Understanding Seasonal Variation in Detection of Martens Using
Radio-Marked Individuals
William J. Zielinski1, Katie M. Moriarty2, Thomas A. Kirk1 and Keith M. Slauson1
1
2
USDA Pacific Southwest Research Station, Arcata, California 95521
Oregon State University, Department of Fisheries and Wildlife, Corvallis, OR 97331
Final Report to the Lassen National Forest
7 September 2011
SUMMARY
We sought to understand the effects of season of detection on the development of a habitat model
for the American marten on the Lassen National Forest (Lassen NF). Monitoring the effects of
land management practices requires a habitat model, and resulting map, that is an unbiased
representation of important habitat areas. However, previous research on the Lassen NF
revealed differences in the locations where martens are detected in summer and winter vs.
primarily in winter, leading to marked differences in the habitat maps that are developed from
these location data. The demographic structure (age, sex ratio) and residential status of martens
differs by season, which may explain some of this difference, especially if locations where
detections occur primarily in the winter include animals that are predominately young and
dispersing. Habitat models developed using the “winter only” locations may not represent the
most important habitat of breeding age animals, in particular females. We radio-marked 12
individual martens captured in areas where previous surveys have detected them primarily in the
winter (Swain Mountain [Swain] and Humboldt Peak [Humboldt]) and monitored their fates. In
doing so we tested the working hypothesis that areas where martens are detected primarily in the
winter include locations without year-round occupancy and detections in these locations occur in
the winter only because young, dispersing animals spend a short period of time assessing the
general habitat quality and, finding it lacking, either move on, die, or persist for unknown
periods of time but do not breed. These animals are much less likely to contribute to population
persistence and, thus, the habitat choices they make during their movements may be
unrepresentative of the resident breeding segment of the population. Ten of the 12 animals were
male and 50% of the individuals that could be aged were < 1 year old. Monthly survival rates
declined most during the period from July to September. There was no evidence, from either
telemetry or seasonal camera surveys, for pronounced changes in the size of the area used by
individual martens in winter versus summer. Camera surveys verified the same phenomenon
reported in previous surveys: that occupancy rates – adjusted for detectability – were
significantly higher in winter than in summer. As of July 2011 (14 - 18 months after their initial
captures) only 3 of 12 animals were alive, but each was located within the boundaries of their
original survey grid. The majority of the mortalities were of animals < 1 year of age. Martens in
our study areas exhibited marked seasonal variation in occupancy and were primarily males and
young animals that exhibited increased mortality during the summer. Importantly, our sample
included only 1 breeding-age adult female. Collectively, these characteristics are indicative of
relatively poor habitats, where breeding is rare, leading to the conclusion that they are likely
“sink” habitats, where death and immigration rates exceed birth and emigration rates. We
conclude that our working hypothesis was supported by our data on age and sex structure and
mortality. Habitat identified on the basis of surveys conducted during the summer is the most
likely to identify areas that are important to the marten population, not only because it is less
extensive than habitats occupied during the non-breeding season, but also because it is known to
support adults during the parturition and breeding season. Including in the development of
habitat models survey locations where martens are primarily detected only in winter (the nonbreeding season) may confound the important relationship among the habitat predictors
associated with year-round residence. This is because many of these locations will be sites where
juveniles or males occur during the non-breeding season only. However, some of these
detections will also be females and we don‟t know yet if their ranges increase in winter. Thus,
new work should focus on the spatial dynamics of breeding-age males and females and the
potential seasonal variation in home range size and habitat use. Ideally, our ultimate goal should
be to distinguish two classes of suitable habitat: “suitable for reproduction” and “suitable for
non-reproductive activities”. Future research will attempt to achieve this standard.
INTRODUCTION
The American marten (Martes americana), a member of the weasel family (Mustelidae),
is associated with structurally complex, high elevation late-seral coniferous forests (Spencer et
al. 1983, Buskirk and Powell 1994, Payer and Harrison 2003, Zielinski et al. 2005). Martens are
considered sensitive to habitat fragmentation (e.g., Bissonette et al. 1997), but they can tolerate
some forms of forest management and other disturbances. Lassen National Forest (NF) has a
rich history of research and monitoring for sensitive species, including martens. Marten habitat
use patterns were investigated at Swain Experimental Forest and vicinity (Ellis 1998), landscape
habitat suitability was modeled throughout the Greater Southern Cascade area (Kirk 2007, Kirk
and Zielinski 2009) and connectivity analyses using Least Cost Path modeling (LCP) have been
undertaken (Kirk and Zielinski 2010). Marten distribution has also been described for the nearby
Lassen Volcanic National Park (Perrine 2005). We have also conducted systematic and rigorous
camera surveys that demonstrate significant seasonal effects on occupancy (Zielinski et al.
2009).
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The Lassen NF is part of a greater land management area defined within the HergerFeinstein Quincy Library Group (HFQLG) Forest Recovery Act. This pilot project encompasses
over 10,000 km2 within 3 national forests in northern California: the Lassen, Plumas, and
Sierraville Ranger District of the Tahoe National Forest (Davis and King 2001). The HFQLG
Forest Recovery Act supports collaborative projects which aim to balance timber management
and local job stability with long-term conservation of natural resources, including wildlife
sustainability and watershed restoration. Essentially, this plan outlined a 5-year (now 15-year)
design to intensively monitor the effects of strategic timber extraction, largely focused on fuel
reduction for fire barriers, ecosystem processes, and wildlife species (HFQLG-Revision 2008).
Given the HFQLG‟s goals, and the previous work on martens in the area, Lassen NF is an ideal
location to further assess the effects of management activities on martens.
Monitoring the effect of HFQLG projects on martens requires a habitat model that
faithfully represents important habitat areas. However, martens are detected at different
locations during different times of the year, particularly when the results of summer surveys are
compared with those conducted during winter (Kirk and Zielinski 2009, Zielinski et al. 2009).
These seasons probably correspond to times of the year when population sizes differ (e.g.
significantly larger in the fall/winter due to the presence of young-of-the-year), food is
differentially available (e.g., more abundant in summer), and whether reproduction is occurring
(summer) versus dispersal (winter). Seasonal differences in abundance or behavior have
implications for the results of landscape habitat suitability modeling because most models for
small carnivores are constructed using detection / non-detection data from track plate or camera
surveys (Carroll et al. in press). However, empirical landscape habitat suitability models created
using locations where martens are detected primarily in the winter identify very different habitat
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areas than similar models created using survey data from summer or from surveys where martens
were detected in summer and winter (Rustigian-Romsos and Spencer 2010).
Typically, adult breeding martens have strong fidelity to their home range for their
lifetime and do not engage in significant seasonal changes in home range (Phillips et al. 1998,
Gosse et al. 2005, Hearn 2007). Thus, habitat associations created from summer survey data
represent habitats chosen as home ranges by adults for the purposes of reproduction. Some of
the animals detected in winter are undoubtedly dispersing juveniles, which are much less likely
to be selective about the areas they traverse when searching for their ultimate home range. This
may result in detections that occur in a wide variety of habitats, some of which happen to be
traversed during the course of searching for suitable sites. Conversely, the greater variety of
conditions where martens are detected in winter may instead occur because all martens are
simply more detectable at baited stations during the winter when, compared to summer, food is
more limiting and martens make extensive forays as they are attracted to carrion (including baits
used at detection stations). We also don‟t know whether martens in the southern Cascades
increase their home ranges in winter, which may account for some of the increase in areas where
martens are detected in winter. Studies elsewhere on martens are inconsistent in their findings
on seasonal change in the size of home ranges (e.g., Phillips et al. 1998, Fuller and Harrison
2005) but Ellis (1998), who conducted work in our study area, did not reveal substantial seasonal
changes in home range size in either males or females. Male martens, however, make prebreeding and breeding-season forays to new areas (K. Slauson, pers. obs., K. Moriarty, pers.
obs).
