The Importance of Prey and Habitat Structure

Martens in a novel habitat – The importance of prey and habitat structure

by Charlotte Eriksson

Master degree thesis in Conservation Biology, 60 credits Department of Biology Lund University

Supervisors Dr. Taal Levi Department of Fisheries and Wildlife Oregon State University

Dr. Katie Moriarty Pacific Northwest Research Station U.S Forest Service

June 2016

Abstract

North American martens (Martes americana and Martes caurina) are thought to be forest specialists closely linked to late-successional forests with high canopy cover and a complex understory. The Pacific marten (Martes caurina) has only been found in two isolated populations in Oregon (one on the central coast and one on the south coast). Interestingly, despite extensive survey effort, nearly all marten observations in the Central Coastal

Population of Oregon have been found in a narrow strip of partially forested coastal sand dune habitat to the west of the Pacific Coast Highway despite the presence of large areas of older forest adjacent to this coastal dune habitat. The surprising degree to which martens are isolated to this novel habitat type motivated a research effort to explain this highly restricted distribution of Pacific martens. We predicted that the range of martens might be restricted by improved foraging opportunities in the coastal dunes and an inadequate prey base in the forest, or relaxed predation and competition in the coastal dunes relative to the forest. To address these hypotheses, we characterized differences in the relative abundance and diversity of potential marten prey, and presence of other omnivorous medium-sized mammals that might compete with martens, and likely mammalian predators. We deployed 674 camera stations across four different vegetation types (beach grass, deflation plain, ericaceous forest and interior forest) within the Central Coast Ranger District area of Siuslaw National Forest

(NF), Oregon, United States between October and December 2015. We also undertook vegetation surveys at each camera location to determine the difference in plant composition and structure among the vegetation types. The ericaceous forest had the highest amount of understory structure with total shrub cover and fruit-producing shrub cover being significantly higher compared to the other vegetation types. As predicted, the relative abundance and diversity of potential prey species was higher in the ericaceous forest compared to the interior forest. The presence of potential competitors and predators were often equal or higher within

2 the ericaceous forest and deflation plain compared to the interior forest. Our study suggests that martens are more flexible in their habitat use than previously believed, and that relative abundance of prey and understory habitat structure may be more important than forest age.

This study provides important indications that may partly explain why martens are inhabiting the dune forest which is important for management of conservation efforts.

Introduction ...... 5 Materials and methods ...... 7 Small mammal surveys ...... 10 Carnivore surveys ...... 12 Data analysis ...... 12 Results ...... 14 Vegetation structure ...... 14 Diversity and relative abundance ...... 15 Discussion ...... 19 References ...... 24 Appendix 1 ...... 30

Introduction

Carnivores are declining worldwide due to anthropogenic stressors including direct persecution, habitat loss and fragmentation (Di Minin et al. 2016). The loss of carnivores has resulted in changes in prey abundances and behavior (Ripple et al. 2001), mesopredator release (Prugh et al. 2009; Ripple et al. 2013) and facilitation of zoonotic diseases (Levi et al.

2012). However, not all species are at equal risk facing these threats. Ecological and life history traits such as energetic requirements, body size, vulnerability to predators and interspecific competition often determines how sensitive a species is to human-induced eco- system changes (Cardillo et al. 2004; Pardo Vargas et al. 2016; Linnell & Strand 2010). These factors also influence the extent to which species use different types of habitats (Pandit et al.

2015). Habitat specialists are typically more vulnerable to habitat loss or fragmentation compared to generalist species that often benefit from human disturbance (Crooks & Crooks

2002; Devictor et al. 2016; Vergara et al. 2013).

