Small Mammal Response to Vegetation and Spoil
Conditions on a Reclaimed Surface Mine in Eastern Kentucky
Jeffery L. Larkin, David S. Maehr, James J. Krupa, John J. Cox,
Karen Alexy, David E. Unger, and Christopher Barton
Southeastern Naturalist, Volume 7, Number 3 (2008): 401–412
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2008 SOUTHEASTERN NATURALIST 7(3):401–412
Small Mammal Response to Vegetation and Spoil
Conditions on a Reclaimed Surface Mine in Eastern Kentucky
Jeffery L. Larkin1,2,*, David S. Maehr2,**, James J. Krupa3, John J. Cox2,
Karen Alexy4, David E. Unger2, and Christopher Barton2
Abstract - Ecologically effective mine reclamation is characterized by the return of
pre-mining fl oral and faunal communities. Excessive soil compaction typically results
in delayed succession and low species diversity on reclaimed mine lands. We compared
small mammal abundance and diversity among three levels of compaction in reforestation
plots on an eastern Kentucky surface mine during 2004 and 2005. Compaction levels
included 1) no compaction (loose-dumped), 2) light compaction (strike-off), and 3)
high compaction (standard reclamation). Peromyscus leucopus (White-footed Mouse)
made up 98% (295 of 300) of all individuals captured. In 2004, loose-dumped plots
had more White-footed Mice (n = 108, mean = 36, SE = 0.58) than high-compaction
plots (n = 62, mean = 20.6, SE = 3.10). Strike-off plots had more White-footed Mice
(n = 59; mean = 19.6, SE = 0.66) than loose-dumped (n = 46, mean = 15.3, SE = 1.45) or
high-compaction (n = 20, mean = 6.6, SE = 2.19) plots in 2005. Canopy cover and large
rocks that created crevices appear to have been the factors that most infl uenced Whitefooted
Mouse abundance on our study sites. Low small-mammal species diversity
across all treatments was likely due to the presence of low quality habitat resulting from
a poorly developed ground layer and soil compared to that found in undisturbed forest.
Additionally, an insufficient amount of time since reclamation for small-mammal
colonization from surrounding forests and a relatively large matrix of non-forested reclaimed
mineland between our plots and potential source habitats may have also limited
small-mammal diversity. To promote biodiversity and provide better wildlife habitat,
we suggest that mine operators consider using reclamation methods that promote surface
and vegetation heterogeneity and connectivity to source habitats.
Surface mining and reclamation impacts both plant and animal populations
(Holl and Cairns 1994). Since 1984, more than 219,000 ha have been
surface mined for coal in Kentucky (Environmental Quality Commission
1997). In eastern Kentucky, botanically and structurally diverse deciduous
forests (Braun 1950) have been replaced by reclaimed exotic grasslands with
low plant diversity. Several factors such as low soil pH, increased surface
temperatures, drought conditions, exotic invasive plant species, lack of
nutrients, and soil compaction are thought to contribute to delayed succession
and low plant species diversity on reclaimed mine lands (Bendfeldt et
al. 2001, Bradshaw 1987). Compaction may be most responsible inasmuch
1Department of Biology, Indiana University of Pennsylvania, Indiana, PA 15705.
2University of Kentucky, Department of Forestry, Lexington, KY 40546. 3University
of Kentucky, Department of Biology, Lexington, KY 40506.4Kentucky Department
of Fish and Wildlife Resources, #1 Game Farm Road, Frankfort, KY 40601. *Corresponding
author - firstname.lastname@example.org. **Deceased.
402 Southeastern Naturalist Vol.7, No. 3
as it negatively impacts tree colonization and survival on reclaimed mines
(Graves et al. 2000, Torbert and Burger 2000), which in turn limits structural
complexity necessary to support a number of terrestrial fl ora and fauna.
Effective mine reclamation is characterized by the return of pre-mining
biotic communities with their attendant structure and function (Steele and
Grant 1982). However, reforestation is not a common post-mining land use in
the Appalachian Coalfields (Sweigard 1999). This situation is largely due to
federal regulations that promote high soil compaction in an attempt to return
mined landscapes to their original contour and reduce soil erosion. Reclamation
methods that reduce compaction can decrease tree seedling mortality and
improve growth (Graves 1999). Although results from compaction reduction
research suggest improved plant community development (Graves 1999), the
response to such practices by vertebrate communities is unknown.
