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Rodent populations on the northern Great Plains respond to weather variation at alandscape scaleAuthor(s): Leanne M. Heisler , Christopher M. Somers , and Ray G. PoulinSource: Journal of Mammalogy, 95(1):82-90. 2014.Published By: American Society of MammalogistsDOI: http://dx.doi.org/10.1644/13-MAMM-A-115.1URL: http://www.bioone.org/doi/full/10.1644/13-MAMM-A-115.1

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Journal of Mammalogy, 95(1):82–90, 2014

Rodent populations on the northern Great Plains respond to weathervariation at a landscape scale

LEANNE M. HEISLER, CHRISTOPHER M. SOMERS, AND RAY G. POULIN*

Department of Biology, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada(LMH, CMS)Royal Saskatchewan Museum, 2340 Albert Street, Regina, Saskatchewan S4P 2V7, Canada (RGP)

* Correspondent: ray.poulin@gov.sk.ca

Extreme weather variation on the northern Great Plains of North America can potentially influence the

abundance of grassland rodents across vast areas. We used the remains of 33,697 small mammals collected from

owl pellets in central and western Canada over 15 years to determine the influence of weather on the annual

abundance of deer mice (Peromyscus maniculatus), sagebrush voles (Lemmiscus curtatus), and meadow voles

(Microtus pennsylvanicus). Weather variation affected the annual abundances of all 3 species; however,

influence on deer mouse and sagebrush vole annual abundances was relatively small compared to that on

meadow voles. This finding may indicate that factors other than weather (i.e., habitat availability) are more

important for the abundance of deer mice and sagebrush voles at the landscape scale. In contrast, meadow voles

were positively associated with the duration of snow cover above the hiemal threshold (20 cm), exhibiting up to

5-fold increases (i.e., irruptions) in abundance following winters of persistent, deep snow cover. Our study is the

first to examine the effects of weather on landscape-scale abundance of rodent species on the northern Great

Plains of North America, providing further insight into the role weather plays in driving rodent population

fluctuations in this highly seasonal environment.

Key words: abundance, grassland, owl pellet, small mammal, snow, vole irruptions, weather

� 2014 American Society of Mammalogists

DOI: 10.1644/13-MAMM-A-115.1

Rodents are characterized by high intrinsic rates of increase

that can enable rapid population responses to acute factors that

affect survival and reproduction. For example, substantial

evidence suggests that changing predator densities can cause

oscillations in prey populations. In particular, specialist

predators may induce regular, high-amplitude oscillations,

whereas generalist predators may stabilize rodent communities

by preventing irregular outbreaks (Hansson and Henttonen

1988; Gilg et al. 2003; Korpimaki et al. 2004). Similarly,

diseases and parasites may cause large declines in high-density

populations (Hansson and Henttonen 1988; Korpimaki et al.

2004). Mounting evidence suggests that these top-down

processes (e.g., predation and disease) occur within the

confines of bottom-up processes (e.g., weather and habitat)

that also may acutely affect survival and reproduction (directly

or indirectly—Power 1992; White 2004; Mutshinda et al.

2009). However, the specific features of weather that are most

important to small mammal populations at large geographic

scales remain poorly studied.

Weather can profoundly influence temporal fluctuations in

the availability of food and shelter, factors known to affect

rodent abundance. Most rodents exhibit diets and foraging

strategies that allow them to take advantage of temporal

fluctuations in primary productivity (Hansson and Henttonen

1988). Effects of food limitation on rodent dynamics have been

documented extensively in arid and semiarid regions, where

population densities respond to the changes in primary

productivity associated with the distribution of precipitation

and temperature (Hansson 1979; Brown and Ernest 2002;

Holmgren et al. 2006). Some species also are positively

associated with winters characterized by deep, prolonged snow

cover (Kausrud et al. 2008; Bilodeau et al. 2013). These effects

have been documented in northern North America and northern

Eurasia, where snow cover depth is often above the hiemal

threshold (i.e., 20–30 cm)—a depth of snow that usually results

in small mammals remaining under the snowpack as opposed

to regularly venturing to the surface. These rodents use the

subnivean space to avoid predation while foraging, and

w w w . m a m m a l o g y . o r g

82

continue to reproduce during the winter season. Thus,

precipitation and temperature appear to be key factors

regulating rodent populations, but the effects of these extrinsic

factors have been studied in only a few ecosystems, and on

relatively small spatial scales.