American marten (Martes americana) has a widespread distribution in North America from the east coast, west to Montana and north to interior Alaska while the Pacific marten (Martes caurina) occupies the west coast from southeastern Alaska south to northern California (Stone et al. 2002). In the Pacific Northwest, martens occupy coniferous forests dominated by Tsuga heterophylla, Pseudotsuga menziesii, Thuja plicata, Abies grandis and Pinus contorta (Shirk et al. 2014; Munzing & Gaines 2008; Bull & Heater 2001). Like other medium-sized carnivores, martens live in a landscape of fear where they have to balance foraging with avoiding terrestrial and avian predators (Hairston et al. 1960; Ritchie & Johnson 2009; Drew

1995). Complex habitat structures are therefore important as they provide protection against predators, habitat for prey species (Andruskiw et al. 2008) as well as resting and denning sites

(Slauson & Zielinski 2009). As a result of their habitat requirements, martens are highly sensitive to timber harvesting (Thompson 1991).

The relative abundance of prey may be particularly important to mustelids like martens.

Their elongated and thin body shape make them efficient hunters in habitats under-utilized by other carnivores such as underground and arboreal refuges of prey species (Brown &

Lasiewski 1972). However, a slender body with limited fat reserves and little insulation from fur, means martens have increased thermoregulatory costs and consistently high metabolic demands in comparison to other mammals (Buskirk & Harlow 1989; Scholander et al. 1950).

To balance these demands martens must eat approximately 25% of their body weight daily, which, for instance, could include 7 red-backed voles (Clethrionomys gapperi) and require significant foraging effort (Gilbert et al. 2009). Not surprisingly, marten density have been strongly correlated with the abundance of prey species such as voles (Flynn & Schumacher

2009), mice and squirrels (Fryxell et al. 1999; Kleef & Wijsman 2015). Furthermore, relative to other similar sized mammals, martens have a slow life history with late sexual maturity, long interbirth intervals, and high longevity, further increasing their sensitivity to additional sources of mortality (Buskirk & Harlow 1989).

The Pacific marten has only been found in two isolated populations in Oregon (one on the central coast and one on the south coast which may be connected to a population in northern California) (Zielinski et al. 2001). The populations appear to be isolated and small both in geographic extent and abundance and are therefore threatened by not only the aforementioned threats of habitat loss and fragmentation, but also high-intensity fires and disease outbreaks (Moriarty et al. in press). In addition, the Central Coastal Population may be of even further risk of local extirpation due to its immediate proximity to the ocean (Fig. 1).

The long-overdue predicted Cascadia subduction zone earthquake and following tsunami

(Long & Shennan 1998) could potentially wipe out the whole population.

Interestingly, despite extensive survey effort, nearly all marten observations in the Central

Coastal Population of Oregon have been found in a narrow strip of partially forested coastal sand dune habitat to the West of the Pacific Coast Highway despite the presence of large areas of older forest adjacent to this coastal dune habitat (Fig. 1; Moriarty et al. in press). The surprising degree to which martens are isolated to this novel habitat type motivated a research effort to explain this highly restricted distribution of Pacific martens.

We predicted that the range of martens might be restricted by improved foraging opportunities in the coastal dunes and an inadequate prey base in the forest, or relaxed predation and competition in the coastal dunes relative to the forest. To address these hypotheses, we characterized differences in the relative abundance and diversity of potential marten prey, and presence of other omnivorous medium-sized mammals that might compete with martens, and likely mammalian predators.

Materials and methods

Study area

We focused our study in a novel system for martens in the Central Coast Ranger District area of Siuslaw National Forest (NF), Oregon, United States (43°42’ N, 124°10’W) (Fig. 1).

Siuslaw NF has a pacific maritime climate with mild temperature averages of 15.1°C in summer and 5.6°C in winter and a mean annual rainfall of 148 cm (Wiedemann & Pickart

1996).

We broadly defined four vegetation communities within the area based on structure and species dominance adapted from Christy et al. (1998): (1) beach grass, (2) deflation plain, (3)

Pinus contorta/ericaceous shrub forest, hereafter “ericaceous forest” and (4) forested stands

>50 years of age often east of the coastal zone, hereafter “interior forest”.

