Quantification Of Roaming Behaviour Of Free-ranging Domestic Dogs To Inform Z...
DissDefensePPT
1. Effects of non-motorized recreation
on mid-size and large mammals in
the San Francisco Bay area
1
Dr. Mathias Tobler – San Diego Zoo Institute; Committee Member
Steve Rosenstock- AZ Game & Fish Dept; Committee Member
Michelle Reilly- NAU; PhD Candidate
Dr. Paul Beier- NAU; P.I. & Committee Chair
Dr. Derek Sonderegger- NAU; Committee Member
4. Goals
• Determine how
habitat use is
affected by
recreation in natural
areas.
• Provide guidance
managers
• locate trails and
manage non-motorized
recreation
• does not degrade the
habitat value of natural
areas
4
5. Background
• Lenth et al. (2008)-
domestic dogs; red fox vs
bobcat, deer, sm.
mammal activity
• Papouchis (2001)- Bighorn
Sheep in Canyonlands NP,
UT; hikers most impactful;
recreation causes
avoidance of suitable
habitat
5
6. Background
• Barja et al. (2007)- Pine
marten in Alps; increased
adrenal activity ;
increase in stress
response
• MacArthur (1982)-
Bighorn sheep &
strongest neg reaction to
leashed dogs
• Increased heart rate
• Krausman et al. (1995)-
Bighorn sheep;
Population declines due
to human activities
6
7. SF Bay Research
Reed and Merenlender (UC-
Berkeley)
2008
• Coyote and bobcat
densities, 5x lower in
areas with rec. as to
those without
2010
• Species richness 1.7x
greater in areas that
excluded rec.
• Abundance of carnivores
decreased as human
visitor use increased
7
8. Methods
• Include woodlands and
forested protected areas
• Attempt to include areas
with varying levels of
recreation use
• Type of rec
• Include mid-size to large
mammals
8
14. Plot Design
14
• Plot cameras were
located 180 from one
another
• Offset 33 left
Camera location (at apex
of triangle)
Approximate field of view
(triangle)
15. Analysis
• Use occupancy modeling
to model ‘habitat use’
• Site-occupancy models =
hierarchical logistic
regression models
• Hierarchical view allows
for separation of
ecological component
and observation
component
15
16. Hierarchical
Occupancy
Models
Hierarchical model- a model with
explicit component models that describe
variations in the data due to
(spatial/temporal) variation in ecological
process and due to an imperfect
observation process
16
17. Occupancy Models
• Sample units
• Multiple observations are
recorded at each site
• multiple visits per site
• detection histories
17
18. Analysis
• Failing to account for
imperfect detection can
lead to spurious
associations
• Detection differs due to
body size, habitat,
camera placement, etc.
18
19. Occupancy
Model
• Multi-species Model
• Analyze multiple
species simultaneously
• Royle-Nichols Model
• Latent occupancy is
modeled as abundance
• Represents the # of
individuals using the
area around the
camera
19
20. Results
• 284 surveys conducted from
2012-2014
• 8 counties and 87 protected
areas
• 20,574 camera trap days
• 96% of cameras functioned
for full 15 day sampling
period
• 18 species detected
• Recreation (max)
• 456 hikers/day
• 263 bikers/day
• 55 recreationists with
dogs/day
• 28 equestrians/day 20
28. Summary
• Mt lions and feral pigs had
the strongest association
with recreation (both
negative)
• Feral pigs are most
impacted by recreation (2
strongest associations)
• Lion most impacted native
species
• For native species:
• Hikers = 2 negative
• Bikers = 2 negative
• Dogs = 3 positive; 1 negative
• Equest = 1 positive
• No negative association
between coyote & bobcats
(contrary to Reed &
Merenlender) 28
29. Management
Implications
• Protected areas have
numerous benefits
• Must balance losses and
gains in conservation and
management
• Should not limit recreation
if there is only a behavior
response
29
31. Research Goals
• Determine if presence of
recreation correlates to
shifts in diel activity
patterns of wildlife
• Describe temporal
patterns of recreation
and wildlife in protected
areas
31
33. Background
• George & Crooks (2006):
Bobcats and coyotes
exhibit spatial and
temporal avoidance to
rec
• Tigas et al. (2002):
bobcats and coyotes
activity “somewhat
lower” in residential
areas with fragmented
habitat
33
34. Methods
• Use data from camera
traps
• Parse events to remove
multiple triggers at one
camera ( >60 minutes)
• Recreation
• > 10 seconds
• “High” = Sites with ≥ 8
recreationists / day
• “None” = Sites without
any humans during
sampling period
• “Any” ≥ 1 recreationist
/day
34
35. Analysis
• Fit circular distribution to
time-of-detection
