The Effect of Season on Detectability of Martens in the
Greater Southern Cascade Region: Lassen National Forest, California
William J. Zielinski, Thomas A. Kirk, and Keith M. Slauson
USDA Forest Service, Pacific Southwest Research Station
Redwood Sciences Laboratory, 1700 Bayview Drive, Arcata, CA 95521
12 May 2009
Introduction
The historical and contemporary distributions of carnivore species in the Southern
Cascade and Sierra Nevada mountains were recently summarized (Zielinski et al. 2005).
The contemporary surveys were conducted by the USDA Forest Service Pacific
Southwest Research station, using a grid-based, systematic sampling design. These
baited track plate and camera surveys suggested that several species of carnivores have
experienced a decline in distribution or have been extirpated during the past 75 years.
The fisher (Martes pennanti) and the marten (M. americana) have experienced reductions
in their occupied ranges; a suspected ~430 km gap in the distribution of fisher
populations was documented and the once contiguous distribution of martens has become
fragmented in northeastern California. Both species are forest habitat specialists and
their declines have been hypothesized to be due to the latent effects of historical trapping
and changes in the amount and distribution of late-successional forests (Zielinski et al.
2005). The wolverine (Gulo gulo) and the Sierra Nevada red fox (Vulpes vulpes) have
been extirpated, except for a small population of red foxes in the vicinity of Lassen
Volcanic National Park (Perrine 2005), but these species are not as closely associated
with mature forest conditions. The decline in the carnivore community underscores the
2
need to recognize the cause of the observed changes in their distributions.
We used a multiple-scale approach to develop a model to predict the occurrence
of martens in the Greater Southern Cascades Region (GSCR) of California (Fig. 1). The
model was developed based on the association of environmental variables with the
locations of marten detections from the systematic survey locations (Kirk 2007, Kirk and
Zielinski, in press). We used a modified version of the California Wildlife-Habitat
Relationships (CWHR) system to represent vegetation variables. The CWHR program
rates the value of habitat for wildlife species found in California, providing a numerical
value (0-1) for cover, forage, and reproductive habitat. We used only the values from
CWHR corresponding to marten high quality reproductive habitat (i.e., classes within the
lodgepole pine, riparian, red fir, and white fir habitat types). Reproductive habitat is
considered the most critical, and is the habitat type that the CWHR program predicts to
occur over much more limited area than habitat for cover or foraging. This permitted a
more strategic assessment of habitat associations because reproductive habitat is
considered the most important, and is more restricted in its distribution than habitat for
cover or foraging. Habitat associations were assessed at three different landscape-scale
extents, following previous research which indicates marten habitat selection is affected
by landscape conditions (Bissonette et al. 1997, Hargis et al. 1999, Potvin et al. 2000).
Multivariate logistic regression was used to identify a combination of variables
that best explained the occurrence of martens. Variables that were considered included:
measures of landscape vegetation pattern (using the modified CWHR marten model),
land ownership, elevation, stream density, road density, and nearby marten detections. A
set of likely candidate multivariate models was created and then ranked for their
3
performance using Akaike’s Information Criteria (AIC) (Akaike 1973), for each of 3
spatial scales separately. The model that best explained the patterns of occurrence from
the survey data was developed at the largest spatial scale (80 km2) and included the
following predictor variables: the total amount of reproductive habitat, the number and
size of contiguous habitat (habitat patches), and land use designation and ownership
(Kirk and Zielinski, in press). Land use was an effective predictor, but is obviously
related to some other ecological attributes of importance to martens. Because this
variable provides managers very little opportunity to affect change, when subsequent
models are necessary they will probably be developed without including land ownership
and designation as potential predictors.
The spatial analysis program FRAGSTATS, and ArcInfo software, were used to
generate a map representing the relative predicted probability values for the best model
(Fig. 2). This map illustrated a low probability of detection for some areas on the Lassen
National Forest (LNF) where marten have been repeatedly detected by separate surveys
conducted by the LNF staff in the winter. This observation precipitated a more formal
evaluation of the model using, as independent test data, the results of surveys conducted
by the LNF. The test data were collected over a 9-year period, beginning 5 years prior to
the systematic surveys that were used to create the predictive model (which occurred in
2000 and 2001 in the Lassen region). Remotely-triggered camera stations collected data
during a 28-day period, primarily during the winter months, and in most cases following
a standard protocol (Kucera et al. 1995). These data provided a unique opportunity to
assess the performance of our landscape habitat model using data that were not used in its
development. A point coverage was created from the test data set representing marten
4
detections and non-detection sites, which was then overlaid on the predicted suitability
map. Model performance was assessed using Receiver Operator Characteristic (ROC)
curves, Area Under Curve (AUC) scores, and relative classification success (Kirk and
Zielinski, in press)
Model performance varied dramatically when using the test data set, with
classification success depending largely on the season in which the data were collected.