We created a random point layer in ArcGIS 10.2 to randomize site selection within the vegetation types. All sites within the same vegetation type were separated by at least 1.5 km to reduce spatial autocorrelation among replicates. We also took edge effect into account by placing all sites at a minimum of 50 m from roads. We deployed 674 camera stations between

October and December 2015 (612 small mammal cameras and 62 carnivore cameras) across

31 sites (interior forest n= 12, ericaceous forest n=7, deflation plain n=8 and beach grass n=4).

Vegetation Survey

We recorded percentage cover of dominant species and the cover as well as structure of each layer (canopy cover, shrub cover, fruit-producing shrub cover, fern cover, grass/sedge cover, height of shrub and canopy layer (m) and diameter at breast height (DBH) (cm) according to

Jennings et al. (2004), within a radius of 10 m centered at each camera location (interior forest n=260, ericaceous forest n=154, deflation plain n=175, and beach grass n=85) (Table 1; Fig.

2). Vegetative composition varied widely among our four vegetation types despite adjacent proximities, occasionally with all four communities spaced within 2.5 km from the ocean

(Fig. 1). Because of the short distance and relatively minor difference in elevation among the vegetation types (Table 1), confounding factors associated with climate or topography are reduced. Hence differences in mammal communities should reflect local environmental characteristics important for the distribution of species.

Figure 1. A: We surveyed for small mammals and carnivores in coastal Oregon, United States between October and December 2015. Stars show sites where camera trapping was conducted (black stars indicate sites with Pacific marten (Martes caurina) detections). Inset shows the study area within the state of Oregon. B: Photo examples of the different vegetation types defined within the study area. (1) beach grass, (2) deflation plain, (3) ericaceous forest and (4) interior forest. C: Drawing depicting the vegetative gradient from the Pacific shore through the dunes and into the interior forest. Not drawn to scale.

Table 1 Vegetation composition (dominant species) and elevation range of each vegetation type. Species are listed in order of dominance.

Beach grass Deflation plain Ericaceous forest Interior forest Ground Ammophila cover arenaria Carex obnupta Mostly absent Polystichum munitum Elymus mollis Gaultheria Shrub layer Cytisus scoparius shallon V. ovatum V. ovatum Myrica californica G. shallon G. shallon Vaccinium ovatum Rhododendron macrophyllum Rubus spectabilis Salix hookeriana R. macrophyllum Canopy Pseudotsuga layer - Pinus contorta Pinus contorta menziesii Picea sitchensis Picea sitchensis Tsuga heterophylla Picea sitchensis Alnus rubra Thuja plicata Elevation (m) 2-8 m 4-7 m 5-149 m 83-587 m

Small mammal surveys

While camera trapping has been used widely to survey large mammals (O’Brien et al. 2003;

Rovero & Marshall 2009; Treves et al. 2010), live-trapping is still the most common method

for studying small mammals (Torre et al. 2016). Live-trapping has been successfully used for

estimating density, body condition, sex-ratio, and reproductive status of animals (Schulte-

Hostedde et al. 2005; Suzuki & Hayes 2003). However, live-trapping surveys may fail to

detect animals that are trap shy (Torre et al. 2016), and can cause major physiological stress to

captured animals (Shonfield et al. 2013). Camera trapping provides a non-invasive alternative,

particularly useful when the aims of the study is distribution or relative abundance of multiple

species at a large scale (De Bondi et al. 2010; McCleery et al. 2014).

We used two types of camera setups – one for the purpose of small mammals and one for

detecting martens and other carnivores. Small mammal grids were comprised of 20 baited