The development of a credible habitat model is critical to achieving the goals monitoring
questions that are mandated to be answered by HFQLG. One question, in particular, asks for a
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consistent definition of suitable habitat for martens; a goal that was highlighted by the Pinchot
Institute‟s recommendations (Anonymous 2008). Achieving this goal will not be possible until
we can understand the modeling implications of using locations from different times of the year,
when they represent different activities and perhaps different cohorts of individuals. Our
previous work on the Lassen NF has demonstrated that martens have significantly lower
occupancy rates during the summer (Zielinski et al. 2009), despite the fact that they are also
statistically less detectable at camera stations during the summer (most likely due to the removal
of bait by black bears [Ursus americana]). Our central working hypothesis, based on
preliminary data, is that the areas where martens are detected primarily in the winter include
relatively poor habitat that is only occupied in the winter because young, dispersing animals
spend a short period of time assessing the habitat quality and, finding it lacking, move on or die.
If this is true, including these observations in the development of an empirical habitat model may
be misleading. Thus, understanding the reasons for seasonal differences in occupancy and –
most importantly – determining how the season of data collection affects the habitat model that
is created, is an important task.
Intensive monitoring of the fate of individual martens, via radio-telemetry, is necessary to
understand the cause of the seasonal change in occupancy pattern. To achieve this objective, we
radio-marked martens in locations on the Lassen NF where seasonal variation in detection was
greatest, the Humboldt Peak and Swain Mountain areas (Zielinski et al. 2009). These locations
also are predicted to have relatively poor habitat (Kirk and Zielinski 2009, Rustigian-Romsos
and Spencer 2010). We assume, for the purposes of this paper, that areas identified by these
previous modeling efforts to have high probability of detection are areas with the best quality
habitat and areas that have the lowest probability of detection area areas with the poorest quality
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of habitat. By capturing and collaring animals at Humboldt Peak and Swain Mountain in the fall,
and monitoring them through the following summer, we could determine whether they persisted
in these regions throughout the winter and into the summer when they establish breeding home
ranges, or whether they ultimately die there or seek more favorable habitat elsewhere. Our
previous survey data were too coarse to answer these questions. We also deployed remotelytriggered digital cameras in winter and summer to determine how detectable martens were by
season. The primary objective of this work is to understand the fate and characteristics of the
American martens that appear to occur in specific regions on the Lassen NF primarily during the
winter. This information will help us understand how annual variation in detection and
occupancy can affect the development of habitat models, and other aspects of conservation
planning for this species that are affected by changes during the annual life cycle of martens.
METHODS
Study Area
Lassen NF is located in the mountains of northeastern California at the junction of the
Southern Cascades and Sierra Nevada ranges. Our work focused on areas that were a subset of
previous marten survey grids (Zielinski et al. 2009) at elevations ranging from 1500-2100 m
(Fig. 1). Primary vegetation types include red fir (Abies magnifica), white fir (Abies concolor),
lodgepole pine (Pinus contorta) and subalpine conifer forest types (Mayer and Laudenslayer
1988). Sierran mixed conifer, Douglas-fir (Pseudotsuga menziesii), montane hardwood,
montane hardwood conifer, ponderosa pine (Pinus ponderosa), Jeffery pine (Pinus jeffreyi) and
chaparral vegetation types are also present, but more widespread at lower elevations. Seasonal
and perennial streams occur in both study areas, wet meadows are also common landscape
features. The regional weather pattern is typical of California‟s Mediterranean climate with cool,
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wet winters followed by summer growing seasons that are hot and dry. Precipitation ranges from
50 – 203 cm with most occurring as snow above 1800 m (Bailey et al. 1994).
Marten Trapping and Processing
We set traps within two of the three previously surveyed grids: Swain Mountain (Swain)
and Humboldt Peak (Humboldt) (Fig. 2) (Zielinski et al. 2009). We did not trap, or do any new
work, within the Mineral grid, located primarily within the Lassen Volcanic National Park (Fig.
1, 2). Initial trapping and radio-marking was focused within the perimeter of the two grids, each
occupying approximately 15,000 ha. Each grid was comprised of 20 sites, about 3-km apart
(Fig. 1, 2), which were previously used to conduct detection surveys via remote camera
(Zielinski et al. 2009). We set traps at all previously surveyed stations within the Humboldt and
Swain grids and at other locations within each grid where previous habitat modeling (Kirk and
Zielinski 2009) predicted marten reproductive habitat.
We used Tomahawk live-traps (model 108) that were modified to decrease potential
injury and stress to the animals. A piece of masonite was fitted onto the bottom of each trap to
discourage digging. Wooden „cubby boxes‟ were attached to the back of each live trap which
provided trapped animals with a dark enclosed area for refuge (Wilbert 1992). It also contained
pieces of fleece and conifer boughs for warmth. Martens were chemically anaesthetized using
either ketamine-diazepam or ketamine-midazolam. The size of the animal was visually
estimated and given an appropriate dose, based on previous marten research (Kreeger and
Arnemo 2007).
Processing included attachment of a VHF radio-collar, the collecting of morphometric
data, and tooth removal. Each radio collar had a unique visual pattern consisting of three 1-cm
reflective bands to aid in individual identification in remote-camera photographs (3M®
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ScotchliteTM Very High Gain Reflective Sheeting 3000X). We collected Dacron swab samples at
the nasal, ocular, and rectal regions for disease testing, we collected hair from the nape and tail
for genetics or isotope analysis, we measured standard features (neck circumference, total length,
tail length, fore and hind foot length and width), and we took ventral photographs. The vestigial
first upper premolar was removed for cementum annuli ageing (Matson 1981, Bodkin et al.
1997). Our trapping and handling protocol was authorized by the Oregon State University
Institutional Animal Care and Use Committee (ACUP permit 3944) and the California
Department of Fish and Game (Scientific collecting permit 803099-01; Memorandum of
Understanding).
Locating Martens and Estimating Areas of Use
We attempted to relocate each radio-marked individual once per week using radio-signal
triangulation. We used Yagi three-element directional antennas and R-1000 telemetry receivers
(Communications Specialists, Orange, CA) to obtain these locations. To maximize accuracy we
attempted to triangulate from at least 3 locations, a minimum of 20° apart, in a period less than
20 min (Powell 2000, Millspaugh and Marzluff 2001). Approximately once per month we also
attempted to locate each individual in a resting location. During these attempts the signal was
approached on foot until, typically, it could be received without an antenna. This usually meant
that we were within 20 m of the animal. Once in the rest site area, we attempted to narrow down
the marten‟s location to the specific structure used (e.g. live tree, snag, log).
All locations, resting sites, triangulations and capture locations were used to estimate 3
versions of the area used by each radio-marked individual: one that includes all locations, from
the initial capture in the fall of 2009 to the conclusion of the study on 15 September 2010 (“total
use area”), a second that reflects only those locations from the period beginning 2 weeks before
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and extending to 2 weeks after the winter camera survey (“winter survey use area”), and a third
that reflects only those locations from the period beginning 2 weeks before and extending to 2
weeks after the summer camera survey (“summer survey use area”). Each area was a 100%
minimum convex polygon, created by joining the perimeter locations for each individual. When
a perimeter location was derived from a triangulation, the outside margin of its error polygon
(estimated using LOAS; Ecological Software Solutions) was used to denote the location. Note
that we refer to these as “use areas” because we did not believe that we would be collecting
sufficient information to denote an animal‟s home range.
