Volume 15, Issue 8 e4981

ARTICLE

Open Access

Western spotted skunk spatial ecology in the temperate rainforests of the Pacific Northwest

Marie I. Tosa,

Corresponding Author

Marie I. Tosa

[email protected]

orcid.org/0000-0002-9020-5098

Department of Fisheries, Wildlife and Conservation Science, Oregon State University, Corvallis, Oregon, USA

Correspondence

Marie I. Tosa

Email: [email protected]

Search for more papers by this author

Damon B. Lesmeister,

Damon B. Lesmeister

orcid.org/0000-0003-1102-0122

Department of Fisheries, Wildlife and Conservation Science, Oregon State University, Corvallis, Oregon, USA

Pacific Northwest Research Station, U.S. Department of Agriculture Forest Service, Corvallis, Oregon, USA

Search for more papers by this author

Taal Levi,

Taal Levi

orcid.org/0000-0003-1853-8311

Department of Fisheries, Wildlife and Conservation Science, Oregon State University, Corvallis, Oregon, USA

Search for more papers by this author

Marie I. Tosa,

Corresponding Author

Marie I. Tosa

[email protected]

orcid.org/0000-0002-9020-5098

Department of Fisheries, Wildlife and Conservation Science, Oregon State University, Corvallis, Oregon, USA

Correspondence

Marie I. Tosa

Email: [email protected]

Search for more papers by this author

Damon B. Lesmeister,

Damon B. Lesmeister

orcid.org/0000-0003-1102-0122

Department of Fisheries, Wildlife and Conservation Science, Oregon State University, Corvallis, Oregon, USA

Pacific Northwest Research Station, U.S. Department of Agriculture Forest Service, Corvallis, Oregon, USA

Search for more papers by this author

Taal Levi,

Taal Levi

orcid.org/0000-0003-1853-8311

Department of Fisheries, Wildlife and Conservation Science, Oregon State University, Corvallis, Oregon, USA

Search for more papers by this author

First published: 27 August 2024

https://doi.org/10.1002/ecs2.4981

Handling Editor: Bryan Kluever

Share a link

Email
Facebook
x
LinkedIn
Reddit
Wechat

Abstract

A major threat to small mammalian carnivore populations is human-induced land use change, but conservation and management are inhibited by limited knowledge about their ecology and natural history. To fill a key knowledge gap of the western spotted skunk (Spilogale gracilis), we investigated their spatial ecology at the landscape and home range scale in the temperate rainforests of the Oregon Cascades during 2017–2019. For the landscape scale analysis, we used detections of western spotted skunks at 112 baited camera traps and fitted a dynamic occupancy model to investigate spatial distribution and drivers of inter-seasonal and inter-annual changes in occupancy. Concurrently, we radio-collared 25 spotted skunks (9 female, 16 male) and collected 1583 relocations. Using continuous-time movement models, we estimated large home range sizes for both male and female spotted skunks, relative to their body mass, and highly overlapping home ranges that indicated a lack of territoriality. Using these home ranges, we fitted a resource selection function using environmental covariates that we assigned to various hypotheses such as resources, predator avoidance, thermal tolerance, and disturbance. Overall, western spotted skunks were widely distributed across our study area (seasonal occupancy up to 63.7 ± 5.3%) and highly detectable (weekly detection probability = 41.2%). At both spatial scales, spotted skunks selected wetter areas and local valleys, which we attributed to areas with more food resources. At the home range scale, spotted skunks selected locations with lower predation risk and areas surrounded by more previously logged forests. In this montane environment, inter-seasonal contractions in the spatial distributions of spotted skunks were strongly driven by their response to cold temperature and accumulated snow. This was especially evident when seasonal occupancy declined significantly following a severe heavy snow event in February 2019. Given that there is little information available on the natural history of the western spotted skunk, these results provide essential information about their ecology to focus future monitoring efforts and may help identify potential threats (e.g., forest management, severe snow events, or wildfires) to this species.

INTRODUCTION

Small carnivores (<21.5 kg) are among the most sensitive mammals to changes in environmental conditions (Jachowski et al., 2023; Marneweck et al., 2022). Recent evidence suggests that small carnivore populations are declining globally (Belant et al., 2009; Marneweck et al., 2021), threatening their ecological function as predators, insectivores, frugivores, and seed dispersers (Do Linh San et al., 2022; Marneweck et al., 2022). One of the major threats to small carnivores is human-induced land use change such as the conversion of forest into large-scale agricultural plantations, which can cause habitat fragmentation and degradation (Marneweck et al., 2021) and rapid localized extinction even for common species (Gompper & Hackett, 2005; Lindenmayer et al., 2011). Some mechanisms for localized extinction of small carnivores in these degraded landscapes could include an increase in the abundance of other larger predators or a decrease in the amount of cover, which would hinder their ability to avoid predation. Therefore, understanding the habitat requirements of species and restoring their habitat can strongly influence the success of conservation efforts (Wilcove et al., 1998), but determining habitat requirements of a species can be challenging once habitat has been converted or once the species is rare or extirpated from a region. This has led to a growing recognition of the need to study common species while they are still common (Lindenmayer et al., 2011).

In western North America, the decline of small forest carnivores such as fishers (Pekania pennanti) (Aubry et al., 2013; Aubry & Lewis, 2003; Zielinski et al., 2004) and martens (Martes caurina humboldtensis) (Moriarty et al., 2011; Slauson et al., 2007; Tweedy et al., 2019) is associated with even-aged forest management practices that convert complex multilevel forests into simple single-canopy plantations (Hayes et al., 1997) that reduce the availability of large-diameter trees, snags, logs, dense cover, and plant diversity. Another small forest carnivore, the western spotted skunk (Spilogale gracilis), is still relatively common within a large geographical range that spans from southwestern Canada to Mexico and as far east as Wyoming and Colorado (McDonough et al., 2022). Its congener, the eastern spotted skunk (now split into the plains spotted skunk [Spilogale interrupta] and the eastern spotted skunk [Spilogale putorius]), which was also common throughout its range in the Midwest and Southeast United States, declined by >90% within a decade (1940–1950) and >99% within four decades (1940–1980) in comparison with precrash population estimates (Gompper & Hackett, 2005). Although the causes of decline are poorly understood, population declines have been linked to habitat loss (Gompper & Hackett, 2005). The eastern spotted skunk is now listed as Vulnerable by the IUCN (Gompper & Jachowski, 2016), and although the US Fish and Wildlife Service ultimately decided that listing the species was not warranted, the plains spotted skunk was being considered for listing under the Endangered Species Act (US Fish and Wildlife Service, 2012, 2023). Eastern spotted skunk recovery efforts have been hindered by limited information due to low capture rates (<2.8% for live captures, <1.9% for camera traps, and <6.5% track plates; Hackett et al., 2007) and have relied on a handful of studies conducted after the population had already declined (Lesmeister et al., 2008, 2009, 2013).

The limited available research on western spotted skunks, including island spotted skunk subspecies (Spilogale gracilis amphiala), is restricted to non-forested ecosystems (Crooks, 1994a, 1994b; Crooks & Van Vuren, 1995), mesquite shrubland of Texas (spotted skunk in this area is now classified as the desert spotted skunk [Spilogale leucoparia]; Doty & Dowler, 2006, Neiswenter et al., 2006, Neiswenter & Dowler, 2007), and the chaparral biome of the Sierra Nevada mountains of California (Carroll, 2000). In these ecosystems, spotted skunks selected for dense cactus patches (den-site selection; Doty & Dowler, 2006), large mesquite (Prosopis glandulosa) trees (within home range selection; Neiswenter & Dowler, 2007), canopy cover, logs, snags, shrubs (den-site selection; Carroll, 2000), and ravines (within home range selection; Crooks & Van Vuren, 1995). Western spotted skunk ecology in forests in mountainous environments remains understudied, particularly in areas with large spatial and interannual variation in snow, but some evidence suggests that the species may be associated with old-growth forests in the temperate rainforests of the Pacific Northwest (Carey & Kershner, 1996).

To better understand the natural history of the western spotted skunk in forested ecosystems, we studied the species' spatial ecology in the Oregon Cascade Mountains using camera traps and radio-collars. The objectives of our study were to quantify the habitat selection of the western spotted skunk at the landscape scale and at the individual home range scale and to investigate the seasonal changes in space use. We designed our analyses to reflect hypotheses related to anthropogenic disturbance and the need for resources, thermoregulation, and cover from predators (Table 1). We predicted that western spotted skunk space use would be negatively impacted by anthropogenic disturbance (Carey & Kershner, 1996), positively related to areas that provide food resources and water (Tosa et al., 2023), positively related to areas with coarse woody debris (Buskirk et al., 1989; Lesmeister et al., 2008) and lower snow fall, and driven by a need for cover from predators (Delheimer et al., 2023; Lesmeister et al., 2009, 2013; Tweedy et al., 2019) as suggested in other spotted skunk and small carnivore studies.

TABLE 1. Descriptions of candidate environmental variables used to develop occupancy and resource selection models for the western spotted skunk (Spilogale gracilis) in temperate rainforests in the Oregon Cascades.