Rodents are a key part of terrestrial communities in the

grasslands of the northern Great Plains, but few studies have

examined landscape-scale fluctuations in their abundance.

Rodents are prey to a wide diversity of predators, alter plant

communities through consumption of vegetation and seeds,

and provide habitat for other species through burrowing

activities (Sieg 1987; Gibbons 1988). The highly variable,

seasonal weather associated with the continental climate of the

northern Great Plains has the potential to influence grassland

rodent abundance across the landscape in several ways.

Precipitation and temperature during the growing season may

indirectly influence rodent abundance through their direct

effect on primary productivity (Garsd and Howard 1981; Reed

et al. 2007), whereas extreme winter conditions may influence

the survival of these species. However, few studies have

examined the effects of landscape-scale weather variation on

the annual abundance of rodents in grassland ecosystems over

long periods of time.

The objective of this study was to identify species-specific

rodent population responses to seasonal weather variation on

the northern Great Plains of North America. Annual variation

in factors such as snow cover, rainfall, and temperature were

hypothesized to influence annual rodent abundance.

MATERIALS AND METHODS

Study area.—Our study area encompassed 1.3 million

hectares of the northern Great Plains in Alberta, Manitoba,

and Saskatchewan, Canada (Fig. 1). This region has been

heavily influenced by anthropogenic activities since the late

1800s; nearly 70% of the mixed-grass prairie has been

converted for agricultural purposes, such as the production of

cereal crops and livestock ranching (Samson et al. 2004). The

northern Great Plains experiences a continental climate with a

short growing season of 3–5 months (May–September),

followed by long, cold winters (October–April). Annual

mean precipitation is 454 mm and annual mean maximum

and minimum temperatures are 8.18C and�4.18C, respectively

(McGinn 2010). Extreme seasonal variation results in summer

mean temperatures that are 22–288C higher than temperatures

during the winter months. The majority of the annual average

precipitation (70–80%) falls during the summer, most of which

falls in June and July (McGinn 2010). The spatial distribution

of precipitation also varies; Saskatchewan and Alberta receive

less precipitation with annual averages of 300–350 mm falling

as rainfall during the summer, and approximately 40 mm as

snowfall during the winter. Manitoba receives more

precipitation with annual averages of 500–550 mm as rainfall

and 60 mm or more as snowfall (McGinn 2010).

Small mammal sampling.—We characterized the annual

changes in abundance of several small mammal species by

identifying their remains within burrowing owl (Athenecunicularia) pellets collected across the study area.

Burrowing owls forage primarily for small mammals and do

so within 2 km of their nest (Haug and Oliphant 1990; Sissons

et al. 2001; Poulin and Todd 2006; Marsh 2012). They select

nest sites primarily in native grass and are known to forage in

most habitats surrounding their nest (Poulin et al. 2005; Marsh

2012). The indigestible parts (i.e., bones and teeth) of the prey

eaten by owls are regurgitated as pellets and deposited in large

quantities at their nest or roost sites (Marti 1974). The bones

within these pellets allow identification and quantification of

the animals eaten by the owls. This sampling method assumes

that the abundance of small mammal species in the owl diet is a

reflection of their abundance in the environment. This

assumption was validated through several comparisons of

owl diet composition to conventional trapping, in which only

minor differences were found (Avenant 2005; Terry 2010a,

2010b). In addition, burrowing owls are known to exhibit

functional relationships to fluctuating prey abundances,

indicating that they consume the most available (and likely

abundant) species within flying distance of their nest (Silva et

al. 1995; Poulin et al. 2001; Marsh 2012). Burrowing owls also

cache prey items, suggesting that they capture variation in

annual rodent abundances above the limits of predator satiation

(Poulin and Todd 2006).

