Open Access
Highlights
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- Lynx crossed two-lane paved highways an average of 0.6 times per day.
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- Lynx crossed roads more at dusk and night, coincident with lower traffic volumes.
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- Forest cover was predictive of lynx highway crossings at fine and landscape scales.
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- Predictions from remotely-sensed covariates validate well with independent data.
Abstract
Carnivores
are particularly sensitive to reductions in population connectivity
caused by human disturbance and habitat fragmentation. Permeability of
transportation corridors to carnivore movements is central to species
conservation given the large spatial extent of transportation networks
and the high mobility of many carnivore species. We investigated the
degree to which two-lane highways were permeable to movements of
resident Canada lynx in the Southern Rocky Mountains based on highway
crossings (n = 593) documented with GPS telemetry. All lynx crossed
highways when present in home ranges at an average rate of 0.6 crossings
per day. Lynx mostly crossed highways during the night and early dawn
when traffic volumes were low. Five of 13 lynx crossed highways less
frequently than expected when compared to random expectation, but even
these individuals crossed highways frequently in parts of their home
range. We developed fine- and landscape-scale resource selection
function (RSF) models with field and remotely sensed data, respectively.
At the fine scale, lynx selected crossings with low distances to
vegetative cover and higher tree basal area; we found no support that
topography or road infrastructure affected lynx crossing. At the
landscape scale, lynx crossed highways in areas with high forest canopy
cover in drainages on primarily north-facing aspects. The predicted
crossing probabilities generated from the landscape-scale RSF model
across western Colorado, USA, were successful in identifying known lynx
crossing sites as documented with independent snow-tracking and
road-mortality data. We discuss effective mitigation based on model
results.
Keywords
- Highway crossing;
- Lynx canadensis;
- Habitat connectivity;
- Highway crossing probability;
- Colorado;
- Highway mitigation;
- Canada lynx
1. Introduction
Road distribution and density can have a significant impact on the connectivity of wildlife populations (Andrews, 1990 and Forman and Alexander, 1998).
Increased human activity, vehicle-related mortality, and behavioral
avoidance of roads can all contribute to changes in movement, survival,
and reproductive success of individuals and populations (Forman & Alexander, 1998; Ferreras, Aldama, Beltran, & Delibes, 1992; Trombulak & Frissell, 2000). Roads may also reduce gene flow for some species (Jackson and Fahrig, 2011 and Riley et al., 2006).
In particular, carnivores are susceptible to reduced population
connectivity due to roads given their large home ranges, long-distance
movements, and low recruitment rates (Noss, Quigley, Hornocker, Merrill, & Paquet, 1996; Woodroffe & Ginsberg, 2000).
Actions
that promote highway permeability for carnivores require an empirical
basis so that highway mitigation is most effective. Methods used to site
animal-crossing structures and to identify animal crossing zones
include expert opinion (Clevenger, Wierzchowski, Chruszcz, & Gunson, 2002), wildlife-vehicle collision patterns (Clevenger, Chruszcz, & Gunson, 2003; Malo, Suarez, & Diez, 2004), remote cameras (Cain, Tuovila, Hewitta, & Tewes, 2003), track surveys (Clevenger & Waltho, 2005; Grilo, Bissonette, & Santos-Reis, 2009), and telemetry (Dodd, Gagnon, Boe, & Schweinsburg, 2007; Tigas, Van Vuren, & Sauvajot, 2002).
However, the use of actual crossing locations to determine attributes
that carnivores select at highway crossings ensures that already limited
funds are expended on conservation measures that truly enhance highway
permeability and reduce carnivore mortality. Physical structures that
increase permeability of highways to carnivores, such as underpasses and
overpasses, must be placed in areas that are consistent with the
species’ resource-use (Clevenger & Waltho, 2000).
For
many species, crossing zones and vehicle-related mortalities tend to be
spatially clustered, an indication that animals may cross highways
non-randomly in response to habitat or road characteristics (Malo et al., 2004 and Neumann et al., 2012; Ramp, Caldwell, Edwards, Warton, & Croft, 2005).
The types and spatial distribution of these characteristics vary by
species, depending on life history and habitat preferences (Chetkiewicz & Boyce, 2009; Ramp, Wilson, & Croft, 2006). Vegetation characteristics tend to be important for many species. For instance, Seiler (2005) found that moose (Alces alces) and vehicle collisions were more likely to occur in areas with greater forest cover and proximity to forest edge. Clevenger et al. (2003) found that small mammal vehicle collisions tended to occur along roads near vegetative cover, and Finder, Roseberry, and Woolf (1999) showed that white-tail deer (Odocoileus virginianus) collisions were more likely in areas nearer to forest cover, gullies, or riparian zones. Lewis et al. (2011) modeled black bear (Ursus americanus)
road-crossing probability and found that bears were more likely to
cross in areas with less human development and greater forest cover.
Thus, species-specific models that predict highway crossing zones should
provide more accurate information on the likelihood of a given area to
be used as a crossing, and therefore increase our ability to manage
highway permeability and reduce direct vehicle-related mortality of rare
carnivores.
The need
for connectivity may be particularly important for reintroduced species
at their range periphery, given low density and high degree of
geographic isolation (Devineau, Shenk, Lukacs, & Kahn, 2010). Populations that are small and geographically isolated from their core range are generally vulnerable to local extinctions (Harrison, 1991 and Lawton, 1993) that may be exacerbated by collision-mortality of dispersers and road avoidance (Forman et al., 2003). This concern is particularly acute for reintroduced populations of Canada lynx (Lynx canadensis)
at their southern range periphery. Canada lynx are a medium-sized felid
that generally occupy spatially distinct home ranges, but are also
capable of long-distance exploratory or dispersal movements ( Aubry, Koehler, & Squires, 2000; Squires & Oakleaf, 2005). Canada lynx are specialist predators of snowshoe hare (Lepus americanus) and are associated with moist, high-elevation spruce-fir forests in the Rocky Mountains of North America ( McKelvey, Aubry, & Ortega, 2000). Vehicle collisions accounted for nearly half of mortalities for reintroduced lynx in the Adirondack Mountains, New York (McKelvey et al., 2000). Vehicle collision was also an important mortality factor for reintroduced lynx in Colorado (20% of mortalities; Devineau et al., 2010) and 45% of Eurasian lynx (Lynx lynx) mortalities in Germany ( Kramer-Schadt, Revilla, & Wiegand, 2005).