• Want to ‘measure’ how
similar activity patterns
are in areas with and
without recreation
• ‘Coefficient of overlap’
measures amount of
agreement of 2
probability distributions
35
36. Analysis
• ‘Coefficient of overlap’
( Δ ) is a nonparametric
estimator based on
kernel density
estimation
• Difference in probability
for the 2 distributions is
at most
Δ – 1
• Δ lies in the interval [0,
1]
• =1 Iff the densities are
identical
• =0 Iff the densities = 0 for
all values of the
distribution 36
37. Analysis
• Generate Δ for each
species in areas of no
rec and high rec & no
rec and any rec
• Create a “permutation
distribution” and
corresponding CI
• Assess if overlap is
significantly different
• diel activity
• weekend vs weekday
activity patterns
37
39. Results
• 3 species shifted activity
patterns in areas with
any amount of recreation
• Coyotes, fox, feral pigs
• 1 species shifted in areas
that had high recreation
• Mule deer
• Weekend and weekday
patterns of recreation
differs
• But not animal activity
patterns
39
46. Summary –
Recreation
• diel activity of non-
motorized recreation on
weekdays differed from
weekends
• 48% more of weekends
• Weekend = 1 peak
• Biking & equest – weekend
peak before weekday peak
• Hikers & dogs – weekend
peak after weekday peak
• Biking pattern most
divergent
46
47. Summary –
Wildlife
• Coyotes and grey fox
shifted activity
patterns for both any
and high levels of rec
• Mule deer shift in
areas with high
recreation
• Feral pig shifted
activity in areas with
any recreation
• Shift away from
daylight hours and
toward crepuscular or
nighttime hours 47
48. Summary
• Two canid species shifted
activity patterns to limit
exposure to recreation
• No shift in activity
patterns for bobcat and
skunk
• Small sample sizes
• Lions
• Other species (no rec)
• Why?
48
49. Summary
• Spatial analysis- no neg
association between
coyote and fox and rec
• Mule deer & feral pigs
• Temporal and spatial
• Sylvilagus – no
association
• Bobcats, opossums,
Sylvilagus – no negative
associations
49
50. Discussion
• Response influenced by :
• Variation in experience
with humans
• History of exposure to
human
• Availability of alt. habitat
• Presence of other
predators
• Responses vary by species
• recreation impacts—either
spatially or temporally—a
subset of species found in
protected areas
50
51. Discussion
• Managers and
future plans
• Corridors/buffers
for carnivores
• Species rarely
detected
• What are the costs?
• Behavioral
response vs
numerical response
51
52. Special Thanks to:
My committee: Paul Beier,
Derek Sonderegger, Mathias Tobler,
Steve Rosenstock
My Funders: The Gordon and Betty
Moore Foundation
NAU Business Office (Kris Bellmore
and Beth Wixom)
GIS Analyst: Jeff Jenness
California Agencies: NPS, Cal State
Parks, Napa LT, Sonoma LT, Sonoma
Ecology Center, SF Public Utilities,
Conservation Fund,
ALL SF AGENCIES
52
My technicians: Tom Batter, Elden Holdorff, Sarah Espinosa,
Morgan Gray, Cody Griffin, Tucker Volk, Leanna Lucore, Jacob
Humm, Canyon Miller, Audrey Nickles, Nick Gengler, Kayla Lauger,
Greg Pfau, Ally Coconis, Vanessa Lane-Miller, Kate Galbreath,
Megan Sutton, Bri Halliwell, Caitlyn Cooper
Past research in the field of recreation ecology (took off in 1960s) which is the scientific study of recreation impacts and here impacts to wildlife….
Lenth, knight & brennan- Boulder CO. compared activity level of wildlife in Areas closed to dogs and areas that allowed dogs. Deer and sm mammal activity decreased within 50-100 m of trail
Used an index for camera trap data ( # photos for species / camera trap nights)
Activity of native carnivore higher on trails that allowed dogs than trails where dogs weren’t allowed.
Bobcats, Mule deer, and sm mammals, activity was inversely correlated to dog presence.
Papouchis – flight as a measure of perceived risk, hikers, bikers, vehicles (animals fled in 61% of encounters with hikers). Animals also used road corridors less in high use recreation areas
There is also research that goes beyond observing behavioral responses of wildlife…..
Krausman- wilderness area in Santa Catalina mountains (backcountry users had most impact) Fire suppression made habitat unsuitable; human encroachment limit ability of population to increase
macArthur- wildlife sanctuary in Alberta Canada; Increase in HR for humans within 50 meters of sheep (the researchers did not observe a reduction in heart-rate response with repeated trials.)
