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 spring- summer 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

4 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

5 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. loss and fragmentation of forest habitat 2. marten movement 3. marten seasonal occupancy and space use 4. marten abundance and survival 5. marten age structure and sex ratio 6. 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

6 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

8 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

9 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 2009- 2011) 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

12 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 z- tests (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-to- year 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

14 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 area- level, or individual-level.

2.7a2 Comparing Marten Density Estimates

We compared marten density using winter and spring-summer capture-probability- adjusted 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 Spring- summer 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.

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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 sub- adults 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 operations- area 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).

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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 8- 10). 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 area- control 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 19 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-At- Tahoe 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 class- related selection results likely reflect the dominance hierarchy between adult and sub- adult 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 session- specific 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 sub- adults, 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 2009- 2011 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 capture- recapture 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 2009- 2011 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).

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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 2009- 2011 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 kit- rearing 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 treatment- level 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 west- slope 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 spring- summer 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 spring- summer 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.

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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 non- denning 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.

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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 sex- specific 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

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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.

Literature Cited

Aebischer, N.J., Robertson, P.A., Kenward, R.E., 1993. Compositional analysis of habitat use from animal radio-tracking data. Ecology 74, 1313–1325.

Anderson, D. R., K. P. Burnham, W. L. Thompson. 2000. Null hypothesis testing: problems, prevalence, and an alternative. Journal of Wildlife Management 64(4): 912-923.

Andruskiw, M., Fryxell, J., Thompson, I.D., and Baker, J.A. 2008. Habitat-mediated variation in predation risk by the American marten. Ecology (in press).

Bartholomew, H. and Associates. 1993. Heavenly ski resort forest carnivore surveys. Unpublished report. 2233 Watt Ave, Suite 330, Sacramento, CA.

Bissonette, J.A., D.J. Harrison, C.D. Hargis, and T.G. Chapin. 1997. The influence of spatial scale and scale-sensitive properties on habitat selection by American marten. Pages 368-385 in J.A. Bissonette, editor. Wildlife and Landscape Ecology. Springer-Verlag, New York, New York, USA.

Bunnell, K. D., J. T. Flinders, and M. L. Wolfe. 2006. Potential impacts of coyotes and snowmobiles on lynx conservation in the intermountain west. Wildlife Society Bulletin 34:828-838.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model inference: A practical information-theoretic approach. 2nd Edition. Springer-Verlag, New York, USA.

Buskirk, S. W. and S. L. Lindstedt. 1989. Sex biases in trapped samples of the Mustelidae. Journal of Mammology. 70:88-97.

Buskirk, S. W. and L. L. McDonald. 1989. Analysis of Variability in Home-Range Size of the American Marten. Journal of Wildlife Management 53: 997-1004.

Buskirk, S. W., J. Bowman, and J. H. Gilbert. 2012. Population biology and matrix demographic modeling of American martens and fishers. In K. Aubry et al. (eds):

33

Biology and Conservation of Martens, Sables, and Fishers: a New Synthesis. Cornell University Press.

Cablk, M. E. and S. Spaulding. 2002. Baseline and initial monitoring assessment of Martes americana, the American marten, at Heavenly Ski resort, Lake Tahoe. U. S. Forest Service, Lake Tahoe Basin Management Unit. 87 p.

Caswell, H. 1989. Matrix population models. Sinauer Associates, Sunderland, Mass., USA.

Cushman, S. A., Raphael, M. G., Ruggiero, L. F., Shirk, A. S., Wasserman, T. N., & O’Doherty, E. C. (2011). Limiting factors and landscape connectivity: the American marten in the Rocky Mountains. Landscape ecology, 26(8), 1137-1149.

Drew, G. S. 1995. Winter habitat selection by American marten (Martes americana) In Newfoundland: Why old growth? Dissertation, Utah State University. Logan. 83 p.