Here
we examine the road crossing characteristics of a reintroduced
population of Canada lynx in the Southern Rocky Mountains of Colorado,
USA. We first evaluated highway-crossing behavior of Canada lynx in
terms of diel timing and road avoidance. We then evaluated the extent to
which environmental variables at two spatial scales (fine scale and
landscape scale) could be used to predict the probability of highway
crossings by lynx. At lynx highway crossings, we quantified fine-scale
environmental covariates in the field to evaluate crossings using
variables not easily evaluated with remote sensing, such as forest
structure and composition, presence of highway guard rails and barriers,
and the distance that oncoming traffic was visible. Next, given that
lynx are highly mobile (Devineau et al., 2010),
our landscape-scale analysis evaluated if environmental heterogeneity
quantified with remotely-sensed data could be used to predict highway
crossings throughout western Colorado for region-wide planning. Given
that lynx generally prefer spruce-fir forests with high horizontal cover
(Fuller and Harrison, 2010 and Koehler et al., 2008; Squires, DeCesare, Kolbe, & Ruggiero, 2010),
we predicted that lynx at both fine and landscape scales would
preferentially select forested crossing zones and generally avoid open
habitat types.
2. Material and methods
2.1. Study area
Our
study areas were in western Colorado, USA and included portions of the
San Juan National Forest (37.6°N, 108.0°W) (referred to as SJNF
hereafter) in Ouray, San Miguel, and Dolores counties, and the White
River National Forest (39.5°N, 106.2°W) (referred to as WRNF hereafter),
in Summit County (Fig. 1).
The SJNF area occurred within the western San Juan Mountains and
encompassed portions of the upper Animas, Dolores, and San Miguel River
watersheds. The San Juan Mountain range was the core area in which the
Colorado Division of Wildlife reintroduced lynx between 1999 and 2006 (Devineau et al., 2010).
The SJNF included portions of two-lane U.S. Highway 550 and State
Highway 145, with average daily traffic volumes between 2000 and 2500
vehicles per day (Colorado Department of Transportation, 2014).
In the WRNF, the primary highways included Interstate 70 (I-70; 23,000
vehicles/day), a four-lane highway, and two-lane State Highway 91 (4000
vehicles/day; Colorado Department of Transportation, 2014).
Study
areas were typical of the Southern Rockies with steep mountains and
narrow valleys at elevations ranging approximately 2000–4300 m asl.
Steep elevation gradients and high topographic variation across the
study area produced a mosaic of conifer and aspen forests extending to
alpine tundra, with herbaceous and shrub openings occurring as avalanche
paths, meadows, and wetlands. Conifer-dominated forests, which provide
most lynx habitat, occur between 2500 m to 3500 m asl in elevation and
were composed primarily of Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa). Aspen (Populus tremuloides) and willow (Salix spp.) were common on disturbed slopes and intermixed with conifers in mid-seral stands, while Douglas fir (Pseudotsuga menziesii) occurred at low elevations. Lodgepole pine (Pinus contorta)
dominated relatively drier forests on the WRNF but was largely absent
from the SJNF. Winters were relatively long and cold; summers were drier
but included monsoonal rain patterns that resulted in regular but brief
afternoon precipitation. Maximum snow depth averaged 138 cm
(range = 97–201 cm; Natural Resources Conservation Service, 2015), and snow generally persisted from November through May (low elevations) or June (high elevations and northerly aspects).
2.2. Lynx capture and highway-crossing behavior
During winters 2010–2012, we captured lynx in box traps according to Kolbe, Squires, and Parker (2003).
Lynx were captured and handled under the guidelines in Animal Care and
Use Permit CDOW-ACUC File#13-2009. We fitted captured lynx with global
positioning system (GPS) collars (Sirtrack Ltd., Havelock North, New
Zealand) programmed to collect locations every 20 or 30 min, from
January to April. We programmed collars to automatically drop off
between April and May. Using GPS-collar data, we defined lynx movement
segments as straight-line vectors between consecutive GPS locations. We
identified lynx crossing segments as movement segments intersecting
highway centerlines (Laurian et al., 2008 and Schwab and Zandbergen, 2011). We limited analyses to crossing segments with at least one lynx location within 200 m of a highway to ensure accuracy.
We
investigated lynx avoidance of highways by quantifying movements within
home ranges relative to simulated movements. We created home ranges
using package ‘adehabitatHR’ (Calenge, 2006) in R (R Development Core Team, 2014)
and calculated a utilization distribution for each lynx with a 90%
kernel density estimate and reference bandwidth as the smoothing
parameter (Worton, 1989).
In each 90% home range, we compared the number of times that lynx
actually crossed a highway to the number of random highway crossings
simulated by correlated random walks (CRW; Kareiva & Shigesada, 1983). We used the Geospatial Modeling Environment (GME; Beyer, 2012)
to generate 500 CRW simulations per lynx. Each CRW simulation started
at the lynx capture location and drew from the observed distribution of
movement segment lengths and turning angles to create an equal number of
random movement segments within the home range. At each CRW iteration,
we tallied the number of movement segments that crossed highways and had
either the start or end point within 200 m of a highway, to be
consistent with how lynx crossings were counted. We then compared the
empirical frequency distribution of random crossing segments generated
for each lynx to the observed number of highway crossing segments per
lynx as a non-parametric bootstrap test of highway avoidance. We defined
significant avoidance of highways to have occurred when the observed
number of highway crossings was equal to or less than the bottom 5% of
the simulated crossing segment distribution (Shepard, Kuhns, Dreslik, & Phillips, 2008).
Although lynx are active throughout diel periods (Kolbe & Squires, 2007; Olson, Squires, DeCesare, & Kolbe, 2011), we expected most highway crossings would occur at night or during twilight periods when traffic volumes were low (Colorado Department of Transportation, 2014).
We defined the time of highway crossing as the midpoint between the
start and end times of lynx crossing movements. We categorized crossing
times into four time periods: (1) dawn (2 h; sunrise ±1 h), (2) day
(10 h; sunrise + 1 h to sunset − 1 h), (3) dusk (2 h; sunset ±1 h), and
(4) night (10 h; sunset + 1 h to sunrise − 1 h); daily sunrise and
sunset times were obtained from the National Oceanic and Atmospheric
Earth Systems Research Laboratory (Cornwall, Horiuchi, & Lehman, 2015).
