The Effect of Ski Resorts on Population Dynamics
of the Pacific Marten in the Lake Tahoe Region
of California and Nevada, 2009 - 2011
Final Report
16 September 2013
Keith M. Slauson and William J. Zielinski, Principal Investigators
USDA Forest Service, Pacific Southwest Research Station, Redwood Sciences Laboratory,
1700 Bayview Dr., Arcata, CA 95521 USA
Southern Nevada Land Management Act
Project #: P022
Project Collaborators:
Lake Tahoe Basin Management Unit
El Dorado National Forest
Tahoe Region Planning Agency
Heavenly Ski Resort
Homewood Mountain Resort
Sierra-At-Tahoe Resort
Wildlife Genetics Laboratory, Rocky Mountain Research Station, U. S. Forest Service
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Executive Summary
From 2009-2011 we investigated the effects of developed ski areas on the distribution
and population dynamics of Pacific martens (Martes caurina) in the Lake Tahoe region
of California and Nevada. Our study design included the comparison of 3 ski areas with
3 paired control study areas. Using hair snares (winter) and live traps (summer) we
systematically identified or captured martens at each study area to determine the
occupancy and demographic characteristics. Over 3.5 years, including the 1-winter and 3
spring-summer capture seasons, a total of 96 (66M:33F) martens were included in the
study.
The development of the 3 ski areas involved the conversion of 30-34% of forest
habitat to largely non-forest habitat. Remnant forest habitat within ski operations areas
was fragmented into 65-100 patches, with >85% of all patches <10 hectares in size.
Marten movement was strongly affected by the width of individual ski runs and by the
cumulative width of runs that had to be crossed to move between capture stations.
Martens typically did not cross individual ski runs that exceeded 20 m or combinations of
runs that exceeded 30 m in cumulative crossing width. Female martens used smaller
average ski run crossings, <15 m, than males. Adult males positioned their use areas to
minimize the inclusion of ski runs and habitat within the ski operations areas.
During winter, marten occupancy was significantly reduced within ski area operations
boundaries. Martens occupied 52% of stations in operations areas compared to 88%
outside operations areas. Station visitation rates were also significantly reduced in
operations areas compared to outside them, suggesting that martens made less frequent
use of habitat in operations areas during the winter. The amount of habitat affected
during the winter, due to avoidance or reduced use, represented 15-37% of the total ski
area study areas.
During the spring-summer season marten occupancy was not significantly different
between ski areas and controls or inside or outside the operations areas. This suggest that
ski area impacts are greatest during the winter season and the combination of habitat
alteration and winter recreation activities are the cause for the winter impact on marten
occupancy. Although spring-summer occupancy did not differ between ski areas and
controls, the processes underlying the changes in occupancy from winter to springsummer differed. In controls, residents contracted the space they used and dispersers that
were present in winter left the area by summer. In ski operations areas, occupancy
declined from winter to spring similarly outside operations areas as residents contracted
their space use but increased from winter to spring inside operations areas due in large
part to the arrival of new individual martens.
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We did not find significant effects of ski areas on estimates of population density
during spring-summer, female survival, reproduction, or age structure. The primary
effects on martens were season- and sex-specific. Winter ski recreation activities
significantly affect marten habitat use within ski operations areas. However, this affect is
mediated somewhat by females seasonal avoidance of habitat located in ski operations
areas during winter recreation activities. Adult males avoided using habitat within ski
operations areas year-round. A number of indicators of habitat use by males
demonstrated negative effects on survival of males. Our results suggest that martens and
ski areas can coexist if habitat across ski areas is connected, seasonal impacts are limited
to avoid the denning and kit rearing season (March-August), and reproductive habitat is
maintained and enhanced.
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1.0 Introduction
High-elevation conifer forests of the Sierra Nevada have historically provided some
refuge from human impacts including trapping and timber harvest, but these forests have
increasingly become focal areas for winter recreation. Because American martens
(Martes caurina) are active year-round and are most energetically stressed during winter,
winter recreation has the potential to have significant negative impacts. The two types of
winter recreation most likely to negatively affect martens are snowmobiling and creation
and operation of ski resorts. There have been very few studies on the effects of these
types of recreation on martens. However, a recent study in the Sierra Nevada found that
managed snowmobile use did not affect marten occupancy or activity patterns and that
this recreational activity largely occurred during the daytime, when martens are not
typically active during the winter (Zielinski et al. 2008).
In comparison to snowmobile recreation, developed ski resorts have the potential to
have more permanent and concentrated effects on martens and their habitat. Ski resort
development includes forest habitat alteration in the form of habitat loss and
fragmentation of remnant forest to create ski runs, roads, and resort infrastructure.
Furthermore, during the ski recreation season, high densities of ski recreationists are
present and distributed across resorts. There are approximately 25 ski resorts in the
Sierra Nevada, nearly all occur within the range of the marten. The Lake Tahoe region
includes about half of these resorts, constituting the highest density of resorts in the
Sierra Nevada and one of the highest in North America.
Martens typically avoid open areas lacking overhead cover or tree boles that provide
vertical escape routes from predators (Drew 1995) and select foraging routes to avoid
entering open areas in favor of remaining in areas with forest cover (Cushman et al.
2011). Martens have been shown to avoid areas when 25-30% of mature forest is
removed (Bissonette et al. 1997, Potvin et al. 2000), favoring areas composed of higher
proportions of high quality habitat providing the necessary prey resources, complex
physical structure near the ground surface to increase prey vulnerability to capture
(Andruskiw et al. 2009), and resting locations in large diameter live and dead woody
structures (Thompson et al. 2012), conditions typically most abundant in mature and old
growth forests. Thus, by the nature of their development ski resorts force martens to
cross openings in order to use habitat within their operational footprints. However, on
many ski resorts in California and Nevada, remnant forest patches are left relatively
intact, potentially providing suitable habitat between run crossings.
In addition to habitat alteration, activities during the winter recreation season have the
potential to negatively affect martens. Snow compaction from grooming activities alters
surface consistency making it easier for larger-bodied carnivores (e.g., coyotes [Canis
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latrans]) which, unlike martens are not adapted for deep, soft snow, to expand their
winter ranges (Bunnell et al. 2006, Whiteman and Buskirk 2013) and compete with or
prey on martens. Skiers and staff are active during the majority of the day at high
densities and during the night conducting grooming activities, creating a higher
likelihood for marten-human encounters and their associated disturbances; such as
decreased frequency of prey captures due to interruptions while hunting. Finally, while
potential ski resort effects may appear to be greatest in winter, the permanent effects of
habitat loss and fragmentation are present year-round, and may be increasing during the
summer season as more resorts developing summer recreation programs (e.g., hiking,
mountain biking).
Ski areas may also have potentially beneficial effects on martens. Martens have been
reported using anthropogenic food sources (e.g., dumpsters), using resort structures (e.g.,
chalets, buildings) as rest sites, and their tracks in snow are occasionally detected beneath
lift lines where they may find discarded food items or prey that are attracted to them.
Food available at ski areas, from humans, may also attract small mammals or support
increased population sizes which, in turn, may provide food for martens. Evaluating the
sum total of costs and benefits of ski areas on marten populations can best be achieved by
contrasting the demographic health of populations found in ski areas to those in similar
areas unaffected by ski operations.
Evidence exists that martens are present at many of the ski resorts in the Lake Tahoe
region. Surveys conducted at Heavenly ski resort have demonstrated that martens
primarily occupy the central and southern portions of the resort, particularly during
winter (Bartholomew & Associates 1993, Cablk and Spaulding 2002). Surveys detecting
martens and sightings of martens have occurred on or near several other resorts in the
region (e.g., Sierra at Tahoe, S. Yasuda pers. comm.; Alpine Meadows, K. Boatner pers.
comm.; Homewood, K. Slauson pers. obs.). Although survey detections and sightings
provide information on occurrence, occurrence alone is insufficient to evaluate the effects
of a ski resort on martens. First, these surveys did not compare marten data from ski
resorts with unaffected (control) areas. Second, these surveys documented presence of
martens only, which provides no information on the demographic health of the
population. Martens can occur in the area but at very low densities, or the populations
may have skewed sex or age ratios or high turnover rates, all suggesting a population that
is not sustainable.
Kucera (2004) conducted the only intensive study of martens in a ski area in North
America. His work occurred at Mammoth Mountain ski area from 2002-2003. Within
the 1855 ha operations area 12 martens were captured, yielding a density of 6.47 martens
per 1000 ha. However, 10 individuals were males, only 1 was female, and 1 was of
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unknown sex, resulting in a highly skewed proportional sex ratio of 0.91. Studies of
martens adjacent to Mammoth (Kucera 1997) and in five other areas of North America
(Buskirk and Lindstedt 1989) obtained much more balanced proportional sex ratios of
0.57 and 0.52-0.62, respectively. The single female at the Mammoth ski area did raise
two kits, but did not use developed areas and only used natural rest sites. Martens
appeared to move away from the ski area and into unmanaged forest after winter. Kucera
(2004) suggested this fits a seasonal use pattern where martens occupy ski areas during
winter, when natural prey is least available and human-supplied food is most plentiful,
then move into unmanaged forests in spring.
Kucera’s (1994) study provides compelling, if only preliminary, information
suggesting that Mammoth ski area does not support a self-sustaining marten population
due to lack of females and seasonal use. If these results are consistent across ski resorts,
their high density in the Lake Tahoe Region could have significant effects on the
regional marten population. To investigate this possibility, we conducted a research
project focused on marten occupancy and demographic parameters (e.g., sex ratio, age,
survival, reproduction) and which compares data from ski resorts to nearby control areas.
The overall goal of this study was to determine whether ski resorts have a net
negative, neutral, or positive effect on Pacific marten populations in the Lake Tahoe
region. Specifically, we gathered information necessary to evaluate the influence of ski
resorts on:
1.
2.
3.
4.
5.
6.
loss and fragmentation of forest habitat
marten movement
marten seasonal occupancy and space use
marten abundance and survival
marten age structure and sex ratio
proportion of females that reproduce
2.0 Methods
2.1 Study Design
Our study design compares 3 pairs of treatment (ski resorts) and control (non-ski
resorts) study areas. To select ski resorts, we reviewed information on the distribution of
martens in the Lake Tahoe region and selected 3 resorts that were within the current
range of martens that were also composed largely of forested habitat. This process
resulted in the selection of the Heavenly, Sierra-At-Tahoe, and Homewood ski resorts as
the 3 treatment study areas (Figure 1). Each resort was then paired with a single control
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study area that best matched it with respect to the overall amount, composition, and
suitability of marten habitat as well as major topographic characteristics.
2.1a Control Area Selection
The 3 control areas for this project were selected based on the topographic (elevation
range and major aspect) similarity, vegetation similarity, and proximity to each ski resort
operations area. Topographic and vegetative similarity was assessed using digital
elevation models and remotely sensed existing vegetation data (hereafter EVEG; USDA,
Pacific Southwest Region, Remote Sensing Lab, Updated Feb 2010) based on California
Wildlife Habitat Relationships system (hereafter CWHR, Mayer and Laudenslayer 1988)
habitat types in a geographic information system (GIS). Control areas were chosen that
had the same basic forest type and size class distributions present at each ski resort
operations area prior to its development (Figure 2). We used pre-development era aerial
photos in combination with existing vegetation conditions to reconstruct the vegetation at
ski resorts (see “Quantifying the Effect of Ski Resort Development on Forest Habitat”,
below, for details) to determine what the contemporary composition of forest habitats
would be at each ski area if development had not occurred. Sites were selected that were
in close proximity to each ski resort operations area to facilitate assessing seasonal
movement, directional dispersal of young, and to better control for environmental
variability (Figure 1).
