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Traffic volume and highway permeability for a mammalian community in the Canadian Rocky Mountains.

Introduction

Roads are vital to our economy but impose significant ecological costs including habitat loss and fragmentation, road mortality and avoidance, reduced access to vital resources, population fragmentation and disruption of ecological processes (Forman and Alexander 1998; Spellerberg 1998; Jackson 1999; Trombulak and Frissel 2000; Bolger et al. 2001; Malo et al. 2004). In fact, roads may pose the greatest mortality threat to wildlife. Most recently, Malo et al. (2004) estimated that vehicle-related wildlife mortality totals several millions per year.

Whether through direct mortality or indirect effects such as avoidance of habitat near traffic, roads can partition previously connected habitat into isolated patches (i.e., islands of habitat). The inability of wildlife to move between patches of habitat due to some disturbance is known as the barrier effect (Forman and Alexander 1998). Because traffic on roads acts more like a filter to movement rather than an absolute barrier, this effect is also described as relative permeability: the greater the movement rate across it, the greater the permeability of the road. It is well understood that the barrier effect can alter animal community composition, create meta-populations, reduce biological diversity and increase the threat of extinction (Oehler and Litvaitis 1996; Forman and Alexander 1998; Spellerberg 1998; Trombulak and Frissel 2000). Particularly at risk are sensitive, threatened and rare species, and this threat is as great within as outside our North American National Parks (Noss et al. 1996; Paquet et al. 1996; Reed et al. 1996; Chruszcz et al. 2003). Carnivores tend to disappear first in isolated patches (Daimond 2001), because relative to their prey they have comparatively low resilience due to individual life-history traits, including (among others) late age of first reproduction, low reproductive rate and low fecundity (Weaver et al. 1996; Cardillo et al. 2004). To illustrate, if a species cannot reproduce rapidly enough to offset vehicle mortality or navigate through traffic to access food, shelter or mates, a population decline is likely. Over time and across space, such a decline can force populations into extinction (Cardillo et al. 2004).

The creation of habitat linkages between isolated patches of habitat is a method for improving biotic exchange across barriers (Swart and Laws 1996; Forman and Alexander 1998). In highway design and habitat management disciplines, such linkages are termed mitigation. In North America, these include faunal crossing structures, such as underpasses or overpasses that connect habitat on each side of the highway and fencing to prohibit access onto the roadway (Jackson 1999). A growing body of research examines the design, implementation and efficacy of such mitigation (Malo et al. 2004). The proliferation of mitigation research and efforts focuses on mortality sites, by implementing tools such as Geographic Information Systems (GIS) to identify landscape criteria predicting such sites (Malo et al. 2004). Or, the research focuses on the relative effectiveness of crossing structures to improve permeability (e.g., Clevenger and Waltho 2000), not the entire mitigation effort. Research on road-related barrier effects is relatively scant (Trombulak and Frissel 2000).

Although the study of mortality sites and relative effectiveness of crossing structures are important, we raise two critical related issues. First, understanding where mortality occurs may not address where animals prefer to cross roads. Previously, we used GIS with the same crossing data used here to determine where mitigation should be placed based on crossing preference (Alexander et al. 2004). Arguably, the approaches of both Malo et al. (2004), who examined mortality sites, and Alexander et al. (2004), who studied crossing locations, should be used when determining where to mitigate a road. Second, studies of mitigation efficacy examine how individual structures compare with each other, but not whether a mitigation effort across one highway is successful. That is, they do not consider the total permeability attained through mitigation (i.e., the total crossings for wildlife along the entire road segment) relative to what would be present in less or non-perturbed habitat (i.e., bisected by a survey line of equal length). The former is a critical distinction. Road ecologists require baseline data that explain how animals move in similar but less or undisturbed habitats. For example, if 1,000 animal crossings per year were observed on a mitigated road section, what would one expect to observe if there were no road or traffic? In the future, our data could be used with existing mitigation data in the area to address the later question of mitigation effectiveness (e.g., compare our data with those used by Clevenger and Waltho 2000 or Chruszcz et al. 2003).