We tallied the number of crossing segments within each time period for
each lynx and then used a Poisson generalized linear mixed model to fit
the number of crossings as a function of time period. We included time
period as a fixed effect, individual lynx as a random intercept, and an
offset term of log(time period hours) to account for differences in the
length of each time period. We further qualitatively examined whether
lynx crossed highways during times when they were most active by
plotting the temporal pattern of lynx highway crossings relative to the
temporal pattern of active lynx movement segments. Active movement
segments were defined as those longer than the spatial error of
stationary collars (92.5 m; Squires et al., 2013); segments shorter than this distance were considered to be resting or stationary.
2.3. Modeling resource selection
We
developed resource selection functions (RSFs) at a fine
(field-collected variables) and a landscape (remotely-sensed variables)
scale to predict highway crossing probability by lynx (Manly, McDonald, Thomas, McDonald, & Erickson, 2002).
We restricted our model-fitting to data from two-lane paved highways
because of their prevalence in lynx home ranges; however, we did apply
the model predictions (see Model Validation section) to I-70,
the only four-lane highway in lynx habitat in western Colorado. We also
provide anecdotal observations of lynx crossing I-70 due to the central
role that this high-volume, four-lane highway could have on lynx
population connectivity. At fine and landscape scales, we used the glmer
function in package ‘lme4′ ( Bates, Maechler, Bolker, & Walker, 2014)
in R to build RSF models using mixed-effects logistic regression, and
accounted for differences in crossing behavior of individual lynx with a
random intercept for individual. Predictor covariates were standardized
by subtracting the mean and dividing by the standard deviation to
facilitate comparison between variables measured at different scales. We
developed plausible a priori multivariate candidate models ( Appendix A)
with covariates that were more informative than the null model in a
univariate sense based on Akaike’s Information Criterion (AIC; Burnham & Anderson, 2002). We excluded covariates with high collinearity (|r| > 0.6);
if correlated, we retained the variable that was most biologically
meaningful and available to managers. We estimated logistic regression
models describing the probability of lynx highway crossing as:
equation 1

For
fine-scale resource-use modeling, we quantified predictor covariates in
the field at lynx highway crossings. We buffered used points by 100 m
then selected available points from outside the buffers. This ensured
that used and available points were non-overlapping to reduce the
potential of used crossings being also considered as available (sample
contamination; Johnson, Nielsen, Merrill, McDonald, & Boyce, 2006; Keating &Cherry, 2004).
We randomly selected 15 actual crossing locations per lynx and 15
“crossings” randomly available in each lynx home range. For three lynx
with <15 total highway crossings, we sampled all used crossing points
regardless of overlap. We fit 13 multivariate candidate models (see Appendix A).
At the landscape scale, we evaluated lynx highway crossing behavior by comparing used lynx crossings (n = 593) to available crossing locations (n = 4331)
distributed across highways in western Colorado. Since a large
available sample is required to minimize bias in RSF models (Hooten, Hanks, Johnson, & Alldredge, 2013; Northrup, Hooten, Anderson, & Wittemyer, 2013),
and to allow prediction across all highways in western Colorado within
the elevation zone of lynx, we sampled available crossing points
systematically spaced 1 km apart along all highways within the elevation
zone used by lynx in our sample (2000–4183 m asl). We considered 29
multivariate candidate models (see Appendix A).
Our mixed model framework required an available sample specific to each
individual lynx; however, since our available landscape was common to
all lynx, we used a bootstrap procedure to refit the model with a
different random sample of all systematic points to verify model
performance. We performed 1000 bootstrap iterations that randomly
sampled each lynx’s used and all available crossing points with
replacement and fitted all 28 candidate models at each iteration. We
used AIC values for model selection, and verified this using the number
of times each model was ranked best across bootstrap iterations. We then
spatially extrapolated our best-performing model to predict probability
of crossing along major highways in western Colorado above 2000 m asl
elevation.
2.4. Predictor covariates
We quantified fine-scale vegetation covariates at crossing points with eight plots aligned in an “X” configuration (Appendix B1; Fig. 2).
At each vegetation plot, we quantified tree basal area with a 10-factor
prism and recorded diameter at breast height (DBH) by species. We also
measured vegetative horizontal cover in each cardinal direction using a
cover-board viewed at 10 m away, consistent with Squires et al. (2010).
We measured distance to vegetative cover as the shortest distance to
continuous vegetation greater than 2 m tall and in patches >25 m2. We measured roadside covariates at three points to account for the spatial uncertainty of crossing locations (Appendix B1; Fig. 2).
We quantified the slope of approaches to highways at 10 m perpendicular
to the road with a clinometer. We used a rangefinder to measure the
length of highway visible to a crossing animal, defined as the
line-of-sight distance of continuous pavement in both directions. Given
that highway structures can have physical or visual impact on wildlife
crossings (Gunson, Mountrakis, & Quackenbush, 2011),
we mapped the locations of physical barriers (e.g., guard rails, jersey
barriers, vertical cliffs). We calculated the mean and standard
deviation for all variables across all eight vegetation or three
roadside plots at each crossing point.
At the landscape scale, we used remotely-sensed topographic and vegetation data (Appendix B2)
at two spatial scales (200 m and 500 m radii circular moving windows)
that we selected arbitrarily to capture the environment associated with
highways. We selected landscape-scale covariates that best represented
important variables associated with crossings identified during fine
scale sampling and those that we thought were most biologically
meaningful for landscape-level modeling. Topographic variables including
slope, aspect, and terrain roughness were obtained from a 10 m digital
elevation model (DEM; Gesch, 2007). Terrain roughness was calculated from the standard deviation of elevation values (Wilson & Gallant 2000).