2.1b Statistical Considerations
Prior to committing to a particular study approach, we conducted a power analysis to
investigate the relationship between the potential number of martens that could occupy a
typical ski area and the parameters of interest (Slauson and Zielinski 2007). The
Heavenly ski area contained approximately 1500 ha of suitable habitat while Sierra at
Tahoe and Homewood ski areas contained about 1000 ha of suitable habitat which we
conservatively estimate could support 7-12 and 4-8 martens, respectively.
We investigated the density parameters, including male, female, and total density,
assuming a 10-50% change in marten density between ski area and controls. Using our
design of 3 pairs of treatment and controls, we predicted that we would be able to detect a
33-36%(2-4 marten difference) decline in the density of martens. A difference in total
density of ≥ 4 martens begins to become biologically relevant, given only 4-16
individuals likely exist in an area of similar habitat, constituting a 25-100% reduction in
the number of individuals present. Furthermore, a reduction of female density >2 can
begin to significantly reduce reproductive capability in small populations. Thus, we
concluded that our design was sufficient to detect differences in marten density that are
biologically relevant.
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2.2 Marten Sampling Design
To sample martens in each study area, a systematic grid was established that
encompassed the entire ski operations area footprint for each ski area. The size and shape
of each control area’s sampling grid mirrored that of its paired ski area. The grid was
composed of hexagonal cells with an area of 100 ha, which is a size similar to a small
female marten home range (Buskirk and McDonald 1989). Within each grid cell, 3
candidate station locations were established 500 m apart in the center of each grid cell
and in a triangular array. Only 2 of the 3 station locations were selected from each
hexagonal cell for sampling because we considered this density of stations, 2 stations/100
ha, sufficient to detect any martens exhibiting the smallest reported home range sizes. At
each station selected, depending on the season, either live traps (spring/summer) or hair
snares (winter) were established (Figure 3). Hair snares were used in the winter because
live capture was too risky given the low overnight temperatures and potential for high
overnight snow accumulation. During the spring-summer live trapping was conducted in
order to collect the full suite of demographic information from each individual captured.
The number of stations sampled at each ski and control study area pair was dependent on
the size of the each resort’s operations area. The Heavenly ski and paired control study
areas each had 36 stations, Sierra at Tahoe ski and paired control areas each had 24
stations (Figure 3), and the Homewood ski and paired control study areas each had 20
stations. Winter sampling occurred in a single year and live trapping occurred over 3
years because the majority of our research questions required the annual live capture of
martens in each study area.
2.2a Marten Winter Sampling
Winter sampling occurred in 2009 from January to March. Each paired ski and
control study area were simultaneously sampled. The two stations selected in each
hexagonal cell were surveyed during non-overlapping 15-consecutive day survey periods
such that each study area was effectively sampled over a total of a 30-day period. At
each station, a newly designed winter hair snare (Kendall and McKelvey 2008) was
attached to the bole of a large diameter tree and consisted of a plastic (coroplast) snow
shield with 2 hair snares attached, 2 chicken drumsticks as bait, and a coroplast collar
below the bait with 3 hair snares attached (Figure 4). An olfactory lure (Gusto,
Minnesota Trapline Products, Pennock, MN) soaked sponge was hung on a nearby tree
branch. Each hair snare was a 30-caliber gun-cleaning brush (Hoppe’s, Bushnell Outdoor
Products, Overland Park, KS). Once established, each station was checked 3 times, every
5 days, for a total of 15 days. Hair samples were collected and stored in dessicant prior to
DNA analysis. DNA was extracted and individual identification was conducted using
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microsatellite variation (Schwartz et al. 2012), conducted by the USFS Wildlife Genetics
Laboratory.
2.2b Marten Spring-Summer Sampling
Spring-summer sampling occurred from May through July in 2009, 2010, and 2011.
Each year, a paired ski and control study area were sampled during each month. Over the
course of the 3-year sampling period each ski area-control pair was rotated through each
sampling month such that each study area was sampled once in May, June, and July. In
each study area, a wire mesh live trap (Tomahawk Co, Tomahawk, WI, USA, Model
#105) was established at every station location where a hair snare had been placed
during the winter of 2009. Traps were modified with a plywood cubby box on the end to
provide trapped animals with a more secure and insulated location to rest while in the
trap (Wilbert 1992). Traps were baited with chicken and the same olfactory lure used for
the snares. Once established, each live trap was checked at least once daily for a total of
13-15 consecutive days.
During the spring-summer season, each individual captured was chemically
immobilized and examined to determine their sex, age class, body condition (weight,
evidence of injury), and reproductive status (females lactating, number of teats from
which milk can be expressed, number of suckling rings). To age most individuals, one
upper first premolar was removed for cementum annuli analysis to determine age (Poole
et al. 1994). Teeth were sent to Matson’s Laboratory (Milltown, MT ) to be aged. For
individuals missing all pre-molars, or when tooth samples were too poor to enable
accurate age estimation, tooth wear was used to estimate an age class based on its
similarity to known age individuals of the same sex.
Blood was collected from tooth extraction sites using a Whatman card and was used
for DNA fingerprinting (Riddle et al. 2003). In addition, small tufts of tail and dorsal
body hair was removed and used for DNA fingerprinting when blood was not available.
All genetic samples were sent to the USFS Wildlife Genetics Laboratory (Rocky
Mountain Research Station, Missoula, MT). Each individual received a uniquely
numbered passive integrated transponder (PIT) tag for future individual identification
(12.5mm; Biomark, Boise, ID). Use of PIT tags facilitated scanning and individual
identification of martens in traps allowing for immediate release of recaptured martens.
2.3 Quantifying the Effect of Ski Resort Development on Forest Habitat
We quantified the changes in habitat composition that ski resort development caused
in each study area by using the combination of pre-development aerial photography and
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existing vegetation structure. This information was used to reconstruct what present day
ski areas would look like without development. The first step was to digitize all ski area
development from high resolution aerial digital ortho-photography (National Agriculture
Imagery Program [hereafter NAIP], Updated 2012). The second step was to use
historical (1940-1969) aerial photography to identify the type of vegetation (e.g., forest,
chaparral, wet meadow, barren) that occurred in each developed area prior to
development. The final step was to use the present day habitat conditions in the most
proximal undeveloped areas with the same habitat type to identify the structural
characteristics, CWHR tree size class and canopy cover, that would be present in
developed areas if development had not occurred.
Post development habitat change metrics included the area of forest and non-forest
habitat types converted to ski runs, roads, and facilities, size distributions of remnant
forest patch types, distribution of ski run widths, and proportion of ski runs with residual
cover versus those completely cleared. Summary statistics were calculated to determine
the overall amounts of each CWHR habitat type and tree size class lost due to ski area
development and their relative proportions of each habitat type at each ski study area.
The number and size of all remnant habitat patches, defined by their isolation from
contiguous habitat by ski area development such as ski runs or roads, were calculated in
GIS.
2.4 Spatial Scales of Analysis and Development of Potential Stressor Covariates
We used two ways of evaluating effects at the study area scale: (1) Comparing ski
versus control study areas, hereafter referred to as the treatment-level comparisons, and
(2) Comparing areas found in and out of each ski areas’ operational boundary, hereafter
referred to as the operations area-level comparisons. We defined the ski operations area
as the entire area in which habitat-altering actions, such as ski run creation, had taken
place and whose outer boundaries typically included the outer-most ski run or roads used
in the ski area’s operation. Analyses at the treatment-level will evaluate whether effects
are present and more or less uniform across the entire ski or control study areas or not.
Comparing areas at the operations area-level, accounts for the fact that some sampling
stations within ski area study areas occur outside the operational boundary and are
substantially less exposed to any potential effects from ski operations. Areas sampled
outside ski operations boundaries at ski study areas were grouped with controls for these
operations area-level comparisons. The value of using these two levels in subsequent
analysis is that it permits the evaluation of whether any detected effects of ski areas
extend beyond the operations area boundaries or whether they are confined to the
operations area.
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In each study area and operations area, there is finer scale heterogeneity in both the
distribution of potential stressors, conditions that may cause stress to martens, at ski study
areas and in habitat characteristics important to martens across all the study areas. For
example, some portions of ski areas are more fragmented by ski runs than others and
some portions have larger ski run crossings than others. Similarly, habitat conditions,
including the distribution of stands with the largest trees size classes, are not uniformly
distributed in both ski and control study areas. Separating the relative influences of these
habitat and potential stressor effects is critical to evaluating whether and how ski area
development affects martens.
To account for this finer scale heterogeneity in the distributions of potential stressors
and habitat characteristics we also evaluated the effects of habitat and potential stressor
variables at the station-level and individual-level. Station-level analyses included the area
in the immediate vicinity of each sampling station using 250 m radius circles or by
identifying the remnant forest patch encompassing the station. Individual-level analyses
were conducted by aggregating all stations where each individual marten was detected
(either via hair snare or live trap) to identify seasonal habitat use areas and whether the
composition of seasonal use areas affected survival or reproduction. The multi-scale
approach to the analysis permitted the evaluation of how potential ski area stressors
affected marten responses at different spatial scales.
2.5 Marten Movement: Evaluating the Effects of Ski Runs
We used the previously described digitized ski run coverage and station grid to
identify both the number of ski runs and ski run crossing distances necessary for martens
to cross in order to move between adjacent stations and to reach contiguous forest at the
closest edge of the operations area boundary.
To identify likely candidate ski run crossings between adjacent pairs of stations we
conducted a least cost path analysis to identify the apparent movement path between
adjacent stations that minimized the total distance of ski run required to cross. First, we
constrained the search area for paths between adjacent stations by buffering the direct
line of travel between station pairs by 250 m to identify all the possible ski run crossing
locations between each station pair. Next, for every ski run crossing that involved
moving from a unique pair of remnant forest patches we measured the shortest crossing
distances at 5 m intervals at all locations in the 250 m buffer. Then for each ski run
crossing between unique forest patches we calculated the minimum, maximum, and mean
crossing distances. Finally, because martens are highly sensitive to crossing open areas
(e.g., Cushman et al. 2011), we identified the single apparent movement path that
minimized the cost, total distance of ski run required to cross, between each unique
station pair.
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To evaluate whether ski runs have altered the functional habitat connectivity we
compared the proportion of movement paths used versus unused between ski and control
study areas and between stations located in and out of ski operations areas. Used
movement paths were defined as any path connecting stations where a unique individual
marten was captured (via either hair snare or live trap) on sequential captures. Unused
was defined as any movement path connecting a station where a marten was detected to
an adjacent station where a marten was not detected. The proportions of used versus
unused movement paths were compared using Z-tests.
2.6 Marten Occupancy and Space Use
2.6a Occupancy Modeling
We used single-season (winter 2009 only) and multiple-season (spring-summer 20092011) occupancy modeling in program PRESENCE, version 2.4 (Hines and McKenzie
2006), to evaluate whether ski areas have influenced the distributional responses by
marten populations. For each analysis, we used a two-step modeling approach. First, we
developed multiple candidate models accounting for detection heterogeneity, variation in
dataset from either the survey protocol and temporal or spatial variation in its
performance. Second, we used the top model(s) from the first step as a base model to
then develop a candidate set of models evaluating multiple hypotheses of how marten
occupancy may be affected by potential ski area stressors. Occupancy estimates for the
winter and spring-summer seasons from top models were compared either between ski
areas and controls (treatment-level) or between stations in and out of ski operations areas
(operations area-level) using McNemar’s Chi-square test.
2.6b Seasonal Changes in Occupancy Rates
We evaluated seasonal changes in occupancy rates by first comparing occupancy
estimates from top models for the winter season to the spring-summer season within ski
study areas and within control study areas at the treatment- and operations area-levels
using McNemar’s Chi-square test.