Variable name	Range	Units	Description	Rationale
Disturbance
D.ROAD¹	0–1385	m	Euclidean distance from nearest road using rgeos::gDistance Transformations tested: linear, log	Areas closer to roads are more disturbed and exhibit more edge characteristics
P.LOGGED¹	0–83	%	Percent within buffer that was logged within the last 100 years calculated with landscapemetrics::sample_lsm Buffer sizes tested: 0.1, 0.5, 1.0, 5.0 km	More previously logged areas, more disturbed
P.MATURE¹	5–21	%	Percent within buffer categorized as OGSI 80 but not OGSI 200 calculated with landscapemetrics::sample_lsm Buffer sizes tested: 0.1, 0.5, 1.0, 5.0 km	More mature stands, more legacy of disturbed
P.OLDGROWTH¹	7–92	%	Percent within buffer categorized as OGSI200 calculated with landscapemetrics::sample_lsm Buffer sizes tested: 0.1, 0.5, 1.0, 5.0 km	More old-growth stands, less disturbed
YRS_SINCE_DISTURB¹	3–103	Year	Years since location was logged Transformations: linear, log	More time since disturbance, less disturbed
OGSI²	0–88		Old-growth structural index as defined by Spies and Franklin (1988)	More old-growth characteristics, less disturbed
Predation
COVER³	0–1	%	Vegetation cover based on the proportion of total returns between 4 and 16 m	More cover, more visual obstruction from avian predators
CANOPY_HT³	2–83	m	25th percentile height for first returns (P25)	Lower canopy height, more obstruction from predators
ROUGH	0–22		Topographic roughness index (TRI) at site calculated from DEM and Raster::terrain	Rougher terrain, more opportunities to escape predators
TREE_DENSITY²	1–4247	trees/ha	Density of live trees, conifers, hardwoods ≥2.5 cm dbh	Lower tree density, more area between trees for avian predators
Resource
STAND_DIVERSITY²	1–837		DDI = measure of the structural diversity of a forest stand, based on tree densities in different dbh classes SDI = Reineke's stand diversity index	More stand diversity, more food and rest site resources
TOPO_POS	−453-685		Topographic position index (TPI) at site calculated from DEM and Raster::terrain Buffer sizes tested: 0, 0.5, 1.0 km	Lower topographic position, more food resources
D.WATER^4,5	0–1507	m	Euclidean distance from nearest waterbody or perennial stream using rgeos::gDistance Transformations: linear, log	Closer to water, more food resources
LANDSAT_VEG	0–5340		Landsat8 reflectance bands: 2 (blue), 3 (green), 4 (red), 5 (near-infrared), 6 (Shortwave infrared 2), 7 (Shortwave infrared 2)	Greener areas and more moist areas, more food resources
STAND_AGE²	1–5486	Years	Basal area weighted stand age based on field recorded or modeled ages of dominant/codominant trees	Older stand, more coarse woody debris for potential rest sites
SNAG²	0–310 (STPH); 1–257,697 (SBPH); 0–704 (SVPH)	trees/ha	Snag density per hectare (STPH) = Density of snags ≥25 cm dbh and ≥2 m tall Snag biomass per hectare (SBPH)snag volume per hectare (SVPH)	More snags, more potential rest sites
Thermal tolerance
ELEVATION³	368–1590	m	Elevation Transformations: linear, quadratic, log	Higher elevation sites, lower ambient temperature and more snow precipitation
BASAL_AREA_SP²	0–2609	m²/ha	Basal area of Abies amabilis (ABAM), Acer macrophyllum (ACMA), Pseudotsuga menziesii (PSME), Tsuga heterophylla (TSHE)	More ABAM, lower temperatures and more persistent snow More ACMA, more riparian area
BASAL_AREA_TYPE²	0–12,004	m²/ha	Basal area of live trees, conifers, or hardwoods ≥2.5 cm dbh	Larger trees provide more insulation for rest sites
ASPECT	0–6.28	Radians	Aspect at site calculated from digital elevation model (DEM) and Raster::terrain northness = COS(Aspect); eastness = SIN(Aspect)	South-facing slopes get more sun exposure

Source: 1. USDA Forest Service; 2. GNN structure: https://lemma.forestry.oregonstate.edu/; 3. LiDAR. 4. National Hydrography Dataset; 5. Oregon Explorer, https://spatialdata.oregonexplorer.info/geoportal/; 6. Landsat 8, https://earthexplorer.usgs.gov/.

STUDY AREA

This study was conducted in the McKenzie River District of the Willamette National Forest and the H. J. Andrews Experimental Forest (HJA), which are located on the western slope of the Cascade Mountain Range near Blue River, Oregon (Figure 1). Elevations range from 410 to 1630 m. The maritime climate is typical of the Pacific Northwest region and consists of warm, dry summers and mild, wet winters. Mean monthly temperatures range from 1°C in January to 18°C in July. Precipitation falls primarily as rain, is concentrated from November through March, and averages 230 cm at lower elevations and 355 cm at higher elevations.

Details are in the caption following the image — **FIGURE 1**
Open in figure viewer PowerPoint

Study area and western spotted skunk (*Spilogale gracilis*) home range centroids for females and males. White areas indicate previously logged stands. Trail camera locations are shown as black circles.

Lower elevation forests not only are dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata) but also include deciduous trees such as bigleaf maple (Acer macrophyllum). Upper elevation forests are dominated by noble fir (Abies procera), Pacific silver fir (Abies amabilis), Douglas-fir, and western hemlock. The understory is variable and ranges from open to dense shrubs. Common shrubs include Oregon grape (Berberis spp.), salal (Gaultheria shallon), sword fern (Polystichum munitum), vine maple (Acer circinatum), Pacific rhododendron (Rhododendron macrophyllum), huckleberry (Vaccinium spp.), and blackberry and salmonberry (Rubus spp.).

Before timber cutting in 1950, 65% of the HJA was covered in old-growth forest. Approximately 30% of the HJA was clear cut or shelterwood cut to create plantation forests varying in tree composition, stocking level, and age. In 1980, the HJA became a charter member of the Long Term Ecological Research network and no logging has occurred since 1985. The Willamette National Forest immediately surrounding the HJA has a similar logging history, but logging continued through the duration of our study. The HJA consisted of a higher percentage of old-growth forest than the surrounding Willamette National Forest (approximately 58% in the HJA vs. 37% in the study area; Davis et al., 2022). In addition to logging, wildfires are a primary disturbance, followed by windthrow, landslides, root rot infections, lateral stream channel erosion, and tree fall caused by heavy snow events. On 24 February 2019, near the end of the study, the Oregon Cascades experienced a large, heavy snow event that resulted in a massive tree fall event (DiGregorio, 2019; Stoelb, 2020; The White House, 2019). There were no fires during the study, but mean fire return interval of partial or complete stand-replacing fires for this area is 166 years and ranges from 20 to 400 years (Teensma, 1987; Morrison & Swanson, 1990).

METHODS

We studied the resource selection of western spotted skunks at two scales because we were interested in the spatiotemporal dynamics of the population as a whole and the responses of individual animals to their environment (Johnson, 1980; Mayor et al., 2009). To investigate the inter-seasonal and interannual variability in the spatial distribution of western spotted skunks across the study area (second-order habitat selection; Johnson, 1980), we implemented multiseason occupancy models (MacKenzie et al., 2018) using detections of spotted skunks at baited camera traps. Specifically, we were interested in the changes in occupancy between seasons and years because spotted skunks may have different habitat needs depending on their reproductive cycle and because our study was conducted in mountainous terrain where precipitation in winter can fall as rain at low elevations and heavy snow at high elevations and the weather was quite variable during the study (e.g., wet cold spring in 2017, warm dry spring in 2018, drought conditions during summer 2018, severe snow event during winter 2019). Next, we used fine-scale locations of spotted skunks from radiotelemetry and GPS data to estimate western spotted skunk home range sizes and overlap and to model western spotted skunk resource selection within home ranges (third-order habitat selection). Below, we present environmental covariates used for both scales of models, methods for field data collection, and then statistical analyses. We conducted all analyses using Program R (R Core Team, 2019) and produced figures using the ggplot2 package (Wickham, 2016).

Model covariates

We assessed western spotted skunk habitat at local and landscape levels with environmental covariates derived from satellite imagery, light detection and ranging (LiDAR) maps (collected from 2008 to 2016), and other maps acquired from the USDA Forest Service and Oregon Explorer (https://oregonexplorer.info/) (Table 1). We assigned each covariate to a category based on our hypotheses related to resources, predation, thermal tolerance, and disturbance. In addition to linear functional forms of each variable, we explored the quadratic form for elevation and the logarithmic form of distance to features of stream, waterbody, road, and areas logged within the last 100 years. We averaged landscape-level variables using buffer sizes of 100 m, 500 m, 1 km, and 5 km to represent minimum and mean step length traveled in 1 h when skunks were active (estimated from GPS locations), approximate core area size (also maximum step length traveled in 1 h), and home range size of a western spotted skunk (this study), respectively (McGarigal et al., 2016). To determine the appropriate scale (i.e., buffer size) and functional forms (i.e., linear, quadratic, logarithmic) of variables, we fitted single covariate models and compared models using corrected Akaike information criterion (AIC_c) for small sample size (Burnham & Anderson, 2002). For each covariate, the scale or functional form of the model with the lowest AIC_c score was identified as the most-supported scale or functional form, respectively, and was used in subsequent models. To prevent multicollinearity and confounding factors, we computed Pearson's correlation coefficients between each pair of covariates and retained the covariate with the greater average deviance explained when two or more covariates were highly correlated (i.e., |r| ≥ 0.6) (Wan et al., 2017). All covariates were centered and scaled prior to fitting models to facilitate effect size comparisons and model convergence.

Field methods

Camera traps

We set and maintained 112 baited camera traps (HC500, Reconyx, Holmen, WI or TrophyCam, Bushnell, Overland Park, KS) between April 2017 and August 2019. Camera traps were placed ~0.3 m off the ground and ~2 m from bait to ensure detection of small carnivores. Cameras were programmed to take a burst of three photos with each trigger and had a quiet time of 1 min. We stratified placement of camera traps based on disturbance (i.e., old-growth stand characteristics) and elevation gradients. Camera traps were part of a larger biodiversity study (Frey, Hadley, & Betts, 2016; Frey, Hadley, Johnson, et al., 2016; Kim et al., 2022; Weldy et al., 2019). The camera traps located in the surrounding Willamette National Forest were deployed in May and June 2018 (n = 58). To increase the detection probabilities of carnivores, we baited each camera trap with a can of fish flavored cat food or sardines (partially opened), a fresh dead mouse (Mus musculus), and a commercially available carnivore lure (e.g., Gusto, Caven's Lure, Pennock, MN) (Avrin, Pekins, Sperry, & Allen, 2021; Avrin, Pekins, Sperry, Wolff, & Allen, 2021; Siegfried et al., 2023). Camera traps were rebaited, and lure was reapplied each month when we were able to safely access sites. We did not check or rebait cameras in unsafe road or working conditions with high snow accumulations that hindered our ability to drive the roads. We manually tagged species presence in camera trap photographs in DigiKam (V6.1.0, www.digikam.org), extracted metadata using the exifr package (Dunnington & Harvey, 2021), and converted western spotted skunk detections into weekly encounter histories.

