Data Brief. 2016 Mar; 6: 783–792.
Published online 2016 Jan 28. doi: 10.1016/j.dib.2016.01.032
PMCID: PMC4749941
aGreat Basin Institute (GBI), 16750 Mount Rose Highway, Reno, NV 89511, USA
bCalifornia Department of Fish and Wildlife (CDFW), 601 Locust Street, Redding, CA 96001, USA
Rick A. Sweitzer: moc.liamg@kcirreztiews
Abstract
These
data provide additional information relevant to the frequency of fisher
detections by camera traps, and single-season occupancy and local
persistence of fishers in small patches of forest habitats detailed
elsewhere, “Landscape Fuel Reduction, Forest Fire, and Biophysical
Linkages to Local Habitat Use and Local Persistence of Fishers (Pekania pennanti) in Sierra Nevada Mixed-conifer Forests” [10]. The data provides insight on camera trap detections of 3 fisher predators (bobcat [Lynx rufus]). Coyote [Canis latrans], mountain lion [Puma concolor], 5 mesocarnivores in the same foraging guild as fishers (gray fox [Urocyon cinereoargenteus]) ringtail [Bassariscus astutus], marten [Martes americana], striped skunk [Mephitis mephitis] spotted skunk [Spilogale gracilis], and 5 Sciuridae rodents that fishers consume as prey (Douglas squirrel [Tamiasciurus douglasii]), gray squirrel [Sciurus griseus], northern flying squirrel [Glaucomys sabrinus], long-eared chipmunk [Neotamias quadrimaculatus], California ground squirrel [Spermophilus beecheyi].
We used these data to identify basic patterns of co-occurrence with
fishers, and to evaluate the relative importance of presence of
competing mesocarnivores, rodent prey, and predators for fisher
occupancy of small, 1 km2 grid cells of forest habitat.
Keywords: Carnivores, Competition, Distribution, Foraging guild, Predation, Tree squirrels
Value of the data
- • These data provide new insights on how the distribution and habitat use of fishers is influenced by presence of multiple co-occurring carnivores and rodent prey in California, USA.
- • These data indicated that fishers co-occurred with multiple species of rodent prey, multiple other mesocarnivores in the same foraging guild, and 3 larger predators that commonly attack and kill them.
- • These data identified a positive association between fisher occupancy and presence of known prey of fishers, which was suggested previously but without supporting data [7].
- • Mesocarnivores consume similar prey [12], and these data identified a negative association between fisher occupancy and presence of other mesocarnivores, indicative of interspecific competition.
- • Previous research used presence records to predict the range of fishers [6], [7], [14], and data we provide on local occupancy of fishers with prey and competing mesocarnivores can improve models of their distribution in forest ecosystems.
1. Data
In
this Data in Brief article we summarize camera trap detections of 3
fisher predators (bobcat, coyote, mountain lion), 5 mesocarnivores in
the same foraging guild as fishers (gray fox, ringtail, American marten,
striped skunk, spotted skunk), and 5 Sciuridae rodents that fishers
prey on (Douglas squirrel, gray squirrel, northern flying squirrel,
long-eared chipmunk, California ground squirrel) in the Sierra Nevada
region of California, USA. These data identify basic patterns of
co-occurrence of rodent prey and other carnivores with fishers, as well
as how presence of these species influence fisher occupancy within
small, 1-km2 patches of forest habitat in California, USA.
2. Experimental design, materials and methods
2.1. Study area
The overall research area was 1127 km2,
and encompassed the non-wilderness region of the Bass Lake Ranger
District in the Sierra NF, and a relatively small portion of Yosemite NP
where camera trap surveys were completed between October 2007 and
October 2014 [10]. The study area was centered in the California Wildlife Habitat Relations (CWHR) Sierran mixed-conifer forest habitat type (http://www.dfg.ca.gov/biogeodata/cwhr/wildlife_habitats.asp).
Additional details on the diversity of trees and shrubs, and historic
and current land use within the study area were provided elsewhere [9], [10], [11].
