Canadian Journal of Zoology · 2019. 10. 21. · Draft Effects of industrial disturbance on small mammal abundance and activity Journal: Canadian Journal of Zoology Manuscript ID

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Effects of industrial disturbance on small mammal abundance and activity

Journal: Canadian Journal of Zoology

Manuscript ID cjz-2019-0098.R1

Manuscript Type: Article

Date Submitted by the Author: 12-Jul-2019

Complete List of Authors: Shonfield, Julia; University of Alberta, Biological SciencesBayne, Erin; University of Alberta, Biological Sciences

Is your manuscript invited for consideration in a Special

Issue?:Not applicable (regular submission)

Keyword:BOREAL < Habitat, compressor station, deer mouse (Peromyscus maniculatus), energy sector, live-trapping, roads, southern red-backed vole (Myodes gapperi)

https://mc06.manuscriptcentral.com/cjz-pubs

Canadian Journal of Zoology

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J. Shonfield and E. M. Bayne

Author affiliations and addresses:

Julia Shonfield, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G

2R3, Canada, [email protected]

Erin M. Bayne, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G

2R3, Canada, [email protected]

Author responsible for correspondence:

Julia Shonfield

Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada

Phone number: 780-200-1224

Email address: [email protected]

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mailto:[email protected]



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Julia Shonfield and Erin M. Bayne

Abstract

Anthropogenic disturbance can negatively impact animal populations and alter the

behaviour of individuals. Disturbance associated with the energy sector has been increasing in

the boreal forest of northern Alberta. Disturbances associated with the oil and gas industry vary

in the infrastructure present and sensory stimuli generated. Two common types are compressor

stations and roads. It is important to assess population consequences of disturbance on small

mammals because they serve as prey, predators, and seed/spore dispersers in the terrestrial

ecosystems they inhabit. To test the effects of disturbance from the energy sector on small

mammal abundance and activity, we used mark-recapture methods and live-trapped in forested

areas with one side adjacent to a clearing with industrial infrastructure present (road or

compressor station) or absent (control sites). We found no difference in abundance or activity of

deer mice (Peromyscus maniculatus (Wagner, 1845)) and southern red-backed voles (Myodes

gapperi (Vigors, 1830)) between sites, and did not detect an edge effect on abundance within

sites, regardless of the presence of industrial infrastructure. Our results suggest minimal effects

of industrial disturbance on the abundance and activity of these species, and the infrastructure

and sensory stimuli generated are unlikely to be key drivers of their population dynamics or

behaviour.

Keywords: boreal forest, compressor station, deer mouse (Peromyscus maniculatus), energy

sector, live-trapping, roads, southern red-backed vole (Myodes gapperi).

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Introduction

Exploration and production of hydrocarbons has resulted in considerable alteration and

fragmentation of vegetation conditions throughout North America (Allred et al. 2015), and

specifically in the boreal forest of northern Alberta (Schneider and Dyer 2006; Pickell et al.

2015). Such changes have been shown to alter habitat conditions to the benefit and detriment of

different species (e.g. Fisher and Burton 2018; Riva et al. 2018). The emphasis of most research

and monitoring in the region has been on the physical effects (i.e. physical changes to the

vegetation and soil conditions). While physical impacts are crucial to understand, it is plausible

that human activity and associated sensory stimuli (e.g. noise, light, movement of equipment,

etc.) in disturbed areas may further influence wildlife response. However, how these stimuli alter

wildlife responses to physical effects of altered habitat are not clearly understood.

A number of studies have specifically examined the effects of oil/gas compressor stations

on songbirds (Habib et al. 2006; Bayne et al. 2008; Francis et al. 2009, 2011a, 2011b).

Compressor stations are an area of disturbance typically a few hectares in size with a distinct

edge. Compressor stations are required to move oil and gas throughout pipeline systems via a

series of large pumps and motors that generate loud chronic noise. Past work at compressor

stations found lower songbird breeding success, abundance, and diversity near compressor

stations (Habib et al. 2006; Bayne et al. 2008; Francis et al. 2009), as well as changes in the

vocalizations of some songbird species (Francis et al. 2011a, 2011b). These results have been

interpreted as being caused by negative effects of noise through changes in conspecific

communication. Far less work has focused on the effects of this type of energy sector disturbance

on other taxonomic groups (but see Bunkley et al. 2017). In particular, for mammalian species

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where acoustic signals are not the primary mode of communication, little is known about the

potential effects of compressor stations on small mammal abundance and activity. As well,

compressor stations are often lit at night to facilitate maintenance. Ecological light pollution

from various sources has been broadly shown to affect a species’ orientation, foraging and

predation risk (Longcore and Rich 2004) which may create additional effects.