10 cameras systematically placed in two parallel transects. Due to the narrow and linear shape of some of the vegetation types, some sites had all cameras placed in one transect to ensure all cameras were placed within the specific vegetation type. Small mammal cameras were deployed every 30 m to reduce the likelihood of detecting the same individual animal with multiple cameras. Prior to starting the survey, we conducted several nights of trial experiments to determine the ideal spacing between the camera and the bait as well as camera settings that would enable identification of small mammal species (Rowcliffe et al. 2011). We placed the cameras on trees, 20 cm off the ground and 1.5 m from the bait. Plastic fence posts were used in the beach grass where trees were absent. We set all the cameras facing north to reduce direct sunlight. Cameras were set to operate 24 h per day for at least 7 consecutive nights per site. Bait consisted of a mix of sunflower seeds, peanut butter, oats, commercial rabbit food and strawberry jam (approximately a tablespoon of each) which was placed within a pocket (20 x 10 cm) made of metal mesh material and nailed to the ground. A wooden stick with reflective tape displaying 5 cm was placed next to the bait to aid in size estimation of animals. We angled the cameras slightly downward by placing a stick behind the camera to ensure the area in front of as well as behind the bait was in view. The sensitivity level was set to high to maximize the likelihood of detecting small fast-moving animals. We cleared branches and ground cover vegetation in the range of the camera view to allow for clear photographs and to minimize false triggering. We set the cameras to take three consecutive photographs per trigger and a capture delay of 1 minute to prevent the memory cards (8 GB) from filling up and to avoid excessive numbers of photographs of the same individuals. We used Bushnell 2015 Aggressor No-Glow with a black led night vision flash instead of the more commonly used infra-red or white flash which have been shown to affect animal behavior and thus potentially causing detection bias (Meek et al. 2014).

Carnivore surveys

The carnivore setup followed the protocol by Moriarty et al. (in press) with the exception that the cameras were left out for at least 7 days and we did not re-bait within that period. We placed the baited carnivore camera at least 75 m away from the small mammal cameras to ensure the carnivore bait or the presence of predators would not influence the detection of small mammals. We also setup one un-baited trail camera at least 50 m away from the baited carnivore camera. To increase our sample size we combined our carnivore data with recent survey data from the area by Moriarty et al. (in press). We only combined data from sites that were separated by at least 500 m from our own carnivore and trail cameras.

Data analysis

We used Google Picasa to classify remote-camera images with information regarding species, vegetation type, and bait status. Shrews and voles were difficult to identify to species so we pooled them at the genus level for shrews (Sorex) and subfamily for voles (Arvicolinae).

Since our study was mainly focused on mammals, all non-mammalian animals detected such as birds and amphibians were not identified to species. We excluded photographs where species identification was not possible mostly because only part of the animal was visible. If the bait was no longer in view due to branches falling in front of the camera or animals moving the camera, the camera was considered ‘inoperable’ and the images were excluded.

We extracted the tags for each photo along with its associated metadata using Exiftool

(Exiftool 2016). All additional data processing was done using the statistical software R, version 3.2.3 (R Development Core Team 2015).

We used the proportion of cameras that detected a species in a site as an index of abundance for small mammals commonly used in other non-invasive surveys (Connors et al. 2005;

Harrington et al. 2008; Drennan et al. 1998). It was therefore important that we used a relatively large number of cameras per site (n= 20) to be able to capture the variability within

12 the site for a reliable inference. Abundance indices from camera trapping have previously been shown to be correlated with density estimates from live-trapping and line transect surveys (Villette et al. 2015; O’Brien et al. 2003). Thus, we assumed that the number of cameras that detected a species was related to the density of the species in the area such that if a species was locally abundant it would be detected by several cameras (Meyer et al. 2015;

Rendall et al. 2014).

We used the proportion of cameras (e.g. n out of 20) detecting each species within seven days as an index of abundance. To assess differences in relative abundance across vegetation types, we compared the mean abundance index (± standard error) among our four habitat types to test whether each species was significantly more abundant in a particular habitat on the dunes

(beach grass, deflation plane, ericaceous forest) relative to the interior forest sites. We calculated the total relative abundance of the vegetation types by summing the relative abundance of each prey species per site.

We used separate baited carnivore and trail camera sets to indicate presence/absence of martens and other carnivores within the vegetation types. If a carnivore species was detected incidentally by a small mammal camera but not by the paired carnivore or trail camera it was counted as present.