Randomly generated potential sites in forested sections of protected areas throughout the study area using ArcGIS. Again managers are not aware of the amount of rec that occurs on protected areas so there was no way to attempt to stratify sites by recreation levels
a lot of the previous research on impacts of recreation on wildlife that used either visual description of the distance an animal moved as an indication of aversion or scat as an index presence, We Used camera
With these we were able to quantify the amount of recreation in protected areas and also to record presence of wildlife species
California has the largest state park system in the US.
We also included regional park and county openspaces, ecological reserves, public utility lands.
Talk about diversity within “woodlands”
Sites were set up at a site for 15 days.
25 m detection range
Other variable were included in the model that may help explain where animals are located
Distance to the urban edge, road density (proxy for human population density), trail density, forest type, precip, temps, elevation.
The initial state of the system
2. State dynamics (occupancy dynamics)-Latent variable and not necessarily known (unless a detection is made)
1. Observation process
To account for imperfect detection extra data about the observation process are needed
Repeated sampling allows for the distinction between a non-detection and an absence
Observed non-detections can result from the site being unoccupied (fixed zero) or a non-detection despite the site being occupied (sampling zero).
True occurrence is imperfectly observed,
Because detection probability can take on any value between 0 and 1, counts or use of count data almost always underestimates true occupancy.
If detectability is non-random (if its related to a covariate) and not accounted for, your model will overstate the importance of that variable (rabbit example)
Multi-species model: non-detections can be distinguished from absence through repeated sampling
Species-specific estimates of occurrence can be improved using collective data on all species observed during sampling
RN model- recognized that heterogeneity in detection probability can be induced by variation in abundance among sites. However this parameter does not have to be interpreted as abundance..it can be any random effect that yields variation in detection probability.
Heterogeneity in detection caused by factors such as cameras being closer to animal activity center, cameras placed on game trails, etc.
RN model is similar to “Model Mh” in capture recapture studies but in the RN model heterogeneity in det prob is related to sites not animals
accounting for differences in abundance or other factors that would result in model habitat use by modeling abundance as a Poisson function of measured covariates. This model includes a logistic model for detection with random effects for species and camera placement.
Survey length was 15 days but not all cameras functioned for the full survey. The model can account for surveys of different length and thus accommodates camera failure.
Again, remember that this number (Detection Probability) is a probability therefore it can take any value between 0 & 1.
The closer to 1 the value is an indication that very few individuals escaped detection. For many survey techniques, this value is usually far below 1.
The discrepancy between counts and true occupancy increases with decreasing detection probability. This further illustrates the need to incorporate detection probability because counts may not reflect true occupancy or habitat use.
Only bobcats, coyotes, and Sylv. had no significant association with recreation
6 negative and 5 positive associations between species and rec
5 strongest associations were all negative
Only coyotes, and Sylv. had no significant association with recreation
Protected areas provide psychological and spiritual benefits, and educational opportunities. Allowing recreation in PA provides communities with opportunities for health benefits as well as providing political and financial support for land and species conservation
Shifts in activity patterns may result from interference
Activity levels are a key metric in understanding the trade off between time spent active and time spent resting.
Activity levels include consideration of energy budgets and ecological constraints
Optimal time for activity = sensory adaptations to light conditions & trade-offs between predation and starvation
George and crooks – Nature Reserve of Orange Co. 49 camera sites; bobcats (& to lesser degree coyotes) detected less frequently along trails with higher human activity. Also shifted to become more nocturnal. (Used Index of Relative Activity = # photos divided by # days camera functioned (indices may not be reliable estimates of density of abundance due to variability in metric used) (there can be non-linear relationships)
temporal analysis = Percent Daytime Activity , where 6am -6pm =day & 6pm – 6am was night) 4ppl/day=high
Tigas-bobcats and coyotes (radio-collared animals); both species persist in urban env by adjusting behaviorally (spatial or temporal changes)
Camera traps provide a efficient method of collecting data on activity patterns of animals in the wild…
Direct observations are time consuming and costly…lab experiments on physiology and chronobiology don’t provide understanding into behavior in the field…
time of day is a circular random variable whose density may be bimodal or multi-modal…
Problem with time of day arise due to arbitrary nature of the time origin…also the end of the scale meets the beginning, or origin.
circular probability density function is needed to capture the complexity of activity patterns address (arbitrary timescale)
Use of nonparametric functions is important because animal activity can be bimodal or even multi-modal
Von mises kernel density
Coefficient of overlap was introduced in 1970 (Weitzman), defined as the common area under 2 probability densities
Was used as a measure of agreement of 2 income distributions
Nonparametric circular kernel density function is used to fit camera trap data and then estimate overlap using total variation distance function (L1)-
As usual in kernel density estimation, a critical issue is the choice of the smoothing parameter,
represented here by the von Mises concentration parameter ν
Rowcliffe et al 2014- with a sample size of 500, bias was less than 10% in all cases. When sample size reached 100 coefficient of variation declined to 10%.