Capture

We captured spotted skunks from August to May during 2017–2019 using Tomahawk live traps (Model 102 [12.7 × 12.7 × 40.6 cm] and 103 [15.2 × 15.2 × 48.3 cm], Tomahawk Live Trap Co., Hazelhurst, WI). We placed traplines of 10–30 live traps at 100–250-m intervals along accessible trails and roads near detections of western spotted skunks on camera traps. To reduce trap injuries, we modified Tomahawk traps with corrugated plastic to eliminate gaps between the trap door and floor. We also included polyester insulation in traps during winter to help trapped animals with thermoregulation. We covered traps with burlap and bark or leaf litter. We baited each trap with one or a combination of the following: sardines, a frozen dead house mouse, or commercially available carnivore lures. We checked traps daily between 0600 and 1000.

Once captured, we physically restrained and chemically immobilized skunks using an intramuscular injection of 15-mg/kg ketamine HCl (Zoetis Services LLC, Parsippany, NJ) and 40-mcg/kg dexmedetomidine HCl (Zoetis Services LLC, Parsippany, NJ). Following chemical immobilization, we determined sex, recorded mass and morphometric measurements, and examined tooth wear. We fit each skunk with two metal ear tags (Monel size 1; National Band and Tag Co., Newport, KY) and a very high frequency (VHF) radio-collar <5% of their body mass (M1545, 16 g; Advanced Telemetry Systems, Isanti, MN). We also fit four large male skunks (>550 g) with GPS collars (LiteTrack20, 20 g; SirTrack, New Zealand) during the breeding season (September–October). All capture and handling protocols were approved by the United States Department of Agriculture Forest Service Animal Care and Use Committee (USFS 2016-015) under Oregon Department of Fish and Wildlife Scientific Take Permits (ODFW 107-17, ODFW 059-18, and ODFW 081-19) and followed guidelines of the American Society of Mammalogists (Sikes et al., 2016).

Relocations

For our third order of resource selection analysis, we collected spatial data on western spotted skunks from August 2017 to August 2019 in three ways: (1) we recorded location information for any skunk that we captured in a trap, (2) we triangulated collar VHF signals from the roads nightly when skunks were more active, and (3) we used homing techniques on collar VHF signals daily to identify diurnal rest site locations. We derived skunk locations from triangulations of ≥3 signal bearings with ≥20 degrees difference within 20 min. from a hand-held 3-element Yagi antenna in the program Locate III (Nams, 2006). Conducting radiotelemetry on small-bodied animals in this montane system was challenging because of limited accessibility and topography and prevented more robust triangulation protocols. To gain finer-scale movement data, we programmed the four GPS collars to collect locations at 30-min fix intervals during the night (21:00–09:00) when skunks were more active and at 4-h fix intervals during the day (09:00–21:00) when skunks are less active (Moriarty & Epps, 2015) (Appendix S1: Text S1). We conservatively censored GPS locations where the horizontal dilution of precision was >20 (n = 14) (D'Eon et al., 2002).

Statistical analyses

Occupancy models

To evaluate the inter-seasonal and interannual variability in second-order selection by western spotted skunks across the landscape, we conducted dynamic occupancy analyses using camera trap data. Dynamic occupancy analyses allowed us to formally address our anecdotal observations of declines in spotted skunk detections at higher elevations during winter when there was consistent snow cover. We separated encounter histories into 17- and 18-week biologically meaningful seasons for summer (June–September), fall (October–January), and spring (February–May). Summer corresponded to the dry season in the Oregon Cascades, fall represented the wet season in the Oregon Cascades, and spring represented a period in the Oregon Cascades when there was consistent snow cover at high elevations and food resources were likely most scarce. These seasons also roughly correspond to the reproductive cycle and ontogeny of western spotted skunks where mating occurs in fall, gestation and birth occurs in spring, and rearing of young occurs in summer (Verts et al., 2001). This resulted in seven seasons (summer 2017 through summer 2019) and allowed us to investigate seasonal shifts in space use by western spotted skunks. Since it was possible for western spotted skunks to visit multiple cameras, occupancy model results should be interpreted as space use instead of occupancy of sites.

We calculated naïve occupancy for each season by dividing the number of sites where western spotted skunks were detected on baited camera traps by the total number of sites monitored during that season. We estimated each of the four parameters—detection (p), initial occupancy (ψ), colonization (γ), and extinction (ε)—as a function of environmental covariates (Table 1) using the unmarked package (Fiske & Chandler, 2011). Since the unmarked dynamic occupancy models require balanced encounter histories for each season, we augmented encounter histories with NA values so that each season consisted of 18 weeks. We started with the null model (p ~ 1, ψ ~ 1, γ ~ 1, ε ~ 1) and fit a detection only model with temporal variables: season, skunk year, and number of weeks since bait. Then, for occupancy, colonization, and extinction parameters, we fitted univariate models using environmental or temporal covariates for each hypothesis category and separately evaluated which covariates to include in each additive hypothesis model (i.e., resource model, predation model, thermal model, disturbance model, and temporal model) and ranked univariate models based on AIC_c scores (Burnham & Anderson, 2002). Only covariates from univariate models that were more supported than the null model, and those that were not highly correlated with one another (|Pearson's r| < 0.6) were included in each hypothesis model. We fitted a parameter model for occupancy, colonization, and extinction by combining all four hypothesis models. To prevent overparameterization and to be conservative in our biological interpretations of covariates, we removed variables with 95% CIs overlapping 0. We combined all parameter models into the final global model (Figure 2).

For our final model, we conducted a goodness-of-fit test using a parametric bootstrap with 1000 replicates and the chi-square statistic (Fiske & Chandler, 2011; Kellner et al., 2023; Kéry & Chandler, 2012). Finally, we estimated SEs of predicted occupancy in each season using a nonparametric bootstrap with 1000 replicates using the nonparboot function and predicted occupancy across the landscape. To explore whether occupancy probability was declining over time, we fit a post hoc linear regression to the predicted seasonal occupancy (predicted occupancy ~ season).

Home range estimation

We calculated home range metrics using a continuous-time movement model in the ctmm package (Fleming & Calabrese, 2022) and incorporated all relocation data (i.e., capture, homing, telemetry, and GPS locations) and the corresponding date and time of those locations. For capture and homing locations, we assumed there was no error in location because we either had the animal in hand or were close to our physical location. For each telemetry location, we incorporated variance and covariance of 95% error ellipses. We removed outlier locations when indicated by the outlie function. For GPS locations, we assumed that 1 horizontal dilution of precision (HDOP) value was equivalent to 10-m error and set df to 2. Then, we estimated core range (50% isopleth) and home range (95% isopleth) for each spotted skunk with ≥5 locations using autocorrelated kernel density estimation with the akde function (Fleming & Calabrese, 2017). We used this method because it incorporates error in locations, performs well with low sample sizes, and allows us to combine multiple sources of location information with different fix intervals (e.g., rest sites, capture locations, VHF triangulation, and GPS locations). We calculated the degree of overlap in home ranges using the Bhattacharyya method (Bhattacharyya, 1943) in the overlap function to assess territoriality within and between sexes of western spotted skunks (Winner et al., 2018). In addition, because there were apparent differences in home range and core area sizes, we explored clustering within the home range sizes using the kmeans function (k = 1–5). To verify significant differences between home range sizes of groups, we ran an ANOVA and Tukey's honestly significant difference (HSD) test. Post hoc, we also explored whether the large differences in home range size we observed in males but not females could be explained by body mass, total body length, or environmental variables (i.e., mean elevation, proportion of logging, or proportion of old growth within the home range) using linear regressions.

To explore whether skunks shifted their home ranges by season, we calculated seasonal home ranges using the same methods as above. We split locations based on the same seasons used in the occupancy modeling: summer (June–September), fall (October–January), and spring (February–May). We calculated seasonal home ranges if an animal had ≥5 locations multiple seasons. Using the Bhattacharyya method in the overlap function, we calculated fidelity of home ranges across seasons (e.g., summer vs. fall).

Within home range resource selection

To investigate resource selection at the home range scale, we used relocation data and explored the same environmental variables from our occupancy models. Again, we tested different scales for landscape variables and linear, quadratic, and log functional forms for environmental variables with these data to determine which was the most supported. Since seasonal home range fidelity was high, we selected 25 available points for each used location within each home animal's overall home range to estimate resource selection (Appendix S1: Figure S1). We fitted a binomial generalize linear mixed effects regression to used and available points within the home range and included individual as a random intercept in the blme package (Chung et al., 2013). To determine which variables to include in the global model, we fitted univariate models to used and available points. We ranked univariate models against the null model using AIC_c and included any variables with a univariate model that ranked higher than the null and were not correlated with one another (|Pearson's r| < 0.6) in an additive global model. Similar to the dynamic occupancy analysis, we fit additive hypothesis models for resources, predation, thermal, and disturbance and combined all hypothesis models into the final global model. We took this approach for the resource selection analysis because our analysis was largely exploratory, and a global model would better reflect relationships of western spotted skunks with variables of interest for forest management such as amount of logging. We excluded skunks with <25 used locations from this analysis because of model convergence issues.

From observations in the field, we suspected that some western spotted skunks had opposite relationships with some environmental variables, specifically elevation, percentage of area logged, and percentage of area mature. To test this, we added a random slope by individual term for one environmental covariate at a time to the final global model and ranked these models using AIC_c. We only added a random slope by individual term for one environmental covariate at a time to prevent overparameterization and facilitate model convergence.