2.6c Comparing Seasonal Space Use
We evaluated the geographic location and composition of marten seasonal use areas
by analyzing the distribution of capture locations by season, for all individuals that were
captured at ≥ 2 stations. For each of these individuals we created “use area polygons”
using the minimum convex polygon methods. These polygons included 100% of each
individual’s capture locations. For individuals or portions of individual’s use areas where
capture stations were linear, we buffered each detection station by 250 m and connected
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adjacent stations with the cylindrical area that encompassed the outer edges of each
station buffer.
For each individual’s use area, in ski study areas only, we calculated the proportion of
the use area that was composed of the operations area, ski run widths <20 m, and remnant
forest patch sizes. In addition, we calculated the proportion of stations located in and out
of the ski operations area for each individual’s use area. For each metric, we first
calculated the proportions of each individual marten’s use area described by each of the
metrics listed above. Then, we took the mean of the metric’s proportion across all
individual martens and compared it to availability across all 3 ski study areas using ztests (Aebischer et al. 1993). For each metric, availability was defined as the amount of
the metric contained within the outer hexagonal boundary for each ski study area. For the
winter analysis we compared male, female, and all martens combined to availability. For
the spring-summer analysis we compared sex, and sex by age classes, to availability.
2.7 Marten Demographic Responses
2.7a Evaluating Abundance and Survival
We used the Pradel, survival and seniority, robust design capture-recapture model in
PROGRAM MARK (version 5.1) to estimate marten abundance and survival (White and
Burnham 1999). The Pradel model involves the estimation of 5 parameters, initial
capture probability (P), recapture probability (C), apparent survival (S), seniority (G), and
population size (N). The capture-recapture data collected in this study did not include
fate data, thus only apparent survival can be estimated. Apparent survival represent both
true survival and emigration, because if a individual is not caught in successive trapping
sessions it may be due to either it emigrating to another area outside the study area or
actually dying. Seniority is unique to the Pradel model and is the probability that if and
individual is alive and in the population at time i (this year), that it was also alive and in the
population at time i-1 (the previous year). Thus, seniority provides a measure of stability
in terms of population membership and for martens can be assumed to represent year-toyear home range fidelity . The robust design allows for the inclusion of within session
capture results and uses this information to better account for capture heterogeneity
resulting from time or prior capture (e.g., trap-happy or shy response; Kendall 1999). By
explicitly using the within session capture information, the robust design yields more
precise survival rate estimates than Jolly-Seber methods (Kendall 1999). Based on field
experience and review of the raw capture data, we anticipated that capture heterogeneity
from time (month trapped) and group (sex) would potentially be important to account for
in the dataset and explicitly incorporated these elements into the modeling design.
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The robust design includes 3 assumptions: (1) Populations are closed to emigration,
immigration, and mortalities across capture sessions within each 30 (winter) and 15
(spring) day encounter period, (2) If temporary emigration occurs within encounter
periods it is assumed to be either completely random, Markovian, or a short term
behavioral response to initial capture, and (3) Survival rate is assumed to be the same for
all individuals in the population. The assumption of closure within encounter periods is
likely to be valid and if not, the simultaneous sampling of paired ski areas and their
controls make the study design more robust to violations of this assumption. The issue of
temporary emigration is of interest and could be a treatment effect, where individuals
occupying ski areas may be more likely to be temporary emigrants due to having
increased home ranges as a response to reduced habitat suitability (e.g., Thompson 1994).
The assumptions of equal survival rates across all martens is not likely valid. To
explicitly address this, we included groups for sex-age classes and subgroups for
treatment-level comparisons.
There were 4 encounter periods in the dataset, winter 2009 and spring-summer 2009,
2010, and 2011. During the single winter 2009 encounter period 6, 5-day capture
intervals (hair snare) were included and during the summer 15, 1-day capture intervals
(live trap) were included in each encounter period. Five-day intervals within the winter
encounter period were used because this was the highest resolution of capture
information available using the hair snares. During the spring traps were visited daily, so
capture results were known each day in the spring/summer of 2009-2011.
To evaluate whether treatment-level and operation area-level effects occurred, a
grouping covariate for ski area/control and for in and out of operations areas were used.
The grouping variables were created to compare survival rates between: (1) martens on
ski areas and controls, and (2) martens in operations areas versus those out of operations
areas. Individuals captured only on the edges of capture grids were identified with an
edge covariate to determine whether these individuals have different capture-recapture
probabilities than individuals captured in grid interiors. Sex and age classes included
adult male, adult female, and sub-adult (<2 years old), and were incorporated into the
modeling structure by designating them as groups in the design phase. Individual-level
covariates were used to evaluate whether metrics of the composition of an individual’s
use area affected survival and seniority.
2.7a1 Model Development, Selection, and Evaluation
We developed candidate models that incorporated capture covariates to explain
variation in capture probabilities and treatment and habitat covariates to estimate
abundance and survival in ski and control areas. We used a 2-step modeling process.
The first step involved the development and comparison of multiple competing models to
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explain capture heterogeneity. Sources of capture heterogeneity hypothesized to
influence the encounter histories include: sex, survey month, and capture on the edge
versus interior of the capture grids. The top ranked model(s) from this step were then
used as the base model in an information-theoretic framework (Burnham and Anderson
2002) to develop an a priori set of competing models representing each of the research
hypotheses about how ski area and forest fragmentation affect marten survival.
The set of models were ranked using Akaike’s Information Criterion for small sample
sizes, (AICc), (Burnham and Anderson 2002). Models were interpreted by the
comparison of ΔAICc values, which provides a measure of strength of evidence and a
scaled ranking for candidate models (Anderson et al. 2000). To further interpret the
relative importance of a model, given the a priori model set, Akaike weights (wi) were
calculated using ΔAICc values and created a 95% confidence set of models by
considering all models whose cumulative weights equaled 0.95 (Burnham and Anderson
2002). Coefficients from the top ranked model(s) were used to determine whether
apparent survival and abundance differed either at the treatment-level, operations arealevel, or individual-level.
2.7a2 Comparing Marten Density Estimates
We compared marten density using winter and spring-summer capture-probabilityadjusted estimates from top models. We estimated density by dividing the area of each
site by the number of individuals estimated to occur there during the Winter and Springsummer seasons. The size of each study area was calculated by summing the areas of
each hexagonal sample unit surveyed (100 ha each). Density estimates were standardized
at # martens/1000 ha for comparability between pooled ski and control study areas.
Variance estimates for density were computed using the delta method: var (Density) =
(1/Area)2 (var N). Treatment-level density estimates were compared between the pooled
ski and control estimates by season using t-tests.
2.7b Age Structure and Sex Ratio
2.7b1 Age Structure Analysis
To age martens we extracted one vestigial pre-molar and sent them to Matson’s
Laboratory (Milltown, MT) for cementum annuli analysis. For all individuals where
either all pre-molars were missing upon initial capture or that had a partial tooth extracted
that could not be accurately aged we used a secondary aging method using combinations
of indexes of tooth wear and sexual maturity. The accuracy of using the secondary aging
method to age individuals with known ages was >95% correct for assignment to adult (≥
2 years old) and sub-adult (< 2 years old) age classes.
15
We summarized the age structure for the entire study population by summing the
number of individuals in each age class by sex across all three spring-summer capture
seasons. Comparisons between age class compositions, using the proportion of subadults of age 1 and age 1 and 2 relative to the comparison sub-population, were made at
the treatment-level and operations-area level using Z-tests.
2.7b2 Sex Ratio Analysis
Sex ratios were calculated by totaling the number of observed unique individuals
captured for each sex. Sex ratios were compared at the treatment-level and operationsarea level using Z-tests.
2.7c Evaluating Reproductively Active Females
To determine the reproductive state for females, raising kits or not raising kits, we
assessed the characteristics of the teats during all spring-summer captures. We captured
female martens during the late lactation through post-weaning period. Females actively
raising kits during the same year of capture would exhibit 1 or more of the following
characteristics (1) at least one teat that expressed milk, (2) at least one sucking ring
present, and (3) enlarged teats, with ≥ 3 mm width on at least one teat. We defined a
female reproductive attempt as an individual female exhibiting evidence of attempting to
raise kits in a single year and totaled the number of reproductive attempts by study area.
We calculated the mean density of reproductive attempts from 2009-2011 by dividing
each study area’s total number of reproductive attempts by its’ total area (ha). We
standardized the density of reproductive attempts as # reproductive attempts / 300 ha for
comparability between all study areas. We compared mean densities of reproductive
attempts between ski areas and controls using t-tests.
3.0 Results
3.1 Effect of Ski Resort Development on Forest Habitat
Ski resort development, including the creation of ski runs, roads, and developed areas
involved a total of 546 hectares, representing 7.3% of the total area of the 3 control study
areas combined and 32.7% of the total area of the 3 operations areas combined. The
relative proportion of each ski area’s operations area developed was very similar, ranging
from 30.0% to 34.1% among the 3 ski areas. The majority of the developed portion of
each ski area (81-89%) represented conversion from forest to non-forest habitat types
(Figure 5). The relative changes in forest habitat types reflected the relative abundance
of the dominant forest types at each ski area. The majority of forest habitat lost (91%)
included CWHR tree size class 4 (i.e. stands of trees 11-24 inch diameter breast height).
16
Remnant forest habitat within the ski operations areas was fragmented into distinct
patches by the presence of or combination of ski runs, roads, or developed areas
completely encircling each patch and separating it from adjacent forest habitat. The
number of remnant forest patches in each ski area was 169, 100, and 65 at Heavenly,
Homewood, and Sierra-At-Tahoe, respectively. Remnant patches averaged <5 ha in size
across all 3 ski areas, but ranged from <1 to >40 ha. Overall few (2.9%) remnant forest
patches exceeded 20 ha, while the majority (89.7%) of remnant forest patches were <10
ha (Figure 6).
Open areas, ski runs and roads, created during ski area development separating
remnant forest habitat patches varied in width from < 5 m to >250 m. The mean widths
of openings at each ski area were significantly different (ANOVA, F = 295.2, df = 2, P
<0.0001). Homewood ski area had the smallest mean width at 27.7 m (SE = 0.23) and
Heavenly and Sierra-At-Tahoe had the largest mean width, 33.3 (SE = 0.16) and 35.1 (SE
= 0.19), respectively. The proportion of ski run and road widths in <10 m, 10-20 m and
large >20 m categories varied amongst the 3 ski areas, with the Homewood ski area
composed of proportionally more widths <20m (Figure 7).
3.2 Marten Winter Detections
From the 4th of January through 28th of March 2009 we sampled a total of 160 hair
snare stations across the 6 study areas (3 ski areas and 3 controls). The hair snare stations
performed well at collecting hair from martens, with hair collected on ≥3 gun brushes on
>90% of occasions. In total, 316 hair samples were collected, 256 (81%) were marten
and 60 (19%) were either not marten or marten, but with DNA of insufficient quality for
determination of individual identification. Of the 256 marten samples, 38 unique
individuals (26M:12F) were identified. The Wildlife Genetics Laboratory reported no
observations of any samples that contained DNA from >1 individual. Thirty-three of 38
individuals (87%) were represented by >1 hair sample, however the total number of
samples were extremely male-biased with 204 (80%) from males and 52 (20%) from
females.
Martens were detected on all 6 study areas, with a total of 17 (13M:4F) individuals
detected on the ski areas and 23 (14M:9F) on the control areas (Table 1). While the
number of male martens detected differed by only 1, the proportion of the stations used
by male martens were lower at the resorts compared to the controls (Table 1, Figures 810). More than twice the number of female martens were detected on controls (n = 9)
versus ski areas (n = 4; Table 3, Figures 8-10), yielding sex ratios (M:F) of 1.56 for
controls versus 3.25 for ski areas. Females were detected at 62% more stations on
controls than ski areas (Table 1).