Owl researchers made a focused effort to collect burrowing

owl pellets from 1997 to 2011 at nesting sites across the

Canadian prairies (Fig. 1). A total of 1,181 burrowing owl

nests were visited an average of 5 times during the breeding

season (April–July). All nests visited between 1997 and 2002

were located on the Regina Plain south of Regina, Saskatch-

ewan (Fig. 1). Nests visited between 2003 and 2011 were

located in all 3 provinces; however, the nests located in

Manitoba were visited in either 2010 or 2011 only (Fig. 1). To

standardize the data for uneven sampling effort between nests

and years, we included data only from nests where 2 or more

visits occurred between May and June of each year, with the

FIG. 1.—Burrowing owl (Athene cunicularia) pellet collection sites

(black circles) and weather station locations (white circles) on the

mixed-grass prairie (dark gray) and aspen parkland (light gray) of the

northern Great Plains of North America. Gray lines represent

provincial boundaries, labelled at the top of the figure. The black

circles outlined in white are reference points (right: Calgary, Alberta,

Canada; left: Regina, Saskatchewan).

February 2014 83HEISLER ET AL.—GRASSLAND RODENT RESPONSES TO WEATHER

1st and last visit occurring at least 14 days apart. Standard-

ization was important because rodents identified from a single

nest approximate all individuals depredated in May and June of

each year. Of the 1,181 nests visited, 682 met criteria to be

used in the statistical analyses. From these nests, a mean of 66

6 43 SD nests were visited annually with a mean of 36 6 5 SDdays between the 1st and last pellet collection.

Pellets were processed in a 10% sodium hydroxide solution

to dissolve the fur and provide clean bones and teeth for

identification and quantification of species. Cranial and dental

traits were used to identify species, and the minimum count of

craniomandibular elements from either the left or right side was

used to determine the minimum number of individuals of each

species present in the pellets. Species abundances were then

summed for each nest per year (using collections from May

and June only). This produced a data set representing the

spring abundances of rodent species at each nest per year; raw

abundances were used in all subsequent statistical analyses. A

total of 33,697 individuals were identified, representing 11

different rodent species (Table 1). We focused our analyses on

deer mice, which made up 64% of these individuals, followed

by meadow voles at 19%, and sagebrush voles at 13% of the

total individuals sampled (Fig. 2). The remaining species were

not included in analyses because combined they made up less

than 4% (n ¼ 1,128) of the total individuals sampled, and all

had a mean abundance of less than 1 individual per sample

(Table 1).

Weather data.—Weather measurements the year each pellet

collection occurred were used to examine immediate responses

to changing weather conditions, as well, weather measurements

taken from the year prior to pellet collection were used to

examine potential lagged responses. Daily precipitation, snow

depth, minimum temperature, and maximum temperature

between 1995 and 2011 were taken from 35–64 weather

stations maintained by Environment Canada (Fig. 1). Weather

stations were an average of 31 6 SD 14 km from burrowing

owl nests. We used a weather data set generated from

interpolating weather measurements from known locations to

estimate values for surrounding locations, assuming that

weather exhibits spatial autocorrelation. Fifteen spatially

interpolated maps were created for each weather variable, 1

for each year (1997–2011). Daily snow depth between October

and April was used to determine the number of days snow

cover was at least 20 cm deep (snow cover duration) for the

winter of the current year and the winter before small mammal

sampling. Daily rainfall was used to determine total rainfall

during May and June of the current spring and from May to

September of the previous summer. Growing degrees were

calculated as the sum of average temperatures above the base

temperature required for plant growth, in this case 58C (Miller

et al. 2002). Growing degrees of the current spring included

only May and June, whereas growing degrees of the previous

summer included May to September. Growing degrees,

rainfall, and snow cover duration of the current and previous

year for each nest location were then interpolated among

weather stations using the Inverse Distance Weighted tool in

ArcGIS 10 (Environmental Systems Research Institute 2011).