RESULTS

Over the course of 7970 trap nights, we captured 31 western spotted skunks (12 female, 19 male) 177 times (2.2% capture success) and tracked 25 of those individuals (9 female, 16 male) for various durations between October 2017 and August 2019 (Appendix S1: Figure S2). Males weighed 1.5 times more (mass_male = 595.3 ± 22.3 g, mass_female = 392.4 ± 11.2 g; F_1,29 = 47.2, p < 0.001) and total body lengths were 4.5 cm longer (length_male = 42.2 ± 0.5 cm, length_female = 37.7 ± 0.5 cm; F_1,29 = 36.7, p < 0.001) than females. We recorded 170 skunk capture events, 293 rest site uses, 1011 telemetry locations, and 109 GPS locations. We excluded 34 reuses of a single site by a female (SG-008) during summer that we suspect was used for denning and rearing kits. During the spring of 2019, many of our VHF collars failed because the VHF antenna broke off the collars within 1 month of deployment. We recovered location data from 2 GPS collars (one SWIFT fix and one standard fix): the SWIFT fix collar recorded 121 points and 107 points met our error threshold criteria (11.0% fix success rate; Appendix S1: Figure S3), whereas the standard fix collar only recorded two successful locations, both of which met the error threshold criteria (0.8% fix success rate).

The western spotted skunks in this study had relatively large home ranges relative to their body mass compared with other mammalian carnivores (Figure 3). One skunk (SG-001) had two distinct home ranges, suggesting the spotted skunk dispersed on 10 May 2018 (Appendix S1: Figure S4). Therefore, we treated the two home ranges as separate for the remainder of the analysis. K-means clustering revealed two groups of home ranges (F_2,22 = 15.2, p < 0.001): a smaller home range consisting of females (mean = 10.93 km², 95% CI = 6.63–16.86) and males (mean = 16.38 km², 95% CI = 12.83–20.49) and a distinctly larger home range consisting of males (mean = 35.83 km², 95% CI = 31.52–40.40) (Table 2). Home range size and clustering were independent of the number of locations used to calculate the home range (β_{total locs} = −0.05, p < 0.56; Appendix S1: Figure S5), length or body mass of the skunk (β_length = 2.21, p = 0.23, β_mass = 0.05, p = 0.09), and other environmental variables including mean elevation within the home range, percent of forest logged, or percent old growth (Appendix S1: Figure S6). Females had similar home range size across seasons, and male home range sizes differed by season, but there was no consistent trend among individuals (Figure 4A). Some males had larger home ranges during spring, whereas other males had larger home ranges during fall (Figure 4A). Home range overlap between neighboring skunks was high regardless of sex (Table 3; Appendix S1: Figure S7), and individual home range fidelity was high across seasons (fall–spring overlap: 87.6 ± 2.6%, fall–summer overlap: 80.2 ± 4.5%, spring–summer overlap: 82.0 ± 5.8%; Figure 4B).

TABLE 2. Western spotted skunk (Spilogale gracilis) home ranges (95% utilization distributions) and core areas (50% utilization distributions) estimated using continuous-time movement models in the Willamette National Forest, Oregon during August 2017–August 2019 (mean with 95% CI in parentheses).

Group	Core area (km²)	Home range (km²)
Female	2.52 (1.55–3.83)	10.93 (6.63–16.86)
Male (small)	3.55 (2.68–4.60)	16.38 (12.83–20.49)
Male (large)	6.81 (6.00–7.69)	35.83 (31.52–40.40)

TABLE 3. Home range overlap between western spotted skunk (Spilogale gracilis) estimated using continuous-time movement models and the Bhattacharyya coefficient (mean with 95% CI in parentheses).

Dyad	Home range overlap
Female–Female	0.78 (0.67–0.86)
Male–Female	0.76 (0.61–0.89)
Male–Male	0.83 (0.65–0.94)

Note: Values range between 0 and 1, where 0 indicates no shared areas and 1 indicates identical distributions of 95% utilization distributions.

Occupancy models

We detected western spotted skunks at 80 of 112 camera sites (naïve occupancy = 71.4%; Appendix S1: Figure S8). The final multiseason occupancy model revealed that detection probability declined with more weeks since bait (β_bait = −0.09 ± 0.01) (Figure 5; Appendix S1: Table S2). Detection probability was highest in fall and in 2019 and lowest in summer and in 2017 (β_p.SPRING = −0.56 ± 0.10, β_p.SUMMER = −1.03 ± 0.10, β_p.2018 = 0.25 ± 0.08, β_p.2019 = 0.36 ± 0.23). Initial occupancy was also higher when there was more mature forest in the landscape (β_P.MATURE.5KM = 0.99 ± 0.67), in local valley bottoms (β_{TOPO_POS.1KM} = −0.71 ± 0.45), and areas with dense vegetation (β_B4 = −1.23 ± 1.19). Colonization probability was higher in areas with more moisture (β_B6 = −0.69 ± 0.26), areas with rougher topography (β_ROUGH = 0.25 ± 0.20), and areas with lower basal area of Pacific silver fir (β_ABAM = −0.53 ± 0.32) (Figure 6). Extinction probability was higher at low and high elevations (β_ELEVATION = −5.14 ± 1.53, β_ELEVATION2 = 5.76 ± 1.67), at ridge tops (β_{TOPO_POS.0.5KM} = 0.45 ± 0.19), and in areas with more Pacific silver fir (β_ABAM = 0.65 ± 0.46). Extinction probability was also higher immediately following a disturbance (β_{YRS_SINCE_DIST} = 0.88 ± 0.76, β_{LOG(YRS_SINCE_DIST)} = −1.47 ± 0.92) and in areas with less mature forest in the landscape (β_P.MATURE.5KM = −0.41 ± 0.20). Probability of colonization was highest between summer and fall (β_γ.SUMMER = 1.70 ± 0.51), and probability of extinction was highest between spring and summer (β_ε.SPRING = 1.10 ± 0.43) and between 2018 and 2019 (β_ε.2018 = 1.04 ± 0.35). After accounting for detection, colonization, and extinction, predicted seasonal occupancy was highest during the fall of 2017 (63.7 ± 5.3%) and lowest during the summer of 2019 (19.9 ± 4.3%) following the severe heavy snow event (Figure 7). Overall, predicted occupancy declined by 30.3% between summer 2017 and summer 2019, and seasonal occupancy had a declining trend over the duration of the study (β_season = −0.05 ± 0.02, p = 0.038), but this trend was primarily driven by the low predicted occupancy rate during summer 2019.

Home range resource selection

We censored five skunks from the resource selection analysis due to small sample size (n < 25). Used locations within the western spotted skunk home range were best predicted by disturbance variables, followed by predation, resource, and thermal variables (Appendix S1: Table S3). The best performing model was one that included a term for random slope by individual on elevation (Appendix S1: Table S4). When we fitted the global model with random slopes for each skunk, we found that each skunk had distinct responses to elevation where some individuals selected for low elevations whereas other selected for intermediate elevations (Appendix S1: Figure S9). Coefficients for covariates were similar between the global model that only included random intercepts by individuals (Appendix S1: Table S5) and the global model that included random intercepts and random slopes for elevation by individuals (Appendix S1: Table S6). Overall, spotted skunks selected for resources such as local valley bottoms (β_{TOPO_POS.1KM} = −0.29 ± 0.04), areas near streams (β_DIST.STREAM = −0.10 ± 0.05, β_{LOG(DIST.STREAM)} = 0.02 ± 0.05), and wetter areas (β_B4 = −0.10 ± 0.04) (Figure 8) and variables related to predation avoidance including shorter understory canopy (β_{CANOPY_HT} = −0.13 ± 0.03) and flatter terrain (β_ROUGH = −0.22 ± 0.03) (Figure 9). Spotted skunks also selected for variables related to thermal tolerance including northerly aspects (β_NORTH = 0.30 ± 0.04), intermediate elevations (β_ELEVATION = 0.02 ± 0.15, β_ELEVATION2 = −0.39 ± 0.13), and more disturbed areas such as locations closer to roads (β_DIST.ROAD =−0.14± 0.06, β_{LOG(DIST.ROAD)} = −0.15 ± 0.03) and locations with more previously logged areas in the landscape (β_P.LOGGED.1KM = 0.17 ± 0.04).

DISCUSSION

This study provides the first detailed movement and habitat analysis of western spotted skunks in temperate rainforests of the Pacific Northwest. By studying habitat selection at multiple spatial and temporal scales and with multiple methods, we found that responses to some environmental variables, such as complex forest structure, were scale specific, whereas responses to other environmental variables, such as topographic position index, were consistent at all scales. Given that there is little information available on the natural history of the western spotted skunk, these results provide evidence for key aspects of their ecology to focus monitoring efforts and may be beneficial to understand and identify potential threats (e.g., forest management, severe snow events, or wildfires) to this species.

We found that western spotted skunks were widely distributed across our study area (seasonal occupancy up to 63.7 ± 5.3%) and highly detectable (weekly detection probability = 41.2%), suggesting that populations are common in the Willamette National Forest. We also found that western spotted skunks exhibited sexual dimorphism in body mass, body length, and home range size. Females were consistently lighter and smaller and had smaller home ranges. Some male western spotted skunks had relatively small home range sizes (16.38 km²) that were similar to female western spotted skunks (10.93 km²), but other males had home ranges that were 2.4-fold larger (35.83 km²). The differences in male home range size were independent of the physical characteristics of the individuals, proportion of forest type, elevation, or the number of relocations we were able to collect (Appendix S1: Figure S6). The home ranges of western spotted skunks in our study were considerably larger than those previously reported for other western spotted skunk populations (0.50 km² for males, 1.59 km² for females; Carroll, 2000), and for island spotted skunks (0.29–0.61 km²; Crooks & Van Vuren, 1995; Jones et al., 2008). We also found that this population of western spotted skunks had larger home ranges than other similarly sized or closely related carnivores (Doty, 2003; Gehring & Swihart, 2004; Jachowski, 2007; King & Powell, 2007; Lindstedt et al., 1986) (Figure 3).