When the model was evaluated using the entire test data set (summer-fall and winter) it
performed poorly based on traditional diagnostics, a result that can be seen by examining
the distribution of marten detections relative to categories of predicted probability of
occurrence (Fig. 3A). However, using only the test data that were collected during the
same seasons as the systematic survey data used to develop the model (i.e., summer-fall),
the model performed substantially better (Fig. 3B). The model also performed well in the
northern and eastern sections of the forest, regardless of the season of the data, correctly
predicting low probability of occurrence in areas where the LNF surveys did not detect
martens. The test data indicated, however, that martens occurred east of the Caribou
Wilderness and in the Humboldt Peak area of the LNF, both areas where the original data
predicted low habitat suitability. Marten detections in both of these target areas occurred
primarily during winter months and were at the margin of the systematic sampling grid
used to develop the GSCR marten model.
A number of hypotheses have been suggested to explain the shortcomings of the
GSCR model predictions. First, and foremost, is the potential effect of survey season.
One logical explanation is that martens are detected in some areas only in winter, because
they are dispersing animals that do not persist there to establish home ranges, and are
5
therefore not detected the subsequent summer. This may be because the habitat is not
sufficient to permit year-round residency or that these habitats have, for some reason,
higher predation risks. Another possibility is that martens may be more detectable in
winter, when food is limiting, and resident animals are more readily drawn to baits.
Alternatively, martens may expand their home ranges in winter, making them more likely
to be detected at more stations during this season. These issues were discussed with the
Lassen NF biologists and we were encouraged to develop a rigorous experimental
approach to verify the phenomenon of seasonal variability in detection and to test
whether probability of detection varies with season. In addition, any new survey data
could ultimately be used to revise and improve the predicted suitability map.
The objective of this study was to resurvey the areas where the test data from the
Lassen NF reported marten during the winter, but where the model predicted low
probability of occurrence. During these new surveys we would use identical detection
methods in each season, so that this factor would not confound the results, and we would
subject the data to an analysis that would test the hypothesis that the probability of
detection was roughly equivalent during summer and winter. Probability of detection
(lower case p) is the likelihood that at least one individual is detected, after a single visit
to a detection station, if the animal is present. Probability of detection (upper case P) is
the probability of detection compounded over all visits in the protocol, given that the
species is present. P is of interest for its own sake but, importantly, its critical role in
surveys is to adjust estimates of occupancy (ψ) (MacKenzie et al. 2006). Occupancy is
the estimate of the number of sample sites where the species is present or, when adjusted
to account for P, the number of sites where the species is estimated to be present.
6
Ideally the findings would help determine whether martens that were detected in
areas of low - moderate predicted value were overlooked during the original summeronly systematic surveys, and are in fact year-round residents, or whether individuals only
persist in these areas during the summer.
Methods
We conducted detection surveys in 3 study areas or grids: east of Caribou
Wilderness on Swain Mountain (Swain), in the Humboldt Peak region (Humboldt), and
within and immediately adjacent to Lassen Volcanic National Park (Mineral) (Fig. 1,4-6).
Swain and Humboldt were chosen because the test data had a particularly poor fit to the
GSCR model in these 2 areas. The third area (Mineral) was included for comparative
purposes given its partial overlap with Lassen Volcanic National Park (LVNP) which has
high predicted habitat value compared to the other 2 grids (Kirk and Zielinski, in press).
Surveys were conducted using remotely triggered cameras (Cuddeback 2.0
Megapixel digital) directed toward a 6 lb chicken bait attached to the bole of a tree. A
commercial scent was applied when a camera was set and reapplied each time it was
revisited. Twenty cameras, on a grid with 3 km spacing, were established in each of the 3
study areas. The size of the study areas (approximately 160 km2) was chosen to
approximate the modal size of the areas of the poorest model fit. Twenty stations, at 3km spacing, provide sufficient sampling resolution to estimate the occurrence of multiple
individual marten, whose home ranges may vary from 300-1100 ha in size (e.g., Simon
1980, Spencer et al. 1983, Ellis 1998). Cameras were serviced weekly to download
images, check camera functions, and replace bait; 3 service occasions resulted in a 4week sample period for each camera. If, however, there was evidence that a camera had
7
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 so that there were
always 4 effective survey weeks (although see Results and Discussion sections, in terms
of the effect of bait presence on the definition of an ‘effective’ survey week).