One of the most pressing questions in road ecology is 'when does traffic volume become a barrier to wildlife movement and require a road to be mitigated?' Such understanding is important to managers, because under financial constraints there is a need to prioritise which roads need to be mitigated, and when. Beyond management and road design, a better understanding of how traffic changes habitat permeability can be of great value to predicting species persistence in road-fragmented ecosystems. Minta and Kareiva (1994) and Tieboult and Anderson (1997) all emphasised the need to document the natural patterns of intrinsic movement, to quantify anthropogenic disruption of natural connectivity and to describe species-specific mechanisms of inter-patch dispersal, in order to quantify processes governing local extinction and colonisation.

Importantly, North American studies of highway mitigation have focused on large mammals such as elk (Cervus elaphus), wolf (Canis lupus) and grizzly bear (Ursus arctos). Although barrier effects have been studied quite extensively for small mammals, reptiles and birds (Jackson 1999), and regionally for black bears (Serrouya 1999), little attention has been paid to smaller carnivores such as marten (Martes americana), and few empirical studies document multiple-species crossing behaviour before highway enhancement. Moreover, none quantified community- or guild-level effects. Yet, the representation of multiple species is critical to attain functional connectivity in the landscape. To illustrate, Tischendorf and Fahrig (2000) refer to two components of effective habitat connectivity: structure and function. Structure refers to crossing location, whereas function refers to taxon or trophic representativeness. Therefore, if only a subset of resident species use crossing structures or less movement occurs across an entire road segment or valley than expected under natural conditions, then functional connectivity has not been attained.

Our study area is a popular international, national and regional scenic destination that is under pressure from increased recreation, tourism and transport. Accompanying this growth is a steady rise in traffic volume on roads across the study area. Concerns were raised that higher traffic volume on the Trans-Canada Highway (TCH) in Banff has reduced wildlife movement and habitat permeability (Banff-Bow Valley Study 1996; Chruszcz et al. 2003). Hence, we tested whether traffic volume significantly reduced movement rates (or habitat permeability) for ten mammalian species. We had four objectives:

* to compare permeability values for four unmitigated roads of different traffic volume (low, moderate, high and very high);

* to examine whether traffic volume-permeability relationships were consistent across the community level (all ten species), the carnivore guild (six species) and the ungulate guild (four species);

* to identify the winter average daily traffic (WADT) volume that significantly reduced permeability for the three species assemblages;

* to make management recommendations regarding the surveyed roads.

Our research makes several important and unique contributions. The research examines how traffic volume affects landscape permeability at a community level, contributing to the disciplines of landscape, community and road ecology. The multiple-species scope of the research is unique, because it applies a consistent methodology simultaneously to a community of species, over an extensive area and during three consecutive years. Moreover, community ecology studies have relied often on 'meta-analytical' techniques, using databases that differ in time, place and method of collection. Lastly, the method of calculating permeability with track density is a novel unique approach to the study of the barrier effect, advancing spatial analytical methods in ecology and geography.

Study Area

Research was conducted in Banff National Park (BNP) and Kananaskis Country (KC), approximately 110 km west of Calgary, Alberta, Canada (Figure 1). BNP is approximately 6,640 [km.sup.2] and is the most visited national park in Canada with more than five million visitors annually (Banff-Bow Valley Study 1996). KC is a 4,250-[km.sup.2] forestland-use zone allows that hunting, ranching, resource development, recreation and tourism activities. The study region is rugged, with steep mountains and narrow (2-5 km), flat valley bottoms, characteristic of the Canadian Rocky Mountains. Monthly precipitation peaks in May through July, and snow thickness maximums occur in November to December and March to April (Alexander and Waters 2000).

[FIGURE 1 OMITTED]

In BNP, data were collected on the two-lane, paved, unfenced section of the TCH from Castle Junction to the British Columbia and Yoho National Park border, and the entire extent of the two-lane, paved Bow Valley Parkway (Highway 1A) (Figure 1). In KC, research was conducted on the two-lane paved Highway 40 (Highway 40) and the gravel covered Smith Dorrien Trail. We surveyed only roads that had not been mitigated with fencing or crossing structures. A few wildlife crossing signs exist to indicate sites where vehicular-wildlife collision risk is high.

Methods

Ground data

In winter, we observed where species moved in the landscape, by observing their tracks in snow along road right-of-ways and transects. Here, transects are permanent, marked (i.e., with flagging tape) survey lines adjacent to roads. We collected track data between November and April from 1997 to 2000, using methods adapted from Van Dyke et al. (1986), Thompson et al. (1988), Beier and Cunningham (1996) and Oehler and Litvaitis (1996).