Western spotted skunk occupancy was higher in local valleys and areas with wetter areas and close to streams, which typically corresponded to areas near ephemeral creeks and was consistent with previous studies (Brown, 1985; Carey & Kershner, 1996; Crooks & Van Vuren, 1995). These areas likely provide spotted skunks with food resources such as invertebrates (e.g., wasps, millipedes, and beetles) (Tosa et al., 2023). Occupancy was also higher in areas surrounded by higher proportions of mature forests, not of old-growth forest, at the 5-km buffer scale, which was not aligned with our predictions given a past study that suggested that spotted skunks were associated with old-growth forest (Carey & Kershner, 1996).

Seasonal occupancy (i.e., landscape use) estimates were highest during fall, which may have been driven by higher movement rates by males during the breeding season. This could have resulted in individual western spotted skunk detections on multiple camera traps. Lesmeister et al. (2009) found that the highest movement rates of plains spotted skunks occurred during spring when males quested for reproductive females. Because seasonal home range fidelity of western spotted skunks was high (>80%), increases in seasonal occupancy rates during fall may also have been due to birth and dispersal events. Conversely, we suspect decreases in seasonal occupancy rates during spring may be due to increased mortality in winter.

Seasonal extinction probabilities appear to reflect sensitivity to recent forest harvest (harvest within 25 years) and thermal tolerance of western spotted skunks. Extinction probability was highest in areas with a recent disturbance but dropped off sharply thereafter (Figure 6; Appendix S1: Figure S10). Extinction probability was also higher at higher elevations, on ridges, and in areas with more Pacific silver fir. These attributes represent areas that experience greater volumes of precipitation in the form of snow, consistent snow cover, especially during spring, and prolonged snowmelt. This was especially apparent when we mapped predicted extinction probabilities across our study area (Appendix S1: Figure S12). Western spotted skunk sensitivity to snow and cold temperatures was apparent when we observed the greatest decline in seasonal occupancy between spring and summer 2019 following the severe wet snow event that occurred in February 2019 (Figure 7). Not only did this event provide a downfall of heavy, wet snow, but also it caused widespread tree damage, tree falls, and landslides (Stoelb, 2020). Therefore, declines in spotted skunk occupancy may have resulted from tree mortality or ground movement (e.g., landslides). It remains uncertain what population-level impact these extreme events may have on western spotted skunks, but this warrants further investigation given extreme weather events are projected to increase in frequency and severity with climate change (Seneviratne et al., 2022).

Other similarly sized carnivores are also sensitive to snow and cold temperatures because of their morphology, high metabolic demands, and limited energy reserves (Buskirk & Harlow, 1989). For example, long-tailed weasels (Neogale frenata) may be limited in their northward distribution because of snow cover (Simms, 1979). Other species such as ermine (Mustela erminea) and American marten (Martes americana), however, may be better suited to these conditions through behavioral adaptations. Ermines are highly adept predators that can balance their energetic demands by hunting voles in the subnivean zone (Simms, 1979). American marten behaviorally cope with colder temperatures and heavy snow by selecting subnivean rest sites (Buskirk et al., 1989; Wilbert et al., 2000). Although we were generally unsuccessful in tracking western spotted skunks when there was deep snow cover, we regularly detected spotted skunks in camera trap photos walking on top of the snow, which suggests that spotted skunks were not restricted to the subnivean zone, at least when active.

While dynamic occupancy models revealed general patterns of space use by western spotted skunks, resource selection at the home range scale revealed more detailed responses to environmental variables. In both models, predicted use by western spotted skunks was higher in local valleys and in wetter areas. Use of wetter areas was further supported by the greater use of northerly aspects, which typically remained moister, compared with southerly aspects. Our hypotheses about spotted skunk use of local valleys were further refined by the home range scale analysis that showed use was higher with less bigleaf maple basal area and lower canopy height. Spotted skunks may have avoided lowland riparian forests prone to flooding. Western spotted skunks are susceptible to predation by barred owls (Strix varia) (Tosa et al., 2022), which may be a driving factor for selecting areas with more shrubby vegetation that could reduce predation risk (Figure 9). Selection for vegetative cover to reduce predation risk is consistent with other studies on plains spotted skunks and eastern spotted skunks (Eng & Jachowski, 2019; Lesmeister et al., 2009; Sprayberry & Edelman, 2018).

At the home range scale, western spotted skunk use was greater in locations surrounded by more logged area at the 1-km scale, also contrary to the observations by Carey and Kershner (1996). This was also counter to our findings in the landscape-level occupancy analysis where occupancy was higher in areas surrounded by more mature forest at the 5-km scale and extinction probability was higher in areas with recent disturbances. A possible reason for selection for previously logged forest could be because light is able to penetrate through the canopy and create denser shrub cover (Bunnell, 1990), which in turn could provide more cover from predators.

In addition, we found that individuals had different responses to environmental variables, especially elevation. Most skunk home ranges were at intermediate elevations, but some individuals selected low elevation sites and one individual selected high elevation sites (Appendix S1: Figure S9). This suggests that western spotted skunks exhibit high plasticity and can employ a variety of strategies to survive in the forests of the Pacific Northwest. Conflicting selection of resources by individuals has also been noted by a previous study on island spotted skunks, where one individual preferred a vegetation type that was avoided by all other monitored animals (Crooks & Van Vuren, 1995). Although spotted skunks may be highly adaptable to their environmental, they may experience varying mortality risks in these different environments (Lesmeister et al., 2010). Together, our results suggest that western spotted skunks are a common, habitat generalist species.

Although spotted skunks have been described as having more of an “area of familiarity” instead of a home range (Crabb, 1948), we found that western spotted skunks generally used the same area over the course of the study, apart from one dispersing skunk. We found that western spotted skunk home ranges of both males and females overlapped considerably, suggesting that western spotted skunks are not territorial, unlike other solitary small carnivores (Inman et al., 2012; Moriarty et al., 2017; Powell, 1979). There were no obvious correlations between seasonal male home range sizes and body mass or body length (Appendix S1: Figure S6). Male spotted skunk home ranges appear to be driven by the size of their summer home ranges where males with large summer home ranges had large overall home ranges, whereas male skunks with small summer home ranges had small overall home ranges. Male home ranges in fall and spring were similar (Figure 4A). Since food resources are most abundant during summer, male skunks with large home ranges may require larger home ranges to acquire enough food resources to meet their energetic requirements. Female western spotted skunk home ranges, on the other hand, were small overall and consistently small across all seasons.

The montane temperate rainforests of the Pacific Northwest can be a challenging landscape to conduct animal GPS and VHF tracking studies. Dense canopy cover in valley bottoms are known to hinder communication between GPS devices and satellites and may prevent fixes from occurring in those location (Moriarty & Epps, 2015) (Appendix S1: Figure S3). VHF signal strength was limited by mass of the battery in comparison to the small body size of the western spotted skunk and prevented us from locating animals far from roads or if the animal was inside a dense structure (Frair et al., 2010). Therefore, our sample of locations may be biased, so the apparent selection of areas close to roads by spotted skunks may be artificial. Still, many carnivores take advantage of road networks for traveling and hunting because there are fewer obstructions. Furthermore, we found that western spotted skunks consistently selected areas of low topographic position index, even with these biases, suggesting that selection for these areas may be higher than quantified in our analyses.

Although the use of bait at cameras may bias the detection rates and occupancy rates by increasing detection rates for western spotted skunks, this method was necessary to obtain adequate observations of our focal species. Other studies were not able to detect spotted skunks without bait, whereas those with bait in the same area were able to detect spotted skunks (Kelly & Holub, 2008; Siegfried et al., 2023; Thorne et al., 2017). Moreover, when Siegfried et al. (2023) compared detection rates and activity rates of carnivores at sites with and without bait, they found increased detection rates of most carnivores with bait, but general activity patterns remained similar to when there was no bait.

Forest stand age was not an important predictor of western spotted skunk space use or resource selection at any scale as it was for plains spotted skunks (Lesmeister et al., 2013). This may be because forest structure and understory complexity are not strictly correlated with forest age in the Pacific Northwest. Most federally managed forest, regardless of age, typically consist of complex vegetation structure that can provide ample rest sites and protection from predators. Stands in the stem-exclusion stage, however, have simple structure and may lack the necessary resources and protection needed for stable occupancy. Thorne et al. (2017) found that for eastern spotted skunks, occupancy was high in both young-aged forest and mature stands that had complex forest structure. If we had not studied western spotted skunks in old-growth stands that have high vegetation complexity and structure, we may have concluded that western spotted skunks favor young stands. Therefore, studying western spotted skunk spatial ecology in a variety of forest types provides important conservation and forest management information.

As small carnivore populations face anthropogenic threats and changes to their environmental conditions, it is increasingly important to understand species' habitat requirements. This study adds to our knowledge about a relatively common small carnivore in Pacific Northwest coniferous forests. Western spotted skunks had disproportionately large home ranges relative to their body mass that overlapped neighboring individuals, regardless of sex, indicating that western spotted skunks were not territorial. Our results suggest that western spotted skunks navigate their environment by balancing their needs for food resources, cover from predators, and thermal tolerance with the effects of anthropogenic disturbance. Multiple lines of evidence suggested that western spotted skunks selected against, and were negatively affected by, snowpack. Otherwise, western spotted skunks were a widely distributed habitat generalist throughout lower to mid-elevation sites in this montane, coniferous forest. Future studies of western spotted skunks should explore co-occurrence patterns with other species and finer scales of selection, such as the characteristics and availability of rest sites, which may limit spotted skunk distribution (Lesmeister et al., 2008).