We calculated an index of “northness” using the percentage of cells in a
200 m or 500 m neighborhood with slope >10% and northerly aspects
(>270° and <90°). Topographic position index (TPI), a measure of
terrain concavity or convexity (Jenness, 2006),
was calculated at a 1000 m scale, in addition to 200 and 500 m; the
1000 m radii plot was added to better characterize drainages in
mountainous topography. Euclidian distance to hydrologic features was
determined using the National Hydrography Dataset (NHD; United States Geological Survey, 2013). We obtained six 30 m resolution Landsat 5 Thematic Mapper (http://earthexplorer.usgs.gov/)
scenes dated 8 June to 24 June 2011, each with less than 1% cloud
cover. From these images, we derived the Normalized Difference
Vegetation Index (NDVI; Jensen, 2005), an index of vegetation biomass, and performed tasseled cap transformations (Crist & Cicone, 1984),
which created variables that index soil reflectivity (brightness),
vegetation presence (greenness), and soil or surface moisture (wetness).
We calculated the mean and standard deviation of NDVI, Brightness,
Greenness, and Wetness. Finally, we evaluated forest structure based on a
30 m LANDFIRE v. 1.2.0 (Rollins, 2009) layer of canopy cover.
2.5. Model validation
We evaluated our best fine-scale model using four-fold cross validation (Boyce, Vernier, Nielsen, & Schmiegelow, 2002).
We randomly divided all used locations into four groups, sequentially
withheld each group, fit the model on the remaining three groups, and
used the model to predict the outcome of the withheld group according to
Boyce et al. (2002). This method should generate a high Spearman’s rank correlation coefficient (rs)
between predictions from the withheld sample and the bin numbers
generated from the entire dataset if the model is predicting the
relative probability of road crossings given the range of probabilities
over the entire area sampled ( Boyce et al., 2002).
We evaluated the landscape-scale RSF model using two methods. First, we conducted a 10-fold cross validation according to Boyce et al. (2002),
similar to the fine scale. Second, we used an independent dataset of
lynx highway crossings in Colorado that consisted of winter lynx
back-tracks from 2000 to 2009 (n = 117; Colorado Parks and Wildlife, unpublished data) and lynx highway mortalities from collisions with vehicles 1999–2015 (n = 11;
Colorado Parks and Wildlife, unpublished data). We believed these
independent data provided our best evaluation of model performance that
mimicked actual field application. We extracted the RSF predicted
probability value at each independent crossing location using our
landscape-scale model; higher crossing probabilities indicated better
predictive performance.
3. Results
We
collected an average of 4810 GPS locations (SD = 2415, range: 752–8300)
on each of 14 lynx (7 M, 7 F). Data collection ranged between 27 Jan
and 17 Jun (Appendix C). Home ranges of all but one lynx were bisected by 4.0–52.9 km of two-lane highway (
= 18.7 km,
SD = 14.8). We documented 735 total lynx highway crossings; 88 of these
were lower quality crossings (GPS locations >200 m off the highway
and/or >40 min between locations) that were eliminated from further
analysis. We used 11 of 13 lynx to model resource selection at 593
crossings; data from two lynx were not available for resource-use
modeling due to late collar drop-offs. Elevation of lynx crossings
averaged 3041 m (SD = 134 m, range: 2778–3451).

3.1. Highway crossing behavior
Lynx crossed highways more frequently during dusk and night than during dawn and day (βdawn = −0.17, SE = 0.13, p = 0.18; βdusk = 0.76, SE = 0.09, p < 0.001, βnight = 1.31, SE = 0.05, p < 0.001).
Lynx crossed highways at increased frequency after sunset until 0100 h;
crossing frequency remained relatively high until sunrise, after which
it declined (Fig. 3). Lynx crossed highways during all hours, but crossings were 1.85 times more frequent during night (n = 393) than day (n = 212). Also, observed diel pattern of lynx highway crossings appeared to deviate from the general pattern of lynx activity (Fig. 3).
For example, lynx movement activity generally decreased from sunset
(1800 h) to 2400 h, while the frequency at which lynx crossed highways
increased during this period.
Lynx crossed two-lane highways an average of 0.6 times per day (SD = 0.4, range: 0.2–1.4; Appendix C).
The mean number of highway crossings per lynx was 50 (SD = 45.4; range:
6–148) compared to CRW paths that crossed an average of 90 times
(SD = 60.0; range: 20–221; Appendix C). Correlated random walk simulations suggested that 5 (3 F, 2 M) of 13 lynx crossed highways significantly less than expected (p < 0.05) whereas 8 lynx exhibited no highway avoidance (0.07 < p < 0.52; Appendix C); all lynx with highways in their home ranges crossed more than once (Fig. 4).
- Fig. 4.
Examples that illustrate most avoidance (top) and least avoidance (bottom) of 2-lane highways by Canada lynx based on GPS locations, western Colorado. Night locations (20:00 h–06:00 h) are shown in blue, while day locations (07:00 h–19:00 h) are shown in yellow. Even the individual exhibiting most highway avoidance (top) frequently used habitats immediately adjacent to the road. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Three
of 5 lynx with adjacent home ranges crossed the four-lane interstate
I-70 on 25 occasions. These crossings provided important anecdotal
observations of behavior associated with crossing a high traffic volume
highway, but the number of observations was insufficient for statistical
evaluation with a resource selection function. These lynx mostly
crossed I-70 near first- and second-order stream tributaries where
eastbound interstate lanes were elevated by bridges 75–100 m long and
15–25 m in height with continuous tall woody vegetation underneath. The
highway median between east and west-bound traffic in these areas was
approximately 150–200 m wide and included patches of forest cover.
Although traffic averaged approximately 1200 vehicles/hr during the day,
volume was reduced to <200 vehicles/hr between 0100 h and 0500 h (Colorado Department of Transportation, 2014).
Seven of 25 crossings occurred during this 0100–0500 h period of low
traffic, while 9 crossings occurred during other dark hours. Snow
tracking data from an independent data set of lynx not included in this
study indicated that lynx successfully crossed I-70 on at least three
occasions, all about 30 km east of where collared individuals crossed.
Large elevated bridges over natural habitat were absent from this
stretch of the interstate and these crossings occurred at grade, over
the road surface. However, two lynx in the independent data set were
killed while attempting to cross at grade in this area and two were
killed attempting to cross at grade near the underpasses described
above. It is unclear whether those killed while attempting to cross I-70
had crossed successfully in previous attempts.