We repeated surveys 3 times in the Swain and Humboldt Peak areas: during the
summer of 2007, the winter of 2007-2008, and again in the summer of 2008. This
allowed us to examine the cyclicity of a seasonal pattern in detection or occurrence.
Changes apparent between only 2 time points could be attributed to other reasons, but
with 3 seasons of sampling we could determine whether the seasonal pattern was
repeated. Funding constraints limited the sampling of the Mineral grid to summer 2007
and winter 2007-2008 only.
We used program PRESENCE (version 2.0, Hines 2006) to estimate the
probability of detection given presence (P), by pooling the detection data from all 3 study
areas and comparing summer 2007 and winter 2007-2008. Pooling occurred because
preliminary analysis determined that although the study grid did influence probability of
detection in the winter (with Mineral exceeding the other areas; Table 1, Fig. 7), there
was no effect of study area during the summer. The seasonal difference in this respect is
probably due to inadequate data in the summer. The camera data were aggregated by 24hour period, with a ‘0’ representing a non-detection and ‘1’ representing a detection for
each period, yielding 28-day “detection histories” for each camera station. Thus, the
‘visit’ for the purpose of estimating p (and, ultimately, P) was a 24-hour period. Prior to
selecting a model to fit the detection histories, we evaluated the pattern of detections for
each season by plotting the proportion of detections for each survey day. We used these
8
plots to help select the best functions by which to model detection probability and thus
account for detection heterogeneity.
Review of plots of proportions of detections per survey day across the entire
survey duration revealed two important issues. First, 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. Therefore, for the purposes of
estimating probability of detection for each season, we truncated all datasets at 28
calendar days (Fig. 8). Second, the data from both seasons revealed a low initial
detection period in the first survey week (Fig. 8).
Personnel on the LNF also brought to our attention the possibility that the
presence or absence of bait, which they believed differed by season, might also affect
probability of detection. Their field observations also suggested that black bears were
very common in the summer and that martens were less likely to be detected at a camera
station when the bait had been removed earlier by a bear. The relatively long interval
between servicing the stations (1 week) made it likely that bait may be absent from some
camera stations during a portion of the survey period. The status of bait at each camera
check was determined by reviewing field notes and photographs. These data made it
possible to investigate the effect of the covariate “bait” (present or absent) on probability
of detection. In sum, custom models were developed to estimate probability of detection
(p) independently for subsequent weeks, during summer, and for all weeks separately
during winter. These models also included the bait covariate. Models were compared
using AIC, with lower AIC values indicating better fitting models. The AIC weight (w)
was also calculated for each model, providing a measure of relative fit compared to other
9
candidate models.
In addition to our interests in the seasonal differences in probability of detection
(P) for its own sake, it is necessary to estimate P so that occupancy estimates (ψ) can be
adjusted accordingly. 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 will 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 will not be much higher than the naïve
estimate of ψ. Season-specific estimates of the overall detection protocol (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
Naïve Estimates of Occupancy
Seasonal differences in naïve estimates of occupancy (ψ) were quite pronounced
in all 3 study areas (Fig. 9). The seasonal variation in proportions was most pronounced
in the Humboldt Peak study area where the proportion of stations with a detection was
5%, 65% and 0% in summer 2007, winter 2007-08, and summer 2008, respectively.
Swain varied from 5.0% to 60.0% to 10.0% over the same period. Seasonal variation
was conspicuous in all 3 grids but, as expected, it was the least in the Mineral grid
(varying from 20% to 50% in summer and winter, respectively). Combining all 3 grids,
the naïve estimates of occupancy were 6/60 (10.0%) for the summer of 2007 and 35/60
(58.3%) for the winter of 2007/2008. The naïve estimate of occupancy for summer 2008
10
(2 grids only: Humboldt and Swain) was 2/40 (5.0%).
Probability of Detection
The top models for each season included custom parameters, including weekspecific estimates for probability of detection (p). The top ranked models estimated
probability of detection separately for survey weeks, with the top models from each week
estimating probability of detection for week 1, weeks 2-3, and week 4 separately (Table
1). For both seasons, probability of detection increased during the course of the 4-week
survey duration (Fig. 8).