We used a binomial generalized linear mixed model (GLMM) to identify the effects of vegetation type (fixed effect) on the relative abundance of each potential prey species

(presence in k out of N cameras). We included a site-level random effect to account for overdispersion of our binomial data. We used a generalized linear model (GLM) to model the effects of vegetation type on presence/absence of carnivores.

We examined trends in prey and mesopredator diversity among the vegetation types using the

Simpson’s index and then applying a reciprocal conversion. The inverse Simpson’s index is more intuitive than Simpson’s index because it represents the number of equally abundant

13 species rather than an entropy (Hill 1973). We modelled the effects of vegetation type on diversity using GLMs.

We corrected for potential increase in type 1 error associated with multiple comparisons by using Tukey honest significant difference (HSD) for all tests of significance among vegetation types. Statistical significance level for all tests was set to 0.05.

Results

Vegetation structure

The ericaceous forest had the highest amount of understory structure with total shrub cover, fruit-producing shrub cover being significantly higher compared to the other vegetation types

(Fig. 2a). Understory structure did not differ significantly between the interior forest and deflation plain but was significantly lower in the beach grass (Fig. 2a,c). The overstory structure was the highest in the interior forest with higher canopy cover, tree height and larger tree DBH (Fig. 2a,b,d).

Figure 2: We surveyed vegetation types in areas where we camera trapped small mammals and carnivores between October and December 2015. Mean values (±SE) of vegetation structure within the four vegetation types. Vegetation types are arranged from closed canopy to open beach grass (left to right). (A) represents cover (%) of canopy, shrubs, fruit-producing shrubs and ground cover. (B) and (C) shows the mean tree and shrub height (m) respectively and (D) depicts the mean DBH (cm) among the vegetation types. Different letters denotes significant difference.

Diversity and relative abundance

We obtained 657,865 photographs of animals over 6,636 camera nights. Approximately 0.4% of the photos resulted from inoperable cameras and another 0.4% were not of sufficient quality for species identification. We detected 26 mammal species (not including the number of shrew or vole species as they were only identified to genus and subfamily respectively).

Ten of these were carnivores with raccoon (Procyon lotor) and gray fox (Urocyon

15 cinereoargenteus) being detected the most frequently and American mink (Neovision vision) and mountain lion (Puma concolor) the least (Appendix 1). We detected Pacific marten in 45 camera points including in two previously unknown locations. Two invasive species, Virginia opossum (Didelphis virginiana) and black rat (Rattus rattus) were also detected.

The mean relative abundance and composition of small mammals varied among the vegetation types (Fig. 3). Deer mice (Peromyscus maniculatus) were detected at every site and accounted for 83% of the total number of photos. Bird species were incidentally captured and were the second most detected animal group (6%). Deer mice, birds, shrews and brush rabbits (Sylvilagus bachmani) were found across all vegetation types (Fig. 3). Mountain beaver (Aplodontia rufa) and California ground squirrel (Otospermophilus beecheyi) were only detected in the interior forest and beach grass respectively. The mean relative abundance of potential prey species was generally higher in the ericaceous forest relative to the interior forest, with Townsend’s chipmunk (Tamias townsendii) (P= 0.01), voles (P=0.01) and birds

(P=0.0002) being significantly higher (Fig. 3). In terms of total relative abundance of prey species, the ericaceous forest was significantly higher (p = <0.0001) (Fig. 4). In addition, the ericaceous forest was also significantly more diverse in potential marten prey species compared to the interior forest (p = 0.05), deflation plain and beach grass communities (p =

0.0003 and p <0.0001 respectively) (Fig. 4). There was no significant differences in diversity of prey species between the interior forest and deflation plain (Fig. 4). The four communities showed a similar trend in terms of total diversity (including carnivores) with the ericaceous forest being the highest (Inverse Simpson’s index: 8.55±1.58) followed by the deflation plain

(6.80±1.59), interior forest (6.58±1.06) and the beach grass being the least diverse

(4.06±1.35). The difference between the ericaceous forest and interior forest was significant

(p = 0.02).