ACKNOWLEDGMENTS

We thank A. Coombs, B. Doyle, H. Haessler, D. Marsh, B. Murley, R. Peterson, R. Rich, L. Rivera, J. Swingle, and K. Van Neste for field support. We thank C. Boucher, D. Cagle, O. Childress, A. Froelich, N. Griffith, J. Hedges, R. Mock, A. Saucedo, S. Spiers, and H. Thomas for photo tagging. Facilities were provided by the H. J. Andrews Experimental Forest and the Long Term Ecological Research program, which is administered cooperatively by the USDA Forest Service Pacific Northwest Research Station, Oregon State University, and the Willamette National Forest. Funding for this project was provided by Oregon State University, the USDA Forest Service (16-JV-11261952-047), the National Science Foundation (LTER7 DEB-1440409), and the ARCS Oregon Chapter. This publication represents the views of the authors and any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the United States Government.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Open Research

DATA AVAILABILITY STATEMENT

Data and code (Tosa et al., 2024) are available from Zenodo: https://zenodo.org/doi/10.5281/zenodo.12804911.

Supporting Information

REFERENCES

Aubry, K. B., and J. C. Lewis. 2003. “Extirpation and Reintroduction of Fishers (Martes pennanti) in Oregon: Implications for Their Conservation in the Pacific States.” Biological Conservation 114: 79–90.
10.1016/S0006-3207(03)00003-X
Web of Science®Google Scholar
Aubry, K. B., C. M. Raley, S. W. Buskirk, W. J. Zielinski, M. K. Schwartz, R. T. Golightly, K. L. Purcell, R. D. Weir, and J. S. Yaeger. 2013. “Meta-Analyses of Habitat Selection by Fishers at Resting Sites in the Pacific Coastal Region.” The Journal of Wildlife Management 77: 965–974.
10.1002/jwmg.563
Web of Science®Google Scholar
Avrin, A. C., C. E. Pekins, J. H. Sperry, and M. L. Allen. 2021. “Evaluating the Efficacy and Decay of Lures for Improving Carnivore Detections with Camera Traps.” Ecosphere 12: e03710.
10.1002/ecs2.3710
Web of Science®Google Scholar
Avrin, A. C., C. E. Pekins, J. H. Sperry, P. J. Wolff, and M. L. Allen. 2021. “Efficacy of Attractants for Detecting Eastern Spotted Skunks: An Experimental Approach.” Wildlife Biology 2021: wlb.00880.
10.2981/wlb.00880
Web of Science®Google Scholar
Belant, J. L., J. Schipper, and J. Conroy. 2009. “The Conservation Status of Small Carnivores in the Americas.” Small Carnivore Conservation 41: 3–8.
Google Scholar
Bhattacharyya, A. 1943. “On a Measure of Divergence Between Two Statistical Populations Defined by their Probability Distribution.” Bulletin of the Calcutta Mathematical Society 35: 99–110.
Google Scholar
Brown, E. R. 1985. “Management of Wildlife and Fish Habitats in Forests of Western Oregon and Washington.” Part 1, Chapter Narratives and Part 2, Appendices. United States. https://www.osti.gov/biblio/7110353.
Google Scholar
Bunnell, F. L. 1990. “Reproduction of Salal (Gaultheria shallon) Under Forest Canopy.” Canadian Journal of Forest Research 20: 91–100.
10.1139/x90-013
Web of Science®Google Scholar
Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach, ed. New York: Springer.
Web of Science®Google Scholar
Buskirk, S. W., S. C. Forrest, M. G. Raphael, and H. J. Harlow. 1989. “Winter Resting Site Ecology of Marten in the Central Rocky Mountains.” The Journal of Wildlife Management 53: 191–196.
10.2307/3801330
Web of Science®Google Scholar
Buskirk, S. W., and H. J. Harlow. 1989. “Body-Fat Dynamics of the American Marten (Martes americana) in Winter.” Journal of Mammalogy 70: 191–193.
10.2307/1381687
Web of Science®Google Scholar
Carey, A. B., and J. E. Kershner. 1996. “Spilogale gracilis in Upland Forests of Western Washington and Oregon.” Northwestern Naturalist 77: 29–34.
10.2307/3536615
Google Scholar
Carroll, K. N. 2000. Macro-and Microhabitat Characteristics of the Western Spotted Skunk, Spilogale gracilis, in the Sierra Nevada of the Northern California. Sacramento: California State University.
Google Scholar
Chung, Y., S. Rabe-Hesketh, V. Dorie, A. Gelman, and J. Liu. 2013. “A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models.” Psychometrika 78: 685–709.
10.1007/s11336-013-9328-2
PubMedWeb of Science®Google Scholar
Crabb, W. D. 1948. “The Ecology and Management of the Prairie Spotted Skunk in Iowa.” Ecological Monographs 18: 201–232.
10.2307/1948639
Web of Science®Google Scholar
Crooks, K. R. 1994a. “Demography and Status of the Island Fox and the Island Spotted Skunk on Santa Cruz Island, California.” The Southwestern Naturalist 39: 257–262.
10.2307/3671590
Web of Science®Google Scholar
Crooks, K. R. 1994b. “Den-Site Selection in the Island Spotted Skunk of Santa Cruz Island, California.” The Southwestern Naturalist 39: 354–357.
10.2307/3671590
Google Scholar
Crooks, K. R., and D. Van Vuren. 1995. “Resource Utilization by Two Insular Endemic Mammalian Carnivores, the Island Fox and Island Spotted Skunk.” Oecologia 104: 301–307.
10.1007/BF00328365
PubMedWeb of Science®Google Scholar
Davis, R. J., D. M. Bell, M. J. Gregory, Z. Yang, A. N. Gray, S. P. Healey, and A. E. Stratton. 2022. Northwest Forest Plan-the First 25 Years (1994–2018): Status and Trends of Late-Successional and Old-Growth Forests. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. https://www.fs.usda.gov/treesearch/pubs/65070.
Google Scholar
Dawson, F. N., A. J. Magoun, J. Bowman, and J. C. Ray. 2010. “Wolverine, Gulo gulo, Home Range Size and Denning Habitat in Lowland Boreal Forest in Ontario.” The Canadian Field-Naturalist 124: 139.
10.22621/cfn.v124i2.1052
Web of Science®Google Scholar
Delheimer, M. S., K. M. Moriarty, H. L. Munro, D. A. Early, K. A. Hamm, and R. E. Green. 2023. “Structural Complexity Characterizes Fine-Scale Forest Conditions Used by Pacific Martens.” Journal of Wildlife Management 87: e22388.
10.1002/jwmg.22388
Web of Science®Google Scholar
D'Eon, R. G., R. Serrouya, G. Smith, and C. O. Kochanny. 2002. “GPS Radiotelemetry Error and Bias in Mountainous Terrain.” Wildlife Society Bulletin (1973-2006) 30: 430–439.
Web of Science®Google Scholar
DiGregorio, L. 2019. “Snowdown 2019.” HJ Andrews Experimental Forest Long-Term Ecological Research. https://andrewsforest.oregonstate.edu/gallery/snowdown-2019.
Google Scholar
Do Linh San, E., J. J. Sato, J. L. Belant, and M. J. Somers. 2022. “ The World's Small Carnivores.” In Small Carnivores, First ed. 1–38. Hoboken, NJ: John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118943274.ch1.
10.1002/9781118943274.ch1
Google Scholar
Doty, J. B. 2003. “Denning Ecology and Home Ranges of Two Sympatric Skunk Species (Mephitis mephitis and Spilogale gracilis) in West-Central Texas.” Thesis, Angelo State University, Texas. https://www.proquest.com/openview/71c02642e2087696b16b70175fbf0241/1?pq-origsite=gscholar&cbl=18750&diss=y.
Google Scholar
Doty, J. B., and R. C. Dowler. 2006. “Denning Ecology in Sympatric Populations of Skunks (Spilogale gracilis and Mephitis mephitis) in West-Central Texas.” Journal of Mammalogy 87: 131–138.
10.1644/04-MAMM-A-172R2.1
Web of Science®Google Scholar
Dunnington, D., and P. Harvey. 2021. “exifr: EXIF Image Data in R.” R Package. https://CRAN.R-project.org/package=exifr.
Google Scholar
Eng, R. Y. Y., and D. S. Jachowski. 2019. “Evaluating Detection and Occupancy Probabilities of Eastern Spotted Skunks.” The Journal of Wildlife Management 83: 1244–1253.
10.1002/jwmg.21684
Web of Science®Google Scholar
Fiske, I., and R. Chandler. 2011. “unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance.” Journal of Statistical Software 43: 1–23. http://www.jstatsoft.org/v43/i10/.
Google Scholar
Fleming, C. H., and J. M. Calabrese. 2017. “A New Kernel Density Estimator for Accurate Home-Range and Species-Range Area Estimation.” Methods in Ecology and Evolution 8: 571–579.
10.1111/2041-210X.12673
Web of Science®Google Scholar
Fleming, C. H., and J. M. Calabrese. 2022. “ctmm: Continuous-Time Movement Modeling.” https://github.com/ctmm-initiative/ctmm.
Google Scholar
Frair, J. L., J. Fieberg, M. Hebblewhite, F. Cagnacci, N. J. DeCesare, and L. Pedrotti. 2010. “Resolving Issues of Imprecise and Habitat-Biased Locations in Ecological Analyses Using GPS Telemetry Data.” Philosophical Transactions of the Royal Society B: Biological Sciences 365: 2187–2200.
10.1098/rstb.2010.0084
PubMedWeb of Science®Google Scholar
Frey, S. J. K., A. S. Hadley, and M. G. Betts. 2016. “Microclimate Predicts Within-Season Distribution Dynamics of Montane Forest Birds.” Diversity and Distributions 22: 944–959.