We divided roads into categories (low, moderate, high and very high) based on annual average daily traffic (AADT) (Table 1). Each road category was approximately 70 km in length, except for the unmitigated section of the TCH (approximately 35 km). For consistency, we divided all roads into 35-km segments. Road surveys began 24 hours after first snowfall. A road survey involved looking for tracks while driving at 15-20 km/h. We stopped and recorded tracks for coyote (Canis latrans), wolf, cougar (Puma concolor), lynx (Felis lynx), marten, wolverine (Gulo gulo), elk, moose (Alces alces), sheep (Ovis canadensis) and deer (Odocoileus virginianus and Odocoileus hemionus). Repeat road surveys were conducted for large carnivores every 3-4 days.

Data at crossing sites were georeferenced with a handheld Garmin II Global Positioning System (GPS) (UTM, NAD27, [+ or -] 50-100 m). If multiple tracks for the same species were found, we entered a total track count. The roads surveyed function as random transects throughout the study area, and in theory, species natural history characteristics should be similar across the region. Thus, counting all tracks in groups (as with ungulates) should not bias results because there should be an equal likelihood of detecting groups in all areas.

We assumed that tracks entering/exiting the road surface were crossing attempts, and the crossing was marked as unconfirmed if no companion tracks exiting/entering the road were found within 300 m. Tracks of the same species past 300 m were assumed to be independent crossing attempts, unless it was obvious that the animals were using the road as a corridor. Time constraints prohibited backtracking, so the previous protocol was necessary to standardise permeability values among road segments. Although arbitrary, we assumed that tracks separated by 300 m were spatially independent (based on extensive personal ground experience of the authors), and thus, replicate tracks beyond that distance did not constitute pseudo-replication. That is, after 300 m the species has many options of where to cross a road and the influence of the original crossing on the new crossing should be minimal. Our track count methods were adopted from prior research that found this approach rigorous (Van Dyke et al. 1986; Oehler and Litvaitis 1996). Finally, as there are only subtle differences in habitat composition across the study area, we assumed the consistent methodology made valid our comparisons amongst roads.

We fixed 1-km-long transects perpendicular and adjacent to each road segment, spaced 1 km apart. Each transect was flagged every 50 m to signify the start of a new sub-transect. This flagged point was georeferenced using a Trimble Pathfinder GPS (UTM, NAD27, [+ or -] 1 m differentially corrected). Observed track counts were tallied for each sub-transect. In other analyses, the track count was linked to the GPS point for use with a GIS to determine species-environment relationships (Alexander et al. 2004). We surveyed forty transects in BNP and twenty in KC, between 24 and 120 hours after snow. This time period was selected because sampling before 24 hours yielded few data, and as surveys were conducted on foot, they required more time to complete than each road survey.

Analysis

Transect data were standardised by the number of days (24 hours) that had elapsed since snowfall, a common practice to correct for 'time effect' (Thompson et al. 1988). For example, some species can use a point in space with increased intensity over time, so it is important to control for that effect on observed track density. We collected road crossing data within 24 hours from fresh snowfall and did not require standardisation. However, road data were used to compute permeability, which required they be standardised by kilometres surveyed per sample period (Equation 1).

(Equation 1) Road crossings/km = Number of tracks on road/ (number of surveys x length in km)

Standardised transect counts also were divided by the total kilometres surveyed on each transect by sample period (Equation 2).

(Equation 2) Transect crossings/km = [T/(R x 50m)] x 1,000m/km

where T represents the number of species tracks on a given transects and R the number of 50-m sub-transects surveyed per transect; on the rare occasion, conditions caused us to terminate a transect surveys before completing the 1 km (e.g., open water that resulted from a warming temperature trend).

We calculated permeability by standardising the road crossings per kilometre (Equation 1) by the density of tracks observed (Equation 2) on transects adjacent to each road section. This accounts for a 'habitat effect'--the possibility that track count on a given road section could be biased by the quality of habitat adjacent to the road being surveyed. In theory, if a habitat is of a higher quality, there could be more individual animals resident, which could increase the likelihood of observing them crossing the road.