Top models for both seasons also included the bait status variable (Table 1),
indicating that the presence or absence of bait helped account for additional detection
heterogeneity. Bait status was a far more influential variable during the summer, due to
the higher incidence of bait absence during this season versus the winter. The presence
or absence of bait was likely the primary reason for the seasonal difference in detection
probability. During winter, camera stations had both functioning cameras and bait
present for an average of 28.5 days, compared to only 19.8 days during the summer.
During the summer, the presence of bait resulted in a >700% increase in the likelihood of
detecting a marten after accounting for the effects of survey week (Table 2). In winter,
the presence of bait increased the probability of detecting a marten by only 63%, after
accounting for the effects of survey week and study area (Table 2, Fig. 10). The top
models yielded estimates for P of 0.699 (SE = 0.090) for the summer data and 0.844 (SE
= 0.064) for the winter data, which were significantly different (t = 5.68, df = 116, P =
<0.0001). Thus, the probability of detecting a marten, given that it is present, was
significantly higher during winter than summer surveys.
11
Adjusted Estimates of Occupancy
The information about the seasonal difference in detectability was used to adjust
(correct) the naïve occupancy estimates. Applying the top model for P, for summer and
for winter, resulted in adjusted occupancy (ψ) values of 0.250 (SE = 0.153; 95% C.I.
0.063-0.622) and 0.817 (SE = 0.094; 95% C.I. = 0.56-0.93), respectively. These
estimates were significantly different (McNemar’s χ2 = 30.9, df = 1, P < 0.0001)
indicating that despite seasonal differences in detectability, the adjusted estimates of
occupancy still differed significantly between summer and winter. The adjusted
occupancy estimates indicate that martens were likely to be present at a total of 15 of 60
(25%) and 47 of 60 stations (78.3%) (all 3 grids combined) in the summer and winter,
respectively. These estimates reveal that the naïve (observed) number of sites occupied
(10.0% for summer and 58.3% for winter) were substantial underestimates of true
occupancy for both seasons.
Discussion
The probability of detecting martens was significantly lower in summer than in
winter, most likely due to differences in availability of bait. However, despite seasonal
differences in probability of detection, when this information was used to adjust
occupancy estimates there remained a significantly greater proportion of stations with
detections in the winter than in the summer. The significant difference in occupancy
between summer and winter suggests that martens are at either low densities during the
summer (for any number of reasons) or have smaller ranges in the summer and are,
therefore, less detectable at each station. Paradoxically, a marten ecology study was
conducted in 1992-1994 (Ellis 1998), partially overlapping the Swain grid (half of the
12
Ellis study area occurred south of the Swain grid), and martens were found to occupy the
area throughout the year and to reproduce there. And, there has been very little change in
forest structure in this area since then. In addition, martens have been observed by
Lassen NF staff during the summer in the Humboldt Peak area (M. Williams, LNF, pers.
comm.), despite our summer surveys detecting only one marten there. These
discrepancies suggest that additional work is needed if the importance and cause of
seasonal variation, as illustrated in this study, is to be understood.
Interestingly, the seasonal difference in proportion of occupied sites was less in
the Mineral grid, which partially overlaps LVNP, than the other two grids. The predicted
habitat value, as indicated by the habitat model (Kirk and Zielinski, in press), is higher in
the Mineral grid than elsewhere, which is why we expected less seasonal difference there.
Nonetheless, the Mineral grid also demonstrated a noticeable increase in proportion of
sites with martens in winter compared to summer; the seasonal phenomenon affected all
3 grids.
The significant seasonal difference in probability of detection demonstrated in this
study was not discovered in a previous study in the Sierra Nevada (Lake Tahoe basin and
Sierra National Forest) (Zielinski et al. 2008). This disparity is probably due to the
greater portion of survey time when stations were without bait, most likely due to bear’s
removing it. Bears may have been more common in Lassen than in the Lake Tahoe and
Sierra NF study sites, but the effect of the absence of bait was probably exacerbated at
Lassen by the long service interval (1 week) compared to the 3-day interval in the Sierra
Nevada study sites. These sites may also be situated in a high-elevation core of marten
distribution in the Sierra Nevada, compared to the LNF, and may have higher population
13
densities in general.