10.1111/ddi.12456
Web of Science®Google Scholar
Frey, S. J. K., A. S. Hadley, S. L. Johnson, M. Schulze, J. A. Jones, and M. G. Betts. 2016. “Spatial Models Reveal the Microclimatic Buffering Capacity of Old-Growth Forests.” Science Advances 2: e1501392. http://advances.sciencemag.org/content/2/4/e1501392.abstract.
10.1126/sciadv.1501392
PubMedGoogle Scholar
Gehring, T. M., and R. K. Swihart. 2004. “Home Range and Movements of Long-Tailed Weasels in a Landscape Fragmented by Agriculture.” Journal of Mammalogy 85: 79–86.
10.1644/1545-1542(2004)085<0079:HRAMOL>2.0.CO;2
Web of Science®Google Scholar
Gompper, M. E., and H. M. Hackett. 2005. “The Long-Term, Range-Wide Decline of a Once Common Carnivore: The Eastern Spotted Skunk (Spilogale putorius).” Animal Conservation 8: 195–201.
10.1017/S1367943005001964
Web of Science®Google Scholar
Gompper, M. E., and D. S. Jachowski. 2016. “Spilogale putorius.” The IUCN Red List of Threatened Species 2016: e.T41636A45211474. International Union for Conservation of Nature. http://www.iucnredlist.org/details/41636/0.
Google Scholar
Hackett, H. M., D. B. Lesmeister, J. Desanty-Combes, W. G. Montague, J. J. Millspaugh, and M. E. Gompper. 2007. “Detection Rates of Eastern Spotted Skunks (Spilogale putorius) in Missouri and Arkansas Using Live-Capture and Non-Invasive Techniques.” The American Midland Naturalist 158: 123–131.
10.1674/0003-0031(2007)158[123:DROESS]2.0.CO;2
Web of Science®Google Scholar
Hayes, J. P., S. S. Chan, W. H. Emmingham, J. C. Tappeiner, L. D. Kellogg, and J. D. Bailey. 1997. “Wildlife Response to Thinning Young Forests in the Pacific Northwest.” Journal of Forestry 95: 28–33.
10.1093/jof/95.8.28
Web of Science®Google Scholar
Inman, R. M., M. L. Packila, K. H. Inman, A. J. Mccue, G. C. White, J. Persson, B. C. Aber, et al. 2012. “Spatial Ecology of Wolverines at the Southern Periphery of Distribution.” The Journal of Wildlife Management 76: 778–792.
10.1002/jwmg.289
Web of Science®Google Scholar
Jachowski, D. S. 2007. “Resource Selection by Black-Footed Ferrets in Relation to the Spatial Distribution of Prairie Dogs.” Thesis, University of Missouri-Columbia, Missouri. https://hdl.handle.net/10355/4974.
Google Scholar
Jachowski, D. S., C. J. Marneweck, C. Olfenbuttel, and S. N. Harris. 2023. “Support for the Size-Mediated Sensitivity Hypothesis within a Diverse Carnivore Community.” Journal of Animal Ecology 93: 109–122. https://doi.org/10.1111/1365-2656.13916.
10.1111/1365-2656.13916
PubMedGoogle Scholar
Johnson, D. H. 1980. “The Comparison of Usage and Availability Measurements for Evaluating Resource Preference.” Ecology 61: 65–71.
10.2307/1937156
Web of Science®Google Scholar
Jones, K. L., D. H. Van Vuren, and K. R. Crooks. 2008. “Sudden Increase in a Rare Endemic Carnivore: Ecology of the Island Spotted Skunk.” Journal of Mammalogy 89: 75–86.
10.1644/07-MAMM-A-034.1
Web of Science®Google Scholar
Kellner, K. F., A. D. Smith, J. A. Royle, M. Kéry, J. L. Belant, and R. B. Chandler. 2023. “The Unmarked R Package: Twelve Years of Advances in Occurrence and Abundance Modelling in Ecology.” Methods in Ecology and Evolution 14: 1408–1415.
10.1111/2041-210X.14123
Web of Science®Google Scholar
Kelly, M. J., and E. L. Holub. 2008. “Camera Trapping of Carnivores: Trap Success Among Camera Types and Across Species, and Habitat Selection by Species, on Salt Pond Mountain, Giles County, Virginia.” Northeastern Naturalist 15: 249–262.
10.1656/1092-6194(2008)15[249:CTOCTS]2.0.CO;2
Web of Science®Google Scholar
Kéry, M., and R. Chandler. 2012. “Dynamic Occupancy Models in Unmarked.” http://cran.r-project.org/web/packages/unmarked/vignettes/colext.pdf.
Google Scholar
Kim, H., B. C. McComb, S. J. K. Frey, D. M. Bell, and M. G. Betts. 2022. “Forest Microclimate and Composition Mediate Long-Term Trends of Breeding Bird Populations.” Global Change Biology 28: 6180–6193.
10.1111/gcb.16353
CASPubMedWeb of Science®Google Scholar
King, C. M., and R. A. Powell. 2007. The Natural History of Weasels and Stoats: Ecology, Behavior, and Management, Second ed. New York: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195322712.001.0001/acprof-9780195322712.
10.1093/acprof:oso/9780195322712.001.0001
Google Scholar
Lesmeister, D. B., R. S. Crowhurst, J. J. Millspaugh, and M. E. Gompper. 2013. “Landscape Ecology of Eastern Spotted Skunks in Habitats Restored for Red-Cockaded Woodpeckers.” Restoration Ecology 21: 267–275.
10.1111/j.1526-100X.2012.00880.x
Web of Science®Google Scholar
Lesmeister, D. B., M. E. Gompper, and J. J. Millspaugh. 2008. “Summer Resting and den Site Selection by Eastern Spotted Skunks (Spilogale putorius) in Arkansas.” Journal of Mammalogy 89: 1512–1520.
10.1644/07-MAMM-A-361.1
Web of Science®Google Scholar
Lesmeister, D. B., M. E. Gompper, and J. J. Millspaugh. 2009. “Habitat Selection and Home Range Dynamics of Eastern Spotted Skunks in the Ouachita Mountains, Arkansas, USA.” The Journal of Wildlife Management 73: 18–25.
10.2193/2007-447
Web of Science®Google Scholar
Lesmeister, D. B., J. J. Millspaugh, M. E. Gompper, and T. W. Mong. 2010. “Eastern Spotted Skunk (Spilogale putorius) Survival and Cause-Specific Mortality in the Ouachita Mountains, Arkansas.” American Midland Naturalist 164: 52–60.
10.1674/0003-0031-164.1.52
Web of Science®Google Scholar
Lindenmayer, D. B., J. T. Wood, L. McBurney, C. MacGregor, K. Youngentob, and S. C. Banks. 2011. “How to Make a Common Species Rare: A Case Against Conservation Complacency.” Biological Conservation 144: 1663–1672.
10.1016/j.biocon.2011.02.022
Web of Science®Google Scholar
Lindstedt, S. L., B. J. Miller, and S. W. Buskirk. 1986. “Home Range, Time, and Body Size in Mammals.” Ecology 67: 413–418.
10.2307/1938584
Web of Science®Google Scholar
Linnell, M. A., C. W. Epps, E. D. Forsman, and W. J. Zielinski. 2017. “Space Use, Movements, and Rest Site Use by Short-Tailed Weasels Mustela erminea in Managed Forests of Western Oregon.” Wildlife Biology 2017: wlb.00270.
10.2981/wlb.00270
Google Scholar
Linnell, M. A., K. Moriarty, D. S. Green, and T. Levi. 2018. “Density and Population Viability of Coastal Marten: A Rare and Geographically Isolated Small Carnivore.” PeerJ 6: e4530.
10.7717/peerj.4530
PubMedWeb of Science®Google Scholar
Lisgo, K. A. 1999. Ecology of the Short-Tailed Weasel (Mustela erminea) in the Mixedwood Boreal Forest of Alberta. Thesis, University of British Columbia. https://doi.org/10.14288/1.0088967.
Google Scholar
D. I. MacKenzie, J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines, eds. 2018. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurence, 2nd ed. Boston: Academic Press. https://www.sciencedirect.com/science/article/pii/B9780124071971000028.
Google Scholar
Marneweck, C., A. R. Butler, L. C. Gigliotti, S. N. Harris, A. J. Jensen, M. Muthersbaugh, B. A. Newman, et al. 2021. “Shining the Spotlight on Small Mammalian Carnivores: Global Status and Threats.” Biological Conservation 255: 109005.
10.1016/j.biocon.2021.109005
Web of Science®Google Scholar
Marneweck, C. J., B. L. Allen, A. R. Butler, E. Do Linh San, S. N. Harris, A. J. Jensen, E. A. Saldo, et al. 2022. “Middle-Out Ecology: Small Carnivores as Sentinels of Global Change.” Mammal Review 52: 471–479. https://doi.org/10.1111/mam.12300.
10.1111/mam.12300
Web of Science®Google Scholar
Martin, M. E., K. M. Moriarty, and J. N. Pauli. 2021. “Landscape Seasonality Influences the Resource Selection of a Snow-Adapted Forest Carnivore, the Pacific Marten.” Landscape Ecology 36: 1055–1069.
10.1007/s10980-021-01215-9
Web of Science®Google Scholar
Mastro, L. L., D. J. Morin, and E. M. Gese. 2019. “Home Range and Habitat Use of West Virginia Canis latrans (Coyote).” Northeastern Naturalist 26: 616–628.
10.1656/045.026.0318
Web of Science®Google Scholar
Mayor, S. J., D. C. Schneider, J. A. Schaefer, and S. P. Mahoney. 2009. “Habitat Selection At Multiple Scales.” Écoscience 16: 238–247.
10.2980/16-2-3238
Web of Science®Google Scholar
McDonough, M. M., A. W. Ferguson, R. C. Dowler, M. E. Gompper, and J. E. Maldonado. 2022. “Phylogenomic Systematics of the Spotted Skunks (Carnivora, Mephitidae, Spilogale): Additional Species Diversity and Pleistocene Climate Change as a Major Driver of Diversification.” Molecular Phylogenetics and Evolution 167: 107266.
10.1016/j.ympev.2021.107266
CASPubMedWeb of Science®Google Scholar
McGarigal, K., H. Y. Wan, K. A. Zeller, B. C. Timm, and S. A. Cushman. 2016. “Multi-Scale Habitat Selection Modeling: A Review and Outlook.” Landscape Ecology 31: 1161–1175.