In their analysis they used data for species with at least 42 captures.
With Small sample sizes the distribution is oversmoothed, overestimating the proportion of time active and leading to decreased ability to detect slight changes in activity patterns (higher coefficient of overlap)
Mt lions- synchrony in activity patterns maybe a difficult assumption to meet due to feeding behaviors..
Coyotes in SF Bay Area at sites with high rec activity were less active during the day and more active at night compared to coyotes in sites with no recreational activity (sample size = 184 and 69 captures respectively; p-value = 0.001). The y-axis represents the fitted kernel density functions of activity from the two data sets.
Grey fox in SF Bay Area sites with high recreation activity were more active before dawn and less active after dusk compared to grey fox in sites with no recreational activity (sample size = 727 and 220 captures, respectively; ( ∆ 1 = 0.898, CI = 0.906 – 0.977). The y-axis represents the fitted kernel density functions of activity from the two data sets.
Figure 3-9 Mule deer in SF Bay Area sites with high recreation levels had an earlier peak in activity near dawn and later peak after dusk compared to mule deer in sites with no recreational activity (sample size = 1401 and 284 captures, respectively; (p-value= 0.023). The y-axis represents the fitted kernel density functions of activity from the two data sets.
Wild pigs in SF Bay Area sites with any level of recreation were less active during the day and more active at night compared to wild pigs in sites with no recreational activity (sample size = 76 and 17 captures, respectively; (p-value=0.002). The y-axis represents the fitted kernel density functions of activity from the two data sets.
***feral pigs= cathemeral; feral pigs can rapidly modify their behavior to benefit their survival…in different climates they have different activity patterns…even in areas with/without hunting they have different activity patterns.
There was a higher peak in density of all recreation types on the weekends compared to weekdays
On weekends, 48 % more recreationists per day than on weekdays.
Recreation activity patterns during the week= 2peak times; weekend = one peak.
The peak hour for weekend mountain biking and equest occurred before the peak in weekday activity
peak weekend hour for hiking and dog walking occurred after the peak in weekday activity.
Weekend and weekday activity patterns for mountain bikers appears the most divergent with a strong peak in weekend activity occurring before noon and two peaks during the week occurring after noon
Consistent with past research on activity patterns of canid species in natural and altered habitats
Are there potential cost to canid species of use of “sub-optimal” foraging times? They are wide-spread in Bay so maybe not
Consistent with shifts in activity patterns of coyote in suburban and agricultural areas compared to natural areas in western Wy
African dogs shift activity to avoid human persecution
Managers are faced with assessing the costs and benefits of recreation in protected areas. There are trade-offs between recreation experience quality and natural resource quality.
Our research identifies the potential trade-off in terms of wildlife’s behavioral responses to recreation
By acknowledging the trade-off managers can address conservation policy in their protected areas
For grey fox, coyotes, and mt lions perhaps protected areas should maintain core areas without trails to allow these animals refuge from recreation
Mule deer protected areas should contain buffers of thick vegetation along trails or protected area edges to conceal recreation and also initiate education program aimed of reducing fruit trees in residential areas adjacent to protected areas
Costs? Behavioral response such as declines in geographic range are often used to evaluate species status and assign risk categories (IUCN)
Grey fox – coyote interferences, reduced time hunting during optimal light conditions due to presence of humans = more energy spent hunting during lower light conditions = reduced hunting success *investigate interspecific interactions
Research on activity levels can extend into consideration of population level consequences of constraints on activity such as recreation, and implications for population persistence.
End with the elusive “bob-lion”
Coyotes in SF Bay Area sites at sites with any level of recreation were less active during the day and more active at night compared to coyotes in sites with no recreational activity (sample size = 411and 69 captures, respectively; (p-value=0.003). The y-axis represents the fitted kernel density functions of activity from the two data sets.
Grey fox in SF Bay Area sites with any levels of recreation were more active before dawn and less active after dusk compared to grey fox in sites with no recreational activity (sample size = 1462 and 220 captures, respectively; (p-value= 0.011). The y-axis represents the fitted kernel density functions of activity from the two data sets.
Problem with time of day arise due to arbitrary nature of the time origin…also the end of the scale meets the beginning, or origin.
circular probability density function is needed to capture the complexity of activity patterns (arbitrary timescale)