(Equation 3) Permeability = Road crossings per km/ transect crossings per km

Initially, we planned to compare exact winter traffic counts (collected on data loggers) with permeability. However, on completion of field surveys, we learned that traffic data were not collected in KC. This precluded linking crossing counts to traffic counts and applying a Generalised Linear Model of any form. Thus, we tested differences in permeability (Equation 3) amongst four highway classes (low, moderate, high and very high) using a Kruskal-Wallis H-test (Siegel and Castellan 1988). These relative volume categories are consistent across seasons. Although there are temporal closures on the 1A (moderate volume road) in BNP during winter, these closures are voluntary and are during the dawn and dusk period only; there is limited enforcement of the closure, and no evidence to suggest that it has reduced volume.

We compared permeability values amongst road classes at the mean annual (three years) and mean monthly (eighteen months) levels to determine temporal aggregation effects. In addition, comparisons were conducted for each of the previous temporal categories for all species (i.e., community level), the carnivore guild and the ungulate guild. We aggregated species into guilds, which represent assemblages of species that use resources and affect ecosystems in a similar way. Although species-specific differences may exist in habitat selection, the guilds tend to be similar in their negative response to human disturbance (Noss et al. 1996; Paquet et al. 1996). Hence, we considered it critical to differentiate guilds, as a disruption for carnivores may signify negative consequences for community or system functionality (Noss et al. 1996; Weaver et al. 1996). Aggregating mammals into guilds also improved our sample size and statistical reliability of results. Importantly, the aggregation of species into guilds provides the most generous interpretation of traffic disturbance; permeability could appear higher because of more crossings by tolerant species within the guild.

In the absence of WADT volume data for all road categories, we developed some general rules of thumb to identify any traffic volume thresholds for carnivores and ungulates. The moderate volume road segments in BNP (the 1A) had approximately 300 WADT and 3,000 AADT, which suggests a 10 : 1 annual to winter traffic ratio. The TCH had approximately 5,000 WADT versus 14,000 AADT and hence a 3:1 ratio of winter to annual traffic volume. We applied the 10:1 rule of thumb only to one road segment, the Highway 40 (i.e., high volume), for which WADT was lacking. We used the 10:1 rule of thumb to estimate the high volume WADT, because the moderate volume road segment in BNP was more similar to high volume road segments in KC than the TCH. The TCH is the main east-west connector in Canada, and the seasonal changes in TCH traffic volume are confounded with a constant and intense use of this highway. We admit that this assumption is not perfect but provides a more relevant traffic volume threshold for management than the associated AADT. More importantly, the point of this paper is to examine relative differences in permeability and not exact traffic volumes at which permeability decreases.

Results

We surveyed 8,280 km driving and 1,004.5 km on foot, which yielded the following track counts, shown for ungulates and carnivores, each in descending order: elk = 3,229; deer = 1,727; moose = 265; sheep = 119; marten = 3,457; coyote = 1,075; lynx = 359; wolf = 329; cougar = 312 and wolverine = 39.

Kruskal-Wallis H-test

We present permeability values for three species aggregations, including the community (ten species), ungulate guild (four species) and carnivore guild (six species), and by all road volume categories (Table 2). These permeability values were depicted graphically (Figure 2) as relative percentages for the ease of interpretation. Table 3 summarises the results of our Kruskal-Wallis H-test, which compared permeability amongst highway sections for all the three species aggregations. Here, we present some non-significant results (P > 0.10) that were important to our discussion.

[FIGURE 2 OMITTED]

At the community level (n = 10,911), a significant difference was observed in annual average permeability amongst all road categories (P = 0.006) (Figure 2; Table 3). We observed significantly higher movement rates for the community on low traffic volume roads than on high (P = 0.033) and very high traffic volume roads (P = 0.001), and moderate volume roads significantly more permeable than very high traffic volume roads (P = 0.014). Moderate volume roads, however, were not significantly different from low or high volume roads. At the mean monthly aggregation, we showed a significant difference in permeability amongst road categories (P < 0.001) (Table 3). Low volume roads were significantly more permeable than high (P = 0.001) and very high (P < 0.001) volume roads. Moderate volume roads were significantly more permeable than high (P = 0.006) and very high (P = 0.004) volume roads. Low and moderate volume roads were not significantly different from each another.