Figure 3. We surveyed for small mammals in four vegetation types using camera trapping between October and December 2015. The bars shows mean relative abundance (defined as the proportion of cameras that detected a species) of mountain beaver (Aplodontia rufa), brush rabbit (Sylvilagus bachmani), California ground squirrel (Otospermophilus beecheyi), bushy-tailed woodrat (Neotoma cinerea), Douglas squirrel (Tamiasciurus douglasii), northern flying squirrel (Glaucomys sabrinus), black rat (Rattus rattus), Townsend’s chipmunk (Tamias townsendii), voles (Arvicolinae sp), birds (Aves) and shrews (Sorex sp) (±SE). Species plots are arranged based on average body mass (Verts & Carraway 1998) from heavier to lighter (left to right). Different letters denotes significant difference.

Figure 4. We surveyed for small mammals in four vegetation types using camera trapping between October and December 2015. Mean diversity (Inverse Simpson Index) and sum of total relative abundance of small mammals (and birds) (±SE). Vegetation types are arranged in order from lowest to highest mean diversity (left to right).

The mean proportion of cameras detecting mesopredators varied across the vegetation types

(Fig. 5). Virginia opossum, gray fox (Urocyon cinereoargenteus) and bobcat (Lynx rufus) were found in all vegetation types except beach grass. Pacific marten, raccoon and American mink were only detected in the ericaceous forest and deflation plain while western spotted skunk was only observed in the ericaceous forest and interior forest. Both the long-tailed weasel (Mustela frenata) and short-tailed weasel (Mustela erminea) were detected across all vegetation types (Fig. 5).

Figure 5. We surveyed for carnivores in four vegetation types using camera trapping between October and December 2015. We also included data from Moriarty et al (in press) to increase our sample size. The bars shows mean proportion of cameras (±SE) detecting the presence of mesopredators: Bobcat (Lynx rufus), raccoon (Procyon lotor), Virginia opossum (Didelphis virginiana), gray fox (Urocyon cinereoargenteus), western spotted skunk (Spilogale gracilis), American mink (Neovison vison), Pacific marten (Martes caurina), long-tailed weasel (Mustela frenata) and short-tailed weasel (Mustela erminea). Species plots are arranged based on average body mass (Verts & Carraway 1998) from heavier to lighter (left to right). Different letters denotes significant difference.

Discussion

We assessed the support for two hypotheses for why Pacific martens are restricted to a narrow strip of novel habitat on the coastal dunes. Our results support the hypothesis that prey abundance is higher in the dunes, with Townsend’s chipmunks, Douglas squirrels, voles, deer

19 mice, birds, California ground squirrels and brush rabbits all detected more frequently

(although not all were significant) in the dune forest (Fig. 3). In addition martens and the aforementioned species are known to forage on berries which were also more common on the dunes (Fig. 2) (Thompson & Colgan 1990). Several studies have measured the abundance and diversity of small mammals within the mature forests in Coastal Oregon (Hayes et al. 1995;

Suzuki & Hayes 2003; Gomez & Anthony 1998; Carey et al. 1999). Information on the mammals on the Oregon dunes is sparse and consist of life-history descriptions by Maser et al. (1981) and a species list provided by Wiedemann (1984). To our knowledge, no study has examined the relative abundance of small mammals on the dunes.

There may be several factors influencing small mammal distribution and abundance (Carey &

Harrington 2001). Our data support the hypothesis that the relative abundance and diversity of small mammals was higher in the ericaceous forest. This may be explained by the significantly higher shrub cover which has previously been established as an important factor in small mammal habitat selection (Suzuki & Hayes 2003; Carey & Johnson 1995).