Estimating Survival Rates
We estimated the monthly survival rate for martens that had been radio-collared using the
Kaplan-Meier procedure, which allows for periodic entry of animals (“staggered entry”) into the
analytical framework (Pollock et al. 1989). The survival function is the probability of an animal
in a population surviving t units of time (in our case, t = months) from the beginning of the
study. Each month, from September 2009 to September 2010 we accounted for the status of our
sample animals by recording their entry into the marked population and their current status
(alive, dead or censored). A “censored” individual is one whose status is unknown, which occurs
most often when a radio-collared animal‟s collar has malfunctioned or an animal has emigrated
from the study area.
Camera Surveys
Camera surveys were conducted to determine the likelihood of detecting radio-marked,
and any other, martens within each grid. We conducted the first camera survey in the winter (12
January – 21 February 2010 in Swain and 1 March – 9 April 2010 in Humboldt) and the second
camera survey in the summer (8 July – 5 August 2010 in Swain and 10 August – 9 September
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2010 in Humboldt). Several cameras in each survey were run a few days beyond the end dates to
compensate for malfunctioning cameras or those disturbed by black bears. We used standard
camera methods (Kays and Slauson 2008) to replicate similar surveys that were conducted
earlier, during summer 2007, winter 2007/08 and summer 2008 (Zielinski et al. 2009). This
time, unlike the previous surveys, we were able to determine where martens occurred during the
survey period and they were individually marked to be identifiable in photographs. We used
both Cuddeback Excite (Non Typical Inc., Green Bay, WI) and ScoutGuard™ SG550 IR
cameras (HCO Outdoor Products, Norcross, GA) and set them as close to each of the previously
used survey points as possible. Two live trees spaced 2-4 m apart were chosen, with the camera
on one tree facing north towards the second (bait) tree. We used as bait whole chickens
(approximately 2.5 kg) that were staked to the tree at 1-2 m height. We also used a commercial
scent lure (Gusto, Minnesota Trapline Products, Pennock, MN) which was placed on a nearby
branch. Cameras were set on high sensitivity with a 1-min delay. We serviced cameras weekly
to download images, check camera functions, and replace bait; 4 service occasions resulted in an
approximately 4-week (28-day) sample period for each camera. If, however, there was evidence
that a camera had not been functioning properly during the previous week (e.g., due to weather
or black bear disturbance) the survey period for that camera was extended to attempt to achieve
28 effective survey days.
To achieve an objective related to a separate project on the genetics of martens and the
Sierra Nevada red fox (Vulpes vulpes necator), we placed a “hair snare” device (Kendall and
McKelvey 2008, P. Figura, pers. comm.) at each station after it had received its first visit by
either a marten or a red fox. The device used 5 gun-cleaning brushes as snares, each of which
was attached about 5 cm apart along a 25cm strip of corrugated plastic that was tacked to the tree
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below the bait.
Estimating Probability of Detection and Occupancy
Detection of a target species is never certain, but the probability of detection can be
estimated by various methods, and can then be used to adjust occupancy and to interpret seasonal
patterns. The probability of detecting a marten during a given sampling period varies with a
number of factors and is represented by p, the probability for each sampling occasion (sometimes
referred to as a“visit”), and P, the probability of detection over all sampling occasions in the
sampling period (MacKenzie et al. 2006). We estimated probability of detection using the
maximum likelihood method (MacKenzie et al. 2006). This approach uses the pattern of
detections, represented by detection histories, to estimate the probability of detection assuming
the target species is present. Maximum likelihood estimators are the estimated values for
parameters given a set of empirical data. A detection history is such a data set and is the pattern
of detections over the survey period. For example, a detection history of “100100” represents a
survey period with 6 sample occasions where a marten was photographed during the 1st and the
4th occasion. We used program PRESENCE (version 2.4, Hines 2006) to estimate P using the
multi-season analysis and the model parameterization that estimates an initial occupancy rate,
extinction, colonization, and detection probability. “Extinction” (έ) and “colonization” (γ) are
transition probabilities used in occupancy estimation (MacKenzie et al. 2006:206) that were
adapted from the metapopulation literature (Levins 1969). Extinction is the probability that a
sample site has changed from “detected” to “not detected” from one survey season to the next,
and colonization is the opposite pattern. They are not estimable for the initial survey season
because there is no data preceding this year. The camera data were aggregated by 24-hour
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period (from 0000 to 2400 h) yielding a 28-day detection history for each camera station. Thus,
the sampling occasion, for the purpose of estimating P was a 24-hour period.
Previous analyses discovered that the presence of bait had a significant effect on
probability of detection in our study area (Zielinski et al. 2009). As with previous surveys, the
relatively long interval between checking and reprovisioning a station (1 week) made it likely
that bait removed by a bear early in the interval would be absent during the remainder of the
survey period. As such, we investigated the effect of the covariate “bait” (present or absent), as
determined by photographs and field notes, on the probability of detection. Preliminary analysis
included examining when detections occurred within the 28-day sampling period for each
season, and how days added to the 28-day protocol may have affected detection probability.
Based on this information, custom models were developed in PRESENCE to estimate how
probability of detection changes over the course of the 28-day survey duration and by the
presence/absence of bait. Patterns observed in the change in the proportion of detections over
the survey duration were used to create several covariates that reflect variation in “time”, where
estimates of p – the daily probabilities of detection – were modeled as being equal within each
week (1 through 4), but different across weeks. Study area effects were modeled on both p and
occupancy (ψ; see below) and used the variable “Swain” to determine if either probability
differed significantly between the two study areas. Two “season” variables were created,
representing winter and summer (season) or to differentiate winter (December – February),
spring (March) and summer (June – August). Models were compared using Akaike‟s
Information Criteria (AIC) (Akaike 1973) with lower AIC values indicating better fitting models.
The AIC weight (w) was also calculated for each model, providing a measure of model fit
relative to other candidate models.
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Estimates of P generated in this fashion can also be used to adjust estimates of occupancy
(ψ) (MacKenzie et al. 2006). Naïve estimates of ψ are simply the proportion of sample sites in
each grid where a marten is detected (i.e., the observed proportion). If P is low, then the final –
adjusted – estimate of ψ would be substantially higher than its naïve estimate. This occurs when
a detection protocol is not very effective at detecting animals that are present. If, on the other
hand, the estimate of P is high (i.e., most animals that are present are detected) then the adjusted
estimate of ψ would be similar to the naïve estimate of ψ. Season-specific estimates of the
probability of detection (P)were compared using paired t-tests and occupancy estimates (ψ )
were compared using McNemar‟s test to assess the difference between 2 proportions (McNemar
1947).
RESULTS
Martens and their Fates
We trapped 49 sites (401 trap nights) at Humboldt and 58 sites (369 trap nights) at Swain
(Fig. 2) during two trapping sessions: October – November 2009 and March – April 2010. We
captured, processed and attached radio-collars to 12 martens, the majority of which were males
(n = 10), 5 at Humboldt and 7 at Swain (Table 1). The majority of animals were < 2 years of
age, with 50% of the animals for which age could be estimated, < 1 year of age (Table 1). By
the time we had concluded monitoring the animals, on September 23, 2010 (5-10 months after
they had been originally captured) 7 were known to be alive (58.3%), 3 were known to be dead
(25%) and 2 were of unknown fate (Fig. 3). Monthly survival estimates were high from
November through June (91.1%) but then decreased abruptly from July (70.9%) to September
(49.6%) (Fig. 4). Two animals (M04, M11) were killed by a predator, most likely a raptor, and
one died of unknown cause (M06).
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Use Areas
We collected an average (SD) of 35.6 (17.6) locations per individual during the entire
sampling period, but 6.5 (3.2) and 9.4 (2.0) during the winter and the summer camera period,
respectively. The majority (67%) of the total number of locations were triangulations, with
accuracies that averaged 128 m (171.8 m). The balance of locations were from camera stations,
incidental captures, aerial telemetry, or mortality locations.