The carnivore guild showed (n = 5,340) a significant difference in mean annual permeability amongst road types (P = 0.001) (Figure 2; Table 3). Low volume roads were significantly more permeable than high (P= 0.001) and very high (P = 0.005) traffic volume roads. Likewise, moderate volume roads were significantly more permeable than high (P = 0.008) and very high (P = 0.049) volume roads. As with the community results, moderate volume roads were not significantly different from low and high volume roads.

Ungulates (n = 5,571) showed no significant difference in annual average permeability amongst traffic volume categories (Figure 2; Table 3). Very high volume roads had significantly greater permeability than high volume roads (P = 0.043). Yet, very high volume roads did not differ from moderate and low volume roads at P < 0.05. However, at a narrower confidence interval, the very high volume road had significantly lower permeability than moderate (P = 0.171) and low (P = 0.148) volume roads.

The AADT for moderate to high traffic is 3,000-5,000. Using the known WADT for the moderate volume road and extending our 'rule of thumb' for high WADT, we suggest a threshold to carnivore movement was observed between 300 and 500 VPD. Using the same estimated WADT for high volume roads, ungulates respond functionally between 500 and 5,000 VPD. We did not survey traffic volume between 500 and 5,000 VPD. Thus, we cannot provide a more precise threshold for ungulates.

Discussion

At the community level, permeability declined significantly with traffic volume. Although expected, this effect has not been demonstrated for the study area or species. In fact, the only study that has examined barrier effects on mammals larger than on mice or birds was that conducted by Serrouya (1999). His work demonstrated similar results but differed from ours in approach and only compared movement rates on the mitigated and unmitigated sections of the TCH. Moreover, his data relied on radiotelemetry whereas ours were non-invasive and used track observations, which tend to be less costly data to collect.

In our study area, the TCH is the only very high volume road we examined, and we found it had significantly lower permeability for all species. This provides evidence that mitigating the TCH is necessary. Based on available WADT and in combination with the trends we observed in permeability, managers may identify the traffic level a road requires mitigation to restore permeability. For example, a high volume road in BNP (the 1A Highway) has approximately 300 vehicles per day (VPD) in winter versus approximately 5,000 VPD on the very high traffic volume TCH. Thus, we know that permeability decreased for the community between 300 and 5,000 VPD.

The carnivore guild also showed significantly lower permeability with increased traffic volume. We observed that carnivore movement aggregated into two functional levels based on traffic volume, low/moderate and high/very high. Thus, we postulate that traffic volume between moderate and high may be a threshold to permeability for carnivores. Ungulates showed a different response from carnivores; permeability did not decline significantly with traffic volume amongst all highway categories. However, we observed that ungulates clustered into two functional levels relative to traffic volume: low/moderate/high and very high, when we considered P-values less than 0.17. This suggests ungulates may have a higher threshold response to traffic volume than carnivores, which ranges between high and very high volume (5,000 versus 14,000 AADT).

Management Implications

Our traffic volume thresholds to movement provide general guidelines about when a road requires mitigation to improve habitat permeability. Of course, our guidelines will only be generalisable to similar habitats with similar species assemblages. We observed that thresholds differed for carnivores and ungulates in that carnivores appear to be more sensitive to traffic volume. Carnivore habitat permeability significantly decreased at high traffic volume, whereas ungulate habitat permeability was significantly different at very high traffic volume. The most biologically conservative choice would be to mitigate roads when they become a barrier to carnivores, because carnivores have a greater effect (via top-down pressures) on the functioning of ecosystems (Noss et al. 1996).

Hence, high volume roads with more than 5,000 VPD require mitigation to restore permeability at the ungulate and wildlife community level. The Phase IIIB of the TCH that we surveyed is such a road. Given that it is presently undergoing expansion, our data support the use of mitigation. Importantly, the permeability required to restore connectivity is that in the least disturbed conditions possible. In other analyses (e.g., Alexander et al. 2004), we found that the present mitigation has actually reduced permeability rather than ameliorating the barrier effect on the TCH. That is, the permeability on the mitigated section of the TCH in BNP is lower than on the unmitigated section.