10.1007/s10980-016-0374-x
Web of Science®Google Scholar
Moriarty, K. M., and C. W. Epps. 2015. “Retained Satellite Information Influences Performance of GPS Devices in a Forested Ecosystem: Satellite Information Affects GPS Performance.” Wildlife Society Bulletin 39: 349–357.
10.1002/wsb.524
Web of Science®Google Scholar
Moriarty, K. M., M. A. Linnell, B. E. Chasco, C. W. Epps, and W. J. Zielinski. 2017. “Using High-Resolution Short-Term Location Data to Describe Territoriality in Pacific Martens.” Journal of Mammalogy 98: 679–689.
10.1093/jmammal/gyx014
Web of Science®Google Scholar
Moriarty, K. M., W. J. Zielinski, and E. D. Forsman. 2011. “Decline in American Marten Occupancy Rates at Sagehen Experimental Forest, California.” The Journal of Wildlife Management 75: 1774–1787.
10.1002/jwmg.228
Web of Science®Google Scholar
Morrison, P. H., and F. J. Swanson. 1990. Fire History and Pattern in a Cascade Range Landscape. General Technical Report PNW-GTR-254. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.
Google Scholar
Nams, V. O. 2006. Locate III User's Guide. Tatamagouche, Nova Scotia, Canada: Pacer Computer Software.
Google Scholar
Neiswenter, S. A., and R. C. Dowler. 2007. “Habitat Use of Western Spotted Skunks and Striped Skunks in Texas.” Journal of Wildlife Management 71: 583–586.
10.2193/2005-623
Web of Science®Google Scholar
Neiswenter, S. A., D. B. Pence, and R. C. Dowler. 2006. “Helminths of Sympatric Striped, Hog-Nosed, and Spotted Skunks in West-Central Texas.” Journal of Wildlife Diseases 42: 511–517.
10.7589/0090-3558-42.3.511
PubMedGoogle Scholar
Orning, E. K. 2019. Carnivore Competition: Spatial and Dietary Implications of Gray Wolf Recolonization for Cougars in Northeast Oregon. Corvallis, OR: Oregon State University.
Google Scholar
Powell, R. A. 1979. “Mustelid Spacing Patterns: Variations on a Theme by Mustela.” Zeitschrift für Tierpsychologie 50: 153–165.
10.1111/j.1439-0310.1979.tb01023.x
Web of Science®Google Scholar
R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org/.
Google Scholar
Schmidt, G. M., M. K. Jennings, J. G. Smith, E. E. Boydston, L. M. Lyren, and R. L. Lewison. 2023. “Bobcats in Southern California Respond to Urbanization at Multiple Scales.” Biological Conservation 278: 109849.
10.1016/j.biocon.2022.109849
Web of Science®Google Scholar
Seneviratne, X. Z., M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, et al. 2022. “ 2021: Weather and Climate Extreme Events in a Changing Climate.” In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, 1513–1766. Cambridge, United Kingdom and New York, NY: Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/.
Google Scholar
Siegfried, A. C., S. N. Harris, C. Olfenbuttel, and D. S. Jachowski. 2023. “Effects of Sardines as an Attractant on Carnivore Detection and Temporal Activity Patterns at Remote Camera Traps.” Wildlife Research 51: WR22196. https://doi.org/10.1071/WR22196.
10.1071/WR22196
Google Scholar
Sikes, R. S., and Animal Care and Use Committee of the American Society of Mammalogists. 2016. “2016 Guidelines of the American Society of Mammalogists for the Use of Wild Mammals in Research and Education.” Journal of Mammalogy 97: 663–688.
10.1093/jmammal/gyw078
PubMedWeb of Science®Google Scholar
Simms, D. A. 1979. “North American Weasels: Resource Utilization and Distribution.” Canadian Journal of Zoology 57: 504–520.
10.1139/z79-061
Web of Science®Google Scholar
Slauson, K. M., W. J. Zielinski, and J. P. Hayes. 2007. “Habitat Selection by American Martens in Coastal California.” Journal of Wildlife Management 71: 458–468.
10.2193/2004-332
Web of Science®Google Scholar
Spies, T. A., and J. F. Franklin. 1988. “Old Growth and Forest Dynamics in the Douglas-Fir Region of Western Oregon and Washington.” Natural Areas Journal 8: 190–201.
Google Scholar
Sprayberry, T. R., and A. J. Edelman. 2018. “Den-Site Selection of Eastern Spotted Skunks in the Southern Appalachian Mountains.” Journal of Mammalogy 99: 242–251.
10.1093/jmammal/gyx168
Web of Science®Google Scholar
Stoelb, D. 2020. “2019 February Winter Storms Spotlight.” Oregon Office of Emergency Management. https://storymaps.arcgis.com/stories/a60e9116f5f240ae95424e878e344ce7.
Google Scholar
Teensma, P. D. A. 1987. Fire History and Fire Regimes of the Central Western Cascades of Oregon. Eugene, OR: University of Oregon.
Web of Science®Google Scholar
The White House. 2019. “President Donald J. Trump Approves Oregon Disaster Declaration.” Statements and Releases. https://trumpwhitehouse.archives.gov/briefings-statements/president-donald-j-trump-approves-oregon-disaster-declaration-2.
Google Scholar
Thorne, E. D., C. Waggy, D. S. Jachowski, M. J. Kelly, and W. M. Ford. 2017. “Winter Habitat Associations of Eastern Spotted Skunks in Virginia.” The Journal of Wildlife Management 81: 1042–1050.
10.1002/jwmg.21282
Web of Science®Google Scholar
Tosa, M. I., D. B. Lesmeister, J. M. Allen, and T. Levi. 2023. “Multi-Locus DNA Metabarcoding Reveals Seasonality of Foraging Ecology of Western Spotted Skunks in the Pacific Northwest.” Ecosphere 14: e4386.
10.1002/ecs2.4386
Web of Science®Google Scholar
Tosa, M. I., D. B. Lesmeister, and T. Levi. 2022. “Barred Owl Predation of Western Spotted Skunks.” Northwestern Naturalist 103: 250–256.
10.1898/1051-1733-103.3.250
Google Scholar
Tosa, M. I., D. B. Lesmeister, and T. Levi. 2024. “tinytosa/WSpottedSkunkSpatialEcology: Ecosphere_pub_2024 (1.0).” Zenodo. https://doi.org/10.5281/zenodo.12804912.
10.5281/zenodo.12804912
Google Scholar
Tweedy, P. J., K. M. Moriarty, J. D. Bailey, and C. W. Epps. 2019. “Using Fine Scale Resolution Vegetation Data from LiDAR and Ground-Based Sampling to Predict Pacific Marten Resting Habitat at Multiple Spatial Scales.” Forest Ecology and Management 452: 117556.
10.1016/j.foreco.2019.117556
Web of Science®Google Scholar
US Fish and Wildlife Service. 2012. “Endangered and Threatened Wildlife and Plants; 90-Day Finding on a Petition to List the Prairie Gray Fox, the Plains Spotted Skunk, and a Distinct Population Segment of the Mearn's Eastern Cottontail in East-Central Illinois and Western Indiana as Endangered or Threatened Species.” Federal Register 77: 71159–71771.
Google Scholar
US Fish and Wildlife Service. 2023. “Endangered and Threatened Species: One Species Not Warranted for Delisting and Six Species Not Warranted for Listing; Plains Spotted Skunk.” Federal Register 88: 64870–64880.
Google Scholar
Verts, B. J., L. N. Carraway, and A. Kinlaw. 2001. “Spilogale gracilis.” Mammalian Species 674: 1–10.
10.1644/1545-1410(2001)674<0001:SG>2.0.CO;2
Google Scholar
Wan, H. Y., K. McGarigal, J. L. Ganey, V. Lauret, B. C. Timm, and S. A. Cushman. 2017. “Meta-Replication Reveals Nonstationarity in Multi-Scale Habitat Selection of Mexican Spotted Owl.” The Condor 119: 641–658.
10.1650/CONDOR-17-32.1
Web of Science®Google Scholar
Weldy, M. J., C. W. Epps, D. B. Lesmeister, T. Manning, M. A. Linnell, and E. D. Forsman. 2019. “Abundance and Ecological Associations of Small Mammals.” The Journal of Wildlife Management 83: 902–915.
10.1002/jwmg.21641
Web of Science®Google Scholar
Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis, 2nd ed. Use R! New York: Springer-Verlag. https://doi.org/10.1007/978-3-319-24277-4.
10.1007/978-3-319-24277-4
Google Scholar
Wilbert, C. J., S. W. Buskirk, and K. G. Gerow. 2000. “Effects of Weather and Snow on Habitat Selection by American Martens (Martes americana).” Canadian Journal of Zoology 78: 1691–1696.
10.1139/z00-121
Web of Science®Google Scholar
Wilcove, D. S., D. Rothstein, J. Dubow, A. Phillips, and E. Losos. 1998. “Quantifying Threats to Imperiled Species in the United States.” Bioscience 48: 607–615.
10.2307/1313420
Web of Science®Google Scholar
Winner, K., M. J. Noonan, C. H. Fleming, K. A. Olson, T. Mueller, D. Sheldon, and J. M. Calabrese. 2018. “Statistical Inference for Home Range Overlap.” Methods in Ecology and Evolution 9: 1679–1691.
10.1111/2041-210X.13027
Web of Science®Google Scholar
Zielinski, W. J., R. L. Truex, G. A. Schmidt, F. V. Schlexer, K. N. Schmidt, and R. H. Barrett. 2004. “Resting Habitat Selection by Fishers in California.” The Journal of Wildlife Management 68: 475–492.
10.2193/0022-541X(2004)068[0475:RHSBFI]2.0.CO;2
Web of Science®Google Scholar

Volume15, Issue8

August 2024

e4981

Journal list menu

Western spotted skunk spatial ecology in the temperate rainforests of the Pacific Northwest

Abstract

INTRODUCTION

STUDY AREA