During the period that they were alive and being monitored, most of the radio-collared
animals in the Humboldt area were located within the boundaries of the original camera station
grid (Fig. 5). These individuals were not all available to be detected during both winter and
summer (due to collar loss or death; see Fig. 3), but 4 of the 5 were on the grid and available
during the winter camera survey. In contrast, a number of animals at the Swain area were found
off the grid on multiple occasions, almost always to the west (Fig. 5). This behavior includes
periods of time when camera stations were operating in summer (the red polygons; see especially
M08 and M10) and when camera stations were operating in winter (the blue polygons, see
especially M09) (Fig. 5).
Total use areas ranged from 3.0 – 56.1 km2; males averaged 16.0 and females 0.9 km2.
Adult male use areas averaged 9.3 and adult females 0.9 km2. Juvenile males had the 2 largest
areas (M02, M09) but also two of the smallest areas (M07, M11; Table 1, Fig. 5). Size of area
was not related to number of locations; one of the smallest total use areas had the greatest
number of locations (i.e., F02) and one of the largest areas was a male with the fewest number of
locations (i.e., M09) (Table 1).
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The number of locations verified during summer and winter were relatively few, but
casual inspection indicates that summer and winter use areas were not substantially different in
size (Fig. 5).
Camera Surveys
Camera surveys in winter and summer produced results that are consistent with the
seasonal pattern demonstrated for Humboldt and for Swain during previous years. A relatively
high proportion of cameras had marten detections in the winter and a relatively low proportion
had detections in the summer (Fig. 6). The seasonal cycle is dampened, somewhat, in 2009 and
2010, but the pattern is still present with the seasonal change in occupancy especially obvious at
Humboldt (Fig. 6).
At Humboldt in winter, martens were detected at 11 of 20 camera stations (naïve
occupancy rate = 55.0%). Radio- marked martens (M01, M03, M04) were detected at 3 different
stations and unmarked martens at 10 different stations (Fig. 5, 7). At the Swain grid in winter
martens were detected at 4 of 20 camera stations (naïve occupancy rate = 20%); marked martens
(M06, M07 and M09) at 2 stations (Fig. 5) and unmarked martens at 3 stations (Fig. 7).
At Humboldt in summer, martens were detected at 2 camera stations (naïve occupancy
rate = 10%). A radio-marked marten was detected at 1 station and an unmarked marten at 1
station (Fig. 5, 7). At the Swain grid in summer, martens were detected at only 1 camera station
(naïve occupancy rate = 5%); radio-marked and an unmarked marten were both detected at this
single station (Fig. 5, 7).
None of the marked martens that were detected at a camera were detected at more than
one camera, regardless of season. Thus, the camera detections provide no indication that
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individuals used larger areas during winter than during summer, consistent with examination of
the use areas for each season (Fig. 5).
A surprising number of unmarked marten detections occurred (Fig. 7). Like the marked
martens, there were more stations with unmarked marten detections during winter (left
hemispheres; Fig. 7) than in summer (right hemispheres). These martens were either present on
the grids and not captured during one of our two capture sessions, or they arrived at the survey
grids after we had concluded our trapping efforts. Only some of them could be identified (via
their snared hair) but they contributed to the detection history data for each survey, and thus
contributed to estimates of probability of detection.
Probability of Detection and Adjusted Estimates of Occupancy
Review of the relationship between the proportions of detections per survey day across
the entire survey duration (for the first summer and winter; 2007-2008), revealed that survey
days that were added after the standard 28-day survey period – to compensate for inoperable
survey days – varied substantially, compared to the first 28-day period (Fig. 8). Therefore, for
the purposes of estimating probability of detection for each season, for all years, we truncated all
datasets at 28 calendar days.
For the multi-season occupancy analysis, a single top model clearly outperformed all
other candidate models (Model 1, AIC weight = 0.76, Table 2). This model included custom
parameters, including week-specific estimates for probability of detection (p), seasonal variation
in p for the winter and summer seasons, and the effect of the presence of bait. The data
suggested that probability of detection was different for three portions of the survey duration:
week 1, weeks 2-3, and week 4, increasing during the survey duration (Table 3). Estimates of
detection probability for each survey week were nearly double during the winter versus summer
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(Table 3), however probabilities of detection were so high that, when compounded over the 28day survey period, winter and summer surveys both had overall estimates of P that were >0.99
(Table 3). These estimates apply to stations where the complete 28-day protocol was achieved,
when survey days are lost due to camera inoperabilities or bait is not present, station-specific
estimates of P were lower, but in all but the most extreme cases by less than 5%.
The top model also included the bait status variable (Table 2), indicating that the
presence or absence of bait helped account for additional detection heterogeneity. Bait was
removed primarily during the summer, when the most frequent culprit, the black bear is active.
In the summer 2010 surveys 28.5% (SE = 0.03) of the survey days had bait missing whereas in
the winter of 2010, 22.7% (SE = 0.03) of the surveys days had bait missing. Most of the missing
bait in the summer was due to visits by bears, whereas most of the missing bait in winter
occurred after a marten had removed it (bears were in dens and unavailable in winter). The
presence of bait resulted in a >170% increase in the likelihood (odds) of detecting a marten after
accounting for the effects of survey week. This reflects the high incidence of detections that
occur when bait was replenished.
Adjusted Estimates of Occupancy
On the basis of the best-fitting model, derived estimates of occupancy (ψ) varied
significantly by both season and during the winter season by study area (Table 3). These
estimates were significantly different (McNemar‟s χ2 = 30.9, df = 1, P < 0.0001) indicating that
despite the greater detectability in winter, the adjusted estimates of occupancy still differed
significantly between summer and winter. Martens were likely to be, on average, present at a
total of only 5% (2 of 40 total stations) in summer, but 48% (19 of 40 total stations) in winter.
17
Furthermore, occupancies were significantly greater in Humboldt compared to Swain, but only
during the winter (Table 3).
The transition probabilities for colonization and extinction were best modeled using
season and study area effects. As expected, significant differences were present between seasons
in both probabilities (Table 3). Colonization transitions only occurred from summer to winter,
while extinction transitions occurred in both seasonal transitions but were most pronounced from
winter to summer (Table 3). This means that stations without detections in summer were much
more likely to have detections in winter, than vice versa.
DISCUSSION
Martens occupied our survey grids at higher rates during the winter than the summer, a
pattern verified over a 4-year period. They demonstrated 48% occupancy, on average, during the
winter but only 5% of stations were estimated to be occupied during the summer. The higher
occupancy in winter was statistically significant, despite the fact that martens are easier to detect
during winter. Thus, the diminished probability of detection in summer was statistically
overwhelmed by the fact that martens more commonly occur in the study areas during winter
than summer. Sample sites that were without detections in summer were significantly more
likely to have detections in winter (i.e. significant “colonization” of survey sites), but these same
sites were also significantly less likely to stay occupied during the subsequent summer season
(i.e. significant “extinction” of sites). Because our sampling methods were consistent across
years and seasons (consistent survey locations and consistent detection devices) the difference in
occupancy between winter and summer was a function of marten population dynamics or
behavior, rather than variation in survey methods or effort. Thus, our most recent survey seasons
18
– winter 2010 and summer 2010 – confirmed and reinforced the seasonal pattern of occupancy
that we had observed previously, with less comprehensive data.