Road infrastructure is a crucial part of energy sector development as it facilitates

exploration and extraction. The effects of roads have been well documented across a range of

taxa (Fahrig and Rytwinski 2009). Roads have predominantly negative effects on the abundance

of birds, reptiles and large mammals, although the impact can depend on traffic volume. For

small mammals, the effects on abundance are generally neutral or positive (Fahrig and Rytwinski

2009). For example, right-of-ways next to roads with low traffic volume have been found to

support a greater density of multiple small mammal species relative to adjacent habitat (Adams

and Geis 1983), and areas with higher road densities have been found to have a greater relative

abundance of white-footed mice (Peromyscus leucopus (Rafinesque, 1818)) (Rytwinski and

Fahrig 2007). Small mammals serve an important function in terrestrial ecosystems as prey for

mammalian and avian predators (Livezey 2007; Andruskiw et al. 2008), predators of songbird

nests (Bayne and Hobson 1997), predators of seeds and arthropods (Martell and MacAulay 1981;

Schnurr et al. 2002), and dispersers of fungal spores (Maser et al. 1978; Martell 1981). For these

reasons, research on the effects of anthropogenic disturbance on small mammal communities

continues to be relevant and could provide insight into the mechanisms influencing responses

observed in other taxonomic groups.

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Small mammal abundance may be indirectly affected by industrial disturbance if the

combination of infrastructure, movement of equipment, traffic, noise, and light pollution alters

predation dynamics. A proposed explanation for the positive effects of roads on small mammal

abundance is lower predation (Rytwinski and Fahrig 2007; Fahrig and Rytwinski 2009). Small

mammals near roads could experience release from predation if predator populations are

susceptible to road effects (e.g. from road mortality, habitat destruction or road avoidance). The

predator release hypothesis could also influence small mammal population dynamics around

compressor stations, if it provides a refuge from disturbance-sensitive predators (e.g. Francis et

al. 2009), or if the noise negatively affects the foraging efficiency of acoustic predators (Siemers

and Schaub 2011; Bunkley and Barber 2015; Mason et al. 2016). Alternatively, small mammals

could be more vulnerable to predation in disturbed areas if their attention is compromised by

distraction from the noise (Chan et al. 2010; Chan and Blumstein 2011), or if the sound of

approaching predators is masked by the noise, or if the artificial light makes them more

detectable to nocturnal predators (Kotler et al. 1991). Reduced activity of prey species may also

reduce the probability of an encounter with terrestrial predators, and thus activity of prey can

provide an indication of perceived predation risk. Evidence from field and lab-based studies

indicate that activity of Microtus and Myodes voles is lower under increased predation risk

(Norrdahl and Korpimäki 1998; Trebatická et al. 2008). Small mammal abundance and activity

in areas with industrial disturbance could be affected by predation risk, but this is not the only

possible mechanism. The infrastructure, light, noise, and/or emissions produced by industrial

disturbances may act as environmental stressors, potentially reducing habitat quality. Studies

have documented increased stress hormone levels in rodents living near roads (Navarro-Castilla

et al. 2014) and close to wind turbines (Łopucki et al. 2018) suggesting that industrial

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disturbance may elicit physiological responses in addition to possible numerical and behavioural

responses.

Our study objective was to assess the effect of industrial disturbance on small mammal

abundance and activity in the boreal forest of northern Alberta by comparing the effects of

compressor stations to roads and control sites with no industrial infrastructure. We sought to

determine if disturbance of sites resulted in a refuge for small mammals or reduced the habitat

quality for small mammals. We used mark-recapture methods to estimate abundance and activity

of small mammals at forested sites adjacent to compressor stations, adjacent to roads, and control

sites adjacent to forest clearings with no infrastructure. If areas near industrial facilities and roads

are lower quality habitat for small mammals, they may be occupied by lower quality individuals,

so we also examined body mass of individuals across sites. Finally, we considered that effects of

disturbance from industrial infrastructure could be occurring over a more localized scale by

altering the magnitude of edge effects, so we analyzed the number of individual small mammals

caught at varying distances from a forest edge adjacent to a compressor station, road, or a forest

opening with no industrial infrastructure.

