22

J. Pearce, L. Venier / Forest Ecology and Management 208 (2005) 153–175

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