We found similarities among each vegetation type, mainly with species we would expect to be generalists. For instance, all four vegetation types were dominated by deer mice, a highly abundant diet and habitat generalist (Fig. 3) (Dueser & Hallett 1980). The bird, vole and shrew groups were also found across all communities which is likely a result of the specific habitat requirements of different species within the groups (Fig 3). For example the creeping vole (Microtus oregoni) feeds primarily on grasses and forbs and may therefore be present in the beach grass while western red-backed voles (Myodes californicus) forage on hypogeous fungi often found within coniferous forests with abundant woody material (Maser et al. 1978).

Interestingly, the relative abundance of northern flying squirrels was higher in the ericaceous forest compared to the interior forest (Fig. 3). The northern flying squirrel is considered a

20 keystone species in coniferous forest ecosystems in the Pacific Northwest where it is an essential prey item of both terrestrial and avian predators (Maser & Maser 1988; Rosenberg et al. 2003). In contrast to our results, previous studies have found the highest densities of northern flying squirrels in older forests (Carey et al. 1999; Smith 2007; Carey et al. 2002).

However, Rosenberg and Anthony (1992) reported no difference between old and second growth forests and suggested food availability and risk of predation to be more influential in flying squirrel habitat selection. Other mycophagous (fungi-eating) species include the

Douglas squirrel and Townsend’s chipmunk (Carey et al. 2002). However, conifer seeds are also an important component of their diets (Carey & Johnson 1995). Several experimental studies have shown that the three squirrel species are food limited (Ransome et al. 2004;

Sullivan & Sullivan 1982; Carey et al. 1999). Despite the higher diversity and abundance of conifers within the interior forest, the relative abundance of both the Douglas squirrel and

Townsend’s chipmunk was higher in the ericaceous forest (Fig. 3). This may indicate a higher abundance of hypogeous fungi or other food sources on the dunes compared to the interior forest. Ericaceous species such as Gaultheria shallon, Rhododendron macrophyllum and

Vaccinium species form a wide variety of mycorrhizal fungi (Smith et al. 1995). An ectomycorrhizal ecology study within the Oregon dunes revealed that Pinus contorta seedlings from the dunes had a higher diversity of Rhizopogon species than the mature forest

(Ashkannejhad & Horton 2006). Rhizopogon species are one of the most common types of fungi consumed by small mammals in North America (Carey et al. 2002; Claridge et al. 1999;

Dubay et al. 2008). In addition, Carey & Johnson (1995) found a correlation between the presence of ericaceous shrubs and the abundance of Townsend’s chipmunks and northern flying squirrels. However, whether this relationship was due to the ectomycorrhizal production or fruits associated with ericaceous shrubs, or the shrub cover providing protection from predators is not fully understood.

Interspecific competition and predation are other factors known to structure mammal communities (Macarthur & Levins 1964). Competitive interactions are common between deer mice and voles (Lemaître et al. 2010) and among squirrel species (Carey & Harrington 2001).

A structurally complex habitat may mediate competition through promoting niche diversification, allowing similar species to coexist (Brown & Lieberman 1973). Habitat complexity has been correlated with both relative abundance of species (Carey & Harrington

2001), and species diversity (Pianka 1966). Thus, the simultaneously high relative abundance and diversity of small mammal species within the ericaceous forest may result from the complex understory providing both horizontal and vertical cover from predators as well as a variety of food resources (hypogeous fungi, berries, seeds) (Hayes et al. 1995). In addition, the high productivity (small mammals, birds and berries) and structural characteristics of the ericaceous forest and deflation plain may explain the coexistence of several omnivorous mesopredators (Fig. 5) (Evans et al. 2005). This supports the theory by MacArthur (1972) that more species can coexist in productive habitats where overlap in resource utilization is possible.

Similar to Moriarty et al. (in press), all our Pacific marten detections were from the dunes.

Interestingly, Pacific martens were detected more often in the deflation plain than the ericaceous forest albeit the lower relative prey abundance and protective cover. However, a high relative abundance of prey may not necessarily equal high prey availability to the predator (Petrunenko et al. 2016). The dense understory cover in the ericaceous forest may limit the hunting efficiency of martens. Habitat structure in the form of coarse woody debris have been shown to improve the hunting efficiency of martens (Andruskiw et al. 2008; Coffin et al. 1997). However, the dense sedge cover within the deflation plains is far from similar to any boreal marten habitat describe previously and comparisons are therefore limited in usefulness.