Materials and Methods

Study area

The study area was located in the boreal forest of northeastern Alberta, within the Lower

Athabasca Planning Region (LAPR). Specifically, study sites were in upland forested areas south

of Fort McMurray, north of Lac la Biche and northwest of Cold Lake (Fig. 1). The LAPR has

seen increased development in the oil and gas energy sector in recent years (Alberta Biodiversity

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Monitoring Institute 2017), making it a suitable area to research the potential effects of industrial

disturbance on wildlife. Sites were a minimum of 1 km apart, in mixedwood forest stands

composed primarily of trembling aspen (Populus tremuloides Michx.) and white spruce (Picea

glauca (Moench) Voss). Other tree species that were sometimes present included black spruce

(Picea mariana (Mill.) Britton, Sterns & Poggenb.), balsam poplar (Populus balsamifera L.),

jack pine (Pinus banksiana Lamb), paper birch (Betula papyrifera Marshall), balsam fir (Abies

balsamea (L.) Mill.), and tamarack (Larix laricina (Du Roi) K. Koch). The most common

species in the shrub layer included prickly rose (Rosa acicularis Lindl.), Labrador tea

(Rhododendron groenlandicum (Oeder) Kron & Judd), green alder (Alnus viridis ssp. crispa

(Aiton) Turrill), beaked hazelnut (Corylus cornuta Marshall), and willow (genus Salix L.).

Sites were selected based on the industrial infrastructure present and grouped into three

disturbance categories: sites adjacent to compressor stations, sites adjacent to a road, and control

sites. All sites were situated in forested habitat with one forest edge where the tree canopy ended

at an open clearing. The clearings differed in the presence or absence of industrial infrastructure,

either having a compressor station, a road, or an open clearing with no infrastructure (control

sites). Compressor stations are used to pressurize oil and natural gas pipelines and produce noise

continuously, typically at sound pressure levels between 80 and 100 dB (Bernath-Plaisted and

Koper 2016; Scobie et al. 2016; Warrington et al. 2018). Control sites were adjacent to a forest

clearing and were at least 1 km away from a road or any other industrial infrastructure. Sites in

each category were distributed throughout the study area (Fig. 1).

Trapping and sampling design

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To estimate abundance of small mammals at each site, we conducted live-trapping using

standard mark-recapture methods from the beginning of July to the end of August in 2014 and

2015. At this time, young born in the spring are likely emerged from their natal nests, and is a

suitable time to assess abundance of small mammals in the boreal forest (e.g. Bayne and Hobson

1998; Fauteux et al. 2012). One trapping grid was set up at each site within forested areas, with

one edge of the grid along the forest edge adjacent to the clearing (either from a compressor

station, a road or an isolated forest clearing). Grids were 1.1 ha in size, with traps spaced at 15-m

intervals, making an 8 x 8 grid with 64 traps. We trapped small mammals at 14 sites in the

summer of 2014, 5 compressor station sites, 5 road sites, and 4 control sites. In summer 2015, we

trapped small mammals at 23 sites (7 compressor station sites, 8 road sites, and 8 control sites):

12 of the same sites from 2014 and 11 new sites. We did not return to two sites in 2015 due to

access restrictions. In total, we trapped 25 unique sites across both years (Fig. 1).

We used Longworth live traps (13.8 cm x 6.4 cm x 8.4 cm) baited with a handful of black

oil sunflower seeds (Helianthus spp.), a small piece of apple for hydration, and polyester fiber

for bedding. Traps were set between 16:00 and 20:00, and checked in the mornings starting at

06:00 for four consecutive days. Traps were locked open in the middle of the day. Small

mammals captured were transferred from the trap to a mesh handling bag and identified to

species. Individuals were sexed, mass measured to the nearest gram with a Pesola spring balance

(Pesola, Baar, Switzerland), and marked with a uniquely numbered metal ear tag (National Band

and Tag Co.) before being released at the point of capture. Live-trapping and handling protocols

followed guidelines of the American Society of Mammalogists (Sikes et al. 2011) and were

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approved by the University of Alberta Animal Care Committee (AUP00001055) and the

Government of Alberta (Wildlife Research Permit No. 54843 in 2014, and No. 56196 in 2015).

Vegetation sampling

We targeted areas of mixedwood forest with aspen and white spruce as the dominant stand

type in an attempt to control for forest composition, though there was still some heterogeneity

across sites. In 2015, we conducted vegetation sampling on all sites at a subsample of trap

locations within each trapping grid to quantify vegetation characteristics. In each of the 23 grids

trapped in 2015, we sampled vegetation on every other trapline, and at every other trap location,

for a total of 16 sampling locations per grid and 368 sampling locations across all grids. At the

sampled locations, we estimated percent canopy cover and conducted a 15-m radius visual scan

for tree species composition, tree size class, and relative density and size class of downed woody

debris greater than 5 cm in diameter. We assessed tree size class and downed woody debris size

class as follows: over 50% of trees/logs within a 15-m radius of the trap location smaller than 10

cm diameter, 10-20 cm diameter, and >20 cm diameter. We assessed relative density of downed

woody debris using the following categories: low (1-5 logs within a 15 m radius of the trap

location), medium (5-10 logs within 15 m), and high (>10 logs within 15 m).