3.2. RSF models at multiple scales
At
the fine scale, lynx were most influenced by vegetation
characteristics. No topographic or highway infrastructure covariates
performed better than null models in univariate analyses, so they were
not considered further. Based on final multivariate models, lynx
selected highway crossing zones that were closer to vegetative cover
(MaxDistCover) and had greater mean basal area (AvgBasalArea) (Table 1). There were five models within four ΔAIC; following Arnold (2010),
we considered models that differed by one extra parameter but were
within two AIC of the top-performing model to contain uninformative
terms. Thus, only MaxDistCover and AvgBasalArea were meaningful
predictors of lynx crossings, although AvgBasalArea was only weakly
predictive, as its 95% confidence interval slightly overlapped zero (Table 3).
This suggested that lynx were most sensitive to the amount of forest
and other vegetative cover along roads when selecting highway crossings.
The mean MaxDistCover for used lynx crossings was 17.8 m (SD = 16.3 m),
compared to 29.8 m (SD = 34.3 m) for available highway crossings. For
every 1 m increase in distance to cover, the odds of highway crossing
declined approximately 1.9%. Lynx also tended to select crossing zones
with higher tree density compared to random: trees basal area was 78.3 m2/ha (SD = 31.3 m2/ha) at crossings compared to 59.5 m2/ha (SD = 31.3 m2/ha)
at available locations. Mean horizontal cover and the proportion of
spruce and fir trees at a crossing appeared among the top models but did
not contribute to model performance. Lynx appeared insensitive to
roadside slope, the presence of barriers, or line-of-sight distances
when selecting highway crossing locations.
- Table 1. Model selection results for fine-scale mixed-effects logistic regression models predicting Canada lynx highway crossings in western Colorado. The number of fixed effect parameters (K), AIC score, ΔAIC, AIC weight, and log-likelihood (LL) are given. Model variables include maximum distance to cover (MaxDistCover), mean basal area (AvgBasalArea), mean horizontal cover (AvgHorizCover), and the proportion of spruce and fir trees (PropSF). Only the 5 best performing models plus the null are reported.
Model K AIC ΔAIC AICwt LL 1 MaxDistCover+AvgBasalArea 4 409.79 0.00 0.36 −200.90 2 MaxDistCover 3 411.23 1.43 0.18 −202.62 3 MaxDistCover+AvgBasalArea+AvgHorizCover 5 411.29 1.50 0.17 −200.65 4 MaxDistCover+AvgBasalArea+PropSF 5 411.76 1.97 0.13 −200.88 5 MaxDistCover+AvgBasalArea+AvgHorizCover+PropSF 6 413.23 3.43 0.06 −200.62 6 NULL 2 424.77 14.84 0.00 −210.38
At
the landscape scale, lynx selected crossings in areas of high forest
canopy cover within the surrounding 500 m (LfCanCvr_500), concave
topographic positions relative to the surrounding 1000 m (TPI_1000), and
predominately northerly aspects within 200 m of the highway
(PctNorth_200; Table 2).
This top multivariate model ranked best in 57% of bootstrap iterations
and was four times more likely than the next candidate model to explain
the probability of where lynx crossed highways (Table 2).
The second best performing multivariate model ranked best in 42% of
bootstrap iterations and included canopy cover within the surrounding
500 m (LfCanCvr_500) and the standard deviation of brightness within the
surrounding 500 m (StdBrt_500). All four predictors were strong with
95% confidence intervals that did not overlap zero (Table 3).
We averaged predictions from the top 2 multivariate models (<4 ΔAIC)
to produce a statewide RSF surface of potential lynx crossing zones
along 4359 km of highways (i.e., those above 2000 m elevation) in
western Colorado (Fig. 5).
Model results suggest that 80% of highways within the elevation zone of
lynx habitat in Colorado had less than a 50% chance of being used by
lynx for crossings. In contrast, high probability crossing areas were
relatively few and were concentrated in areas of high forest cover on
north-facing slopes (Fig. 6).
- Table 2. Model selection results for landscape-scale mixed-effects resource selection models predicting Canada lynx highway crossings in western Colorado, giving the number of fixed effect parameters (K), AIC score, ΔAIC, AIC weight, log-likelihood (LL), and proportion of bootstrap iterations each model was ranked best (Prop Best). Variables included in the top models were mean percent canopy cover (LfCanCvr_500), topographic position index, percentage of area composed of north-facing aspects, standard deviation of brightness (StdBrt_500), and mean wetness (MeanWet_200). The number after each covariate denotes the size of the radius at which each covariate was calculated. Only the 5 best performing models plus the null are reported.
Model K AIC ΔAIC AICwt LL Prop Best 1 LfCanCvr_500+TPI_1000+PctNorth_200 5 828.03 0.00 0.80 −409.01 0.57 2 LfCanCvr_500+StdBrt_500 4 830.80 2.78 0.20 −411.40 0.42 3 LfCanCvr_500+MeanWet_200+TPI_1000 5 839.22 11.19 0.00 −414.61 0.01 4 LfCanCvr_500+TPI_1000 4 851.11 23.08 0.00 −421.56 0 5 LfCanCvr_500+MeanWet_200+PctNorth_200 5 868.10 40.07 0.00 −429.05 0 6 Null 2 1510.81 682.79 0 −753.41 0
- Table 3. Model coefficients, with 95% confidence intervals, of covariates in top performing models within 4 ΔAIC used to predict Canada lynx highway crossings at two spatial scales (fine and landscape) in western Colorado. Model numbers correspond to Tables 1 and 2. Covariates included are maximum distance to cover (MaxDistCover), mean basal area (AvgBasalArea), mean percent canopy cover (LfCanCvr), topographic position index (TPI), percentage of an area composed of north-facing aspects (PctNorth), and the standard deviation of brightness (StdBrt). Numbers after the landscape scale model covariates indicate the size of the radius at which each covariate was calculated.
Scale Model Variable Coefficient Lower 95% CI Upper 95% CI Fine Scale Models Model 1 MaxDistCover −0.44 −0.80 −0.12 AvgBasalArea 0.24 −0.01 0.51 Model 2 MaxDistCover −0.57 −0.91 −0.27 Landscape Scale Models Model 1 LfCanCvr_500 1.82 1.66 2.01 TPI_1000 −0.56 −0.68 −0.45 PctNorth_200 0.38 0.28 0.48 Model 2 LfCanCvr_500 2.38 0.86 1.05 StdBrt_500 0.86 0.67 1.05
- Fig. 6.