Reclaimed surface mines provide different habitat and microsite
conditions than that which existed prior to mining. We predicted that the
distribution and abundance of small mammals on such sites in eastern Kentucky
would refl ect the simplified topography and vegetative characteristics
associated with these areas (Hansen and Warnock 1978, Hingtgen and Clark
1984, Sly 1976). Krupa and Haskins (1996) documented four small-mammal
species on reclaimed mines in eastern Kentucky; however, microhabitat relations
were not reported. Overall, there is a great paucity of work that has
examined small-mammal microhabitat relations on reclaimed surface mines
in the central Appalachians (Chamblin 2002, Chamblin et al. 2004). Moreover,
no published data exist about small-mammal response to mine reclamation
techniques intended to reduce soil compaction. Such studies are needed to
evaluate whether such techniques promote the recovery of biodiversity and to
make recommendations for their use or their improvement.
We analyzed data collected in reforestation plots on a reclaimed surface
mine in eastern Kentucky. Specifically, we examined small-mammal abundance
and diversity among three compaction regimes (no compaction, light
compaction, high compaction), and looked for relationships between microhabitat
characteristics and small-mammal abundance. We selected small
mammals as our focal taxa because: 1) much is known about their biology,
2) many species are effective colonizers of vacant habitats, 3) individuals can
be easily marked and monitored (Barrett and Peles 1999), and 4) our findings
can be compared to studies conducted in adjacent undisturbed forests.
Our study was conducted on experimental reforestation plots located on the
Star Fire Mine in Perry County, southeastern Kentucky. Elevations ranged from
250–410 m (Krupa and Lacki 2002). Mountain top removal methods were used
to extract coal, and subsequent reclamation have converted rugged, forested topography
into expansive (up to 5000 ha), level to gently sloping grasslands.
Herbaceous plant species commonly found on reclaimed surface mines primarily
are exotic to the area and include Kentucky-31 Festuca elatior L. (Tall
Fescue), Lespedeza cuneata G. Don (Chinese Lespedeza), L. striata Hook.
and Arn. (Japanese Clover), L. stipulacea Maxim. (Korean Clover), Lo2008
J.L. Larkin, D.S. Maehr, J.J. Krupa, J.J. Cox, K. Alexy, D. Unger, and C. Barton 403
lium multifl orum, Lamarck (Annual Ryegrass), Lolium perenne L. (Perennial
Ryegrass), Dactylis glomerata L (Orchardgrass), and Trifolium hybridium L.
(Alsike Clover) (Slaski and Fowler 1980). Woody plant species that tend to
colonize forest-mineland edge in this part of the Appalachians include Rubus
spp. (blackberry), Liriodendron tulipifera L. (Yellow-poplar), Robinia psuedoacacia
L. (Black Locust), and Rhus spp. (sumac).
Where surface mining is not a factor, mixed-mesophytic forests dominate
the region (Krupa and Lacki 2002). Depending on slope and aspect, this forest
type is composed of 55 native and six exotic tree species (Braun 1950,
Krupa and Lacki 2002). Lower slope and cove sites consist of Quercus spp
(oaks), Acer spp. (maples), Yellow-poplar, Fagus grandifolia Ehrh. (American
Beech), Tilia americana L. (American Basswood), Rhododendron
maximum L. (Rosebay Rhododendron), and Tsuga canadensis (L.) Carr.
(Eastern Hemlock). Forests occupying side slopes are dominated by multiple
species of oak and Carya spp. (hickory), whereas xeric ridgetops, southwestern
facing slopes, and areas with rocky shallow soils are dominated by Q.
prinus L. (Chestnut Oak) Q. coccinea Munchh. (Scarlet Oak), Pinus virginiana
Mill. (Virginia Pine), P. echinata Mill. (Short-leaf Pine), and P. rigida
Mill. (Pitch Pine) (Krupa and Lacki 2002, Leopold et al. 1998).
In adjacent forest habitat, Peromyscus leucopus Rafinesque (White-footed
Mouse) is the most abundant small mammal in early succession growth (Krupa
and Haskins 1996, Krupa and Lacki 2002). White-footed Mice comprised 47%
of the small mammals trapped in regenerating clear cuts three years post-harvest
(Krupa and Haskins 1996). Microtus pennsylvanicus Ord. (Meadow Vole)
and M. pinetorum LeConte (Pine Vole) were the next most common species,
representing 32% and 16% of the total mammals trapped, respectively (Krupa
and Haskins 1996).