These general results are quite similar to the results of research on probabilities of
detection and occupancy for the American mink (Neovison vison) (Reynolds et al. 2010) in
which probabilities of detection were consistently lower in summer than in fall/winter, yet
average occupancy estimates did not differ between seasons. Thus, although seasonal
differences in detectability may be a common phenomenon in carnivores (see Hackett et al. 2007
and references therein), this means that there can, and typically are, more individuals available to
be detected in winter than in summer. This is not too surprising given the general ecological
principle that there are insufficient resources for all the young of the year that are produced by a
species to survive the winter and reproduce. Annual cycles of abundance in temperate species,
and the forces of natural selection, generate the phenomenon of overabundance in fall and
density-dependent regulation of population size. We believe that these principles are ultimately
responsible for the seasonal patterns in occupancy we observed. This is probably most evident at
locations like Swain and Humboldt Peak which, over time, have become located at the margin of
the marten‟s geographic range in the southern Cascades.
Testing the Working Hypothesis
Our radio-collared sample of animals provided some insights as to the proximate reasons
for the seasonal pattern. The working hypothesis we sought to evaluate was whether the winter
occupants were young-of-the-year animals that were temporary residents captured while they
were in the process of finding adult home ranges (i.e., dispersers). Indeed, we verified that most
of the animals captured during the fall and winter trap sessions were young animals. Of the
individuals that could be aged via tooth cementum, 50% were < 1 year of age and only 1 of 13
19
individuals (7.7%) we captured was estimated to be > 2 years old. This appears to be a younger
age distribution than in the Lake Tahoe Basin (where the habitat is perceived to be higherquality; W. Zielinski, pers. obs.). In the Basin there is a similar proportion of young animals but
14 of 71 (19.8%) of the individuals were > 2 years old (K. Slauson, unpubl. data). Contrary to
our hypothesis, however, most radio-marked animals in this study resided on the study areas for
longer periods of time than expected. From 5 to 10 months after initial capture most (58.3%)
radio-marked individuals were still found within their respective study areas. Thus, we did not
confirm the limited tenure in the study areas that we assumed would occur if the habitat was
indeed so poor that individuals only needed to reside there long enough to assess, and then reject
it as a home range site.
The primary hypothesis also predicts an increase in mortality rates from the winter to the
following summer. This is because if the habitat was unsuitable, and the animals did not leave,
then we assumed that they were likely to die there. We believe that this prediction was realized
in that survival rates decreased precipitously from July to September. It does not seem likely
that if habitat conditions were favorable within the Swain or Humboldt study areas, martens
would succumb at the rates we observed. Unfortunately, our surveillance of individual animals
was not frequent enough to verify death (for 2 with unknown fate) or to verify cause of death for
the 3 carcasses that were recovered. Nonetheless, we were surprised that if the habitat was
significantly poorer in our study areas – compared to locations where seasonal patterns are less
profound – that the martens would not have either left the study areas sooner, or that mortality
rates didn‟t increase sooner. Unfortunately, we did not have a control area where survival rates
at our study areas could be compared to a location where habitat conditions are predicted to be
20
more benevolent. It is possible that martens that do not find an acceptable home range reside in
suboptimal habitat and wait until better sites become available.
Originally, we also entertained the possibility that the seasonal difference in detections
could be influenced by changes in the size of home ranges in winter compared to summer. We
did not have sufficient data to address this possibility, but a cursory examination of the summer
and winter use areas (Fig. 5) does not indicate marked differences. This agrees with others that
did not report seasonal differences in either movement distances or home range sizes in martens
(Phillips 1994, O‟Doherty et al. 1997, Gosse et al. 2005, Hearn 2006). Payer (1999) found
smaller home ranges in winter, contrary to what we would expect if the higher winter detection
rates were simply due to larger home ranges.
In sum, the 2 study areas exhibited the marked seasonal variation in occupancy reported
previously and they include male and juvenile-biased populations and increased summer
mortality. We also verified the presence of only a single reproductive-age female. All these
measures are indicative of potentially poor habitat, where martens are short lived (0-3 years) and
successful breeding and parturition is rare, leading to the conclusion that these may be “sink”
habitats, where death and immigration rates exceed birth and emigration rates (Pulliam 1988).
An Update on Fate of Individuals: July 2011
Although we truncated the official collection of data at the end of September 2010, most
of the surviving martens continued to be monitored for another purpose (K. Moriarty, unpubl.
data). Thus, at the time of this writing (July 2011), we had additional information on the status
of the subjects of our study. Of the 12 individuals that were originally captured (in the
fall/winter of 2009-2010), 3 of them (25%) – all males – were still alive and located within the
boundaries of the original survey grid. Two other males were alive, but also used areas outside
21
the original grid (i.e., both were Swain animals that were making significant use of the
wilderness area to the west of this study area). Two of the animals of “unknown fate” (Fig. 3)
were now presumed dead, given that substantial trapping effort in their former use areas did not
result in their capture, and two additional individuals (F02, M07) were either dead or presumed
dead. Thus, of the original 12 animals capture and marked, 7 (58.3%) were dead or presumed so
as of mid-July 2011 (from 14 to 19 months after their original captures). This updated view of
survival indicates that most of the 7 animals that died during the course of the study were young;
4 of the 7 (57.1%) were 0 or 1 year of age, the rest were 2 years of age.
In summary, the original data – and the updated information – indicate that the working
hypothesis was supported in terms of the expected age distribution and the mortality schedule.
However the hypothesis was not necessarily supported in terms of the period of time we
expected individuals to reside in the areas. This is because 3 of the original 12 animals (25%; all
males) continued to live exclusively within the boundaries of the grid – for greater than one year
– in an area that earlier work predicted to be of relatively low habitat value (Kirk and Zielinski
2009, Rustigian-Romsos and Spencer 2010) and which we expected few animals to use except
during dispersal. However, we reiterate that none of the demographic information we collected
suggests that the 2 study areas contribute to the populations in any significant way, particularly
due to the juvenile biased age structure, high mortality rate for juveniles, and the fact that we
captured only 1 breeding age female among the 12 individuals. Thus, the prediction of low
habitat value for these study areas was supported by the demographic characteristics of the
martens captured there.
Estimating Probability of Detection
22
The weekly probability of detection (p) was significantly less in the summer than the
winter, confirming our earlier work and the long-standing observation among marten trappers
and researchers that martens are more difficult to capture or detect during the summer than
winter (e.g., Campbell 1979, Soutiere 1979, Steventon and Major 1982, Bull et al. 1992, Wilbert
1992, Buskirk and Powell 1994, Fowler 1995, Ivan 2000). However, when the weekly
probability was compounded over the entire survey period P exceeded 99% regardless of season.
Thus, despite the effect that removal of bait by bears had on detection rates, our bait replacement
rate and the survey duration were apparently sufficient to almost guarantee a certainty of
detection if a marten was present.
Implications for Habitat Modeling
The temporal distribution of animal location data can dramatically affect the predictive
habitat model that is created from these data (Schooley 1994, Arthur et al. 1996, Forsyth et al.
2005, Gayet et al. 2011). For example, Coe et al. (2011) evaluated the temporal performance of
resource selection functions (habitat models) for elk (Cervus elaphus) for 9 monthly or seasonal
periods over 3 years. They found that models were validated for some, but not all, of the months
and seasons. Gayet et al. (2011) also discovered that seasonal differences in locations of mute
swans (Cygnus olor) resulted in different predictors in the best seasonal habitat. In our study
area, Rustigian-Romsos and Spencer (2010) predicted marten habitat using summer data, winter
data and annual data and revealed significant differences in the distribution and abundance of
habitat. Predicted habitat based on summer survey data was more limited in extent than habitat
areas predicted when winter survey data were used. The challenge is in determining how to
define marten habitat under these circumstances.