Fifteen spatially interpolated maps were created for each

weather variable, 1 for each year (1997–2011). These maps

were then spatially joined to the owl pellet data set, providing a

data set characterizing weather variation of the current and

previous year for each owl nest.

Statistical analyses.—The influence of weather on the

annual abundance of each rodent species was examined

using generalized linear mixed models and Akaike

information criterion (AIC) model selection. Generalized

linear mixed models with a Poisson distribution and log link

were used to account for the uneven influence of spatial

variation among nests, and the potential nonlinear relationships

between species count data and weather variation (Gillies et al.

2006). Fixed effects included growing degrees, rainfall, and

snow cover duration of the current and previous year. Nest site

locations were used as a random effect to minimize the

influence of spatial variation among nests (Gillies et al. 2006).

TABLE 1.—Total abundance and average annual abundance per owl

nest (6 SD) of small mammal species found within burrowing owl

pellets. Species in boldface type were used for statistical analyses.

Species

Total

abundance

Annual

average

(6 SD)

Deer mouse (Peromyscus maniculatus) 21,701 14 6 18

Meadow vole (Microtus pennsylvanicus) 6,473 4 6 9

Sagebrush vole (Lemmiscus curtatus) 4,295 3 6 7

Olive-backed pocket mouse (Perognathus fasciatus) 709 0 6 2

Northern grasshopper mouse (Onychomys leucogaster) 472 0 6 1

Western harvest mouse (Reithrodontomys megalotis) 1 0 6 0

Western jumping mouse (Zapus princeps) 31 0 6 0

Meadow jumping mouse (Zapus hudsonius) 2 0 6 0

Prairie vole (Microtus ochragaster) 2 0 6 0

Southern red-backed vole (Myodes gapperi) 3 0 6 0

Ord’s kangaroo rat (Dipodomys ordii) 8 0 6 0FIG. 2.—Average annual abundance of deer mice (Peromyscus

maniculatus; light gray line), sagebrush voles (Lemmiscus curtatus;

dark gray line), and meadow voles (Microtus pennsylvanicus; black

line) per owl nest between 1997 and 2011 across the northern Great

Plains of central Canada.

84 Vol. 95, No. 1JOURNAL OF MAMMALOGY

The model parameter estimates and standard errors were

standardized to a mean of 0 6 0.5 SD prior to model selection,

allowing comparison of the strength of parameter estimates

relative to each other (Grueber et al. 2011).

Maximum likelihoods from all possible subsets of the global

model containing all variables were used to obtain AIC scores

and build the candidate set of explanatory models for each

species. We calculated the number of parameters (k) for each

model as 2 (1 for the intercept and 1 for the random factor)þ 1

for each model parameter (Barton 2013). The most-parsimo-

nious model (top model) was that with the lowest AIC score

(i.e., greatest support) out of the candidate set. Competing

models were those within 2 DAIC units of the top model

(Burnham and Anderson 2002). Model averaging, using the

natural average method, of all models within 10 DAIC units of

the top model were used when competing models were present

to provide more-robust parameter estimates (Grueber et al.