We live-trapped small mammals in nine 1-ha plots (70 m x 155 m) that
were established in 1997 as part of a study on compaction effects on forest
regeneration (Thomas 1999). All nine plots were located on relatively fl at,
high elevation (≈300 m) mine land originally reclaimed to hay/pastureland
during the late 1980s. Treatment plot layout was constrained by mining activity
and regulations, and as a result, distance between plots varied between
70 m and 500 m. Our treatments included 1) standard (high compaction), 2)
strike-off (light compaction), and 3) loose-dump (no compaction). Highcompaction
plots were graded until smooth using a bulldozer to remove all
existing vegetation and then compacted to the industry standard (bulk density
= 1.73 g/cm3). High-compaction plots were level and exhibited no visible surface
variation. Lightly compacted (strike-off) plots were created by dumping
spoil into piles then leveling with one or two passes of a bulldozer. Strike-off
plots exhibited more surface variation than the high-compaction treatment.
Loose-dump plots exhibited the highest degree of surface variation (i.e., rocks
up to 2 m in diameter with shaded recesses). A standard mixture of Q. alba L.
(White Oak), Fraxinus americana L. (White Ash), Pinus strobus L. (Northern
White Pine), Q. rubra L. (Northern Red Oak), Juglans nigra Thunb. (Black
Walnut), Paulownia tomentosa Thunb. (Royal Paulownia), and yellow-poplar
seedlings was planted in each plot. Additionally, all plots were hydro-seeded
404 Southeastern Naturalist Vol.7, No. 3
to a standard mixture of low stature, non-aggressive grasses and legumes to
limit erosion (Thomas 1999). This mixture included Secale cereale L. (Annual
Rye), Perennial Rye, Orchardgrass, Lotus corniculatus L. (Birdsfoot Trefoil),
and Chinese Lespedeza.
We used Sherman traps (7 cm x 9 cm x 23 cm) to capture small mammals
during May 2004 and 2005. We trapped in May because small mammal abundance
and trapping success is the highest locally during the spring (Krupa
and Haskins 1996). We randomly placed trapping grids (50 m x 50 m; 0.25
ha) in each plot. Our traps were placed every 10 m, with a total of 36 traps
per grid. In 2004, we trapped in compacted and loose-dump plots. In 2005,
we were made aware of strike-off plots and incorporated this treatment during
the second field season.
Each trapping bout lasted 3-days. We trapped one replicate of each treatment
each night, and our trapping bouts were spaced 4 days apart. We baited
traps with oats; cotton batting was added for bedding. We set traps in the late
afternoon and checked them each morning starting at 0600 to ensure all animals
were processed before temperatures became too high for the captured
individuals. We determined species identity and applied a uniquely numbered
ear tag to each individual prior to on-site release. Our small-mammal
handling procedures were approved by University of Kentucky Institutional
Animal Care and Use Committee (IACUC) protocol 00695A2004.
We did not establish study plots in adjacent forests as we were primarily
interested in evaluating the effectiveness of each reclamation method relative
to each other rather than adjacent forests. From an experimental design
perspective, we considered our high-compaction plots to be homologous to
control plots because this treatment type is the standard method used for
surface mine reclamation throughout Appalachia. We measured habitat characteristics
within 20 randomly placed 1-m2 plots throughout each trapping
grid. Within each 1-m2 plot, we measured vegetation height, and estimated
% woody canopy cover, % grass, % forbs, and, % bare ground. In each trapping
grid, we also measured the number of woody stems <2 cm dbh, number
of woody stems >2 cm dbh, and number of rocks >20 cm across in 10 randomly
placed 5-m radius circular plots (Bonham 1989). We counted rocks if
they had the potential to provide cover for small mammals (i.e., bare rocks
fl ush to the soil surface were not counted). To reduce observer variability,
one researcher conducted all habitat measures and estimates.
We compared species diversity and abundance among treatments using
95% confidence intervals (Johnson 1999). Statistical similarity was
indicated if confidence intervals overlapped. We analyzed habitat variables
using general linear models (PROC GLM; SAS 2000). If the overall model
for a particular variable was significant, we preformed mean separation
using least-square means (LSMEANS; SAS 2000) to determine which
treatments were responsible for the differences. We arcsin square root
transformed all percentage data prior to analysis. We accepted significance
at an alpha of 0.05.