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3.3 Spring-Summer Captures
From 6 May through the 2nd of August in 2009, 2010 and 2011 we live trapped at the
same 160 stations in the 6 study areas that were surveyed with hair snares during the
winter. In 2009, we captured a total of 38 martens (24M:14F) across the 6 study areas
(Table 2). Eight (57%) of 14 females showed signs of having litters of ≥1 kit. Of the 38
individuals, 19 were captured on ski areas and 19 in control areas (Table 2). Capture
success, defined by the number of martens captured per number of trap nights, declined
from May/mid-June to mid-June/July. We observed a total of 8 breeding females, 4 on
ski and 4 on control areas (Table 2).
In 2010, we captured 36 individual martens (25M:11F) across the 6 study areas (Table
2). Eighteen (50%) of the 36 individuals were live trapping recaptures (12M:6F) and 18
(50%) were new captures (13M:5F). Seven (64%) of 11 females captured in 2010
showed signs of having litters with ≥1 kit. Of the 7 breeding females, 4 were captured on
ski areas and 3 on controls. Of the 36 individuals captured, 19 were on ski areas and 17
on control areas (Table 2).
In 2011, we captured and released 46 individual martens (28M:18F) across the 6 study
areas (Table 2). Eighteen (39%) of the 46 individuals were live trapping recaptures
(11M:7F) and 28 (61%) were new captures (17M:11F). Sixteen (89%) of 18 females
captured in 2011 showed signs of having litters with ≥1 kit, surprisingly including 2
females aged at just over 1-year old. Of the 46 individuals captured, 31 were on ski areas
and 15 on control areas (Table 2). We continued to observe the same consistent decline
in capture success over the course of the 3-month trapping season in 2011 that we
observed in 2009 and 2010. In 2011, we observed a total of 16 breeding females, 11 on
ski areas and 5 on controls. No marten mortalities or significant injuries due to live
trapping or processing occurred during the 3 years of live trapping effort.
Over the 3.5 years, including 1-winter and 3-spring-summer capture seasons, we
captured a total of 96 unique individual martens (63M:33F), 51 on ski areas (33M:18F)
and 49 on control areas (34M:15F) Of these individuals only 14 (37%; 10M:4F) of the
38 that were captured during the winter of 2009 on hair snares were recaptured during the
subsequent 3 years of spring live trapping. In contrast to the observed winter station
detections, martens were detected at the same or more total stations at ski areas than
control areas during the spring-summer from 2009-2011 (Table 3, Figures 11-13).
However, most cumulative captures at individual stations occurred outside the operations
areas at each ski area from 2009-2011 (Figures 11-13). In addition, in 2 of 3 ski areacontrol pairwise comparisons adult males were captured at proportionately more stations
at controls than at ski areas (Table 3). Finally, and importantly, over the 3 year span of
this study we observed evidence of a total of 31 female reproductive attempts, 19 on ski
18
study areas compared to 12 on control study areas. However, at the operations-area
level, only 12 female reproductive attempts occurred in portions of the operations areas,
compared to 19 reproductive attempts outside the ski operations boundaries.
3.4 Marten Movement
3.4a Movement Paths
During the winter season we identified 36 used and 75 unused apparent marten
movement paths between adjacent stations. Overall, martens showed highly significant
selection (p = 0.0001) for movement paths with lower cumulative minimum ski run
crossing distances (Table 4). The mean cumulative minimum ski run crossing distance at
movement paths used by martens was 3-times lower, 17.5 m versus 54.8 m, than present
at unused movement paths (Table 4). There were no significant differences between the
mean cumulative crossing distances between males and females during winter (p = 0.39)
or between adult and sub-adult males (p = 0.87), adult and sub-adult females (p = 0.63),
and adult males and females (p = 0.97).
During the spring-summer seasons from 2009-2011, we identified 39 used and 82
unused apparent movement paths. Only 18 (46%) of the movement paths used in the
spring-summer were also used in the winter season. However, despite the high level of
non-overlap between movement path use between seasons, the characteristics of both
movement paths and single ski run crossings varied little between seasons (Table 4, 5).
During the spring-summer season martens also showed highly significant (p = 0.0004)
selection for movement paths with lower cumulative minimum ski run crossing distances
(Table 4).
3.4b Individual Ski Run Crossings
Among the used and unused movement paths in the winter season of 2009, we
identified 44 individual used and 177 individual unused ski run crossings. The mean
minimum crossing distances for individual ski runs were significantly lower at used
versus unused paths (p = 0.009; Table 4). There were no significant differences between
the mean minimum crossing distances between males and females during winter (p =
0.66).
Among the used and unused apparent movement paths in the spring-summer seasons
we identified 56 used and 182 unused individual ski run crossings. Martens showed
highly significant (p = 0.0006) selection for using ski run crossings with shorter
minimum crossing distances (Table 5). There was no significant difference between
mean ski run widths used only in the winter versus those used only in the spring-summer
season (p = 0.53). Therefore, we pooled all used ski run crossings, used in 1 or both
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seasons, and all unused ski run crossings, not used in both winter and spring-summer
seasons, for the final analysis and to guide the development of metrics for use in
subsequent analysis. The pooled mean ski run crossing widths were significantly lower
(p = 0.001) at used versus unused ski run crossings (Table 5). There were no significant
differences between mean crossing distances used in the spring-summer season between
adult and sub-adult males (p = 0.62), or adult and sub-adult females (p = 0.85), but
females used significantly shorter mean ski run crossing distances (mean = 11.2 m) than
males (mean = 15.1, p = 0.01; Figure 14).
3.4c Functional Habitat Connectivity
At the treatment-level, the comparison between the proportion of used versus
available movement paths between ski and control study areas was not significant (z =
1.68, p = 0.09; Table 6). However, at the operations area-level, the proportion of used
versus available movement paths were significantly reduced within the operations areas
compared to the combination of paths outside the operations areas and in the controls (z =
4.05, p <0.01; Table 6). The high degree of sensitivity for run crossing widths
significantly reduced the functional habitat connectivity on ski versus control study areas,
but these effects were confined to within the operations area boundaries. The resulting
landscape structure on ski areas, due to the interconnected networks of ski runs and roads,
has greatly influenced the apparent movements of martens at ski areas. Effective
isolation of habitat patches in operations areas occurred once stations were more than a
single ski run >20 m wide or by a combination of run crossings > 30 m within a 500 m
linear distance. Based on these results we used 3 categories of individual ski run crossing
distances, <10, 10-20, and >20 m, for inclusion into additional analyses (see Occupancy
and Survival Analyses).
3.5 Occupancy
3.5a Winter Occupancy
We developed 13 candidate models to estimate marten occupancy (ψ) and detection
probability (p). Of those models a single model, model 1 (Table 7), was clearly superior
to all others and received 99% of the Akaike weight (wi). The top model contained
covariates related to effect of treatment versus controls on detection probability and the
effect of being in or out of the ski operations areas on winter occupancy (Table 7).
Specifically, stations located in ski areas had significantly lower single-visit probabilities
of detection p = 0.53 (SE = 0.04) versus stations located in controls p = 0.75 (SE = 0.03;
z-stat = 2.94, p =0.003). Stations located in ski operations areas (n = 32) at ski areas also
had significantly lower probabilities of occupancy ψ = 0.52 (SE = 0.09) compared to
stations located out of ski operations areas (n = 126) ψ = 0.88 (SE = 0.03; McNemar’s χ2,
p <0.001). The overall single-station probabilities of detection (P), which considers the
20
entire 15-day survey period with 3 visits, for the winter hair snare protocol was 0.90 for
ski areas and 0.98 for controls. This suggests that our survey methods had a very high
probability of detecting martens that were present.
3.5b Spring-Summer Occupancy
For the spring-summer occupancy analysis we developed 17 candidate models to
estimate marten occupancy (ψ) and detection probability (p), combining all sex and age
classes in order to be comparable to the winter occupancy results. Several models were
competing for the top model, but they all shared the same base model with p best
modeled using a year and survey month covariate to account for detection heterogeneity
from temporal variation (Table 8). Occupancy (ψ) was best modeled separately for each
study area, resulting in independent occupancy estimates (Table 8). Covariates for
treatment-level or operations area-level effects on ψ or p were not included in the top
models for the spring-summer season (Table 8).
3.5c Seasonal Changes in Occupancy Rates
To compare season-specific occupancy estimates, we had to first derive weighted
mean occupancy estimates for each ski area during the winter using the proportion of
stations located in and out of each ski area’s operations area to account for the different
occupancy rates. This resulted in estimates of ψ = 0.70 for the Heavenly and Sierra-AtTahoe ski areas and ψ = 0.72 for the Homewood ski area. All 3 control study areas
maintained out of operations area estimates of ψ = 0.88. All study area-specific
occupancy estimates show significant declines in occupancy rates from winter to spring
(Figure 15). The relative rates of change in occupancy rates from winter to spring are
larger for control areas (mean = 57.6, SE = 0.14) than ski areas (mean = 32.4, SE =
0.043), however they were only marginally statistically significant (1-tailed t-test, p =
0.10).
The differences in the relative rates of change in occupancy for ski versus control
areas between seasons is due to an increase in occupancy rates in the operations areas
during the spring. The winter occupancy rate for stations located in the operations area
was 0.52 and during the spring, using the observed occupancy rate for the month of May
when station-level detection probability was near 1, was 0.72. Outside the operations
areas, the decline in occupancy rate from winter to spring is due to the combination of the
contraction of use areas by resident martens and the departure of young of the year
dispersing individuals. Within the operations areas the increase in occupancy rates from
winter to spring is due largely to the arrival of new individuals. The total number of
individual martens captured on ski areas versus controls during the winter versus spring
(May), when individual capture probabilities were the highest, showed more than twice
21
the proportional increase of new individuals captured at ski areas (75%) versus controls
(36%).
3.6 Seasonal Space Use: Winter
During the winter 17 individuals (13M:4F) at ski areas were detected at ≥2 stations
across all 3 ski study areas (Figures 17-19). Only 1 of 16 (6.2%) martens at ski areas had
a use area exclusively located inside a ski area operations area boundary. Males showed
significant selection against using stations located in the operations area while females
did not (Table 9). Females showed more selective use of areas within the ski operations
boundary that included more ski runs <20m wide and more remnant forest patches >10
ha, but selective use of these features by females was not significantly different from
availability (Table 9).
3.7 Space Use: Spring-Summer
During the spring-summer seasons from 2009-2011, 45 individuals (28M:17F) at ski
areas were captured at ≥2 stations across all 3 ski study areas (Figures 19-24). Similar to
the winter results, males, specifically adult males, showed selection against (p = 0.06)
incorporating stations located in the ski operations area while sub-adult males and
females did not (Table 9). However, the strength of selection during the spring-summer
(p = 0.06) was much less than in winter (p = 0.00003). Neither males nor females
showed significant selection for including areas with higher proportions of ski runs <20m
wide during the spring-summer (Table 9).