At the same trap locations, we visually estimated the percent cover of shrubs within a 2-m

radius of the trap location, and classified dominant ground cover using the following categories:

bare ground, grass, sedge, forb, litter, moss, or lichen. All vegetation data was collected in 2015.

We summarized vegetation characteristics at each site by determining the modal category for

downed wood debris density class, downed wood debris size class, dominant ground cover type,

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and tree size class. We calculated the average of the continuous vegetation variables for each

site: canopy cover, shrub cover, and forest composition (proportion coniferous trees).

Analysis

All analyses were performed using R version 3.4.3 (R Core Team 2019) in RStudio version

1.1.383 (RStudio Team 2018). We report the abundance of small mammals as the number of

unique individuals captured per 100 trap-nights. When calculating the number of trap-nights, we

used a correction factor of half a trap-night subtracted for each trap sprung (Nelson and Clark

1973; Beauvais and Buskirk 1999). This corrects for closed traps that reduce the number of

animals that can be captured, and this method is applicable to any trapping device that becomes

inoperative when sprung (Nelson and Clark 1973). We conducted a two-factor ANOVA to

determine if the disturbance of sites influenced the abundance of small mammals among years

(14 sites in 2014, and 23 sites in 2015). We performed three ANOVAs, one on the abundance of

all rodent species combined, and separate ANOVAs for the two most abundant species: southern

red-backed voles (Myodes gapperi (Vigors, 1830)) and deer mice (Peromyscus maniculatus

(Wagner, 1845)). Abundance data were not normally distributed, so were log(x + 1)-transformed

prior to analysis. We added 1 to the abundance of each site to allow transformation, because we

had some sites where no deer mice were caught. Initially, the interaction between year and

disturbance category was included in each ANOVA model, but was removed if not significant.

We then fitted more complex linear models (separate models for all rodents, southern red-backed

voles, and deer mice) with year, disturbance category, and vegetation variables that had

significant effects on small mammal abundance (based on α = 0.05) to account for differences

between sites in vegetation characteristics. Each of the three ANOVAs did not include two sites

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because of missing vegetation data, these were trapped in 2014 but not in 2015 when vegetation

data was collected. To test for significant effects of vegetation on small mammal abundance, we

ran univariate linear models with each of the following vegetation variables: wood debris density

class, downed wood debris size class, dominant ground cover type, tree size class, canopy cover,

shrub cover, and forest composition.

We considered that if areas in close proximity to industrial facilities and roads are lower

quality habitat for small mammals, then it is possible that they may be more likely to be occupied

by younger, subordinate, or lower quality individuals. So, in addition to the models on small

mammal abundance described above, we also tested for a difference in small mammal body mass

across sites. We ran two separate ANOVA’s for deer mice and red-backed voles with an average

mass of individuals (including all age classes) for each site as our dependent variable, and

disturbance category as the independent variable. For each species, we also ran two additional

separate models for juvenile and adult individuals, and in this case calculated the average mass

for adults and juveniles separately for each species.

To answer the question of whether industrial disturbance affected the activity of

individuals, we calculated the maximum distance moved between recapture events during the 4-

day trapping session for individuals that were recaptured at least once, as a measure of the spatial

extent of activity by small mammals. We included deer mice and southern red-backed voles only

in this analysis because we caught few individuals and had very few recaptures of any other

species (Table 1). Maximum distance moved was not normally distributed and was log(x + 1)-

transformed prior to analysis. We added 1 m to all distances to allow transformation when the

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maximum distance moved was 0 m. This occurred for individuals that were only ever caught at

the same trap location. The log maximum distance moved by each individual was our response

variable, and we included site disturbance category, year, species and sex as fixed effects in our

model to test for an effect of disturbance while controlling for other variables that we had reason

to believe could affect movement behaviour (i.e. activity) based on previous studies (Norrdahl

and Korpimäki 1998; Trebatická et al. 2008). All fixed effect variables in the model were

categorical: disturbance category (compressor station, road, or no disturbance), year (2014 or

2015), species (deer mouse or red-backed vole), and sex (male or female). Initially, all two-way

interactions were included in the model. We used a backward stepwise model simplification to

eliminate non-significant interactions. We initially ran a linear mixed effects model using the R

package lme4 (Bates et al. 2015), and included site as a random intercept to account for possible

differences in movement behaviour across different sites. However, the random effect of site had

very low variance and a likelihood ratio test indicated that it did not improve the model, so we

simplified the analysis to a linear model with only fixed effects.