2008 J.L. Larkin, D.S. Maehr, J.J. Krupa, J.J. Cox, K. Alexy, D. Unger, and C. Barton 405
We used program R v. 4.2.1 to generate multiple regression models that indicated
habitat variables important for distinguishing between plots with high
and low White-footed Mouse abundance (R Development Core Team 2004).
We created a customized script file to test all possible subsets of our habitat
variables. We limited our models to incorporate up to two habitat variables to
prevent over-parameterized results due to small sample size (n = 9 plots). We
calculated Akaike’s Information Criterion values corrected for small sample
sizes (AICc), differences in AICc values (ΔAICc), likelihood values, and
Akaike weights (ω) for each variable combination. We averaged competing
models (<2 AICc units from the best model) that best predicted White-footed
Mouse abundances (Burnham and Anderson 2002). If there was a potential for
a quadratic relationship between a habitat variable and White-footed Mouse
abundance, we then performed a quadratic transformation on that variable (Zar
1996). Additionally, we standardized all data prior to generating multiple regression
models (Burnham and Anderson 2002).
In 2004, we trapped 648 trap-nights (324 trap-nights x 2 treatments)
and captured 170 different individuals but only of one species, the Whitefooted
Mouse. Loose-dump plots had more White-footed Mice (n = 108,
mean = 36, SE = 0.58) than compacted plots (n = 62, mean = 20.6, SE =
3.10; Table 1).
In 2005, we added the strike-off treatment and trapped 972 trap-nights
(324 x 3 treatments) and captured 130 individuals of 4 species that included
White-footed Mouse (n = 125), P. maniculatus Wagner (deer mouse; n = 2, Mus
musculus L. (House Mouse; n = 2), and Meadow Vole (n = 1). Species richness
was three (Meadow Vole, White-footed Mouse, and Deer Mouse), two
(White-footed Mouse and house mouse), and two (White-footed Mouse, and
Deer Mouse) for strike-off, compacted, and loose-dump plots, respectively.
White-footed Mice made up 98% (295 of 300) of all individuals captured during
both years combined. Because we captured so few individuals of other
species, we limited subsequent analyses to measures of White-footed Mouse
Table 1. White-footed mouse abundance in each of three spoil reclamation treatments during
the May 2004 and 2005 sampling periods on a reclaimed strip mine in eastern Kentucky.
Note: means with different superscripted letters within each year were not significantly different
(P > 0.05).
Replicate Compacted Dumped Compacted Dumped Strike-off
Grid 1a 18 37 5 15 19
Grid 1b 27 36 11 13 19
Grid 1c 17 35 4 18 21
Total 62 108 20 46 59
Mean 20.6A 36B 6.6A 15.3 B 19.6C
Std Error 3.10 0.58 2.19 1.45 0.66
95% CI 14.4–26.8 34.9–37.1 2.3–10.9 12.5–18.2 18.4–20.9
406 Southeastern Naturalist Vol.7, No. 3
abundance. Among the treatments that were sampled in both years of this study
(compacted and loose-dump) we captured fewer White-footed Mice in 2005 (n
= 66) than in 2004 (n = 170) (Table 1). Loose-dumped plots had more Whitefooted
Mice (n = 46, mean = 15.3, SE = 1.45) than compacted plots (n = 20,
mean = 6.6, SE = 2.19), and strike-off plots (n = 59, mean = 19.6, SE = 0.66) had
more White-footed Mice than both compacted and loose-dump plots (Table 1).
We found overall differences in % grass cover (F = 13.73, P < 0.001),
% forbs cover (F = 5.99, P < 0.001), % bare ground (F = 14.96, P < 0.0001),
% woody canopy cover (F = 4.06, P < 0.0002), % litter cover (F = 4.77,
P < 0.0001), number of woody stems >2 cm dbh (F = 11.23, P < 0.001),
and number of rocks >20 cm in diameter (F = 113.26, P < 0.001) among
treatments (Table 2). Bare ground was highest in loose-dump plots (mean =
0.76, SE = 0.04), followed by strike-off plots (mean = 0.32, SE = 0.04), and
lowest in compacted plots (mean = 0.17, SE = 0.04) (Table 2). Loose-dump
and strike-off plots had greater canopy cover (mean = 0.29 with SE = 0.03,
and mean = 0.24 with SE = 0.03, respectively) than compacted plots (mean
= 0.06, SE = 0.03) (Table 2). Strike-off plots contained more litter (mean =
0.85, SE = 0.04) than loose-dump plots (mean = 0.65, SE = 0.04) (Table 2).