Both males and females showed significant selection for the largest available patch
sizes of remnant forest habitat within ski operations areas during the spring-summer
season (Table 9, Figures 19-24). Adult females showed strong selection (p = 0.007) for
incorporating >10 ha remnant patches in combination with contiguous forest, located
outside the operations areas, while sub-adult females showed significant selection (p =
0.04) for incorporation of >10 ha patches within the operations areas. These age classrelated selection results likely reflect the dominance hierarchy between adult and subadult females, with the adults incorporating and defending the highest value resources
and sub-adults selecting from lower quality resources. Males also demonstrated a similar
selection pattern, with sub-adult males showing significant selection (p = 0.04) for use of
larger patch sizes within ski operations areas (Table 9). Adult males, as a group, did not
share the same significant selection for large remnant patches and contiguous forest
habitat as females. However, the 5 adult males that were known to be alive until the end
of the study on ski study areas exhibited significant selection for large remnant patches
and contiguous forest (mean = 93.7%, p = 0.0004) while the 5 adult males that were not
22
known to be alive showed selection against using large remnant patches and contiguous
forest (mean = 48.7%, p = 0.04).
3.8 Abundance and Survival
For the purposes of estimating abundance and survival, 76 candidate models were
developed and fit to the dataset in Program MARK. Seven models were in the 95%
confidence set and all 5 were highly competing (≤2 ΔAICs) for the top model (Table 10).
Model averaging was not considered because the model set was not orthogonal and most
of the candidate models represented attempts to best model capture-recapture
probabilities while holding survival, seniority, and population size parameters constant.
In winter, capture-recapture probabilities were best modeled by using a constant initial
capture probability for both adult males, adult females, and sub-adults and sessionspecific recapture probabilities (Table 10). In the spring-summer season, initial capture
probabilities were best modeled using the same constant initial capture probabilities for
adult males and sub-adults, but a different initial capture probability for females. All
initial spring-summer capture probabilities were best modeled by year. Recapture
probabilities were best modeled using the same structure, adult males and sub-adults
contant and females constant, both varying by year. The base model for capture
probability was nearly identical for all models in the 95% confidence set (Table 10).
Initial capture probabilities (p) were highest in winter, and during that season both all age
classes showed ‘trap-happy’ responses, with increased recapture probabilities (c). During
the spring-summer, females had lower initial capture probabilities than males and subadults, but had a decrease or no change in recapture probability. Both adult males and
sub-adults showed increased recapture probabilities compared to initial capture
probabilities during the spring-summer.
Survival was best modeled with separate estimates for adults and sub-adults for each
season. Using model 1, adult apparent survival for winter to spring 2009 was 0.94 (95%
C.I. = 0.56 to 1.0) and for adult annual apparent survival from 2009-2011 was 0.56 (95%
C.I. = 0.42 to 0.47). Using model 1, sub-adult apparent survival for winter to spring 2009
was 0.36 (95% C.I. = 0.19 to 0.56) and for adult annual apparent survival from 20092011 was 0.51 (95% C.I. = 0.36 to 0.64).Seniority was best modeled using constant
seniority estimates for adults and sub-adults separately. Using model 1, adult seniority
was 0.67 (95% C.I. = 0.54 to 0.78). For sub-adults, 4 of the 7 models in the 95%
confidence set also included treatment effects on seniority, using model 3 sub-adult
seniority was higher in controls 0.47 (95% C.I. = 0.32 to 0.61) than in ski study areas
0.37 (95% C.I = 0.26 to 0.50).
Population size was modeled using single estimates for adult males, adult females, and
sub-adults by treatment and control over all 4 capture sessions. Due to the high capturerecapture probabilities, population sizes were essentially the same as the observed capture
23
totals. Over the duration of the study the total marten population was estimated at 96
individuals (66M:33F). Winter population estimates were 23 (14M:9F) for controls and
17 (13M:4F) for ski areas. Cumulative spring-summer population estimates from 20092011 for adult males were 26 in controls versus 23 in treatments, adult females were 11
in controls versus 10 in treatments, and sub-adults were 43 controls versus 48 in
treatments. Standard errors for each n-hat estimate were < 0.01.
3.9 Marten Density Estimates
Winter density estimates were 33% higher in controls (5.64 martens/1000 ha, var =
<0.01) than ski areas (4.25 martens / 1000 ha, var = <0.01). This difference is largely
driven by the difference in the density of females, which was 2.3 times higher in controls
(2.3 females/1000 ha) versus ski areas (1 female/1000 ha). During the winter, the density
of males was not significantly different between controls (3.59 males /1000 ha) versus ski
areas (3.25 males/1000 ha).
Annualized spring-summer total marten density estimates were nearly equivalent
between controls (6.83 martens/1000 ha) and ski areas (6.75 martens/1000 ha). For adult
males (1.92-ski versus 2.22-control/1000 ha), adult females (0.83 versus 0.94
females/1000 ha), and subadults (4.00-ski versus 3.67-control/1000 ha) all differed by
<15% between ski areas and controls.
3.10 Age Structure and Sex Ratio
The cumulative age structure of the entire study population was overall skewed to
younger age classes of 1 and 2 year old martens (Table 11, Figure 25). Males and
females both had median ages of 2 years, but males typically exhibiting older annual
maximum ages of 5-7 years versus 3-5 for females. To compare age structure between
martens in and out of the operations areas, we assigned any martens to the in the
operations area group if they met all 3 of the following criteria: (1) >20% of their use
areas overlapped a ski operations area (2) if the portion of their use area overlapping the
ski operation area did not show any avoidance of crossing ski runs, and (3) if they did not
shift the position of their home range to minimize the overlap with the operations areas
during subsequent years.
There were no significant differences in the cumulative age structures between
treatment-level and operations-level comparisons for both sexes pooled, males only, and
females only (Table 11, Figure 26). The largest difference among any age classes
occurred at the operations-level for males, where higher proportions of sub-adult males
(1-2 years old) and lower proportions of adult males (>2 years old) occurred in the ski
operations areas versus outside the operations areas (Table 11).
24
The overall observed population sex ratio was 1.9 males to a single female (Table 13).
There were no significant differences between the sex ratio for the treatment- or
operations-level comparisons (Table 12).
3.11 Reproductively Active Females
We captured 29 females on a total of 43 annual occasions to assess reproductive
status. Overall there was very little variation in the proportion of females showing
evidence of lactation during each season, with 100% of females >1-year old showing
evidence of raising kits during the season they were captured (Table 13). From 20092011 we identified 31 reproductive attempts by females. At the treatment-level,
reproductive attempts at ski study areas exceeded those at controls, with 19 (61%) of
female reproductive attempts occurred on ski study areas compared to 12 (39%) on
control areas. However, at the operations-area level, the majority of reproductive
attempts occurred outside the operations areas, with 12 (39%) inside versus 19 (61%)
outside the operations boundaries.
Only 2 (17%) of 12 the reproductively active females in ski operations boundaries
maintained use areas exclusively inside the ski operations area boundary during the kitrearing season. The mean density of reproductive attempts from 2009-2011 was higher at
ski areas (mean = 2.25 repro. Attempts / 300 ha) versus controls (mean = 1.25 repro.
attempts / 300 ha) but the difference was not statistically significant (p = 0.31). The
number of reproductively active females using portion of ski area operations areas
differed by ski area, with Sierra-At-Tahoe ski area including up to 2-4, Heavenly 1-2, and
0 at the Homewood ski area. The distribution of reproductively active females was
patchy overall and most often present in areas of WHR size class 5 red fir and Sierra
mixed conifer forest patches in association with riparian habitats and less commonly in
areas with WHR size class 4 subalpine conifer habitat.
4.0 DISCUSSION
Ski areas have complex effects on marten populations, which depend on the season,
spatial scale, and population metric considered. Overall many of the marten demographic
characteristics we evaluated were similar between ski areas and controls. This may give
the impression that ski areas have limited or no overt effect on marten populations.
However, we did find seasonal and year-round effects on habitat use, effects on marten
movement, and sex and age class-specific effects that are important. At the treatmentlevel we found few differences between ski and control study areas. The most significant
effect we found at this scale was for winter marten density, where female density was
reduced by >50% in ski versus control areas. However, this effect did not continue into
25
the spring-summer period, as females increased their use of the habitat in the largest
remaining patches within ski operations areas once ski recreation activities had ceased.
During the winter martens used the majority of all stations outside of ski operations
areas, but used significantly fewer where ski area recreation activities occur during both
the day (skiing and lift operations) and night (grooming). The probability of detecting a
marten is typically only affected by temporal variation or variation in how survey
protocols are carried out (Slauson et al. 2012). In this study, however, there was also an
operations area effect on detection probability, suggesting that marten movement rates
into operations areas and their rates of habitat use are significantly reduced within ski
operations areas during the winter. At the individual-level, male martens positioned their
use areas to avoid or minimize using habitat in the operations areas during winter.
Together these results support the conclusion that martens are responding to winter ski
recreation by minimizing their spatial and temporal overlap with the operations areas,
where recreation and ski operations activities are most concentrated.
To shift their spatial and temporal use of habitat away from ski operations areas,
martens must have access to additional nearby habitat. Habitat avoided in ski operations
areas represents a direct loss of the available habitat to support the marten population.
One way to view the net loss is to consider the loss of occupied area as a result of the
presence of ski operations areas. For example, the estimate of occupancy (ψ) outside the
operations areas was 0.88, suggesting that 88% of the area in this status is occupied by
one or more martens. Conversely, only 52% of the area inside the operations area was
estimated to be occupied. Given the actual areas of the respective lands, this represents a
loss of 36% (i.e., 88% – 55%) which translates to 600.4 (ha) (1485 acres) of habitat lost
in the 3 ski areas combined. In the other studies where habitat (reviewed in Thompson et
al. 2012) or prey populations (Thompson et al. 1994) have been degraded, martens
typically respond by increasing their home ranges to compensate for the reduction in prey
resources. The rate of home range increase is typically proportional to the proportion of
home range degraded (Potvin et al. 2000). If the same phenonmenon occurs in ski areas,
we can expect that the combined effect of habitat lost and seasonal reduction in use
during the winter to represent 15%-37% of each of the ski study areas.
During the spring-summer season both occupancy and capture rates did not
significantly differ between ski or control areas or between stations located in and out of
ski operations areas. The lack of an operations area-level effect on occupancy or capture
rates during the spring-summer season, when ski recreation activities have ceased,
suggests that winter ski recreation activities are the main cause for marten avoidance and
reduced activity within ski operations areas. This avoidance and reduced activity was
only seen when ski recreation activities were present and when they ceased, martens
26
increased both their spatial use of habitat and had detection rates similar to habitat outside
ski operations areas. This is in contrast to Kucera’s (1994) finding that martens appeared
to use the Mammoth ski area seasonally in the winter, leaving in the summer. Instead,
we found that ski area effects were greatest in the winter season.
This may be due to differences in how marten populations utilize more productive westslope and Sierra crest forest types, where they are known to be largely resident, versus
less productive eastside forest types where their residency and seasonal movements are
not well understood.
Although overall occupancy and capture rates did not differ during the spring-summer
period, the seasonal distributional dynamics differed between ski and control areas. As
winter transitioned to the spring-summer season, occupancy rates declined more rapidly
in controls than ski areas. In controls, this was due to the combination of the reduction in
use areas by resident adult martens and, to a lesser degree, dispersal by some sub-adults.
In the ski study areas, use areas of residents also contracted, but more new individuals
arrived than in controls and these new immigrants primarily used areas within the
operations areas, accounting for the seasonal increase in occupancy. New immigrants
into ski operations areas each spring-summer season were primarily sub-adult males and
few of these individuals were captured in subsequent years. Furthermore, none of the 7
adult males known to survive for the complete duration of the study occurred in any of
the ski operations areas. Together these effects suggest that habitat use within ski
operations areas is not only seasonally reduced but affects the ability to support resident
males. Although we do not know the fates of individuals not recaptured, the loss of
habitat capable of supporting resident individuals reduces the population size an area can
support. And if male martens are also experiencing higher mortality rates in operations
areas, the combination of the direct- and indirect-effects to population size would be
more severe.