To answer the question of whether an effect of disturbance was occurring at a smaller scale

than the size of the trapping grids, we calculated a relative distance of each trap to the forest edge

of the clearing. Traps on the first line of the trapping grid closest to the forest edge were assigned

a distance of 0 m, and the distance increased in 15 m increments. We were interested in testing

for an edge effect on the number of individual small mammals caught, but more importantly

whether the edge effect differed between forest edges with infrastructure and forest edges

without infrastructure. At this scale, an increase in the number of individuals caught could be due

to increased abundance and/or changes in behaviour of small mammals. We determined the

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number of individuals caught across traplines located the same distance from the edge at each

site separately. There were 8 traplines per site, thus our sample size for this analysis was 296

traplines across both years. Our response variable, number of individual rodents captured per

trapline during the 4-day trapping session, showed overdispersion (i.e. the variance was larger

than the mean), so we used a generalized linear mixed effects model (GLMM) with a negative

binomial distribution using a log-link function in the ‘glmmADMB’ package in R (Skaug et al.

2016). Exploratory analyses suggested that the relationship between number of individual

rodents caught and distance to the forest edge was not linear, so a model with a term for inverse

distance (1/distance) was fit to the data. This functional form provided a better fit based on

comparing models using Akaike Information Criterion (AIC; Burnham and Anderson 2002). We

included year as a fixed effect to account for large differences in small mammal abundance

between years. Our final statistical model included number of individual rodents captured per

trapline as the response variable, fixed effects of year, inverse distance to the forest edge, site

disturbance category, the interaction between distance and site disturbance category, and site as a

random intercept to account for differences in small mammal abundance and vegetation

characteristics between sites.

Results

We recorded 1 531 small mammal capture events during 8 484.5 trap-nights, and we

caught and tagged a total of 674 individuals. Abundance of small mammals was higher during

the summer of 2014 (13.7 individuals per 100 trap-nights) than in 2015 (5.4 individuals per 100

trap-nights). Although we trapped more sites in 2015, we only captured 292 individuals during 5

473 trap-nights, compared to 382 individuals during 3 011.5 trap-nights in 2014. Directly

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comparing sites trapped in both years, the number of individuals caught decreased in 2015 for 11

out of the 12 sites trapped. Seven species of rodents were captured (Table 1), but 98% of all

captures and 96% of all individuals caught were southern red-backed voles (n = 956 captures,

450 individuals) and deer mice (n = 544 captures, 200 individuals). We caught red-backed voles

at 100% of sites trapped and deer mice at 68% of sites trapped across both years.

Abundance of small mammals (all rodent species pooled) differed between years (Fig. 2;

F1,33 = 11.64, p = 0.002), but did not differ across disturbance categories (Fig. 2; F2,33 = 1.30, p =

0.29). Abundance of red-backed voles differed between years (F1,33 = 9.77 p = 0.004), but did not

differ across disturbance categories (F2,33 = 1.68, p = 0.20). The interaction between year and

disturbance category approached significance (F2,31 = 3.01, p = 0.06). We caught twice the

number of red-backed vole individuals per 100 trap-nights at compressor station sites in 2015

compared to other sites, whereas we caught similar numbers of red-backed vole individuals

across industrial sites in 2014 (Fig. 2). Abundance of deer mice did not differ between years

(F1,33 = 2.01, p = 0.17), and did not differ across disturbance categories (F2,33 = 0.48, p = 0.62).

For the vegetation characteristics that we summarized for each site, we found significant effects

of forest composition (F1,33 = 7.20, p = 0.01), dominant ground cover (F2,32 = 5.57, p = 0.008),

and canopy cover (F1,33 = 6.54, p = 0.02) on the abundance of all rodent species pooled.

However, there was no effect of site disturbance on small mammal abundance (for all rodents

pooled, red-backed voles, and deer mice) when these three vegetation variables were added to

the models with year and site disturbance, and for this reason we chose to only present the results

of the simpler models (see above) without the vegetation variables.

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We found no difference in mean mass of deer mice across site disturbance categories (F2,21

= 1.31, p = 0.29), and no difference in mean mass of red-backed voles across site disturbance

categories (F2,34 = 0.05, p = 0.95) for all age classes combined. Furthermore, there was no

difference in mean mass of adult deer mice (F2,20 = 2.46, p = 0.11), juvenile deer mice (F2,10 =

1.55, p = 0.26), adult red-backed voles (F2,34 = 1.28, p = 0.29), or juvenile red-backed voles (F2,23

= 0.23, p = 0.80) across site disturbance categories.