Hinze, J. E. and D. L. Mackenzie. 2006. Program Presence, version 2.4. http://www.mbr-pwrc.usgs.gov/software/doc/presence/presence.html

Kendall, K. C. and K. S. McKelvey. 2008. Hair Collection. Pages 141-182 in: Long, R. A., P. MacKay, W. J. Zielinski and J. C. Ray, editors. Noninvasive Survey Methods for Carnivores. Island Press, Washington, D.C.

Kendall, W. L., J. D. Nichols, and J. E. Hines. 1997. Estimating temporary emigration using capture-recapture data with Pollock’s robust design. Ecology 78: 563-578.

Kendall, W. L. 1999. The robust design for capture-recapture studies: Analysis using program MARK. International Wildlife Management Congress, Hungary. p. 357-360.

Kucera, T. 1997. Ecology of American martens on the Inyo National Forest. Unpublished report, Department of Environmental Science, Policy, and Management, University of California, Berkeley.

Kucera, T. 2004. Ecology of American martens on the Mammoth Ski area. Unpublished report.

Mayer, K. E., and W. F. Laudenslayer. 1988. A guide to wildlife habitats of California. California Department of Forestry and Fire Protection, Sacramento, California.

McGarigal, K., and B. J. Marks. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. U.S. Forest Service Pacific Northwest Research Station, General Technical Report PNW-GTR-351.

34

Mead, R. A. 1994. Reproduction in Martes. in S. W. Buskirk, A. S. Harestad, M. G. Raphael, and R. A. Powell, editors. Martens, sables, and fishers: biology and conservation. Comstock Publishing Associates, Ithaca, New York, USA.

Poole, K., G. Matson, M. A. Stirckland, A. Magoun, R. Graf, L. Dix. 1994. Age and sex determination for American martens and fishers. In: Buskirk SW, Harestad A, Raphael M., Powell R. (eds) Martens, sables and fishers: biology and conservation. Cornell University Press, Ithaca, NY.

Potvin, F., L. BClanger, and K. Lowell. 2000. Marten habitat selection in a clearcut boreal landscape. Conservation Biology 14:844-857.

Powell, Roger A. The fisher: life history, ecology, and behavior. 2nd ed. Minneapolis: University of Minnesota Press, 1993.

Riddle AE, Pilgrim KL, Mills LS, McKelvey KS, Ruggiero LF. 2003. Identification of mustelids using mitochondrial DNA and non-invasive sampling. Conservation Genetics, 4, 241–243.

Schwartz, M. K., A. Ruiz-Gonzalez, and R. Masuda. In press. Martes conservation genetics: assessing within species movements, units to conserve and connectivity across ecological and evolutionary time. In K. Aubry et al. (eds): Biology and Conservation of Martens, Sables, and Fishers: a New Synthesis. Cornell University Press.

Simon, T. L. 1980. An ecological study of the pine marten in the Tahoe National Forest. M.S. thesis. California State University, Sacramento, CA. 143 p.

Slauson, K. and W. J. Zielinski (Principal Investigators). 2007. Effects of ski resorts in the Lake Tahoe region on population dynamics of the American marten. Project Proposal: www.fs.fed.us/psw/partnerships/tahoescience/ski_resorts_marten.shtml.

Slauson, K M. J. A. Baldwin, and W. J. Zielinski. 2012. Occupancy Estimation and Modeling in Martes Research and Monitoring. Pages 343-368 in K. Aubry et al. (eds): Biology and Conservation of Martens, Sables, and Fishers: a New Synthesis. Cornell University Press.

Thompson, I. D. 1994. Marten Populations in Uncut and Logged Boreal Forests in Ontario. The Journal of Wildlife Management 58(2): 272-280.

Thompson, I. D., & Colgan, P. W. (1994). Marten activity in uncut and logged boreal forests in Ontario. The Journal of wildlife management, 280-288.

Thompson, I. D. J. Fryxell, nd D. J. Harrison. 2012. Improved insight into use of habitat by American martens. Pages 209-230 in K. Aubry et al. (eds): Biology and

35

Conservation of Martens, Sables, and Fishers: a New Synthesis. Cornell University Press.

White, G C. and K. P. Burnham. 1999. Program MARK—survival estimation from populations of marked animals. Bird Study 46 (supplement): S120-S139.