Our study suggests that martens are more flexible in their habitat use than previously believed, and that relative abundance of prey and understory habitat structure may be more important than forest age. Similar findings have occurred in other marten habitat studies in

Canada (Poole et al. 2004; Hearn et al. 2010). The higher structural complexity in the ericaceous forest and deflation plain may also provide security from known marten predators such as bobcat (Lynx rufus), coyotes (Canis latrans) and great-horned owls (Bubo virginianus) (McCann et al. 2010; Bull & Heater 2001) present in the area (Moriarty et al. in press).

It should be noted that we conducted an exploratory cross-sectional study during a relatively short time period. It is possible that the results would differ if the study was undertaken across several seasons. Another limitation of our study is the potential bias caused by varying detectability of species in different habitats (Sollmann et al. 2013). However, we believe that the results from this study may still have important implications for management of the coastal dunes and coastal forests in Oregon as well as providing hypotheses for future research. This study provides important indications that may partly explain why martens are inhabiting the dune forest which is important for management of conservation efforts. Further research is needed to increase the understanding of how prey, competitors and predators may influence the restrictive distribution of the Pacific marten in central coastal Oregon.

References

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Appendix 1

Appendix 1. All species detected and number of photos per vegetation type and species captured during camera surveys within the study area in coastal Oregon between October and December 2015. Species are listed alphabetically based on order and species name.

Vegetation type* Total # Order Family Common name Species name IF EF DP BC photos Carnivora Canidae Gray fox Urocyon cinereoargenteus 0 458 1049 0 1507 Felidae Bobcat Lynx rufus 45 0 24 0 69 Mountain lion Puma concolor 12 0 0 0 12 Mephitidae Western spotted skunk Spilogale gracilis 599 721 0 0 1320 Mustelidae Pacific marten Martes caurina 0 458 287 0 745 Short-tailed weasel Mustela erminea 30 54 15 15 114 Long-tailed weasel Mustela frenata 6 3 3 12 24 American mink Neovision vision 0 3 3 0 6 Procyonidae Raccoon Procyon lotor 0 704 2253 0 2957 Ursidae American black bear Ursus americanus 0 274 55 0 329 Cetartiodactyla Cervidae Elk Cervus canadensis 9 0 0 0 9 Black-tailed deer Odocoileus hemionus 297 36 576 36 945 Didelphimorpha Didelphidae Virginia opossum Didelphis virginiana 11005 1141 1014 0 13160 Lagomorpha Leporidae Brush rabbit Sylvilagus bachmani 252 1502 972 360 3086 Soricomorpha Soricidae Shrew species Sorex species 5134 5977 3915 63 15089 Rodentia Aplodontiidae Mountain beaver Aplodontia rufa 105 0 0 0 105 Castoridae North American beaver Castor canadensis 0 0 51 0 51 Cricetidae Bushy-tailed wood rat Neotoma cinerea 39 3 0 0 41 Deer mouse Peromyscus maniculatus 192524 165027 136151 54860 548562 Vole species Arvicolinae species 806 2378 1330 1296 5810 Erethizontidae North american porcupine Erethizon dorsatum 0 0 30 24 54 Muridae Black rat Rattus rattus 7 1449 399 0 1855 Sciuridae Northern flying squirrel Glaucomys sabrinus 875 3907 3 0 4785

California ground squirrel Otospermophilus beecheyi 0 0 0 477 477 Townsend’s chipmunk Tamias townsendii 2170 12703 235 0 15108 Douglas squirrel Tamiasciurus douglasii 1045 636 335 3 2019

*IF: Interior Forest, EF: Ericaceous Forest, DF: Deflation Plain, BG: Beach Grass