23
If, as we have suggested, the martens that were in detected in our study areas – which are
locations that are primarily occupied during winter – are young animals searching for suitable
habitat and are unlikely to maintain year-round home ranges there, then including detections of
these animals in predictive habitat models will over represent suboptimal habitat in the resulting
model. A model created with these data will include locations that are regularly assessed by
young animals but rarely occupied for the purposes of breeding. Our data support our belief that
this is the case and, therefore we agree with Rustigian-Romsos and Spencer (2010) that summer
habitat – when females are occupying home ranges where they bear young and breed – is likely
the most important to the marten population, not only because it is more restricted than habitats
occupied during the non-breeding season, but also because it is known to support adults during
the parturition and breeding season. Including survey locations where martens are primarily
detected only in winter (the non-breeding season) in the development of habitat models may
confound the important relationship among the habitat predictors associated with year-round
residence. This is because many of these locations will be sites where juveniles or males occur
during this season. However, some of these detections will also be females, thus future work
should focus on the spatial dynamics of breeding adults and their seasonal variation in home
range size and habitat so that we can understand how modeling habitat primarily for breeding
adults will affect the results.
When a habitat map is created from summer survey data, or from survey sites where
martens are detected during both summer and winter, we can be assured that the animals that are
detected are more likely to be contributing to future population growth. Thus, habitat models
(and maps) created from these data identify important areas for marten management. The same
cannot be said about models that are developed using data from locations where martens are
24
detected only during the winter, non-breeding season. Given that marten distributions have
decreased in portions of the Cascades and Sierra Nevada mountains in California (Zielinski et al.
2005, Moriarty et al. in press), it is important that land management activities be especially
careful not to adversely affect the high-value areas identified by models that were derived from
data collected during the breeding season. However, as Rustigian-Romsos and Spencer (2010)
also noted, the expanded area of occupancy during winter, which includes more lower-elevation
habitats that are less likely to be used during the breeding and kit-rearing seasons, may also be
important for sustaining the marten population by providing habitat for dispersing juveniles
during winter as they search for suitable areas to settle. Alternatively, areas identified as
supporting only winter occupancy may represent opportunities where habitat conditions can be
restored or improved such that summer occupancy could be supported there.
Our data lead us to conclude that, in response to the Pinchot Institute‟s request for “a
suitable habitat definition for marten” (Anonymous 2008), the most defensible approach is to
base the definition of suitable habitat (at the landscape scale) on a habitat model built using
locations where martens are detected during summer or during both summer and winter. Thus,
we support – as definition of suitable landscape-scale habitat – the results of modeling by Kirk
and Zielinski (2009) and the summer model of Rustigian-Romsos and Spencer (2010).
Locations where martens are detected primarily during winter can be included in a secondary
model, which will identify a wider array of habitat types that include locations that support
movement that is largely unrelated to breeding or home range establishment.
Our study brings to light a fundamental shortcoming in the development of habitat
models for martens, as well as for other species: the ambiguity of the definition of “suitable
habitat”. Most often suitable habitat is simply defined as where an individual of a species
25
occurs – whether it be a dispersing juvenile, an adult male or a reproductive female. Obviously,
the key to managing habitat for a species is to understand what habitat features are essential for
maintenance of the population. Thus, managers interested in maintaining and increasing
populations should consider suitability to vary as a function of the population segment of most
importance, the reproductive female. This is an especially important consideration in a
polygamous species such as the marten. Slauson (in prep) is exploring the implications of this
variation in data sources for marten habitat modeling in the Lake Tahoe basin. This work
suggests that knowledge of the spatial locations of breeding females (and males) would be the
most important data source for the development of credible habitat models. Ideally, our ultimate
goal should be to distinguish two classes of suitable habitat: “suitable for reproduction” and
“suitable for non-reproductive activities”. Our study lacked a control area and suffered from
relatively small sample sizes. These shortcomings will be avoided in new, ongoing work in the
Lake Tahoe Basin which will attempt to describe source and sink habitats with better precision
(Slauson, in prep.). Although we did not have the data in this study to assess the breeding status
of the subjects, our data support the notion that martens that are detected in winter only, in the
Swain and Humboldt Peak study areas, are less likely to be individuals that contribute to future
population growth than martens that are detected during the summer.
ACKNOWLEGEMENTS
We thank Tom Frolli and Mark Williams, of the Lassen National Forest (LNF), for their
support of this project. We appreciate the financial support of the Lassen National Forest, the
Pacific Southwest Region and the Pacific Southwest Research Station of the USDA Forest
Service, and the Herger Feinstein Quincy Library Group steering team and staff. Discussions
with LNF biologists were instrumental in developing plans for field work and analysis. We
26
thank the following for their help in collecting and summarizing field data: Cassie Kinnard,
Colleen Heard, Kaley Phillips, Mark Linnell, Mathew Delheimer, Katie Mansfield, and Patrick
Lieske.
LITERATURE CITED
Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. Pages
267-281 in Petran, B. N., and F. Csaki, editors. International Symposium on Information Theory.
Second edition. Akademiai Kiadi, Budapest, Hungary.
Anonymous, 2008. HFQLG independent science panel “red flag” issue monitoring report. Pinchot
Institute for Conservation, Washington, D.C.
Arthur, S. M., B. F. J. Manly, L. L. McDonald, and G. W. Garner. 1996. Assessing habitat selection
when availability changes. Ecology 77:215-227.
Bailey, R. G. 1994. Descriptions of the ecoregions of the United States. Second edition. Miscell.
Publication 1391. US Forest Service, Washington, D.C.
Bissonette, J. A., D. J. Harrison, C. D. Hargis, and T. G. Chapin. 1997. The influence of spatial
scale and scale-sensitive properties on habitat selection by American marten. Pages 368-385 in J.
A. Bissonette, editor. Wildlife and landscape ecology: effects of pattern and scale. Springer, New
York.
Bodkin, J. L., J. A. Ames, R. J. Jameson, A. M. Johnson, and G. E. Matson. 1997. Estimating age of
sea otters with cementum layers in the first premolar. Journal of Wildlife Management 61:967973.
Bull, E. L., R. S. Holthausen, and L. R. Bright. 1992. Comparison of 3 techniques to monitor
marten. Wildlife Society Bulletin 20:406-410.
Buskirk, S. W., and R. A. Powell. 1994. Habitat ecology of fishers and American martens. Pages
283-296 in Buskirk, S. W., A. S. Harestad, M. G. Raphael, and R. A. Powell, editors. Martens,
sables and fishers: biology and conservation. Cornell University Press, Ithaca, New York.
Campbell, T. M. 1979. Short-term effects of timber harvests on pine marten. Thesis, Colorado State
University, Fort Collins.
Carroll, C., W. D. Spencer, and J. C. Lewis. In press. Use of habitat and viability models in Martes
conservation and restoration in Biology and Conservation of Martens, Sables, and Fishers: A
New Synthesis. K.B. Aubry, W.J. Zielinski, M.G. Raphael, G. Proulx, and S.W. Buskirk,
editors. Cornell University Press, Ithaca, New York, USA.
27
Coe, P. K., B. K. Johnson, M. J. Wisdom, J. G. Cook, M. Vavra, and R. M. Nielson. 2011.
Validation of elk resource selection models with spatially independent data. Journal of Wildlife
Management 75:159-170.
Davis, C. and M. D. King. 2001. The Quincy Library Group and collaborative planning within
U.S. national forests. Unpubl. report, Colorado State University, Fort Collins, Colorado.
Ellis, L. 1998. Habitat use patterns of the American marten in the Southern Cascade Mountains of
California, 1992-1994. Thesis, Humboldt State University, Arcata, California.
Forsyth, D. M., W. A. Link, R. Webster, G. Nugent, and B. Warburton. 2005. Nonlinearity and
seasonal bias in an index of brushtail possum abundance. Journal of Wildlife Management
69:976-984.
Fowler, C. H. 1995. Techniques for detecting and monitoring martens and fishers in forest habitats
of California. Thesis. Humboldt State University, Arcata, California.