Whiteman, J. P., & Buskirk, S. W. (2013). Footload influences wildlife use of compacted trails in the snow. Wildlife Biology, 19(2), 156-164.

Wilbert, C. J. 1992. Spatial scale and seasonality of habitat selection by martens in southeastern Wyoming. M.S. Thesis, University of Wyoming, Laramie.

Zielinski, W. J., K. M. Slauson, and A. E. Bowles. 2008. The effect of off-highway vehicle use on the American marten in California, USA. Journal of Wildlife Management 72:1558-1571.

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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 Stations Males Females All Units Martens # % # % # % 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%

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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.

Spring 2009 Capture Results Spring 2010 Capture Results Spring 2011 Capture Results # Sub- # Sub- # Sub- Trap Ind. Adult Ad Adult Sub- Trap Ind. Adult Ad Adult Sub- Trap Ind. Adult Ad Adult Sub- Study Area Mon. Capt. M M F Ad F Mon. Capt. M M F Ad F Mon. Capt. M M F Ad F

Homewood Ski Area May 6 2 2 1 1 July 2 2 0 0 0 June 4 1 2 0 1 Homewood Control 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

Heavenly Ski Area July 5 3 0 1 1 June 5 1 1 2 1 May 11 3 3 5 0 Heavenly Control July 1 1 0 0 0 June 2 2 0 0 0 May 7* 1 3 2 0

Ski Area Totals 19 7 4 4 4 19 5 6 4 4 25 6 8 9 2 Control Area Totals 21 11 4 3 3 17 8 6 3 0 21* 8 6 3 3

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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.

Spring 2009 Live Capture Spring 2010 Live Capture Spring 2011 Live Capture Study Adult Sub-Ad Adult Sub-Ad All Adult Sub-Ad Adult Sub-Ad All Adult Sub-Ad Adult Sub-Ad All Area Male Male Female Female Marten Male Male Female Female Marten Male Male Female Female Marten # % # % # % # % # % # % # % # % # % # % # % # % # % # % # % Home- wood Ski Area 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% Home- wood Control 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%

Sierra Ski Area 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% Sierra Control 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%

Heavenly Ski Area 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% Heavenly Control 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%

Ski Area Totals 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% Control Area Totals 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%

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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 Spring 2009-2011 Used Unused Used Unused

N 36 75 39 82

Mean 17.5 54.8 20 51.7

T -test p -value = 0.0001 p-value = 0.0004

95% Confidence 5.4 - 29.65 42.9 - 66.5 11.4 - 28.6 42.6 - 60.7 Interval

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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 Spring 2009-2011 Pooled 2009-2011 Used Unused Used Unused Used Unused

N 44 177 56 182 73 126

Mean 14.3 20.5 14.2 22.1 14.0 22.1

T -test p-value = 0.009 p-value = 0.0006 p-value = 0.001

95% Confidence 11.3 - 17.3 18.0 - 23.0 11.6 - 16.6 19.7 - 24.5 11.7 - 16.3 19.5 - 25.8 Interval

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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 Available Study Area 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

63 Totals-Control (58%) 108

63 Total-Ski Areas (47%) 133

Totals -In/Out Ops 35 (37%) 91 (63%) 96 145

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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 Ranking

p ΔAIC wia Kb Model # ψ c

1 In vs Out of Ski Ops Area 0 0.99 4 Ski Area vs Control 2 Constant Patch Size 10.8 0.004 4 Ski Area vs Control 3 Constant 10.9 0.001 3 Ski Area vs Control 4 In vs Out of Ski Ops Area In vs Out of Ski Ops Area 13.2 4 0 5 2-groups Constant 21.0 0 4 6 3-groups Constant 25.0 0 6 7 Constant Patch Size 25.2 0 2 8 1-group Constant 31.2 0 2 9 1-group Survey-specific p 32.3 0 4 10 In vs Out of Ski Ops Area Ski Area vs Control Ski Run 33.1 0 5 11 In vs Out of Ski Ops Area Ski Area vs Control Patch Size 38.1 0 5 12 Patch Size Ski Area vs Control 41.2 0 3 13 In vs Out of Ski Ops Area Patch Size Ski Area vs Control 45.2 0 5