Sex-specific effects were evident at the individual-level, as adult males showed the
strongest avoidance of habitat in the ski operations areas, unlike females and sub-adults.
The stronger avoidance of habitat in the operations areas by adult males likely relates to
the relative difference in movement rates within home ranges between males and
females, especially in the denning season. Males typically use larger home ranges than
females and, during the denning season when females centralize their foraging activity to
the vicinity around den sites, males can use >3 times the space as females. Recent
evidence suggests that male martens may traverse their entire or significant proportions
of their home ranges within a single day (K. Moriarty unpubl. data). Due to their
increased movement rates relative to females, male martens may necessarily need to
cross ski runs more frequently than females. The net result is that they may experience a
27
higher predation risk from using habitat in ski operations areas than females, which may
result in stronger avoidance of these habitats.
Unlike adult males, adult females did not exhibit avoidance of habitat in ski operations
areas. Adult females, however, exhibited strong selection for incorporating a
combination of continuous forest outside ski operations areas with the largest available
patches of remnant habitat within ski operations areas into their spring-summer use areas.
No adult females maintained year-round use areas exclusively in ski operations areas,
thus functional movement and habitat connectivity need to be maintained or enhanced to
promote the continued use of habitat supporting reproductively active females within ski
operations areas. The estimated number of adult females in the population in the springsummer period represented only 21.3% of entire study population. Given that they are
such a small proportion of the population, but are solely responsible for the raising
young, maintaining reproductive habitat to support adult females and the production of
young will be critical to marten population persistence both on and off ski operations
areas.
We observed adult females largely associating with large amounts of 2 forest habitat
types: (1) red fir and Sierra mixed conifer forest with large trees >24” dbh interspersed
with riparian habitat, which includes surface water with mesic woody plants such as
thinleaf alder (Alnus tenuifolia) and (2) subalpine conifer forest with patches of trees
>24” with patches with trees 11-24” dbh, not necessarily associated with riparian
features. Adult female martens appear to be much more selective than male martens for
the habitat characteristics capable of supporting their year-round life history needs.
During the denning season female martens experience energetic demands, from lactation
and provisioning of prey for their young, which far exceed anything male martens
experience during the year. In the morphologically similar fisher, this disparity in
energetic demands between females and males during the denning season can require that
females more than double the calories/day needed during the denning season than males
(Powell 1993). This difference in energy requirements to support reproduction requires
them to select the habitats with the most abundant and reliable prey populations to
support their energetic needs. A spatially-explicit habitat model capable of distinguishing
suitable reproductive habitat from habitat supporting only males and non-habitat for
martens will be essential to guiding management actions to benefit the maintenance of
marten populations.
Both male and female martens demonstrated strong responses in winter and springsummer to both individual ski run crossing widths and to the cumulative widths of run
crossings along apparent movement paths. This finding was not surprising, as martens
are well known to exhibit a high degree of sensitivity to entering and crossing open areas
28
lacking overhead (e.g., canopy cover) or vertical (e.g., tree boles) escape cover from
predators (Drew 1995, Cushman et al. 2011). Martens had distance thresholds, above
which, they were rarely noted crossing ski runs. Using the upper limit of the 95%
confidence intervals, martens rarely crossed individual ski runs that were > 20 m and
rarely crossed cumulative runs that totaled > 30 m. Responses to run crossing differed by
sex in that females (all ages) selected to cross shorter distances of openings, typically <
15m. However, adult males were more apt than females to incorporate portions of the ski
areas that were outside the operations area into their use areas. This would, presumably,
reduce their risk to overall exposure to any of the negative effects that operations had on
their habitat. That females did not do the same suggests that they may place a premium
on selecting denning habitat, regardless of where it occurred.
Buskirk et al. (2012) used matrix population models parameterized using fecundity
and survival estimates from wild marten populations across North America. They found
that adult and sub-adult survival had the largest and second largest influence on
population growth rates, respectively. Although we did not find significant differences
between apparent survival rates for martens between ski areas and controls or inside and
out of ski operations areas we found sex differences in survival rates. Female martens
were able to use portions of ski operations area without apparent negative effects on
survival, but we had a number of indications that male were not. First, adult male
martens exhibited the strongest avoidance of the ski operations areas in winter and spring
than any other sex or age class. Second, none of the 7 adult martens known to be alive
for the 3-year duration of the study had a significant portion (>20%) of their use areas
located within ski operations areas. Finally, of the 13 occasions that males survived from
the ages of 1-2 or 2-3 years old, only 2 (15.3%) of these males had significant portions of
their use areas within ski operations areas. Collectively these indicators suggest that male
apparent survival may indeed by lower within ski operations areas.
Our survival estimates have limitations that suggest that additional research will be
necessary to refine our understanding of the effects of ski area development on
demography. We estimated annual apparent survival over a 3-year period, providing only
2 intervals in which to calculate estimates. This was not likely sufficient time to
adequately separate the sources of variation we observed, such as natural levels of
immigration and emigration and edge effects of our sampling grids, from effects on
survival. Multiple indicators of adult male survival did support that habitat in ski
operation areas has lost most of its capacity to support resident adult male martens. The
next step will be to determine whether habitat in the ski operations areas promotes
increased emigration rates, increased mortality rates, or some increased combination of
the two compared to habitat outside operations areas. The data from this study can be
29
used to conduct simulations, and in conjunction with known-fate monitoring can be used
to design a program capable of resolving this important unresolved issue.
Our study design allowed us to make treatment- and operations-level comparisons
between habitat contained in ski areas and non-ski areas. However, the strength of this
design is dependent on making accurate pairings between treatments and controls with
respect to the habitat quality for martens. Initially, we used CWHR habitat types and tree
size classes to match the overall conditions present. During the course of this study it
became clear that these characteristics were capable of generally identifying marten from
non-marten habitat, but they did not predict well how martens actually make use of that
habitat, such as for reproduction, year-round non-reproductive use, or as winter habitat
only. The Sierra ski area contained the largest contiguous patches of red fir forest with
>24” dbh trees and thus supported the most number of reproductively active females
among all treatment and control areas. The Sierra control study area had an equivalent
amount of habitat in that size class, but it was more disaggregated, reflecting the
importance of large contiguous patches of habitat for supporting reproduction in martens.
The Heavenly control study area, while matching the overall CWHR types and size
classes in the ski area, contained the poorest overall habitat for martens of all study areas,
supporting few resident individuals. Poorer site conditions for tree growth and a
management history of relatively more selective logging may explain why the habitat
there was apparently lower quality. It is clear that marten habitat is not well described for
research or management by simple 2-state systems, such as habitat and non-habitat, or
suitability values based on relating marten presence to habitat quality. To provide
improved management of marten populations both on ski areas and beyond, managers
will need spatially-explicit habitat models capable of distinguishing between the key
states of marten habitat, that support reproduction, year-round non-reproductive habitat,
and seasonal use of habitat.
MANAGEMENT IMPLICATIONS
The overall results of our study suggest that winter ski recreation at developed ski
areas may be compatible with the maintenance of marten populations in the Lake Tahoe
region of the Sierra Nevada. However, ski area development and winter ski recreation
activities do have negative, sex-specific effects on martens. Our results suggest that
marten conservation within ski areas will be best achieved by considering the following
suggestions:
1. Maintain reproductive habitat and its use by adult female martens.
30
A. Maintain or enhance suitable reproductive habitat within ski
operations areas by maintaining or increasing the sizes of habitat
patches.
B. Maintain habitat connectivity between reproductive habitat in ski
operations areas and outside them to provide year-round habitat to
support adult females.
C. Maintain or enhance ski run crossings between reproductive habitat
within and outside of ski operations areas by maintaining or increasing
the proportion of ski runs <15m across. Maintain or enhance
sequential run crossings, between suitable patches of reproductive
habitat, to not exceed 30m cumulative crossing distances between ski
operations boundaries and suitable reproductive habitat.
D. Restrict recreational activities in reproductive habitat to the nondenning seasons (fall-winter) to limit their effects on marten
reproduction.
2. Maintain or enhance habitat connectivity within ski operations areas.
A. Maintain or enhance the proportion of ski run crossings < 20m
between non-reproductive habitat patches that are >10 ha and the
operations areas boundary. Where smaller patches function as
potential ‘stepping-stones’ between patches >10 ha and/or the
operations boundary, maintain or enhance the proportions of ski run
crossings that are < 20m.
3. Evaluate expansion using a variety of spatial criteria for habitat and connectivity
that are sex specific (as described above).
A. Expansion or habitat altering activities should avoid reproductive
habitat, or large (>10 ha) patches of remnant habitat on ski areas.
B. Expansion or habitat altering activities should incorporate
consideration for how habitat connectivity will be affected and
minimize effects reducing habitat connectivity between reproductive
habitat and large (>10 ha) remnant patches of habitat.
31
C. Mitigation efforts can be useful, but rarely compensate for the loss or
degradation of female denning habitat.
D. Mitigation efforts should match or exceed the value of habitat or
connectivity lost due to a proposed action.
E. Ski area expansions should be considered as a cumulative effect which
account for impacts from prior ski development (the seasonal and sexspecific effects found in this study) as well as other threats that may
have affected, or will affect, martens or their habitat (e.g., fuel
treatments, climate change effects on habitat).
4. The scope of inference of this study are limited to ski area development that
involves the creation of ski run, road, and development infrastructure with very
limited modification to remnant forest habitat within operations areas.
Modifications such as thinning of trees to increase space for skiing or removal of
logs and rocks to improve surface consistency in remnant forest patches were not
included in the ski study areas used in this study. These activities are known to
used elsewhere for ski area development and likely represent additional
degradation of habitat suitability.
5. The results of this study are applicable to forest habitats within the range of the
Pacific marten on west slope and Sierra crest forest types. Due to the difference
in effects reported in this study, and work reported previously in eastside forest
habitats (Kucera 2004), marten populations may be responding to ski area
development and recreation in different ways in these areas.
ACKNOWLEDGEMENTS
We would like to thank the Lake Tahoe Basin Management Unit staff and especially
Joey Keely for logistical assistance during the period when fieldwork was conducted in
the Lake Tahoe Basin. We also would like to thank Ted Thayer and Shane Romsos and
the Tahoe Regional Planning Agency for assistance in the initial need and design of the
study. We are grateful to Casey Blann and Jim Larmore and the Heavenly resort, Drew
Bray and the Sierra-At-Tahoe resort, and Kent Hoopengarner and Homewood Mountain
resort for assistance with access and logistics while conducting field research activities at
each ski area. This project would not have been possible without the efforts of the crew
leaders: Mark Linnell, Katie Greller, and Wesley Watts as well as the field research
asssistants: Natalie Craven, Devin Crenshaw, Matt Delheimer, Kirstie Lawson, Pete
Lundberg, Katlyn Mansfield, Dustin Marsh, Conor McNamara, Natalie Mesce, Caleb
32
Pharris, Nathan Shea, Kathleen Sholty. We would like to thank our collaborators Mike
Schwartz and Kristy Pilgrim at support staff at the Rocky Mountain Research Station’s
Wildlife Genetics Laboratory. We also acknowledge the contributions from other staff
from Pacific Southwest Research Station: Ric Schlexer for logistical support, and Jan
Werren, Tom Kirk and Diane Montoya for GIS support. We would like to acknowledge
Patricia Buettner and the Modoc National Forest for use of snowmobiles during the
study.
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36
Table 1. Distribution of observed Pacific marten detections at hair snares during the winter of 2009 on 3 ski
areas and 3 paired control areas in the Lake Tahoe Region of California and Nevada, USA.