Fuller, A. K., and D. J. Harrison. 2005. Influence of partial timber harvesting on American martens
in north-central Maine. Journal of Wildlife Management 69:710-722.
Gayet, G., M. Guillemain, M. Benmergui, F. Mesleard, T. Boulinier, J-P. Bienvenu, H. Fritz and J.
Broyer. 2011. Effects of seasonality, isolation and patch quality for habitat selection processes
by mute swans Cygnus olor in a fishpond landscape. Oikos 120:801-812.
Gosse, J. W., R. Cox, and S. W. Avery. 2005. Home-range characteristics and habitat use by
American martens in eastern Newfoundland. Journal of Wildlife Management 86:1156-1163.
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. American Midland Naturalist 158:123131.
Hearn, B. J. 2007. Factors affecting habitat selection and population characteristics of American
marten (Martes americana atrata) in Newfoundland. Dissertation, University of Maine, Orono.
Hines, J. E. 2006. PRESENCE2. Software to estimate patch occupancy and related parameters.
USGS-PWRC. Available from http://www.mbr-pwrc.usgs.gov/software/presence.html
(Accessed 02 August 2011).
Ivan, J. S. 2000. Effectiveness of covered track plates for detecting American marten. Thesis,
Purdue University, West Lafayette, Indiana.
Kays, R. W., and K. M. Slauson. 2008. Remote cameras. Pages 110-140 in Long, R. A., P.
MacKay, W. J. Zielinski, and J. C. Ray, editors. Noninvasive survey methods for carnivores.
Island Press, Washington D.C.
28
Kendall, K. C., and K. S. McKelvey. 2008. Hair collection. Pages 135-176 in Long, R. A., P.
MacKay, W.J. Zielinski, and J. C. Ray, editors. Noninvasive survey methods for carnivores.
Island Press, Washington D.C.
Kirk, T. A. 2007. Landscape-scale habitat associations of the American marten (Martes americana)
in the Greater Southern Cascades region of California. Thesis, Humboldt State University,
Arcata, California.
Kirk, T. A., and W. J. Zielinski. 2009. Developing and testing a landscape habitat suitability model
for the American marten (Martes americana) in the Cascades mountains of California.
Landscape Ecology 24:759-773.
Kirk, T. A., and W. J. Zielinski. 2010. Modeling functional habitat connectivity of the American
marten (Martes americana) in northeastern California using least-cost corridors. Report to the
Lassen National Forest. Pacific Southwest Research Station, Arcata, California.
Kreeger, T. J., and J. M. Arnemo. 2007. Handbook of wildlife chemical immobilization. Third
edition. Self-published, Laramie, Wyoming.
Levins, R. 1969. Some demographic and genetic consequences of environmental heterogeneity for
biological control. Bulletin of the Entomological Society of America 15:233-240.
MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. P. Pollock, L. L. Bailey, and J. E. Hines. 2006.
Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence.
Academic Press, San Diego, California.
Matson, G. M. 1981. Workbook for cementum analysis. Matson's Lab, Milltown, Montana.
Mayer, K. E., and W. F. Laudenslayer, Jr. 1988. A guide to wildlife habitats of California.
California Department of Forestry and Fire Protection, Sacramento, California.
McNemar, Q. 1947. Note on the sampling error of the difference between correlated proportions or
percentages. Psychometrika 12:153-157.
Millspaugh, J. J., and J. M. Marzluff, editors. 2001. Radio tracking and animal populations.
Academic Press, San Diego, California.
Moriarty, K. M, W. J. Zielinski, and E. Forsman. In press. Decline in American marten occupancy
rates at Sagehen Experimental Forest, California . Journal of Wildlife Management.
O'Doherty, E. C., L. F. Ruggiero, and S. E. Henry. 1997. Home-range size and fidelity of American
martens in the Rocky Mountains of southern Wyoming. Pages 123-134 in Proulx, G., H. Bryant,
and P. M. Woodard, editors. Martes: taxonomy, ecology, techniques, and management.
Provincial Museum of Alberta, Edmonton, Canada.
29
Payer, D. C. 1999. Influences of timber harvesting and trapping on scale-specific habitat selection
and demographic performance of American marten. Dissertation, University of Maine, Orono,
Maine.
Payer, D. C., and D. J. Harrison. 2003. Influence of forest structure on habitat use by American
marten in an industrial forest. Forest Ecology and Management 179:145-156.
Perrine, J. D. 2005. Ecology of red fox (Vulpes vulpes) in the Lassen Peak region of California,
U.S.A. PhD dissertation, University of California, Berkeley, California.
Phillips, D. M. 1994. Social and spatial characteristics, and dispersal of marten in a forest preserve
and industrial forest. Thesis, University of Maine, Orono, Maine.
Phillips, D. M., D. J. Harrison, and D. C. Payer. 1998. Seasonal changes in home-range area and
fidelity of martens. Journal of Mammalogy 79:180-190.
Pollock, K., S. R. Winterstein, C. M. Bunck, and P. D. Curtis. 1989. Survival analysis in telemetry
studies: the staggered entry design. Journal of Wildlife Management 531:7-15.
Powell, R. A. 2000. Animal home ranges and territories and home range estimators. Pages 65-110
in L. Boitani and T. K. Fuller, editors. Research techniques in animal ecology: controversies and
consequences. Columbia University Press, New York.
Pulliam, R. 1988. Sources, sinks and population regulation. American Naturalist 132: 652-661.
Reynolds, J. C., T. A. Porteus, S. M. Richardson, R. J. Leigh, and M. J. Short. 2010. Detectability of
American mink using rafts to solicit field signs in a population control context. Journal of
Wildlife Management 74:1601-1606.
Rustigian-Romsos, H. L., and W. D. Spencer. 2010. Predicting Habitat Suitability for the American
Marten on the Lassen National Forest. Unpublished report. USDA Forest Service, Lassen
National Forest, Susanville, California.
Schooley, R. L. 1994. Annual variation in habitat selection: patterns concealed by pooled data.
Journal of Wildlife Management 58:367-374.
Soutiere, E. C. 1979. Effects of timber harvesting on marten in Maine. Journal of Wildlife
Management 43:850-860.
Spencer, W. D., R. H. Barrett, and W. J. Zielinski. 1983. Marten habitat preferences in the northern
Sierra Nevada. Journal of Wildlife Management 47:1182-1186.
Slauson, K. M. In prep. Linking landscape pattern to population process in a carnivorous mammal.
PhD proposal, University of Montana, Missoula, Montana.
30
Steventon, J. D., and J. T. Major. 1982. Marten use of habitat in a commercially clear-cut forest.
Journal of Wildlife Management 46:175-182.
Wilbert, C. J. 1992. Spatial scale and seasonality of habitat selection by martens in southeastern
Wyoming. Thesis, University of Wyoming, Laramie.
Zielinski, W. J., T. A. Kirk, and K. M. Slauson. 2009. The effect of season on detectability of
martens in the Greater Southern Cascades region: Lassen National Forest, California.
Unpublished report. USDA Forest Service, Pacific Southwest Research Station, Arcata,
California.
Zielinski, W. J., R. L. Truex, F. V. Schlexer, L. A. Campbell, and C. Carroll. 2005. Historical and
contemporary distributions of carnivores in forests of the Sierra Nevada, California, USA.
Journal of Biogeography 32:1385-1407.
31
Figure Legends
Figure 1. Previously surveyed carnivore grids (Mineral, Humboldt Peak, and Swain Mountain)
within the Lassen National Forest and Lassen Volcanic National Park, California. Remote
camera sites are designated by circles.
Figure 2. Camera, trap and initial capture locations, relative to the previously surveyed
Humboldt and Swain camera survey grids during two trapping sessions (October- November
2009 and March-April 2010). A: grid locations used for current and previous camera surveys, B:
locations where traps were set, which included previous cameras stations and some new
locations within or just outside the grids, C: locations where individual martens were captured
(closed circles) relative to all trap sites (open circles).