Examples of the predicted resource selection function surface showing the probability of Canada lynx crossing a highway compared to independent known crossing locations (snowtracking and vehicle-related mortalities; indicated by gray dot) in western Colorado (panels A, B). Panel C shows distribution of predicted probabilities of crossing at all available locations in the landscape-scale RSF versus actual probabilities at independent crossing locations; independent crossings occurred with increasing frequency within the top deciles of binned crossing probabilities (panel D).
3.3. Model validation
Cross-validation
of the fine- and landscape-scale models indicated good model fit. A
four-fold cross-validation of the best performing fine-scale RSF model
had a Spearman correlation coefficient of |rs| = 0.94.
The 10-fold cross-validation for the landscape-scale averaged model
yielded a Spearman correlation coefficient of 0.95. The independent data
that we used for the landscape model validation consisted of 117 snow
tracks of lynx crossing highways and 11 road-killed lynx mortalities.
These independent lynx crossings had a predicted average RSF value of
0.75 (range 0.15–0.98; SD = 0.18) from the landscape-scale RSF model (Fig. 6).
Additionally, the predicted RSF values associated with all independent
lynx crossings were largely between 0.6 and 0.8, with only 7% of
independent data associated with modeled values less than 0.5 (Fig. 6).
In contrast, the distribution of RSF values at all available locations
across Colorado was largely between 0 and 0.1, with 78.82% of predicted
probabilities less than 0.5. This suggested the landscape model was
effective at predicting the actual areas that lynx would use when
crossing highways.
4. Discussion
Canada
lynx in the Southern Rocky Mountains of western Colorado crossed 2-lane
highways (traffic volumes of 2000–4000 vehicles/day) approximately
every other day. We found that most lynx (8 of 13) did not appear to
avoid crossing roads, likely due to the habitat configuration of lynx
home ranges in our study area. Lynx whose home ranges included extensive
sections of highways lived in close proximity to them and crossed
frequently. Lynx mitigated the risk of increased highway exposure by
crossing roads at greater frequency during dusk and night, when traffic
volume was lower. Our resource selection models were successful at
predicting the probability of lynx crossing given fine- and
landscape-scale environmental characteristics. At both spatial scales,
lynx were more likely to cross highways in areas with greater vegetative
cover, while at the landscape scale, lynx also preferred north-facing
slopes and areas of topographical concavity, such as river drainages.
Despite
the fact that all lynx crossed highways, we found that 5 of 13
individuals (39%) exhibited some degree of road avoidance behavior as
defined by crossing significantly less than CRW simulations. Other
studies have documented highway-avoidance behavior by lynx (Apps, 2000 and Squires et al., 2013),
although the lynx in our study that exhibited road avoidance behavior
still frequently crossed roads in some regions of their home range,
depending on forest vegetation near crossing zones (Fig. 4).
Lynx reintroduced to the Southern Rocky Mountains occupied habitat in
high-elevation mountain valleys that were bounded at upper elevations by
open rock and tundra. Given the mountainous topography, two-lane
highways in western Colorado were present in valley bottoms with
vegetation too sparse for lynx, while other sections were high on
mountain passes in good lynx habitat. We acknowledge that reintroduced
lynx may exhibit different crossing behavior than native populations.
However, of the 13 individuals in our study, five were born in the
Southern Rockies, and the remaining eight were resident in the Southern
Rocky Mountains for more than 5 years and had established home ranges.
Thus, we believe our results reflected behaviors of established
individuals and were not uninformed movements of naïve individuals in a
new environment.
One
way that lynx accommodated vehicle-related disturbance was to cross
highways more frequently at night when traffic volumes were relatively
low. The proclivity for lynx to cross highways at night was similar to
other wide-ranging felids such as bobcat (Lynx rufus; Cain et al., 2003) and European wildcat (Felis silvestris; Klar, Herrmann, & Kramer-Schadt., 2009), as well as other taxa such as grizzly bears (Ursus arctos; Waller & Servheen, 2005) and elk (Cervus elaphus; Gagnon, Theimer, Dodd, Boe, & Schweinsburg, 2007). Tigas et al. (2002) reported that bobcats and coyote (Canis latrans)
tended to utilize areas with high human activity more often at night.
Nighttime traffic volumes on highways in western Colorado were generally
<5% of peak early-afternoon volumes of 200–400 vehicles per hour ( Colorado Department of Transportation, 2014).
We assumed that increased crossings at night were an avoidance behavior
to vehicle-related disturbance because lynx were generally active
across all diel periods (Fig. 3).
The tendency of lynx to preferentially traverse highways during periods
of low traffic volume may also reduce the risk of vehicle-related
mortality (Neumann et al., 2012). For example, Waller and Servheen (2005) demonstrated that grizzly bears experience lower risk in crossing highways at night compared to peak traffic volumes.
At a fine scale, lynx crossed highways in close proximity to vegetative cover, similar to several other large mammal species (Clevenger & Waltho, 2005).
Vegetative cover was primarily provided by conifers in stands with
higher basal area compared to randomly available along highways. We
assume that road-side vegetation provided security cover and that higher
horizontal cover could support greater snowshoe hare densities (Fuller and Harrison, 2010, Hodges, 2000 and Squires et al., 2010).
Consistent with fine-scale results, lynx at the landscape scale
selected north-facing crossings in areas of high forest canopy cover
primarily in drainage bottoms. The landscape-scale model we developed
generally agreed with other studies of wildlife highway crossings that
identified important crossing areas near drainages with forest cover (Clevenger et al., 2003 and Grilo et al., 2009).
Our landscape model based on remotely-sensed environmental covariates
provides a useful management tool to predict areas of high permeability
to lynx movement, as evidenced by performance with independent crossing
data. The fact that independent lynx crossing locations were generally
associated with high-probability crossing zones supports the use of
model outputs by highway planners to evaluate potential crossing zones
in western Colorado.