2.2. Camera trap surveys
We used a 1-km2
grid matrix overlain on the research area for organizing camera trap
surveys. Motion sensing camera traps (Silent Image Professional,
Rapidfire PC85; RECONYX Inc., Holmen, WI) were systematically deployed
near the center of 1-km2 grid cells at the start of each of 7
camera survey years beginning around October 15 and ending the next
year in early October. We placed camera traps within cells in the grid
matrix by navigating to grid centers with a handheld Global Positioning
System unit (Garmin model 60 CSx; Olathe, KS), and placing camera traps
at the nearest position including one or more habitat elements known
important for fishers [8].
Cameras were focused on the base and lower bole of bait trees, upon
which we attached baits 1.1–1.5 m up from base, and applied scent lures
as attractants. We used small pieces of venison (140–250 g) in a dark
colored sock as meat bait for fishers, and 8–10 hard-shell pecans strung
onto a length of wire and formed into a small ring as a nut bait for
squirrels [8].
Scent lures were Hawbaker׳s Fisher Scent Lure (Fort Loudon, PA),
Caven׳s “Gusto” scent lure (Minnesota Trapline Products, Pennock, MN),
and peanut butter smeared on the nut ring, and we set all cameras to
high trigger sensitivity, 3 pictures per trigger event on a 1 s
interval, and no delay for images between trigger events [10].
2.3. Image interpretation and processing
When
we processed images from camera traps we assigned identity for each
species and summarized data on detections to identify basic patterns of
co-occurrence with fishers (Table 1).
We represented co-occurrence of each species with fishers as the
proportion of all camera trap survey stations where they were detected
that overlapped with fisher detections from 909 m to 2707 m elevation (Fig. 1Fig. 2, Fig. 3). Each camera station was assigned an elevation based on the mean elevation for the 1-km2 grid [10].
For our assessment of general patterns of co-occurrence, we grouped
camera traps into 12 bins (each bin spanned 151 m elevation), and
created histograms representing the distribution of detections for each
species that were plotted together with the distribution of fishers (Fig. 1Fig. 2, Fig. 3). Because we surveyed just 9 1-km2 grids with mean elevations≥2575 (bin 12), we combined species detections for bin 12 with bin 11. We used loglinear χ2
analyses to contrast detection frequencies between fishers and each
species, or pair of species (e.g. long-eared chipmunks+California ground
squirrels), and the statistical data were reported with the histograms (Fig. 1–3).
Distribution of camera trap detections within 1-km2
grid cells for 5 species of squirrels that fishers prey on in the
research area. Douglas squirrel was the most commonly detected rodent
prey (n=588) of all surveyed grids; (a), and this species completely ...
Distribution of camera trap detections within 1-km2
grid cells for bobcat (a), coyote (b), and mountain lion (c) overlain
on fisher detections (bars with dashed lines). Bobcats were detected in
134 of the surveyed grids, and at all elevations where fishers ...
Data on camera trap detections within 1-km2
grid cells for fishers, large predators, medium-sized carnivores
(mesocarnivores), and Sciurid rodents (rodent prey) that fishers are
known to consume in the Sierra Nevada region of California, USA. Camera
traps ...
We
developed 3 covariates from detections of other species at the camera
traps. Metadata from images of bobcats, mountain lions, and coyotes were
used to develop an index of the frequency of predator presence (pred)
based on the number of 24 h calendar days with predator
detections/effective camera days. Data on frequency of detection of 5
mesocarnivores in a similar foraging guild as fishers were included in
the variable “compete” as an index of competition. We reviewed
information on rodents consumed by fishers in the Sierra Nevada [13],
and combined data on camera detections for them for the covariate
“prey”, representing an index of prey availability in each 1-km2 survey grid.
2.4. Basic habitat and biophysical covariates
We
developed local, cell-specific, biophysical covariates for use in
analytical models of occupancy. We calculated the mean elevation (elev)
for each surveyed cell, which was always included in occupancy analyses
with its quadratic term (elev2). This covariate was
standardized. Habitat covariates included an index of canopy cover based
on the proportion of each cell with CWHR conifer and hardwood tree
canopy closure classes M (40–59% canopy closure) or D (60–100% closure)
(denMD; http://www.dfg.ca.gov/biogeodata/cwhr/wildlife_habitats.asp).