Compacted and strike-off plots had more forb cover (mean = 0.52 with SE =
0.03, and mean = 0.52 with SE = 0.03, respectively) than loose-dump plots
(mean = 0.27, SE = 0.03) (Table 2). Grass cover was highest in compacted
plots (mean = 0.51, SE = 0.03), followed by strike-off plots (mean = 0.24, SE
= 0.03), and lowest in loose-dump plots (mean = 0.06, SE = 0.03) (Table 2).
Number of rocks >20 cm in diameter differed among all treatments, with
highest values in loose-dump plots (mean = 100.0, SE = 2.38), followed by
strike-off plots (mean = 26.2, SE = 2.38), and lowest in compacted plots
(mean = 3.7, SE = 2.38) (Table 2). Loose-dump and strike-off plots had more
woody stems >2 cm dbh (mean = 12.1 with SE = 0.87, and mean = 11.2 with
SE = 0.87, respectively) than compacted plots (mean = 2.6, SE = 0.87). No
differences were found among treatments for vegetation height (F = 0.91,
P = 0.51) or number of woody stems <2 cm dbh (F = 1.24, P < 0.29).
Table 2. Habitat characteristics in each of three spoil reclamation treatments on a reclaimed
strip mine in eastern Kentucky, 2004–2005. Note: Means in same column with different superscripted
letters were significantly different at alpha = 0.05.
% grass % forbs % bare % litter % canopy
Treatment mean (SE) mean (SE) mean (SE) mean (SE) mean (SE)
Compacted 0.51A (0.03) 0.52A (0.04) 0.17A (0.04) 0.77AB (0.04) 0.06A (0.03)
Strike-off 0.24B (0.03) 0.52A (0.04) 0.32B (0.04) 0.82A (0.04) 0.25B (0.03)
Dumped 0.06C (0.03) 0.28B (0.04) 0.76C (0.04) 0.65B (0.04) 0.29B (0.03)
# of woody stems # of Vegetation
(>2 cm dbh) (<2 cm dbh) large rocks height (m)
Treatment mean (SE) mean (SE) mean (SE) mean (SE)
Compacted 2.6A (0.9) 8.3A (3.1) 3.7A (2.4) 0.15A (0.02)
Strike-off 11.2B (0.9) 9.1A (3.1) 26.2B (2.4) 0.13A (0.02)
Dumped 12.1B (0.9) 8.6A (3.1) 100.0C (2.4) 0.12A (0.02)
2008 J.L. Larkin, D.S. Maehr, J.J. Krupa, J.J. Cox, K. Alexy, D. Unger, and C. Barton 407
We screened for correlation among habitat variables and eliminated % bare
ground, % herb, % grass, % forb, % water, and number of woody stems >2 cm
dbh; (r2 ≥ 0.6). The remaining habitat variables—% litter, % woody canopy,
vegetation height, number of woody stems <2 cm dbh, and number of rocks
>20 cm across—were analyzed with respect to White-footed Mouse abundance
via multiple regression. Our analysis of these data resulted in two competing
models that both included the variable % woody canopy (Table 3). After
model averaging, based on coefficient estimates for each variable, % woody
canopy (t-value: 2.40) accounted for the most variation in our data and gave our
models better predictive power when included (Table 3). The quadratic transformation
for number of large rocks was included in the 2nd competing model,
but this variable’s effect was indistinguishable from zero (t-value: 0.63).
White-footed Mice occur most often where a combination of vegetative
canopy, rocks, and course woody debris is present (Barry and Franq
1980)—conditions typically found in central Appalachian forest settings
(Hamilton and Whitaker 1979). Kirkland et al. (1996) suggested that the accumulation
of coarse woody debris facilitated White-footed Mouse recolonization
of oak forests in Pennsylvania five months after it burned, whereas
Krupa et al. (2005) attributed persistence of the species in recently burned
forests in Eastern Kentucky to the presence of sheltering emergent rock.
Typical mine reclamation compacts soils and thereby creates conditions
which not only inhibits woody plant establishment and growth, but leaves a
resultant substrate devoid of large surface rocks and course woody debris.