Species
with high adjacency to transportation corridors have a heightened
vulnerability to vehicle-related mortality compared to those with
considerable spatial separation. The high frequency at which lynx
crossed highways suggests that risk of vehicle-related mortality was
high, which in turn justifies appropriate highway mitigation. Model
results at the landscape scale indicate that mitigation actions that
promote forest cover immediately adjacent to highways may increase
permeability by lynx, especially on north-facing slopes and in drainage
bottoms. In addition, the diel crossing pattern of lynx suggests that
lower nighttime speed limits on highways in lynx habitat may decrease
collision mortality. These suggested mitigation measures are based on
resident lynx in winter-spring home ranges that contain highways; we did
not directly investigate movements of dispersers or individuals making
long distance movements from established territories. Thus, we
acknowledge that transient or dispersing felids, or those engaging in
exploratory movements, may cross highways where few predictive factors
occur (Tewes & Hughes, 2001); these lynx may be more susceptible to vehicle collision than resident animals due to unfamiliar terrain (Beier, 1995 and Ferreras et al., 1992).
Physical
crossing structures, such as over/under passes and fencing, effectively
facilitate safe wildlife crossings of major highways (Foster & Humphrey, 1995; Ng, Dole, Sauvajot, Riley, & Valone, 2004; Yanes, Velasco, & Suárez, 1995).
However, the extent to which these improvements benefit lynx may depend
on size of the highway and related traffic volume, as well as the
landscape structures around the passes. Our GPS locations at 20 min
intervals were inadequate to provide detailed depictions of how lynx
responded to physical highway structures, like guard rails and culverts.
In future studies, collars with greater temporal resolution, such as 10
or even 5 min intervals, might be more successful in documenting animal
movement relative to highway structures at a fine spatial and temporal
scale. However, the broad spatial distribution and sheer number of
highway crossings that we documented indicate that lynx mostly crossed
two-lane highways at road grade, and they did not depend on physical
highway improvements to traverse two-lane highways. Similarly, Tigas et al. (2002) reported a preference by bobcats to cross highways at the surface and Crooks et al. (2008)
failed to detect lynx using any of seven underpasses that were
constructed specifically to reduce lynx highway mortalities in Colorado.
Our
anecdotal observations of lynx crossing I-70, a high traffic four-lane
divided highway, suggested that resident lynx did locate safe,
below-grade crossings at large underpasses and used them repeatedly.
They were also capable of crossing I-70 at road-grade during periods of
low traffic volume. The use of underpasses for crossing high volume
roads was consistent with other studies. For example, Beier (1995) observed numerous cougars crossing underneath major highway bridges over watercourses and Henke, Cawood-Hellmund, and Sprunk (2001)
showed that several mammalian species in Colorado, including bobcats,
used below grade highway crossings on major interstate highways. We
assume lynx cross high-volume, four-lane highways similar to other
wildlife in their proclivity to use larger underpasses with dense native
vegetation close to passage entrances (Cain et al., 2003) in favorable habitat with low human disturbance (Beier, 1995 and Ng et al., 2004).
5. Conclusions
We
demonstrated that, at a fine scale, lynx crossed two-lane highways in
forests with higher tree basal area and lower distance to cover. At the
landscape scale, lynx selected highway crossings in areas of high forest
canopy cover, especially in drainages and on north-facing slopes. The
presence of highway infrastructure (guard rails and barriers) was not
predictive of crossing two-lane highways. Model results indicated
considerable individual variation in crossing behavior and the presence
of multiple crossing zones within home ranges when bisected by extensive
highway sections. Thus, appropriate mitigation to enhance connectivity
for Canada lynx across 2-lane highways may include reduced speed limits
at night and vegetation management rather than intensive investments for
physical overpasses in few putative crossing zones. However, our
anecdotal observations (n = 25 crossings) of lynx crossing a
high-volume four-lane highway (I-70) suggest that investment in large
elevated underpasses across drainages, especially in highway sections
with forested medians, may be warranted.
Acknowledgements
We
thank the United States Department of Agriculture, Grand Mesa,
Uncompahgre and Gunnison National Forests, White River National Forest,
and San Juan National Forest for logistical support. We greatly
appreciated the statistical advice provided by S. Baggett and B. Bird,
Rocky Mountain Research Station. Funding was provided by the United
States Forest Service Region 2 and the Colorado Department of
Transportation. We thank the two anonymous reviewers for their valuable
suggestions to the manuscript.
Appendix A. Candidate RSF models
Candidate
fine- and landscape-scale resource selection function models considered
to predict Canada lynx highway crossing locations in western Colorado.
Scale Model # Model Structure FineScaleModels 1 AvgDistCover 2 MaxDistCover 3 AvgBasalArea 4 AvgHorizCover 5 MinHorizCover 6 MaxDistCover+AvgBasalArea 7 MaxDistCover+AvgBasalArea+AvgHorizCover 8 MaxDistCover+AvgBasalArea+AvgHorizCover+PropSF 9 MaxDistCover+AvgBasalArea+PropSF 10 AvgDistCover+AvgHorizCover 11 AvgBasalArea+AvgHorizCover 12 AvgBasalArea+AvgHorizCover+PropSF 13 Null BroadScaleModels 1 MEANBRT500 2 MEANWET200+MEANBRT500 3 MEANWET200+MEANBRT500+STDBRT500 4 MEANBRT500+STDBRT500 5 LFCNCVR500 6 MEANWET200+LFCNCVR500 7 MEANWET200+NDVI200+LFCNCVR500 8 NDVI200+STDBRT500+LFCNCVR500 9 MEANBRT500+PCTNRTH200 10 MEANBRT500+TPI1000 11 MEANBRT500+TPI1000+PCTNRTH200 12 MEANBRT500+ROUGH500 13 MEANBRT500+MEANSLP500 14 MEANWET200+MEANBRT500+PCTNRTH200 15 MEANWET200+MEANBRT500+TPI1000 16 MEANWET200+MEANBRT500+TPI1000+PCTNRTH200 17 MEANWET200+MEANBRT500+ROUGH500 18 MEANWET200+MEANSLP500 19 MEANBRT500+STDBRT500+PCTNRTH200 20 MEANBRT500+STDBRT500+TPI1000 21 MEANBRT500+STDBRT500+TPI1000+PCTNRTH200 22 MEANBRT500+STDBRT500+ROUGH500 23 MEANBRT500+STDBRT500+MEANSLP500 24 LFCNCVR500+PCTNRTH200 25 LFCNCVR500+TPI1000 26 LFCNCVR500+TPI1000+PCTNRTH200 27 MEANWET200+LFCNCVR500+PCTNRTH200 28 MEANWET200+LFCNCVR500+TPI000 29 NDVI200+STDBRT500+LFCNCVR500+TPI1000
Appendix B. Predictor variables
Table B1
Variables
aggregated from eight vegetation plots and three roadside sample points
at used and available lynx highway crossing points, used to evaluate
fine scale resource selection functions predicting Canada lynx highway
crossing locations in western Colorado.