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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 Ranking a Model # ψ p Є ΔAICc wi K 1 Individual Study Area Year, Survey Month Ski vs Control 0 0.32 14 2 Individual Study Area Year, Survey Month Constant 0.13 0.3 13 3 Individual Study Area, Patch Year, Survey Month Constant 1.22 0.17 14 In vs Out of Ski Ops Area 4 Individual Study Area Year, Survey Month 2.13 0.11 14 5 Individual Study Area Year, Survey Month Patch Size 3.56 0.05 13 6 Individual Study Area, Year, Survey Month Constant 4.07 0.04 15 In vs Out of Ski Ops Area 7 Individual Study Area Year, Survey Month Constant 8.73 <0.01 10 8 Ski vs Control Year, Survey Month Ski vs Control 11.48 <0.01 10 9 Ski vs Control Year, Survey Month Constant 11.72 <0.01 9 10 Constant Year, Survey Month Constant 12.76 <0.01 9 11 Constant Year, Survey Month Constant 14.55 <0.01 8 In vs Out of Ski Ops Area 12 Year, Survey Month Constant 16.18 <0.01 9 13 Constant Survey Months 2009, Months 2010-11 Constant 16.94 <0.01 8 14 Constant Individual Study Area Constant 23.69 <0.01 9 15 Constant Ski-Control Pairs Constant 28.12 <0.01 9 16 Constant Constant Constant 29.02 <0.01 3 17 Constant Year, Ski vs Control Constant 32.94 <0.01 7

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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.

Captures In-Operations Areas Ski Runs <20m Patches >10 ha > 10 ha + Contiguous Season Sex/Age n Use Avail. p n Use Avail. p n Use Avail. p Use Avail. p

Winter All 16 36.30% 48. 75% 0.04 13 38.4% 38.9 0.96 17 23.7% 17.9% 0.22 79.9% 78.0% 0.40 Male 12 25.30% 48.75% 0.00003 10 34.1% 38.9 0.54 13 17.1% 17.9% 0.55 79.9% 78.0% 0.42 Female 4 47.60% 48.75% 0.48 3 51.3% 38.9 0.27 4 45.0% 17.9% 0.11 79.9% 78.0% 0.43

Spring Male 19 38.70% 48.75% 0.07 17 36.00% 38.9 0.72 28 31.0% 17.9% 0.03 78.3% 78.0% 0.48 Adult 10 35.30% 48.75% 0.06 9 42.10% 38.9 0.11 10 23.1% 17.9% 0.20 64.8% 78.0% 0.73 Sub-adult 9 42.40% 48.75% 0.28 8 29.10% 38.9 0.84 18 34.2% 17.9% 0.04 82.3% 78.0% 0.25

Female 9 52.60% 48.75% 0.53 7 38.70% 38.9 0.76 17 34.9% 17.9% 0.03 87.9% 78.0% 0.02 Adult 7 58.10% 48.75% 0.24 6 32.10% 38.9 0.78 7 30.2% 17.9% 0.22 90.1% 78.0% 0.007 Sub-adult 2 33.30% 48.75% 0.68 1 39.90% 38.9 NA 10 38.4% 17.9% 0.04 83.6% 78.0% 0.36

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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

Δ Model Abundance wi Likelihood K # Initial Capture (p) Recapture ( c ) (n) Survival (s) Seniority (g) AICc

Winter-Constant Spring- Class x (Male+Sub-adult Winter-Session Season x Winter-AD/SA Adults Constant Base / Female) x YR Spring-Class x YR Year Spring-AD/SA Sub-adults-Constant