In contrast, the two other reclamation techniques used in this study create
uncompacted, structurally diverse surface conditions that appear to promote
White-footed Mouse abundance, a finding congruent with studies conducted
on surface mines elsewhere. Complex topography of mine spoils in Colorado
supported diverse vegetation and more small mammals than compacted
spoils (Steele and Grant 1982). Bramble and Sharp (1949) concluded that
Table 3. Multiple regression results showing only competing models (Δ AICc < 2) with associated
habitat variables that infl uenced White-footed Mouse abundance on a reclaimed strip mine
in eastern Kentucky, 2004–2005.
ΔAICcA ωB % canopy Rocks2
0 0.673 2.439 ± 0.065C -
1.442 0.327 5.309 ± 1.370 -2.257 ± 1.370
Model average estimatesD 3.377 ± 1.407 -0.738 ± 1.165
t-valuesE 2.40 -0.633
ADifference of the “best” model’s AICc (corrects for small sample size) value and competing
model’s AICc value.
BAICc weight, the probability that a model is indeed the “best” model.
CCoefficient and standard error.
DAverage coefficient and standard error for individual habitat variables.
EAssociated t-values for individual habitat variables based on a significance of 1.96.
408 Southeastern Naturalist Vol.7, No. 3
bare spoil on a Pennsylvania surface mine offered numerous crevices for
small mammals, and that such habitats were more heavily used than had been
Strike-off and loose-dumped plots had more White-footed Mice than standard
plots. This finding supports the hypothesis that structural heterogeneity
at the ground surface primarily infl uenced small-mammal relative abundance
(Steele and Grant 1982). Strike-off plots may have offered a more optimal
combination of vegetation and surface structure compared to loose-dump
plots, and this difference may have been the reason we observed slightly higher
White-footed Mouse abundance in strike-off plots compared to loose-dumped
plots. Alternatively, this finding may have been a result of a low treatment replication.
Although % woody canopy cover and number of large woody stems did
not differ between strike-off and loose-dump plots, strike-off plots had more
litter, grass, and forbs (Table 2). These three habitat features are found within
the 0–7.6 cm vegetation stratum which is thought to be the habitat zone that
primarily infl uences White-footed Mouse abundance (M’Closkey and Lajoie
1975). Further, habitats supporting dense mats of grass provided Peromyscus
spp. with cover for travel and feeding (Wirtz and Pearson 1960). Additionally,
our multiple regression models suggest that the presence of a woody canopy is
a good predictor of White-footed Mouse abundance on these mined sites.
Loose-dump and strike-off treatments had higher White-footed Mouse
abundance than the compacted treatment. However, these treatments all had
lower species diversity than in adjacent forests (Krupa and Lacki 2002). The
low species richness (n = 4) on our sites is a result consistent with the findings
from studies conducted on coal surface mines elsewhere in the United States
(Bramble and Sharp 1949, DeCapita and Bookout 1975, Hingtgen and Clark
1984). For example, the White-footed Mouse was the only species captured
on surface mines in Pennsylvania (Bramble and Sharp 1949). In Colorado, rodent
species richness and diversity also were lower on reclaimed mine spoils
than in surrounding natural habitats (Steele and Grant 1982). In two previous
small-mammal studies adjacent to our study site, at least half as many
small-mammal species were recorded on reclaimed mines versus forests and
low-elevation clearings (Krupa and Haskins 1996, Krupa and Lacki 2002).
Moreover, none of the species observed during these studies were unique to
reclaimed habitat (Krupa and Haskins 1996, Krupa and Lacki 2002). All of
the species observed in our study, with the exception of the Deer Mouse, were
found in grassy openings in a forest adjacent to our study sites (Krupa and
Lacki 2002). Yet species found in grassy openings in the adjacent forests such
as Synaptomys cooperi Baird (Southern Bog Lemming), Blarina brevicauda
Say (Northern Short-tailed Shrew), Pine Vole, and Reithrodontomys humulis
Audubon and Bachman (Eastern Harvest Mouse) were not captured in our
study. We suggest that their absence in loose-dump and strike-off plots was
infl uenced more by the distance from a source population (>800 m) rather than
a lack of suitable habitat (Gottfried 1982). For example, forest gaps created
by group selection timber harvest that were closer to existing oldfield habitats
exhibited increased small-mammal richness of early-successional species in
the Coastal Plain of South Carolina (Menzel et al. 2005). Within the central
2008 J.L. Larkin, D.S. Maehr, J.J. Krupa, J.J. Cox, K. Alexy, D. Unger, and C. Barton 409
Appalachians of West Virginia to the northeast of our study site, Francl et al.