2011). Eighty-five percent confidence intervals (CIs) were used

to reduce the probability of type II errors by improving AIC

compatibility between selection of models for averaging and

evaluating parameter effects (Arnold 2010). Inference was

made from parameters whose confidence intervals did not pass

through 0. R2GLMM was calculated for each top model to

determine the amount of temporal variation in the annual

abundance of each species that was explained by the entire

model (R2GLMM(c)), as well as the approximate amount of

temporal variation explained by the weather alone

(R2GLMM(m)—Nakagawa and Schielzeth 2013). ArcMap 10

(Environmental Systems Research Institute 2011) was used for

all species and weather data preparation, and all statistical

analyses were conducted in R Project for Statistical Computing

2.15.2 (R Development Core Team 2012), using packages

lme4 (Bates et al. 2012), arm (Gelman and Su 2013), and

MuMIn (Barton 2013).

RESULTS

Deer mice.—The top model characterizing deer mouse

abundance included all fixed effects of the current year and

summer precipitation of the previous year. However,

R2GLMM(m) values were relatively small compared to

R2GLMM(c) values (Table 2). Snow cover duration of the

previous winter and growing degrees of the previous summer

were included in 3 competing models. However, model-

averaged 85% CIs passed through 0 for both of these

parameters (85% CIGDDT1 ¼ �0.08, 0.02; 85% CISNOWT1 ¼�0.01, 0.06; Table 2), indicating little potential influence on

deer mouse populations. Snow cover duration of the current

winter had the greatest relative effect; deer mouse mean annual

abundance decreased 23% following winters with above-

average deep snow cover duration. Deer mouse abundance also

exhibited negative associations with precipitation of the current

spring and previous summer, corresponding to 9% and 29%

decreases in mean annual abundance with above-average

precipitation, respectively. In contrast, deer mouse abundance

was positively associated with growing temperatures of the

current spring, corresponding to a 5% increase in mean annual

abundance during years of above-average growing

temperatures. Thus, weather variation had a relatively small

effect on deer mouse abundance, but populations generally

responded positively to warm, dry summers and winters with

little snow.

Sagebrush voles.—Sagebrush vole annual abundance was

affected by all fixed effects of the previous year, as well as

summer precipitation and winter snow cover duration of the

current year (Table 3). Similar to the deer mouse model,

R2GLMM(m) of the sagebrush vole model was relatively small

compared to the R2GLMM(c) (Table 3). Three competing models

also included growing degrees of the current spring as a fixed

effect. However, model-averaged 85% CIs suggested that all

parameters except growing degrees of the current spring had an

effect on the spring abundance of sagebrush voles (85% CIGDD

¼ �0.25, 0.08; Table 3). Sagebrush vole mean annual

abundance decreased 50% when summer growing

temperatures and deep snow cover duration were above

average during the previous year. There was a 17% decrease

in mean annual abundance when precipitation was above

average in the previous summer. Fixed effects of the previous

year had the greatest relative influence, indicating that this

species responds to weather variation a year after the change

occurs. These results suggest that sagebrush vole populations

increase following cool, dry summers and winters with little

snow.

Meadow voles.—Weather had the greatest influence on

annual abundance of meadow voles; the top model included all

fixed effects and R2GLMM(m) was greater than 30%, almost half

the R2GLMM(c) (Table 4). Model averaging was not necessary

because there were no competing models and none were within

10 DAIC units of the top model. Evaluation of the 85% CIsfound that all parameters affected the annual abundance of

meadow voles. Snow cover duration of the current winter was

at least 3 times as influential as all other fixed effects, and

associated with meadow vole populations exhibiting up to 5-

fold increases in abundances (population irruption) following

winters with above-average deep snow cover duration. Mean

annual abundance of meadow voles decreased 50% and 11% in

years of above-average precipitation during the current spring

and previous summer, respectively. Thus, meadow voles

appear to be considerably more influenced by weather

variation compared to the other 2 species considered here.

Population abundances increased following summers with little

rainfall and irrupted following winters characterized by

prolonged deep snow cover.