Spring Sub- 1 adult+Edge 0.00 0.23 1.00 29

2 0.17 0.21 0.92 28

Spring Sub-adult x 3 Treatment 0.91 0.15 0.63 29

Adult: Winter/Spring Sub-adult x 4 Treatment 1.04 0.14 0.59 30

Sub-adult: Season x 5 Treatment 1.82 0.09 0.40 31

Spring Sub-adult x 6 Treatment 2.45 0.07 0.29 30

7 Capture Month 2.51 0.07 0.28 30

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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 Adult Z-score 1-tailed Sub-Adult Adult Z-score 1-tailed (1-year) (2-7 years) p-value (1-2 years) (>2 years) p-value

Ski Combined 65 43% 57% 0.32 0.37 65% 35% 0 0.5 Control Combined 55 40% 60% 65% 35%

In -operations Area Combined 35 43% 57% 0.3 0.38 66% 34% 0.6 0.27 Out-operations Area Combined 88 40% 60% 60% 40%

Ski Males 37 49% 51% 0.78 0.21 68% 32% 0 0.5 Control Males 40 40% 60% 68% 32%

In -operations Area Males 19 47% 53% 0.29 0.38 74% 26% 0.91 0.18 Out-operations Area Males 60 43% 57% 63% 37%

Ski Females 28 36% 64% 0.24 0.4 61% 39% 0.06 0.47 Control Females 15 40% 60% 60% 40%

In -operations Area Females 16 38% 62% 0.38 0.34 56% 44% 0.31 0.37 Out-operations Area Females 28 32% 68% 61% 39%

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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 51 33 18 1.83 0.39 0.34 Control 49 34 15 2.27

In -operations Area 24 16 8 2.00 0.09 0.46 Out-operations Area 72 47 25 1.88

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

N 1 2 3 4 5

Entire Population 43 3 /15 10 /10 12 / 12 5 /5 1 / 1

20% 100% 100% 100% 100%

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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

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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% Montane Chaparral 40% White Fir 30% 20% Sierra Mixed Confier 10% Subalpine Conifer 0% Red Fir

B. Study area tree size class composition for the top 4 tree-dominated habitat types.

100% 90% 80% 70% 60% 50% 40% WHR Size Class 3 30% 20% WHR Size Class 4 10% WHR Size Class 5 0%

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Figure 3. Sampling design for American martens at the Sierra At Tahoe Ski Area, California, USA.

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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.

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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%

60.0%

Non-Habitat

Montane Chaparral 40.0% White Fir Sierra Mixed Confier 20.0% Subalpine Conifer Red Fir

0.0% Tree Size Class 3 Proportional Change Proportional Tree Size Class 4 Tree Size Class 5 -20.0%

-40.0%

Heavenly Homewood Sierra-At-Tahoe Ski Area Ski Area 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%

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%

Percent Composition Percent 0.0% Heavenly Homewood Siera At Ski Area Ski Area Tahoe Ski Area

53

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 >20 m

54

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 3- spring-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 3- spring-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 3- spring-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 2009- 2011.

Unused 60.0% Adult Male

50.0% Subadult Male Adult Female 40.0% Subadult Female

30.0% Frequency 20.0%

10.0%

0.0% >0-10 10-20 >20

Ski Run Width (m)

61

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

0.90

0.80 Rate 0.70

ncy 0.60

0.50

0.40

0.30

Station Occupa Station 0.20

0.10 Heavenly Heavenly Homewood Homewood Sierra Sierra Ski Control Ski Control Ski Control 0.00

62

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

35

30 Female

25

20

15

10 Number of Individuals Number 5

0 1 2 3 4 5 6 7

Marten Age (Years)

B. Relative proportions of ages for males and females.

50%

Male

40% Female

30%

20%

10%

Proportion of Individuals of Proportion

0% 1 2 3 4 5 6 7

Marten Age (Years)

72

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.

60.0%

Males-Ski

50.0% Males-Control Females-Ski 40.0% Females-Control 30.0%

20.0%

10.0%

Proportion of Individuals of Proportion

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.

50.0% Male-IN

45.0% Male-OUT 40.0% Female-IN 35.0% Female-OUT dividuals 30.0% 25.0% 20.0% 15.0%

10.0% Proportion of In of Proportion

5.0% 0.0% 1 2 3 4 5 6 7

Marten Age (Years)

73