High traffic volume impairs habitat permeability at the guild and community level, and different thresholds to permeability exist for carnivore and ungulate guilds. We recommend mitigation be implemented at thresholds for carnivores, such that ecosystem functionality most likely will be restored. We showed that carnivore permeability was decreased significantly between 300 and 500 VPD, whereas ungulates showed a higher tolerance that ranged from 500 to 5,000 VPD. Based on the previous data, the TCH requires mitigation but the Highway 40 in KC is a priority for mitigation.

Researchers have outlined what appropriate mitigation might be (see Introduction), which included fencing to reduce mortality, combined with appropriate crossing structures (e.g., wildlife overpasses, culverts, tunnels and elevated sections of highway or open-span structures). We emphasise that mitigation should restore connectivity to permeability that exists in undisturbed or less-disturbed habitat, not simply maintain what exists in the disrupted habitat. Most recent arguments reiterate the appropriateness of raising highways (e.g., open-span bridges) as the most efficient approach to protecting continuous and diverse habitat/movement corridors beneath the highway (Alexander et al. 2004; Bissonette 2004). This ascribes to 'the Cinderella Principle'--making the road ('virtual shoe') fit the movement corridor, rather than the corridor fit the road (Bissonette 2004), which seems particularly apt in protected areas such as BNP, where ecological integrity is mandated by law.

Conclusion

Traffic volume is rising in Canada's protected areas, particularly in the Rocky Mountains, despite growing ecological problems. Hence, managers need information and tools to decide when a road needs to be mitigated. Our work has provided a cost-effective and rigorous tool to examine habitat permeability at a community and guild level. Managers can employ our results to inform management decisions regarding existing roads such as Highway 40, which based on our results should be a priority for mitigation. Our results are generalisable to similar environments and thus may be used to garner support for the mitigation of highways. We provided baseline movement rate or permeability data for different disturbance regimes in Banff and KC. These data can be used in an adaptive framework to assess the performance of existing mitigation on the roads surveyed here; managers could determine whether permeability rates rise to a level consistent with at least those observed on low or moderate volume roads. Lastly, we highlighted community- and guild-level traffic volume thresholds. Combined with our other work on spatial requisites of multiple species (Alexander et al. 2004), our results should improve the success of mitigation planning and decision making.

Acknowledgements

We received support from NSERC Canada, the University of Alberta Biodiversity Fund, the Province of Alberta Graduate Fellowship, Alberta Environmental Protection, Canadian Pacific Corporation, Banff National Park Wildlife and Highways Divisions, Edward Alexander, Dr. Margaret P. Hess, the University of Calgary, the Alberta Sport-Recreation-Parks and Wildlife Foundation, Employment Canada, the Western Forest Carnivore Society and the Paquet Fund for Wildlife Biology. Dr. A. Clevenger, Dr. C. White and T. Hurd from Parks Canada and S. Donelon and J. Jorgensen from Alberta Natural Resources provided logistical support and research direction on behalf of their agencies. We thank the anonymous reviewers for their valuable contributions.

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SHELLEY M. ALEXANDER

Department of Geography, University of Calgary, Calgary, Alberta, Canada T2N 1N4

(e-mail: smalexan@uclagary.ca)

NIGEL M. WATERS

Department of Geography, University of Calgary, Calgary, Alberta, Canada T2N 1N4

(nwaters@ucalgary.ca)

PAUL C. PAQUET

Faculty of Environmental Design, University of Calgary, Calgary, Alberta, Canada

T2N 1N4 (ppaquet@sasktel.net)
Table 1

Road section description and traffic volume class

             Survey                                 Traffic
Location       ID             Description           volume    Category

Banff          B1     Highway 1A East--East Exit     3,000    Moderate
                      to Castle Junction
Banff          B2     Highway 1A West--Castle        3,000    Moderate
                      Junction to Lake Louise
Banff          B3     Trans-Canada Highway (TCH)    14,000    Very high
                      --Castle Junction to the
                      British Columbia Border
Kananaskis     K2     Highway 40--Kananaskis         5,000      High
                      Village to TCH East of
                      Canmore
Kananaskis     K3     Highway 40--Kananaskis         5,000      High
                      Lakes Trail (KLT) to
                      Kananaskis Village
Kananaskis     K4     Smith Dorrien Trail--Shark     2,000       Low
                      Mountain Exit to KLT Road
Kananaskis     K5     Smith Dorrien Trail--Shark     2,000       Low
                      Mountain Exit to Whiteman's
                      Gap

NOTE: Roads were divided into 35-km segments, as detailed under
description. Traffic volume was estimated for Banff National Park from
annual average daily traffic (Banff-Bow Valley Study 1996), and each
segment was assigned to an ordinal class (low to high). Sections with
similar volume were aggregated for analysis.