For the activity analysis, we calculated the maximum distance moved during a trapping

session for each individual red-backed vole (n = 283) and deer mouse (n = 160) recaptured at

least once. The activity analysis included a total of 443 individuals, 257 individuals in 2014, and

186 individuals in 2015. The range of maximum distance moved between recaptures during a 4-

day trapping session varied between 0 m (caught in the same trap) and 129 m (moved diagonally

across the grid), with a mean maximum distance (± SE) of 33.8 m. The mean maximum distance

moved was 31.8 ± 1.53 m in 2014 and 36.4 ± 2.06 m in 2015, but this difference was not

significant between species (Table 2). We found no effect of site disturbance category on the

maximum distance moved by individuals during a trapping session (Fig. 3; Table 2). The mean

maximum distance moved (± SE) by deer mice was 38.5 ± 2.36 m, and for red-backed voles it

was 31.1 ± 1.39 m, but this difference was not significant (Table 2). We found a significant

effect of sex on mean maximum distance moved (Table 2), where males (n = 207 individuals)

moved 38.4 ± 1.95 m on average, and females (n = 236 individuals) moved 29.7 ± 1.55 m on

average. The effect of sex was primarily due to red-backed vole females moving less than males.

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For the edge effects analysis, we tested whether the number of individuals caught varied

with inverse distance to the forest edge, and whether this effect differed with the presence of

industrial infrastructure using a GLMM model. Our analysis included 296 traplines (8 per grid),

and the number of individual rodents caught on a trapline varied from 0 to 16, with a mean of 3.7

individuals. We found a significant effect of year (Table 3), but the interaction between inverse

distance to the edge and presence of industrial infrastructure was not significant (Table 3). We

ran the GLMM model without the interaction term and found the effect of year was highly

significant, but there was no significant effect of inverse distance or presence of industrial

infrastructure on the number of individual rodents trapped.

Discussion

There was little evidence that industrial disturbance due to energy sector development had

an impact on the abundance and activity of two common forest-dwelling small mammal species:

deer mice and red-backed voles. We examined this at two spatial scales and found no effect of

industrial disturbance at the level of the trapping grid, or within the trapping grid at increasing

distances from the industrial disturbance. In contrast, a study in sagebrush habitat found mouse

captures were higher at sites with increased habitat loss (i.e. less shrub cover) from energy

development (Hethcoat and Chalfoun 2015). The industrial sites we chose within the study area

had similar amounts of forest habitat loss, but differed in other features that we hypothesized

could affect small mammal abundance and activity. All three types of sites had forest edges

present, and the road and compressor station sites had infrastructure present. The road sites and

compressor station sites had noise and light pollution, but for road sites these stimuli were

intermittent from vehicle traffic. Presumably the traffic volume at the road sites was insufficient

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to create any kind of noise or light effect on small mammals, but also suggests that road

mortality or edge effects caused by roads were no different than those caused by forest clearings

with or without compressor stations. The compressor stations generate chronic noise and light

pollution from the infrastructure present, but we found no evidence that the infrastructure or

sensory stimuli present at these sites affected the abundance or activity of small mammals.

We hypothesized that small mammals could be indirectly affected by industrial disturbance

through changes in predation risk. Noise from industrial infrastructure could make small

mammals more vulnerable to predation, or this disturbance could provide a refuge from

predators. There is a possibility that these positive and negative effects could be operating at the

same time, leading to no detectable difference in abundance of small mammals between sites.

This could occur if predators that use auditory cues to hunt prey avoid noisy areas, while

predators using olfaction or vision are attracted to noisy areas with more susceptible prey.

Unfortunately, we are still lacking a comprehensive understanding of how predators respond to

noise-producing infrastructure. A few examples indicate that some predators avoid noisy areas

(Francis et al. 2009), while others do not (Shonfield and Bayne 2017). More research into how

the predator community, both aerial and terrestrial predators, is responding to noise-producing

industrial infrastructure is necessary to untangle these potential effects on prey. Light pollution

could also alter predation risk, as has been shown for two gerbil species (Gerbillus spp.) that

suffered higher predation risk by owls under artificial illumination, albeit in an open sand dune

habitat (Kotler et al. 1991). Nevertheless, the net result of our study appears to be that there is no

difference to the abundance of deer mice and red-backed voles across industrial sites in this

boreal forest system.