(2004) emphasized the importance of the configuration and type of surrounding
habitats along with the small-mammal species pool for understanding
species colonization responses to disturbance.
We cannot, however, rule out the possibility that the low small-mammal
diversity and skewed relative abundance of captured individuals was the result
of trap bias as we used only Sherman traps. For example, in the central
and southern Appalachians, pitfalls were much more effective at capturing
extant shrew species compared to other methods (Ford et al. 1997), whereas
Moriarty (1982) found that White-footed Mice were especially susceptible
to being caught with snap traps. Accordingly, had our animal care and use
protocol allowed methods other than Sherman traps, it is possible we might
have observed a greater diversity of small-mammal species on our plots.
Small mammals are an important part of terrestrial ecosystems and drive a
variety of ecosystem processes (Brady and Weil 2002). Small mammals serve
as prey for a variety of mammalian, avian, and reptilian predators (Mindell
1978, Yearsley and Samuel 1980). As such, their return to post-mining landscapes
should be an important biodiversity consideration for reclamation
goals. Conversely, small mammals can modify plant community composition
and species distribution (Siege 1988) through foraging and burrowing in a
manner that has landscape-level implications (Hole 1981, Taylor 1935). For
example, Hingtgen and Clark (1984) suggested that small mammals infl uenced
vegetation community development on reclaimed mines. Their roles as seed
predators, herbivores, detritivores, and seed dispersers affect plant distribution
and succession (Chamblin 2002), with Bramble and Sharp (1949) observing
White-footed Mice seed predation causing failed Northern Red Oak establishment
on Pennsylvania surface mines. Though not a concern in our study plots
because seedlings were planted, a recent project to establish blight resistant
Castanea americana Rafinesque (American Chestnut) on uncompacted mine
spoil in eastern Kentucky failed due to seed predation by small mammals (C.
Barton, University of Kentucky, Lexington, KY, pers. comm.).
Our findings suggest that loose-dump and strike-off plots are the best for
White-footed Mouse abundance, as Graves (1999) found with increased tree
productivity. Low small-mammal diversity, regardless of reclamation treatment,
was likely due to the presence of low-quality habitat due to a poorly
developed ground layer and soil compared to that found in undisturbed forest.
Small-mammal diversity may have also been limited by 1) an insufficient
amount of time since reclamation for some small mammal species to have
colonized from surrounding forests, and 2) a relatively large matrix of nonforested
reclaimed mine land between research plots and source habitats. We
suggest that mine operators use reclamation methods that promote surface
and vegetation heterogeneity and connectivity to source habitats to promote
colonization and to meet the life requisites of a more diverse small-mammal
community (Menzel et al. 2005). Additionally, until forest communities have
been established on reclaimed mine land, it could become necessary for land
managers to find ways to mitigate negative impacts small mammals have on
seed survival if the establishment of ecologically and economically valu410
Southeastern Naturalist Vol.7, No. 3
able oak species using acorns is planned. Alternately, it may be beneficial
to place oak regeneration plots on portions of reclaimed mines beyond the
immediate dispersal capabilities of small mammals or that are surrounded
by compacted mine spoil that inhibits small-mammal colonization. Therein,
limited depredation of acorns by small mammals could promote oak regeneration
success. Ultimately, a patch work of planted oak stands intermixed with
naturally invading vegetation such as Black Locust, Yellow-poplar, Rubus
spp., and associated grasses and forbs will result in the development of more
diverse small-mammal communities on reclaimed surface mines in the region.
We would like to thank Travis Neal, Kevin Rexroat, and Anthony Miller for their
assistance in the field and Joe Duchamp for statistical advice. We are grateful to Don
Graves for inviting us to expand upon his reforestation research. The comments and
suggestions from two anonymous reviewers were very constructive and improved
manuscript clarity. Lodging during the field season was provided by the University of
Kentucky Department of Forestry’s Robinson Forest. This research was funded by a
grant from the Department of Energy. We dedicate this manuscript to our friend and
co-author Dr. David Maehr, who died in a plane crash on 20 June 2008 while conducting
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