Table 2

Permeability values by highway section (volume category in brackets)
by community, ungulate and carnivore guilds

Species assemblage/year(s)    K4 (low)     KS (low)     B1 (mod)

Community 3 years               1.334        1.400        2.940
Community 1999/2000             1.892        1.284        0.747
Community 1998/1999             1.131        0.690        6.440
Community 1997/1998             0.833        2.229        1.632
Ungulates 3 years               1.446        1.401        4.348
Ungulates 1999/2000             3.031        2.768        1.375
Ungulates 1998/1999             0.654        0.278        8.915
Ungulates 1997/1998             0.653        1.157        2.753
Carnivores 3 years              1.266        1.400        2.000
Carnivores 1998/1999            1.448        0.965        4.790
Carnivores 1999/2000            1.133        0.295        0.328
Carnivores 1997/1998            0.953        2.943        0.885

Species assemblage/year(s)    B2 (mod)     K3 (high)    B3 (high)

Community 3 years               0.286        1.600        0.111
Community 1999/2000             0.124        0.288        0.155
Community 1998/1999             0.437        4.338        0.071
Community 1997/1998             0.299        0.240        0.239
Ungulates 3 years               0.196        3.970        0.053
Ungulates 1999/2000             0.075        0.570        0.020
Ungulates 1998/1999             0.355       10.845        0.000
Ungulates 1997/1998             0.159        0.493        0.139
Carnivores 3 years              0.347        0.017        0.149
Carnivores 1998/1999            0.491        0.000        0.118
Carnivores 1999/2000            0.156        0.100        0.246
Carnivores 1997/1998            0.392        0.021        0.306

Three years permeability is an annual average based on 1997-2000
surveys. Yearly permeability estimates are mean monthly for five
months.

Table 3

Kruskal-Wallis H-test: permeability rates by traffic volume

Road class comparisons                   d.f.   Significance    Chi-
                                                               square
A. Mean annual permeability community
level
  Low, moderate, high, very high          3        0.006       12.574
  Low versus moderate                     1        0.507        0.440
  Low versus high                         1        0.033        4.555
  Low versus very high                    1        0.001       10.198
  Moderate versus high                    1        0.082        3.027
  Moderate versus very high               1        0.014        6.009
  High versus very high                   1        0.426        0.035
B. Mean monthly permeability community
level
  Low, moderate, high, very high          3        0.000       21.389
  Low versus moderate                     1        0.258        1.282
  Low versus high                         1        0.001       11.335
  Low versus very high                    1        0.000       13.370
  Moderate versus high                    1        0.006        7.616
  Moderate versus very high               1        0.004        8.521
  High versus very high                   1        0.932        0.007
C. Mean annual permeability carnivores
  Low, moderate, high, very high          3        0.001       16.571
  Low versus moderate                     1        0.326        0.964
  Low versus high                         1        0.001       11.380
  Low versus very high                    1        0.005        7.911
  Moderate versus high                    1        0.008        7.132
  Moderate versus very high               1        0.049        3.883
  High versus very high                   1        0.259        1.274
D. Mean annual permeability ungulates
  Low, moderate, high, very high          3        0.311        3.577
  Low versus moderate                     1        1.000        0.000
  Low versus high                         1        0.734        0.115
  Low versus very high                    1        0.148        2.091
  Moderate versus high                    1        0.734        0.116
  Moderate versus very high               1        0.171        1.872
  High versus very high                   1        0.043        4.083

NOTE: Traffic volume categories are low, moderate, high and very high.
Comparisons are shown for the community (ten species) for three years
mean annual (A) and three years mean monthly (B), and carnivores (C)
and ungulates (D) for three years mean annual values. Kruskal-Wallis
H-test uses a chi-square equivalent for significance testing.
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Author:Alexander, Shelley M.; Waters, Nigel M.; Paquet, Paul C.
Publication:The Canadian Geographer
Geographic Code:1CANA
Date:Dec 22, 2005
Words:6144
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