DISCUSSION

Relationships between weather and rodent abundance on the

northern Great Plains of North America may be more complex,

and subsequently less predictable, than in other locations where

these relationships have been studied more thoroughly. In our

study, all 3 rodent species exhibited responses in annual

abundance to variation in weather conditions, suggesting that

February 2014 85HEISLER ET AL.—GRASSLAND RODENT RESPONSES TO WEATHER

weather does play an important role in rodent population

fluctuations in grassland ecosystems. However, the rodent

populations did not covary dramatically with any single

weather variable, but exhibited individualistic responses to all

variables. In addition, all 3 species responded to immediate

weather variation, as well as exhibited lagged responses to

weather variation that occurred the summer before. Most

previous studies have examined rodent population dynamics in

desert ecosystems limited primarily by precipitation. In these

arid regions, strong positive associations between the amount

and distribution of precipitation and rodent densities in desert

communities clearly illustrate the crucial role precipitation

plays in these communities (Ernest et al. 2000; Brown and

Ernest 2002; Shenbrot et al. 2010; Thibault et al. 2010;

Meserve et al. 2011). The northern Great Plains are

considerably different from desert regions; precipitation is less

limiting to primary productivity, and the region is characterized

by a continental climate of extreme seasonal variation (Coup-

land 1950; Barker and Whitman 1988; McGinn 2010).

Contrary to the results of community studies of desert rodents,

this seasonal variation appears to play a larger role in

determining rodent abundances than any single weather

variable alone on the northern Great Plains of North America.

Species of grassland rodents often respond differently to

weather patterns across different temporal and spatial scales,

resulting in highly dynamic communities. Both short- and

long-term studies of rodent responses to local weather variation

have found differing responses to weather variables between

species (Brady and Slade 2004; Reed et al. 2006). In

paleobiological studies of rodent cave assemblages from

8,000 years ago, Grayson (2000) and Schmitt et al. (2002)

found that sagebrush voles and deer mice were locally

extirpated from cave areas following increased aridity. These

studies suggest that weather plays a dominant role in

determining local species abundances across multiple temporal

scales. However, most of these studies were conducted within

TABLE 2.—Top model, intercept-only model, and all models within 10 delta Akaike information criterion (DAIC) units of the top model

(candidate set) explaining the annual abundance of deer mice, followed by model-averaged, standardized parameter estimates and standard errors

(unconditional SE), and 85% confidence intervals (CIs). Included in all models was the nest collection site as a random effect. Conditional and

marginal R2GLMM values were calculated to determine the approximate amount of variation explained by the entire model (R2

GLMM(c)), as well as

by the weather variables of the top model (R2GLMM(m)). Acronyms represent weather characteristics (GDD¼ accumulated growing degrees; PREC

¼ precipitation; SNOW ¼ number of days snow cover depth greater than 20 cm) and the year they were measured relative to small mammal

sampling (T1 ¼ year prior to small mammal sampling, otherwise weather measured during same year of small mammal sampling).

86 Vol. 95, No. 1JOURNAL OF MAMMALOGY

small spatial scales (Jorgensen 2004). The advantage of our

study using owl pellets is the very large spatial scale that was

examined (see also Heisler et al. 2013); these owls depredated

rodents from 1.3 million hectares of heterogeneous landscape.

Our results further emphasize the importance of weather

variation to influencing the individualistic nature of rodent

responses across the landscape and affecting entire populations

in grassland ecosystems.

Habitat type may influence rodent population responses to

environmental change. Deer mice and sagebrush voles

exhibited the weakest associations with weather variation of

the 3 species considered, indicating that weather had a

relatively small influence on the annual abundance of these 2

species. However, using the same data set, we found both

species make up notably greater proportions of small mammal

composition in regions dominated by cropland and native

grassland compared to other habitat types at the landscape

scale, respectively (data not shown). Sagebrush vole average

annual abundances from all habitat types across the landscape

were higher (8 6 SD 3 individuals/sample) compared to a

subset of the landscape dominated by cropland (2 6 1

individuals/sample), whereas deer mice exhibited the inverse

relationship (entire landscape ¼ 18 6 6 individuals/sample;

cropland-dominated region only¼ 24 6 9 individuals/sample).