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Activity of individual deer mice and red-backed voles varied considerably, as measured by

the maximum distance moved between traps, but did not vary between compressor station sites,

road sites, and control sites. The mean distance moved by red-backed voles in our study was

comparable to another live-trapping study on this species (Vanderwel et al. 2010), and the range

of distances moved was comparable to a study tracking movement of two Microtus spp. using

radio telemetry (Norrdahl and Korpimäki 1998). We predicted that movement, as an indicator of

activity, would be influenced by predation risk based on the results of previous studies (Norrdahl

and Korpimäki 1998; Hegab et al. 2015). Movement differed between sexes, but did not differ

between industrial sites. That male voles moved more than females was similar to a study on two

Myodes vole species where males moved more than females and consequently were more

exposed to predation risk (Trebatická et al. 2008). Our results indicate that activity of deer mice

and red-backed voles did not differ between industrial sites, suggesting perceived predation risk

did not differ with industrial disturbance.

There could be alternative explanations to our results, other than predation risk. Without

data on how predation risk may have varied across sites, it is difficult to say whether this was the

primary factor influencing our results. Small mammals could have been affected by the noise

from compressor stations and intermittent traffic noise, if these sources act as environmental

stressors. A couple of studies have found evidence of physiological stress reactions in small

mammals in areas near roads (Navarro-Castilla et al. 2014) and wind farms (Łopucki et al.

2018), and have attributed these reactions, at least in part, to the noise generated. A logical

follow-up study to our work would be to determine if stress hormone levels of small mammals

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are affected by the presence of industrial infrastructure and the noise generated. We also do not

know about the hearing abilities of deer mice and red-backed voles. Some murid rodents do not

hear appreciably below 1 kHz (Heffner et al. 2001). We would presume that rodents with

restricted low frequency hearing would be less likely to exhibit physiological stress reactions in

response to low frequency industrial noise, and may be less susceptible to potential negative

effects of industrial noise. It is also possible the presence of noise-producing industrial

infrastructure could degrade the habitat, and if that was the case we would expect younger,

subordinate, or lower quality individuals to occupy these areas. However, we did not find a

difference in body mass in deer mice or in red-backed voles living in close proximity to

compressor stations, roads, and control sites. Overall, there was no evidence that industrial

infrastructure and the noise generated degrades habitat for deer mice and red-backed voles,

however, we do not have information on the survival and reproduction of individuals living in

these areas. It is also worth noting that the densities of the two species were relatively low, and

future research is necessary to determine if responses by small mammals to industrial

disturbance change at higher densities.

Despite the fact that we found no difference in small mammal abundance across industrial

sites at the grid level, we considered that there may be an edge effect occurring over a very short

distance. However, our results indicated that there was no interaction between distance from the

edge and presence of industrial infrastructure. We also found only limited evidence of any edge

effect on small mammal abundance. Reviews of studies on edge effects have generally shown

mostly neutral effects of edges on small mammals (Ries et al. 2004). Edge effects can be positive

if they result in habitat enhancement, for example if species are attracted to different resources

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available in each habitat, as edges are the ideal location to gain access to spatially separated

resources (Ries et al. 2004). Edge effects are more likely to be negative if they result in

transitional habitat that lacks important resources for some species, or if they result in higher

predation risk or mortality.

Impacts of anthropogenic disturbance on wildlife can differ between taxonomic groups.

The literature on the effects of roads has shown mainly negative effects on the abundance of

birds, reptiles and large mammals, but for small mammals the effects on abundance are either

neutral or positive (Fahrig and Rytwinski 2009). Our results similarly suggest that for two

common forest-dwelling rodent species, roads in the boreal forest of northern Alberta have a

neutral effect. Our results also suggest the effects of infrastructure producing chronic noise are

minimal on these small mammal species primarily using non-auditory signals for

communication, and unlikely to be key drivers of their population dynamics or behaviour. Deer

mice and red-backed voles are found across the boreal region, so it seems reasonable to expect

similar results across this region; however, other small mammal species inhabiting different

ecosystems (e.g. in the tropics) may respond differently. Our understanding of the impacts of

anthropogenic disturbance on different species and taxonomic groups continues to evolve and

further work on small mammal communities and their response to chronic noise is warranted, but

should be done in conjunction with studies on predator behaviour near disturbed areas.