We did not include covariates representing average tree size and slope
because of their colinearity with forest cover and elevation.
2.5. Single-season occupancy model analyses
Occupancy represents the proportion of an area on which a species occurs [3], [4], and modeling can be used to estimate occupancy while accounting for heterogeneity in detection probability among survey sites [5]. We modeled single-season occupancy (ψ) and detection probability (p) as functions of covariates (x) and parameters (β) where p was defined as the probability of observing fisher during a survey period if it was present.
Single-season Occupancy Model
Detection:logit(p)=βp0+βp1x1+βp2x2+
Occupancy:logit(ψ)=βy0+βy1x1+βy2x2+
We
created a detection history of whether a fisher was observed by a
camera trap within each grid during each consecutive survey period after
set-up or re-baiting for up to 5 8–10 day periods during a survey year,
detailed elsewhere [10]. Models were solved by maximum likelihood estimation (MLE) via R statistical software (Version 3.0.1, www.r-project.org) using the unmarked package [2]. Single-season occupancy models were fit using the occu
function, and we followed an information-theoretic approach for
comparing models containing different combinations of covariates. We
evaluated the top models with AIC weights summing to 0.95 [1].
We based decisions on which covariates were important predictors of
detection probability, and occupancy on the relative AIC weights of the
top models and the magnitude and variation of parameter estimates from
these models.
Covariates for
potentially explaining detection probability included a dichotomous, 1st
order Markov process reflecting whether a fisher was detected in the
previous survey period in a season (auto.y); the number of effective
camera days in a survey period divided by 10 (camdays), the proportion
of CWHR medium and dense canopy closure classes in each grid (denMD),
and a dichotomous variable representing whether the survey was conducted
in summer (summer) instead of in fall to spring [10]. We fit all 16 combinations of these detection covariates in occupancy-intercept-only single-season models (e.g., logit(ψ)=βψ0, logit(p)=βp0+βp1x1+βp2x2+…).
Covariates deemed important in this step were included in the detection
component of all subsequent models. Next, we evaluated the following
occupancy covariates: compete, prey, pred, elev+elev2, and
denMD. While always including the final detection covariates, we fit all
64 possible combinations of the occupancy covariates in single-season
models. We evaluated these models to assess the importance of occupancy
covariates and to identify a “best model” for estimation of detection
and occupancy parameters (Table 2). We used parameter estimates from the best model (Table 3) to investigate potential linkages between local fisher occupancy and presence of competitors (Fig. 4a), presence of rodent prey (Fig. 4b), and presence of 3 larger predators (Fig. 4c).
Single-season model illustrating the relationship between local fisher occupancy in 1-km2
grid cells and frequency of presence of competing mesocarnivores in the
same foraging guild as fishers (compete; panel a), frequency of
presence of rodent prey (panel ...
Candidate
models for single-season occupancy for camera trap surveys and fisher
detections in the Bass Lake District, Sierra National Forest,
California, USA from October 2007 to October 2014.
Acknowledgments
The
field effort would not have been possible without help from a dedicated
team of staff and volunteers including C. J. O’Brien, J. Ashling, S.
Bassing, A. Beaudette J. Busiek, A. Cellar, T. Day, Z. Eads, T. Gorman,
D. Hardeman, D. Jackson, W. Mitchell, M. Ratchford, J. Ruthven, J.
Schneiderman, W. Sicard, T. Thein, S. Vogel, R. Wise, T. Watson, and
others. Local support was facilitated by B. Persson, A. Otto, and A.
Lombardo. The study was associated with the Sierra Nevada Adaptive
Management Project (SNAMP), a joint effort between US Forest Service
Region 5, the University of California, US Forest Service Pacific
Southwest Research Station, US Fish and Wildlife Service, California
Department of Water Resources, California Department of Fish and Game,
California Department of Forestry and Fire Protection, the University of
Wisconsin – Madison, and the University of Minnesota, focused on
investigating the effects of landscape fuel treatments on forest
ecosystems. USDA Forest Service Region 5 funded the majority of the
field research, and the California Agricultural Experiment Station
funded the remainder. This is contribution #46 from the SNAMP.
Footnotes
Appendix ASupplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2016.01.032.
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