Winter 2009 Hair Snare Station Detections
Study Area
#
Sample
Units
#
Stations
Males
All
Martens
Females
#
%
#
%
#
%
Homewood
Ski Area
10
20
12
60%
2
10%
13
65%
Homewood
Control
10
20
18
90%
3
15%
19
95%
Sierra Ski
Area
12
24
13
54%
5
21%
17
71%
Sierra
Control
12
24
22
92%
11
46%
23
96%
Heavenly
Ski Area
18
36
23
64%
7
19%
25
69%
Heavenly
Control
17
34
21
62%
9
26%
27
79%
Ski Area
Totals
40
80
48
60%
14
18%
55
69%
Control
Area
Totals
40
78
61
78%
23
29%
69
88%
37
Table 2. Pacific marten spring-summer live capture results for 2009-2011 on 3 ski areas and 3 paired control areas in the Lake Tahoe Region of
California and Nevada, USA.
Study Area
Homewood
Ski Area
Homewood
Control
Spring 2009 Capture Results
#
SubTrap Ind. Adult Ad Adult SubMon. Capt.
M
M
F
Ad F
Spring 2010 Capture Results
#
SubTrap Ind. Adult Ad Adult SubMon. Capt.
M
M
F
Ad F
Spring 2011 Capture Results
#
SubTrap Ind. Adult Ad Adult SubMon. Capt.
M
M
F
Ad F
May
6
2
2
1
1
July
2
2
0
0
0
June
4
1
2
0
1
May
15
7
3
3
2
July
6
3
2
1
0
June
5
3
1
0
1
Sierra Ski
Area
June
8
2
2
2
2
May
12
2
5
2
3
July
10
2
3
4
1
Sierra
Control
June
5
3
1
0
1
May
9
3
4
2
0
July
9
4
2
1
2
July
5
3
0
1
1
June
5
1
1
2
1
May
11
3
3
5
0
July
1
1
0
0
0
June
2
2
0
0
0
May
7*
1
3
2
0
19
7
4
4
4
19
5
6
4
4
25
6
8
9
2
21
11
4
3
3
17
8
6
3
0
21*
8
6
3
3
Heavenly
Ski Area
Heavenly
Control
Ski Area
Totals
Control
Area Totals
38
Table 3. Distribution of Pacific marten capture locations at live traps during the spring-summer periods (May-July) from 2009-2011 on 3 ski areas
and 3 paired control areas in the Lake Tahoe Region of California and Nevada, USA.
Study
Area
Homewood Ski
Area
Homewood
Control
Sierra Ski
Area
Sierra
Control
Heavenly
Ski Area
Heavenly
Control
Ski Area
Totals
Control
Area
Totals
Spring 2009 Live Capture
Adult Sub-Ad Adult Sub-Ad All
Male Male Female Female Marten
# % # % # % # % # %
Spring 2010 Live Capture
Adult Sub-Ad Adult Sub-Ad All
Male Male Female Female Marten
# % # % # % # % # %
Spring 2011 Live Capture
Adult Sub-Ad Adult Sub-Ad All
Male Male Female Female Marten
# % # % # % # % # %
11 55% 2 10% 1 5% 3 15% 16 80%
4 20% 0 0% 0 0%
0 0% 4 20%
3 15% 4 20% 0 0%
2 10% 8 40%
13 65% 6 30% 7 35% 3 15% 12 60%
8 40% 3 15% 2 10% 0 0% 11 55%
8 40% 4 20% 1 5%
1 5% 11 55%
2 8% 5 21% 3 13% 3 13% 9 38%
3 13% 17 71% 6 25% 5 21% 19 79%
7 29% 7 29% 7 29% 0 0% 17 71%
6 25% 3 13% 1 4% 0 0% 8 33%
9 38% 9 38% 4 17% 0 0% 15 63%
9 38% 4 17% 3 13% 2 8% 13 54%
9 25% 0 0% 2 6% 2 6% 11 31%
2 6% 2 6% 1 3%
2 6% 6 17%
16 44% 9 25% 11 31% 0 0% 25 69%
0 0% 1 3% 0 0% 0 0% 1 3%
4 12% 0 0% 0 0%
0 0% 4 12%
6 18% 7 21% 6 18% 0 0% 15 44%
22 28% 7 9% 6 8% 8 10% 36 45%
9 11% 19 24% 7 9%
7 9% 29 36%
26 33% 20 25% 18 23% 2 3% 50 63%
19 24% # 13% 8 10% 3 4% 21 27%
# 27% 12 15% 6 8%
0 0% 30 38%
23 29% 15 19% 10 13% 3 4% 39 50%
39
Table 4. Comparison of Cumulative Ski Run Crossings Used and Unused by Pacific Martens along Least
Cost Movement Paths Between Capture Locations at Ski Areas In the Lake Tahoe Region from 2009-2011.
Winter 2009
Used
Unused
N
Mean
T-test
95% Confidence
Interval
Spring 2009-2011
Used
Unused
36
75
39
82
17.5
54.8
20
51.7
p-value = 0.0001
5.4 - 29.65
p-value = 0.0004
42.9 - 66.5
11.4 - 28.6
40
42.6 - 60.7
Table 5. Comparison of individual Ski Run Crossings Used and Unused by Pacific Martens Between Capture
Locations At Ski Areas In the Lake Tahoe Region from 2009-2011.
Winter 2009
Used
Unused
N
Mean
T-test
Spring 2009-2011
Used
Unused
Pooled 2009-2011
Used
Unused
44
177
56
182
73
126
14.3
20.5
14.2
22.1
14.0
22.1
p-value = 0.009
95%
Confidence 11.3 - 17.3
Interval
18.0 - 23.0
p-value = 0.0006
11.6 - 16.6
p-value = 0.001
19.7 - 24.5
41
11.7 - 16.3
19.5 - 25.8
Table 6. Summary of the used versus available movement paths between stations where Pacific martens
were detected during the winter season of 2009 and the spring-summer seasons from 2009-2011.
Used
Study Area
Available
Used
Available
In-Ops
Out-Ops
In-Ops
Out-Ops
Heavenly Control
26
48
0
26
0
48
Heavenly Ski
27
58
16
11
47
11
Homewood Control
15
28
0
15
0
28
Homewood Ski
13
36
4
9
19
17
Sierra Control
22
32
0
22
0
32
Sierra Ski
23
39
15
8
30
9
Totals-Control
63
(58%)
108
Total-Ski Areas
63
(47%)
133
35 (37%)
91 (63%)
96
145
Totals-In/Out Ops
42
Table 7. Single-season occupancy modeling results for the combined male and females Pacific martens during the winter 2009
on 3 ski areas and 3 paired control areas in the Lake Tahoe Region of California and Nevada, USA.
.
Model Parameters
Model #
1
2
3
4
5
6
7
8
9
10
11
12
13
Model Ranking
p
ψ
In vs Out of Ski Ops Area
Constant
Constant
In vs Out of Ski Ops Area
2-groups
3-groups
Constant
1-group
1-group
In vs Out of Ski Ops Area
In vs Out of Ski Ops Area
Patch Size
In vs Out of Ski Ops Area
Patch Size
Ski Area vs Control
Ski Area vs Control
Ski Area vs Control
In vs Out of Ski Ops Area
Constant
Constant
Patch Size
Constant
Survey-specific p
Ski Area vs Control
Ski Area vs Control
Ski Area vs Control
Ski Area vs Control
43
Patch Size
Ski Run
Patch Size
ΔAICc
wia
Kb
0
10.8
10.9
13.2
21.0
25.0
25.2
31.2
32.3
33.1
38.1
41.2
45.2
0.99
0.004
0.001
0
0
0
0
0
0
0
0
0
0
4
4
3
4
4
6
2
2
4
5
5
3
5
Table 8. Multiple-season occupancy modeling results for the combined male and females Pacific martens during the spring-summer seasons of
2009-2011 at 3 ski areas and 3 paired control areas in the Lake Tahoe Region of California and Nevada, USA.
Model Parameters
Model #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
ψ
Individual Study Area
Individual Study Area
Individual Study Area, Patch
Individual Study Area
Individual Study Area
Individual Study Area,
In vs Out of Ski Ops Area
Individual Study Area
Ski vs Control
Ski vs Control
Constant
Constant
In vs Out of Ski Ops Area
Constant
Constant
Constant
Constant
Constant
Model Ranking
p
Є
ΔAICc
wia
0.32
0.3
0.17
0.11
0.05
0.04
K
14
13
14
14
13
15
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
10
10
9
9
8
9
8
9
9
3
7
Year, Survey Month
Year, Survey Month
Year, Survey Month
Year, Survey Month
Year, Survey Month
Year, Survey Month
Ski vs Control
Constant
Constant
Patch Size
Constant
0
0.13
1.22
2.13
3.56
4.07
Year, Survey Month
Year, Survey Month
Year, Survey Month
Year, Survey Month
Year, Survey Month
Year, Survey Month
Survey Months 2009, Months 2010-11
Individual Study Area
Ski-Control Pairs
Constant
Year, Ski vs Control
Constant
Ski vs Control
Constant
Constant
Constant
Constant
Constant
Constant
Constant
Constant
Constant
8.73
11.48
11.72
12.76
14.55
16.18
16.94
23.69
28.12
29.02
32.94
In vs Out of Ski Ops Area
44
Table 9. Summary statistics and univariate z-test results for Pacific marten use areas during the winter of 2009 and the spring-summer seasons of
2009-2011 at 3 ski areas and 3 paired control study areas in the Lake Tahoe region of California and Nevada. Bold indicates a p-value ≤0.05.
Season
Sex/Age
Captures In-Operations Areas
n
Use
Avail.
p
n
16 36.30% 48.75%
0.04
12 25.30% 48.75% 0.00003
4 47.60% 48.75%
0.48
13
10
3
Ski Runs <20m
Use
Avail.
p
n
Patches >10 ha
Use
Avail.
p
> 10 ha + Contiguous
Use Avail.
p
Winter
All
Male
Female
38.4%
34.1%
51.3%
38.9
38.9
38.9
0.96
0.54
0.27
17
13
4
23.7%
17.1%
45.0%
17.9%
17.9%
17.9%
0.22
0.55
0.11
79.9% 78.0%
79.9% 78.0%
79.9% 78.0%
0.40
0.42
0.43
0.48
0.73
0.25
Spring
Male
19 38.70% 48.75%
Adult
10 35.30% 48.75%
Sub-adult 9 42.40% 48.75%
0.07
0.06
0.28
17 36.00%
9 42.10%
8 29.10%
38.9
38.9
38.9
0.72
0.11
0.84
28
10
18
31.0%
23.1%
34.2%
17.9%
17.9%
17.9%
0.03
0.20
0.04
78.3% 78.0%
64.8% 78.0%
82.3% 78.0%
Female
Adult
Sub-adult
0.53
0.24
0.68
7
6
1
38.9
38.9
38.9
0.76
0.78
NA
17
7
10
34.9%
30.2%
38.4%
17.9%
17.9%
17.9%
0.03
0.22
0.04
87.9% 78.0% 0.02
90.1% 78.0% 0.007
83.6% 78.0% 0.36
9
7
2
52.60% 48.75%
58.10% 48.75%
33.30% 48.75%
38.70%
32.10%
39.90%
45
Table 10. The 95% confidence set of candidate models explaining Pacific marten capture-recapture probability, survival, seniority,
and population size over four capture sessions from 2009-2011. All models in the 95% confidence set share the structure of the ‘base model’
highlighted in gray.