The method used to estimate probability of detection, and to adjust occupancy,
relies on the methods used in program PRESENCE. This software uses the history of
detections, at survey stations that had at least one detection, to estimate probability of
detection. Thus, martens that never visit camera stations – for one reason or another – do
not factor into the estimation of probability of detection. An alternative method to
estimating probability of detection is to do so directly, by monitoring the whereabouts of
a sample of radio-collared martens from each of the study areas (particularly Humboldt
and Swain). This, more expensive, approach would allow us to monitor the specific
location of each marten, relative to the detection stations, during each season to
determine their probability of detection directly and to monitor their fate over the course
of the year. If martens occupy Humboldt and Swain primarily during the winter,
collaring and monitoring them there as winter progresses to summer will provide ironclad evidence as to whether they are year-round occupants or, instead, dispersers that
temporarily reside there. This approach may also allow us to evaluate the hypothesis that
their home ranges become so small in summer as to render them less detectable overall.
This may occur when reproductive females constrict their ranges when caring for
dependent young.
Ultimately, we will have to determine whether a GSCR habitat model and map,
for the purposes of forest planning, should be developed using only the location of
detections during the snow-free seasons, when martens are occupying what are likely to
be their life-long home ranges, or whether a model should be based on all detections –
including those locations where martens are detected only during the winter. We hope to
14
determine the residency of martens in the Humboldt and Swain grid, via monitoring
telemetered animals, beginning in the fall of 2009 (Zielinski 2009). If this study confirms
that martens are indeed residing within the Swain and Humboldt areas year round, the
habitat model and mapping would need to include the full data set of detections available,
regardless of season.
Acknowledgements
We thank Tom Frolli of the Lassen National Forest for his support of this project, as well
as other biologists on the LNF, particularly Mark Williams and Tom Rickman. We
appreciate the financial support of the Herger Feinstein Quincy Library Group steering
team (forest supervisors) and staff (in particular Dave Wood and Colin Dillingham) as
well as Diane Macfarlane, Regional Office Threatened, Endangered, and Sensitive
Species Program Manager. Discussions with LNF biologists were instrumental in
developing plans for field work and for analysis. These biologists, for example, brought
to our attention the possibility that the status of bait may have influenced probability of
detection. We thank the following for their help in collecting the field data: Don Eastes,
Dan O' Leary, Cassie Parsons, Catie Parsons, Kaley Phillips and Andy Williams of
Lassen National Forest, Mourad Gabriel, Greta Wengert, Steven Breth and Matt Gibons
of MGW Biological, and Katie Moriarty, Ian MacKay and Michelle McKenzie of
Adaptive Management Services.
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Hargis, C. D., J. A. Bissonette, and D. L. Turner. 1999. The influence of forest
fragmentation and landscape pattern on American martens. Journal of Applied
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Greater Southern Cascades region of California. Master’s thesis. Humboldt State
University, Arcata.
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Mountains of California. Landscape Ecology.
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Hines. 2006. Occupancy Estimation and Modeling. Academic Press, NY.
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Spencer, W. D., R. H. Barrett, and W. J. Zielinski. 1983. Marten habitat preferences in
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17
Table 1. Alternative models fit to the detection data for the purposes of estimating
probability of detection per visit (p). Models are compared using Akaike Information
Criteria (AIC), for which a lower value suggests a better-fitting model. Models were
generated separately for summer and winter, pooling the results from all 3 grids. Akaike
weights (w) represent the strength of the model, compared with others, and K is the
number of parameters in each model. Unless noted, all models listed include 1-group and
a constant (ψ). Only the models that account for 95% of the Akaike weight are listed.
The bait covariate was handled in 2 different ways in respect to missing data. “Bait1”
assumes that bait was present for all days when bait status could not be determined
(missing data) and “Bait2” assumes that the bait was absent for days with missing data.
During the summer there were 164 of 1680 days (9.8%) with missing data; during the
winter there were 461 of 1624 days (28.3%) with missing data.
Summer
Model
AIC
∆AIC
w
Model
K
Likelihood
___________________________________________________________________
p (week1_week23_week4_Bait1)
67.4
0.0
0.45
1.00
5
p (week1_week234_Bait1)
68.2
0.7
0.31
0.68
4
p (week1_week234_Bait2)
70.2
2.7
0.11
0.25
4
p (week1_week234)
70.3
2.8
0.10
0.23
3
___________________________________________________________________
Winter
p (week1_week23_week4_
Study Area_Bait2)
436.6
0.0
0.42
1.00
8
p (week1_week23_week4_
Study Area)
436.7
0.1
0.41
0.96
7
p (Week1_week23_week4_
StudyArea_Bait1)
438.5
1.9
0.16
0.38
8
___________________________________________________________________
Table 2. Odds ratios for individual variables in the top models for modeling marten detection probability (p) for each season. The odds ratio
is the ratio of the odds of an event occurring in one group (in this case bait present) to the odds of it occurring in another group (bait absent).