Figure 3. Timeline of fates of individual radio-marked martens, from the date they were first
captured (closed circles), to they were either confirmed to have died (closed squares), or when
their fate became unknown. Time horizon represents one year, from 28 September 2009 – 30
September 2010. Solid lines represent time periods during which the individual was known to
be within the survey grid (either Swain or Humboldt Peak) and dashed lines indicate when the
animal was either known to be off the grid (large dashes: M06, M10, and M11) or suspected to
be off the grid (M09).
Figure 4. Kaplan Meier monthly survival rate estimates for all martens, beginning in November
of 2009 and concluding in September of 2010. Dotted lines indicate standard errors (upper value
not shown when > 1.0).
32
Figure 5. Minimum convex polygons and camera stations for Humboldt Peak (A - D) and Swain
Mountain (E - H) study areas, Lassen National Forest. Camera stations are indicated by circles;
empty half-circles represent no detections during either winter (left hemisphere) or summer (right
hemisphere). Black polygons represent 100% minimum convex polygons for all known
locations (total use area), blue dashed polygons are winter use areas (from 29 December - 7
March 2010 in Swain and from 15 February - 23 April 2010 in Humboldt: 2 weeks before until 2
weeks after the winter camera survey), and the red dashed polygons enclose the summer use
areas (from 24 June – 20 August 2010 in Swain and from 27 July to 23 September 2010 in
Humboldt: 2 weeks before until 2 weeks after the summer camera survey). Numbers near each
polygon identify the individual marten.
Figure 6. (A) Detections at camera stations on the Humboldt, Swain and Mineral grids over
3years, replicated during 3 summers (between June and September) and 2 winters (between
November and January). Open circles are camera stations with no marten detection, closed
circles indicate a detection. Mineral grid sampled only during summer of 2007. (B) Proportions
of stations at each grid with detections for each of the 3 summer and 2 winter sample periods.
Data correspond to the season directly above in panel A.
Figure 7. Detection locations of unmarked martens at Humboldt (A) and Swain (B). Camera
stations are indicated by circles; empty half-circles represent no detections during either winter
(left hemisphere) or summer (right hemisphere). Surveys in Humboldt were conducted from 1
March – 15 April 2010 in winter and 10 August – 17 September 2010 in summer and surveys in
33
Swain were conducted from 11 January – 7 March 2010 in winter and 8 July – 13 August 2010
in summer.
Figure 8. Plot of the proportion of stations where martens were detected using cameras (y axis)
during each survey day (x axis) in the summer of 2007 and the winter of 2007-2008.
34
Figure 1.
35
36
Figure 2.
37
Figure 3.
38
1.0000
0.9000
Survival rate
0.8000
0.7000
0.6000
0.5000
0.4000
0.3000
0.2000
Figure 4.
39
Figure 5.
Proportion of Satations
40
60
50
40
30
20
10
0
Mineral
Figure 6.
Swain
Humoldt Peak
41
Figure 7.
42
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
Summer
Winter
1
Figure 8.
4
7 10 13 16 19 22 25 28 31 34 37 40
43
Table 1. Individual characteristics of martens captured in the vicinity of either the Humboldt or
Swain camera survey grids from October 2009 – March 2010.
Study Site
ID
Initial
Capture Date
Estimated
Age (yrs)1
Sex
Initial
Weight
(g)
No.
Locations
Estimated
Use Area
(km2)
M01
Humboldt
7-Oct-09
2
Male
1050
61
15.60
M02
Humboldt
26-Oct-09
0
Male
840
45
42.06
M03
Humboldt
27-Oct-09
2
Male
970
44
5.99
M04
Humboldt
28-Oct-09
2
Male
950
34
5.28
M05
Humboldt
28-Nov-09
0
Male
815
03
---
M06
Swain
2-Nov-09
1
Male
1002
49
4.51
M07
Swain
4-Nov-09
0
Male
840
47
2.95
M08
Swain
8-Nov-09
1+
Male
1060
53
10.02
M09
Swain
11-Nov-09
0
Male
920
15
56.09
F01
Humboldt
19-Nov-09
Adult2
Female
700
6
0.63
M10
Swain
24-Feb-10
3
Male
1090
20
10.23
F02
Swain
8-Mar-10
1
Female
770
38
1.14
M11
Swain
8-Mar-10
0
Male
1095
via tooth cementum annuli; age = 0 refers to young of the year.
2
estimated; premolars missing.
3
small juvenile, not collared.
15
7.05
1
44
Table 2. Candidate models fit to the detection data for the purposes of estimating probability of detection (p), occupancy (ψ, psi), extinction
(έ, eps), and colonization (γ, gamma). Covariates include “Season” (winter v summer), “Swain” (study area effect),” wk” (week within the
survey duration; 1 - 4), “W” (the months December – February), “Spr” (the month of March), and “Bait” (the presence or absence of bait).
Models compared using Akaike‟s Information Criteria for which a lower value suggests a better-fitting model. Models were generated from 5
seasons of occupancy data in 2 study areas, Swain and Humboldt Peak, combined. Akaike weights (w) represent the strength of the model
and K is the number parameters in each model.
Model #
1
2
3
4
5
6
7
Model
psi,gamma(Season Swain),eps(Season Swain),p(wk_1_23_4 Bait Season_W_Spr)
psi,gamma(Season), eps(Season),p(wk_1_23_4 Bait Season_W_Spr)
psi,gamma(t),eps(t),p(wk_1_23_4 Bait Season_W_Spr)
psi,gamma(Season),eps(Season),p(wk_1_23_4 Bait Season_W)
psi,gamma(Season),eps(Season),p(wk_1_23_4 Bait)
psi,gamma(),eps(),p(wk1_23_4)
psi,gamma(),eps(),p()
ΔAIC
AIC
weight
Model
Likelihood
K
0.0
2.8
5.5
15.6
20.2
28.1
83.4
0.76
0.19
0.05
0.00
0.00
0.00
0.00
1.00
0.25
0.06
0.00
0.00
0.00
0.00
13
11
15
10
9
8
4
45
Table 3. Parameter estimates (SE) for individual variables in the top model for modeling marten detection probability for 5 seasons from
2007 to 2010 in the Humboldt and Swain study areas.
Variable
Summer 2007
Winter 2008
Summer 2008
Winter 2010
Summer 2010
0.03 (0.025)
0.03 (0.025)
0.60 (0.08)*
0.32 (0.08)**
0.06 (0.04)
0.07 (0.04)
0.60 (0.08)*
0.34 (0.08)**
0.06 (0.04)
0.07 (0.05)
Colonization (γ)
NA
0.60 (0.08)
0 (0)
0.60 (0.08)
0 (0)
Extinction (έ)
NA
0.47 (0.35)
0.90 (0.06)
0.47 (0.35)
0.90 (0.06)
Week 1
Week 2-3
Week 4
0.087 (0.03)
0.142 (0.03)
0.288 (0.06)
0.159 (0.03)
0.247 (0.02)
0.444 (0.04)
0.087 (0.03)
0.142 (0.03)
0.288 (0.06)
0.159 (0.03)
0.247 (0.02)
0.444 (0.04)
0.087 (0.03)
0.142 (0.03)
0.288 (0.06)
Detection Probability for
entire 28-day Protocol
0.994
0.999
0.994
0.999
0.994
Occupancy (ψ)
Humboldt
Swain
Detection Probability
*P-value <0.05 for McNemar's chi-squared test for seasonal comparisons; indicating that winter ≠ summer.
**P-value <0.05 for McNemar's chi-squared test for study area comparisons; indicating that Swain and Humboldt are only different during
winter.