Model
Model
#
Initial Capture (p)
Base
Winter-Constant
Spring(Male+Sub-adult
/ Female) x YR
Recapture ( c )
Abundance
(n)
Survival (s)
Seniority (g)
Winter-Session
Spring-Class x YR
Class x
Season x
Year
Winter-AD/SA
Spring-AD/SA
Adults Constant
Sub-adults-Constant
Spring Subadult+Edge
1
2
Δ
AICc
wi
Likelihood
K
0.00
0.23
1.00
29
0.17
0.21
0.92
28
3
Spring Sub-adult x
Treatment
0.91
0.15
0.63
29
4
Adult: Winter/Spring
Sub-adult x
Treatment
1.04
0.14
0.59
30
5
Sub-adult: Season x
Treatment
1.82
0.09
0.40
31
6
Spring Sub-adult x
Treatment
2.45
0.07
0.29
30
2.51
0.07
0.28
30
7
Capture Month
46
Table 11. Cumulative age structure comparisons for Pacific martens captured on Ski and Control study areas from 2009-2011 in the Lake Tahoe
Region.
Comparison
Sex
N
Sub-adult
(1-year)
Adult
(2-7 years)
Z-score
1-tailed
p-value
Sub-Adult
(1-2 years)
Adult
(>2 years)
Z-score
1-tailed
p-value
Ski
Control
Combined
Combined
65
55
43%
40%
57%
60%
0.32
0.37
65%
65%
35%
35%
0
0.5
In-operations Area
Out-operations Area
Combined
Combined
35
88
43%
40%
57%
60%
0.3
0.38
66%
60%
34%
40%
0.6
0.27
Ski
Control
Males
Males
37
40
49%
40%
51%
60%
0.78
0.21
68%
68%
32%
32%
0
0.5
In-operations Area
Out-operations Area
Males
Males
19
60
47%
43%
53%
57%
0.29
0.38
74%
63%
26%
37%
0.91
0.18
Ski
Control
Females
Females
28
15
36%
40%
64%
60%
0.24
0.4
61%
60%
39%
40%
0.06
0.47
In-operations Area
Out-operations Area
Females
Females
16
28
38%
32%
62%
68%
0.38
0.34
56%
61%
44%
39%
0.31
0.37
47
Table 12. Sex ratios, using unique individuals, for a Pacific marten population sampled
from 2009-2011 in the Lake Tahoe region of California and Nevada.
Sex Ratio
N
Males
Females
Ratio
Z-Score
1-tailed
p-value
Entire Population
96
63
33
1.91
Ski
Control
51
49
33
34
18
15
1.83
2.27
0.39
0.34
In-operations Area
Out-operations Area
24
72
16
47
8
25
2.00
1.88
0.09
0.46
Table 13. Proportion of female Pacific martens showing evidence of lactation during the
season of capture from 2009-2011 in the Lake Tahoe region of California and Nevada.
Female Age
Entire
Population
N
1
2
3
4
5
43
3 /15
10 /10
12 / 12
5 /5
1/1
20%
100%
100%
100%
100%
48
Figure 1. Location of the 6 paired ski and control Pacific marten study areas in the Lake
Tahoe Region of California and Nevada, USA.
Lake Tahoe
49
Figure 2. Existing habitat type and tree size class composition of each paired ski and
control study area. Habitat types (A) and tree size classes (B) were defined using the
California Wildlife Habitat Relationships classification and remotely sensed data
available from EVEG (updated 2010).
A. Study Area Habitat Composition for top 5 Habitat Types
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Montane Chaparral
White Fir
Sierra Mixed Confier
Subalpine Conifer
Red Fir
B. Study area tree size class composition for the top 4 tree-dominated habitat types.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
WHR Size Class 3
WHR Size Class 4
WHR Size Class 5
50
Figure 3. Sampling design for American martens at the Sierra At Tahoe Ski Area,
California, USA.
51
Figure 4. Winter hair snare design (1a) and use by American marten (1b) in the Lake
Tahoe basin of California.
4a. Hair snare design.
4b. American marten interacting with hair snare in the Lake Tahoe basin.
52
Figure 5. Proportional changes in California Wildlife Habitat Relationships habitat type
and tree size class composition from development at 3 ski study areas in the Lake Tahoe
region of California and Nevada. Non-habitat is the combination of Barren, Urban,
Annual and Perennial grassland habitat types.
80.0%
Proportional Change
60.0%
Non-Habitat
Montane Chaparral
40.0%
White Fir
Sierra Mixed Confier
20.0%
Subalpine Conifer
Red Fir
Tree Size Class 3
0.0%
Tree Size Class 4
Tree Size Class 5
-20.0%
-40.0%
Heavenly
Ski Area
Homewood
Ski Area
Sierra-At-Tahoe
Ski Area
Figure 6. Frequency distributions of post-development remnant forest patch sizes at 3 ski
study areas in the Lake Tahoe region of California and Nevada.
100.0%
90.0%
Percent Composition
80.0%
70.0%
60.0%
<10 ha
50.0%
10-20 ha
40.0%
20-30 ha
30.0%
30-40 ha
20.0%
>40 ha
10.0%
0.0%
Heavenly
Ski Area
Homewood
Ski Area
53
Siera At
Tahoe Ski
Area
Figure 7. Distribution of ski run crossing widths measured at 5 m intervals across 3 ski
areas in the Lake Tahoe region of California and Nevada.
80.00%
Heavenly Ski Area
60.00%
Homewood Ski Area
Sierra-At-Tahoe Ski Area
40.00%
20.00%
0.00%
<10 m
10-20 m
54
>20 m
Figure 8. Pacific marten hair snare detection results for the Heavenly Ski and Control
study areas during the winter of 2009 in the Lake Tahoe region of California and Nevada.
Males
Females
No Captures
55
Figure 9. Pacific marten hair snare detection results for the Homewood Ski and Control
study areas during the winter of 2009 in the Lake Tahoe basin of California.
Males
Females
No Captures
56
Figure 10. Pacific marten hair snare detection results for the Sierra Ski and Control study
areas during the winter of 2009 in the Lake Tahoe region of California.
Males
Females
No Captures
57
Figure 11. Pacific marten capture results at the Heavenly Ski and Control study areas during the
spring-summer seasons from 2009-2011 in the Lake Tahoe region of California and Nevada.
Numbers next to capture locations are the cumulative number of marten captures over the 3spring-summer capture sessions.
Marten Captured
No Captures
58
Figure 12. Pacific marten capture results at the Homewood Ski and Control study areas during
the spring-summer seasons from 2009-2011 in the Lake Tahoe region of California and Nevada.
Numbers next to capture locations are the cumulative number of marten captures over the 3spring-summer capture sessions.
Marten Captured
No Captures
59
Figure 13. Pacific marten capture results at the Sierra Ski and Control study areas during the
spring-summer seasons from 2009-2011 in the Lake Tahoe region of California and Nevada.
Numbers next to capture locations are the cumulative number of marten captures over the 3spring-summer capture sessions.
Marten Captured
No Captures
60
Figure 14. Ski run crossing widths used by adult and sub-adult Pacific martens and unused in the
Lake Tahoe Region of California and Nevada during the spring-summer seasons from 20092011.
Unused
60.0%
Adult Male
Subadult Male
50.0%
Frequency
Adult Female
40.0%
Subadult Female
30.0%
20.0%
10.0%
0.0%
>0-10
10-20
Ski Run Width (m)
61
>20
Figure 15. Seasonal changes in Pacific marten occupancy rates from winter 2009 to the
combined spring 2009-2011 estimates at 3 ski area and 3 paired control study areas in the Lake
Tahoe region of California and Nevada.
1.00
Station Occupancy Rate
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
Heavenly
Ski
Heavenly Homewood Homewood
Control
Ski
Control
0.00
62
Sierra
Ski
Sierra
Control
Figures 16. Pacific marten use areas in the Heavenly Ski and Control study areas during the
winter of 2009 in the Lake Tahoe region of California and Nevada. Each polygon encompasses
all capture locations (solid black circles) for each individual marten captured during the 3-year
study period.
Males
Females
63
Figures 17. Pacific marten use areas in the Homewood ski and control study areas during the
winter of 2009 in Lake Tahoe basin, California. Each polygon encompasses all capture locations
(solid black circles) for each individual marten captured during the 3-year study period.
Males
Females
64
Figures 18. Pacific marten use areas in the Sierra ski and control study areas during the winter of
2009 in the Lake Tahoe region of California. Each polygon encompasses all capture locations
(solid black circles) for each individual marten captured during the 3-year study period.
Males
Females
65
Figures 19. Adult and sub-adult male Pacific marten use areas at the Heavenly ski and control
study areas during the spring-summer seasons from 2009-2011 in the Lake Tahoe region of
California and Nevada. Each polygon encompasses all capture locations (solid black circles) for
each individual marten captured during the 3-year study period.
Adult (≥2 yrs)
Sub-adult (< 2yrs)
66
Figures 20. Adult and sub-adult male Pacific marten use areas at the Homewood ski and control
study areas during the spring-summer seasons from 2009-2011 in the Lake Tahoe region of
California and Nevada. Each polygon encompasses all capture locations (solid black circles) for
each individual marten captured during the 3-year study period.
Adult (≥2 yrs)
Sub-adult (< 2yrs)
67
Figures 21. Adult and sub-adult male Pacific marten use areas at the Sierra ski and control study
areas during the spring-summer seasons from 2009-2011 in the Lake Tahoe region of California
and Nevada. Each polygon encompasses all capture locations (solid black circles) for each
individual marten captured during the 3-year study period.
Adult (≥2 yrs)
Sub-adult (< 2yrs)
68
Figures 22. Adult and sub-adult female Pacific marten use areas at the Heavenly ski and control
study areas during the spring-summer seasons from 2009-2011 in the Lake Tahoe region of
California and Nevada. Each polygon encompasses all capture locations (solid black circles) for
each individual marten captured during the 3-year study period.
Adult (≥2 yrs)
Sub-adult (< 2yrs)
69
Figures 23. Adult and sub-adult female Pacific marten use areas at the Homewood ski and
control study areas during the spring-summer seasons from 2009-2011 in the Lake Tahoe region
of California and Nevada. Each polygon encompasses all capture locations (solid black circles)
for each individual marten captured during the 3-year study period.
Adult (≥2 yrs)
Sub-adult (< 2yrs)
70
Figures 24. Adult and sub-adult male Pacific marten use areas at the Sierra ski and control study
areas during the spring-summer seasons from 2009-2011 in the Lake Tahoe region of California
and Nevada. Each polygon encompasses all capture locations (solid black circles) for each
individual marten captured during the 3-year study period.
Adult (≥2 yrs)
Sub-adult (< 2yrs)
71
Figure 25. Cumulative age structure of the entire study population (n = 96) of Pacific martens
from 2009-2011 in the Lake Tahoe Region.
40
Male
Number of Individuals
35
Female
30
25
20
15
10
5
0
1
2
3
4
5
6
7
Marten Age (Years)
B. Relative proportions of ages for males and females.
Proportion of Individuals
50%
Male
Female
40%
30%
20%
10%
0%
1
2
3
4
5
Marten Age (Years)
72
6
7
Figure 26. Proportion of total marten captures by age from 2009-2011 pooled by A. Ski and
Control study areas and B. In and out of the Ski operations areas.
A. Age structure of martens captured in Ski (n = 65) and Control (n= 55) study areas.
Proportion of Individuals
60.0%
Males-Ski
Males-Control
50.0%
Females-Ski
40.0%
Females-Control
30.0%
20.0%
10.0%
0.0%
1
2
3
4
5
6
7
Marten Age (Years)
B. Age structure of martens in (n = 35) and out (n = 88) of Ski operations areas.
Proportion of Individuals
50.0%
Male-IN
45.0%
Male-OUT
40.0%
Female-IN
35.0%
Female-OUT
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
1
2
3
4
5
Marten Age (Years)
73
6
7