These habitat affinities suggest that weather variation causing

population-level fluctuations in rodent abundances may be

detectable only within habitats where their densities are notably

higher. Our study occurred at a scale encompassing many

different habitat types and did not differentiate population

fluctuations among them. This may have limited our ability to

identify the true relationship between rodents, their affiliated

habitats, and weather variation. Future studies of fluctuations in

deer mice and sagebrush vole populations should examine not

only other environmental factors occurring across the land-

scape, but also whether temporal patterns in population

fluctuations differ among habitat types.

TABLE 3.—Top model, intercept-only model, and all models within 10 delta Akaike information criterion (DAIC) units of the top model

(candidate set) explaining the annual dynamics of sagebrush voles, followed by model-averaged parameter estimates, standard error

(unconditional SE), and 85% confidence intervals (CIs). Included in all models was the nest collection site as a random effect. Conditional and

marginal R2GLMM values were calculated to determine the approximate amount of variation explained by the entire model (R2

GLMM(c)), as well as

by the weather variables of the top model (R2GLMM(m)). Acronyms represent weather characteristics (GDD¼ accumulated growing degrees; PREC

¼ precipitation; SNOW ¼ number of days snow cover depth greater than 20 cm) and the year they were measured relative to small mammal

sampling (T1 ¼ year prior to small mammal sampling, otherwise weather measured during same year of small mammal sampling).

February 2014 87HEISLER ET AL.—GRASSLAND RODENT RESPONSES TO WEATHER

Meadow voles exhibited the greatest response to weather

variation across the landscape. Population densities were

positively associated with years characterized by deep snow

cover that persisted throughout the winter. Snow cover reaching a

depth of 20 cm or more (hiemal threshold) insulates the ground

surface against cold and variable ambient temperatures, main-

taining a stable ground temperature close to freezing (Pruitt 1957;

Courtin et al. 1991). These temperatures increase individual

survival of species adapted to inhabiting snow cover close to the

ground surface (subnivean space) by reducing physiological

stress associated with maintaining body temperatures in extreme

environments (Courtin et al. 1991; Duchesne et al. 2011; Reid et

al. 2012; Bilodeau et al. 2013). Snow cover also reduces

mortality due to predation by providing protective cover, under

which individuals carry out most daily activities during the winter

(Korpimaki et al. 2004; Duchesne et al. 2011; Reid et al. 2012).

Furthermore, these conditions facilitate rapid population growth

by extending the reproductive season past the limits of the plant

growing season (Reid and Krebs 1996; Reid et al. 2012; Bilodeau

et al. 2013), resulting in irruptions of meadow vole populations.

Weather plays an important role in determining the cyclicity,

or lack thereof, in the dynamics of rodent populations. Rodent

populations characterized by recurring high densities at regular

intervals (population cycles) are often found in highly seasonal

environments, where weather indirectly affects population

growth by increasing resource availability and subsequent

secondary productivity or individual survival (Hansson and

Henttonen 1988; Reid and Krebs 1996; Korpimaki et al. 2004;

Kausrud et al. 2008; Duchesne et al. 2011; Reid et al. 2012;

Andreassen et al. 2013; Bilodeau et al. 2013). Consistent

patterns in the frequency and magnitude of specific weather

events (i.e., snow cover or drought) are proposed to drive

cyclicity in the dynamics of rodent species inhabiting highly

seasonal environments (Korpimaki et al. 2004). For example,

several studies have found that snow conditions (i.e., depth,

duration, and density) can affect the amplitude and periodicity

of lemming population cycles on different continents (Reid and

Krebs 1996; Kausrud et al. 2008; Duchesne et al. 2011; Reid et

al. 2012; Bilodeau et al. 2013). However, regions where snow

cover reaches the hiemal threshold infrequently or irregularly

are characterized by rodent dynamics with corresponding

infrequent or irregular irruptions (Hansson and Henttonen

1988; Kausrud et al. 2008). The irregular irruptions of meadow

vole populations on the northern Great Plains further

emphasize the important role weather plays in the population

fluctuations of rodents in highly seasonal environments.