Acknowledgements

We thank E. Beck, C. Bodnar, N. Boucher, J. Cameron, S. Petzold, L. Valliant, and A. Wong for

their assistance in the field. Thanks to E. Neilson, F. Stewart and two anonymous reviewers for

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providing helpful comments on an earlier version of this manuscript. Funding was supported by

the National Science and Engineering Research Council, the Northern Scientific Training

Program, the University of Alberta North program, the Alberta Conservation Association, the

Environmental Monitoring Committee of the Lower Athabasca, Nexen Energy, and the Oil

Sands Monitoring Program operated jointly by Alberta Environment and Parks, and Environment

and Climate Change Canada. This work was funded under the Oil Sands Monitoring Program

and is a contribution to the Program but does not necessarily reflect the position of the Program.

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Figure Legends

Fig. 1. Map of the study area located within the Lower Athabasca planning region of

northeastern Alberta (bottom right inset) and locations and disturbance categories of sites where

small mammals were live-trapped. Alberta boundary map data from Statistics Canada, planning

region boundary map data from the Government of Alberta.

Fig. 2. Abundance of small mammals for each year in each site disturbance category (mean

number of individuals per 100 trap-nights ± 1 SE). In 2014, 14 sites were trapped (left panel) and

in 2015, 23 sites were trapped (right panel).

Fig. 3. Spatial extent of activity, measured as mean maximum distance moved (mean ± 1 SE)

between traps by small mammals (deer mice (Peromyscus maniculatus (Wagner, 1845)) and

southern red-backed voles (Myodes gapperi (Vigors, 1830)) only) during 4-day trapping sessions

for each site disturbance category. Numbers in the bars indicate the sample size (i.e. number of

individuals caught at least twice).

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Table 1 – Number of individuals caught, and number of individuals recaptured at least once during the 4-day trapping session for each

species in each year. Live-trapping was done in July and August of each year, and each site was trapped for one session per year.

Species Scientific name Individuals

caught in

2014

Individuals

recaptured

in 2014

Individuals

caught in

2015

Individuals

recaptured

in 2015

Southern red-backed vole Myodes gapperi 258 172 192 110

Deer mouse Peromyscus maniculatus 109 85 91 76

Meadow vole Microtus pennsylvanicus 10 1 5 1

Northern flying squirrel Glaucomys sabrinus 2 1 1 0

Red squirrel Tamiasciurus hudsonicus 2 0 0 0

Meadow jumping mouse Zapus hudsonius 1 0 2 1

Least chipmunk Tamias minimus 0 0 1 0

Total small mammals 382 259 292 188

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Table 2 – Results of the activity analysis: a linear model explaining log transformed maximum

distance moved by individual rodents between traps during a 4-day trapping session. Effects are

reported as the effect of the level in parenthesis (e.g. road) relative to the reference category. In

this case the reference categories are control sites for the site category, 2014 for year, females for

sex, and deer mice (Peromyscus maniculatus (Wagner, 1845)) for species. Significant p-values

are in bold (significance level α = 0.05).

Effect Estimate ± SE t p

Intercept 2.97 ± 0.15 20.11 <0.0001

Site category (compressor station) -0.21 ± 0.15 -1.44 0.15

Site category (road) -0.03 ± 0.16 -0.19 0.85

Year (2015) 0.08 ± 0.12 0.67 0.50

Sex (male) 0.35 ± 0.12 2.84 0.005

Species (southern red-backed vole

Myodes gapperi)-0.01 ± 0.13 -0.10 0.92

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Table 3 – Results of the edge effect analysis: a negative binomial generalized linear mixed model

(GLMM) explaining number of individual rodents caught per trapline during 4-day trapping

sessions. Effects are reported as the effect of the level in parenthesis (e.g. road) relative to the

reference category. In this case the reference categories are control sites for the site category, and

2014 for year. Significant p-values are in bold (significance level α = 0.05).

Fixed effect Estimate ± SE Z p

Intercept 1.60 ± 0.22 7.29 <0.0001

Year (2015) -0.80 ± 0.08 -10.29 <0.0001

Site category (compressor station) 0.07 ± 0.31 0.23 0.82

Site category (road) -0.12 ± 0.31 -0.40 0.69

Inverse distance to edge -0.10 ± 0.17 -0.60 0.55

Site category (compressor station) ×

Inverse distance to edge0.40 ± 0.22 1.81 0.07

Site category (road) × Inverse distance

to edge0.27 ± 0.24 1.16 0.25

Random effect Variance SD

Site 0.356 0.597

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Fig. 1.

215x279mm (300 x 300 DPI)

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Fig. 2.

203x101mm (300 x 300 DPI)

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Fig. 3.

101x101mm (300 x 300 DPI)

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Canadian Journal of Zoology · 2019. 10. 21. · Draft Effects of industrial disturbance on small mammal abundance and activity Journal: Canadian Journal of Zoology Manuscript ID