Type Variable Name Description VegetationPlots PropSpruceFir Percentage of “In” trees on plots that were Engelmann spruce or Subalpine fir. AvgBasalArea Average basal area (sq. meters/ha) of plots, measured with a 10-BAF prism. MaxBasalArea Maximum basal area among plots, measured with a 10-BAF prism. AvgHorizCover Mean horizontal cover of plots. MinHorizCover Minimum horizontal cover among plots. AvgPlotSlope Average slope (%) of plots. MaxPlotSlope Maximum slope (%) among plots. PctTreesLess Percentage of “In” trees on plots with diameter <5”. PctTreesGE5Less9 Percentage of “In” trees on plots with diameter ≥5 and <9”. PctTreesGE9Less20 Percentage of “In” trees on plots with diameter ≥9 and <20”. PctTreesGE20 Percentage of “In” trees on plots with diameter ≥20”. Roadside Sample Plots AvgRoadSlope Average roadside slope (%) at sample points. MaxRoadSlope Maximum roadside slope (%) among sample points. AvgRoadVisibility Average distance of continuous pavement visible from sample points. AvgDistCover Average distance from sample points to the nearest stand of continuous trees or shrubs >2m tall and ≥25m2. MaxDistCover Maximum distance among sample points to the nearest stand of vegetation >2m tall and ≥25m2. MinDistCover Minimum distance among sample points to the nearest stand of vegetation >2m tall and ≥25m2. RoadCliff Tally of vertical roadside cliffs >5m high within 25m of sample points RoadManBarrier Tally of man-made structures, including guard rails and jersey barriers, within 25m of sample points.
Table B2
Variables
extracted from GIS at used and available lynx highway crossings and
used to evaluate landscape scale resource selection functions to predict
Canada lynx highway crossing locations in western Colorado. Variables
were calculated at two spatial scales: within a 200 or 500 m buffer
around each crossing point.
Type Variable Name Description Topography MEANSLOPE Average slope (%) from a 10m digital elevation model. ROUGH An index of terrain roughness, calculated as the standard deviation (SD) of elevations. PCTNORTH Percentage of area composed of north-facing aspects (>270° and <90°) for slopes >10%. TPI Relative topographic position index, where negative values represent topographic concavities and positive values represent ridges. DISTHYDRO Average distance to the nearest 14th-level (HUC) national hydrography dataset stream or waterbody. Vegetation LFCANCVR Average of LANDFIRE canopy cover values, expressed as a percentage. NDVI Average Normalized Difference Vegetation Index values derived from Landsat 5 TM images. MEANBRT Average spectral variations in soil background reflectance (Brightness) derived from a Tasseled Cap transformation of Landsat 5 TM images. STDBRT Standard deviation of spectral variations in soil background reflectance (Brightness) derived from a Tasseled Cap transformation of Landsat 5 TM images. MEANGRN Average spectral variations in the vigor of green vegetation (Greenness) derived from a Tasseled Cap transformation of Landsat 5 TM images. STDGRN Standard deviation of spectral variations in the vigor of green vegetation (Greenness) derived from a Tasseled Cap transformation of Landsat 5 TM images. MEANWET Average spectral variations related to canopy and soil moisture (Wetness) derived from a Tasseled Cap transformation of Landsat 5 TM images. STDWET Standard deviation of spectral variations related to canopy and soil moisture (Wetness) derived from a Tasseled Cap transformation of Landsat 5 TM images. MEANPCA1 Average of values from the first Principal Component transformation of Landsat 5 TM image band ratios, which generally correspond to image brightness. MEANPCA2 Average of values from the second Principal Component transformation of Landsat 5 TM image band ratios, which generally describes variations in vegetation cover.
Appendix C. Lynx Highway Crossing Summary
Table C1
Summary
information for each Canada lynx used to assess highway crossing
avoidance within a home range in western Colorado, 2010–2012. Columns
show the lynx ID, sex, start and end date of collaring, number of days
the animal was collared, number of GPS points collected during this
time, percent of GPS fix attempts that were successful, number of road
crossings exhibited during this time, number of crossings per day, mean
number of crossings as simulated by correlated random walk (Avg Sim
Cross), and the non-parametric p-value from the comparison of actual
crossings against the simulated distribution. Bold values indicate
significantly fewer crossings than expected by chance at α = 0.05.
Lynx Sex Start Date End Date # Days # Points % Success # Cross Cross/Day Avg Sim Cross p-value F02 F 16-Mar-10 16-Apr-10 31 1925 86 24 0.77 64 0.01 F03 F 28-Feb-12 31-May-12 92 5602 85 62 0.67 61 0.52 M01 M 19-Feb-12 31-May-12 101 6730 93 68 0.67 88 0.35 F04 F 22-Mar-10 10-Apr-10 19 1096 80 6 0.32 19 0.13 M02 M 11-Mar-11 14-Apr-11 34 752 92 9 0.26 79 0.01 F06 F 22-Feb-12 31-May-12 98 5693 81 33 0.34 114 0.04 M04 M 25-Feb-12 31-May-12 95 6510 95 105 1.11 142 0.17 F07 F 27-Jan-12 17-Jun-12 141 8300 82 106 0.75 221 0.02 M05 M 12-Feb-12 31-May-12 108 7399 95 148 1.37 184 0.21 M06 M 18-Feb-12 31-May-12 102 6658 91 27 0.26 53 0.29 M07 M 28-Feb-12 31-May-12 92 5883 89 19 0.21 41 0.24 M08 M 17-Feb-11 14-Jun-11 117 2611 93 29 0.25 71 0.01 F08 F 5-Feb-11 15-Jun-11 130 2890 93 11 0.0 32 0.07
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