Irruptions in meadow vole populations may have significant

effects on rodent community dynamics and predator–prey

TABLE 4.—Top model, intercept-only model, parameter estimates, standard errors (SEs), and 85% confidence intervals (CIs) explaining annual

meadow vole dynamics. Included in both models is the nest collection site as a random effect. Conditional and marginal R2GLMM values were

calculated to determine the approximate amount of variation explained by the entire model (R2GLMM(c)), as well as by the weather variables of the

top model (R2GLMM(m)). Acronyms represent weather characteristics (GDD ¼ accumulated growing degrees; PREC ¼ precipitation; SNOW ¼

number of days snow cover depth greater than 20 cm) and the year they were measured relative to small mammal sampling (T1¼ year prior to

small mammal sampling, otherwise weather measured during same year of small mammal sampling).

88 Vol. 95, No. 1JOURNAL OF MAMMALOGY

relationships on the northern Great Plains. Over the last 15

years, meadow vole abundances increased 2- to 5-fold above

average annual abundance for this time period following

winters of above-average snow cover duration (i.e., irruptive

years). Furthermore, average annual abundance of meadow

voles changed from 26% of the total individuals from all 3

species combined to 53% in irruptive years, supplanting deer

mice as the most abundant rodent during these irruptions.

These high densities may alter rodent composition further

through the propensity for meadow voles to exclude deer mice

and other species from local habitats on the northern Great

Plains (Findley 1954; Morris 1969; Reich 1981). Previous

studies also have observed the effects of these irruptions

cascade through higher trophic levels as regional predator

densities respond by increasing reproduction or immigration in

regions experiencing local meadow vole irruptions (Poulin et

al. 2001). These cascading effects have significant implications

for population fluctuations and geographic distributions of

many species, illustrating the importance of the mechanisms

behind meadow vole irruptions (i.e., persistent deep snow

cover) for trophic dynamics on the northern Great Plains.

Meadow vole irruptions on the northern Great Plains have

been observed in previous studies; however, until now we

knew little about the mechanisms driving these population

changes. Previous studies focused on identifying predator

responses to changing prey densities, noting in particular the

meadow vole irruption in 1997 (Poulin et al. 2001; Poulin

2003). Our study is the first to examine the effects of weather

on these irruptions at the landscape scale, the scope of which

was logistically only possible through the use of owl pellets.

Our results identify the influence of the duration of deep snow

cover (i.e., greater than 20 cm) on meadow vole densities and

provide further insight into the role weather plays in driving

fluctuations in the abundances of grassland rodent species.

ACKNOWLEDGMENTS

We thank everyone involved in collecting owl pellets from across

the Canadian prairies, particularly T. Wellicome, H. Trefry, G.

Holroyd, and A. Froese for collecting and allowing use of their pellet

samples for this study. We also thank T. Schowalter for identifying the

majority of individual small mammals collected. Thanks to J. Piwowar

for sharing his expertise regarding ArcGIS 10, and S. Davis for

sharing his knowledge of statistics for ecology. LMH was supported

by Mathematics of Information Technology and Complex Systems—

Accelerate internships, Friends of the Royal Saskatchewan Museum,

and the University of Regina. Additional financial and logistic support

was provided by Environment Canada—Interdepartmental Recovery

Fund, Canadian Museum of Nature, Agriculture and Agri-Food

Canada—Prairie Farm Rehabilitation Administration, the Canada

Research Chairs Program, and Grasslands National Park.

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Submitted 12 May 2013. Accepted 24 September 2013.

Associate Editor was Bradley J. Swanson.

90 Vol. 95, No. 1JOURNAL OF MAMMALOGY