Model
Rank
Variable
Coefficient
Odds
ratio
Season
Model
Summer
p (week1_week23
_week4_Bait1)
1
1
1
1
Bait1
Week1
Week23
Week4
2.10
-22.65
-2.27
-1.02
8.16
0.00
0.10
0.36
716% increase p when bait is present vs. absent
100% decrease in p in week 1 versus 2-4
3% increase in p in weeks 2-3 versus 1 and 4
36% increase in p in week 4 versus 1-3
p (week1_week234_Bait2)
3
Bait2
1.22
3.39
239% increase in p when bait is present
p (week1_
week23_week4_Study
Area_Bait2)
1
Bait2
0.48
1.62
62% increase in p when bait is present
1
1
1
1
1
1
Week1
Week23
Week4
Mineral
Humboldt
Swain
-1.37
-0.91
1.12
0.36
-0.20
-1.32
0.25
0.40
3.07
1.44
0.82
0.27
75% decrease in p in week 1 versus 2-4
60% decrease in p in week 2-3 versus 1 and 4
207% increase in p in week 4 versus weeks 1-3
44% increase in p versus Humboldt or Swain
19% decrease in p versus Mineral and Swain
73% decrease in p versus Mineral and Humboldt
3
Bait1
0.19
1.21
21% increase in p when bait is present
Winter
p (week1_week23_week4_
StudyArea_Bait1)
Interpretation
19
Figure 1. The Greater Southern Cascades Region (A), evaluation region (B),
and Lassen National Forest boundary (irregular polygon) with the 3 study
areas noted with rectangles.
20
Figure 2. A map of the predicted probability of marten
occurrence for the Greater Southern Cascades region,
based on the best-fitting model developed from the
systematic surveys (Kirk and Zielinski, in press). Darker
regions indicate areas of higher predicted probability;
crosses indicate sample units without a marten detection
and circles are units with a marten detection.
21
Figure 3A. Maps of the locations of ‘test’ survey data represented by camera stations
run by biologists from the Lassen National Forest. Open circles are survey locations
that did not detect martens; white circles are survey locations that did. They represent
the locations and results of all test survey data, regardless of season.
22
Figure 3B. Maps of the locations of ‘test’ survey data represented by camera stations
run by biologists from the Lassen National Forest. Open circles are survey locations
that did not detect martens; white circles are survey locations that did. They represent
the test survey data that were collected during the summer (that same season as the
previous systematic survey data).
23
Figure 4. Swain study area, east of Caribou Wilderness. White dots are camera
locations and thin black lines are forest roads. Green represents Lassen National
Forest and gray represents private land.
24
Figure 5. Humboldt Peak study area. White dots are camera locations and thin black
lines are forest roads. Green represents Lassen National Forest and gray represents
private land.
25
Figure 6. Lassen National Volcanic Park study area. White dots are camera locations
and thin black lines are forest roads. Green represents Lassen National Forest and gray
represents private land.
26
0.8
Detection Probability
0.7
0.6
0.5
0.4
0.3
Mineral
0.2
Humboldt Peak
Swain Mtn.
0.1
0
Week 1
Week 2-3
Week 4
Figure 7. Probability of detection, as it varies among study grids, and over the survey duration,
during winter. Insufficient data to conduct similar analysis in summer.
27
Proportion of Survey Days with Marten
Detections
Proportion with Detection
35.0%
Summer
Winter
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
1
3
5
7
9
11
13 15
17
19
21
23 25
27
Survey Days
Figure 8. Plot of the proportion of stations where martens were detected (y axis), using
remotely triggered cameras, during each survey day (x axis) in the summer of 2007 and the
winter 2007-2008; 3 study areas combined.
28
Figure 9. Locations where martens were detected at each of the 3 study areas during
summer 2007, winter 2007-2008, and summer 2008. Dark circles represent camera sites
that detected martens, open circles sites that did not. Polygons represent protected areas
(either Lassen National Volcanic Park or designated wilderness on national forest land).
Note that the park study area was not sampled in summer 2008.
29
0.8
Detection Probability
0.7
0.6
0.5
Bait P
0.4
Bait A
0.3
0.2
0.1
0
Week 1
Week 2-3
Week 4
Figure 10. Detection probability (p) for American martens at remote camera stations during
winter on the Lassen National Forest, when bait is present (P) or absent (A).