Modelling Trends in Cetacean Habitat Use and Density on the Southern CalCOFI Lines
Greg Campbell1, Cornelia Oedekoven2, Dominque Camacho3
Lisa Munger1, Karlina Merkens1, Andrea Havron3, Annie Douglas4, John Calambokidis4 and John Hildebrand1
Sara Kerosky
1Scripps Institution of Oceanography, La Jolla 2University of St Andrews, St. Andrews, UK 3Spatial Ecosystems, Olympia, WA 4Cascadia Research Collective, Olympia, WA
• Detailed knowledge of cetacean distribution,
habitat use, density and abundance critical
for management/impact assessment. Importance
•Cetacean habitat use patterns, density and
abundance in SOCAL are not clearly defined.
• Characterize cetacean distribution and
habitat, estimate density/abundance,
and assess trends over time.
Status
Goals
Background
• Examine cetacean distribution relative
to oeanographic variables to
elucidate habitat use patterns.
Habitat
Modelling
• Develop cetacean density and abundance
estimates across seasons and years.
Distance
Sampling
• Create predictive models of cetacean
distribution and densities. Density
Surface
Modelling
Approach
Methods – Visual Monitoring
• Visual monitoring conducted during daylight transits between
CalCOFI stations.
• Two trained marine mammal observers on port and starboard,
scanning 90°with 7-power and 18-power binoculars.
• Species, group size, distance, angle and environmental data.
Habitat
Modelling
Distance
Sampling
Density
Surface
Modelling
Visual
Sightings
Visual
Sightings
Visual
Sightings
Thermal
Fronts
Detection
Functions
Distance
Sampling
Habitat
Predictions
Density &
Abundance
Density
Predictions
Habitat Modelling
r
x
d
What is a Thermal Front?
A boundary between two dissimilar water masses,
characterized by a temperature gradient.
Importance to Cetaceans?
Thermal fronts increase surface nutrients that
support primary and secondary productivity.
http://www.micrographia.com http://www.nmfs.noaa.gov/
Thermal Front Activity Detection
AVHRR (Advanced Very High Resolution Radiometer) Pathfinder 5.0
• 4km resolution satellite images from NOAAs NESDIS
WIM (Windows Image Manager) – SIED (Single Image Edge Detection)
Monthly and Seasonal Shifts in Thermal Front Distribution
Seasonal and Annual Frequency of Thermal Front Activity
Seasonal Thermal Front Activity
Thermal Front Activity: 2004-2008
Linking Cetacean Presence to Fronts
ArcGIS 9.3
• Euclidean distance from sightings to thermal fronts
• Generated random points for comparison
Matlab 7.0
• Statistical Analysis: Two Sample Kolmogorov-Smirnov Test
Photo: D. Camacho
Euclidean Distance to Fronts
Summer H=1 P<0.001 D=0.6233 n=44
Fall H=1 P<0.001 D=0.4719 n=25
Winter H=0 p=0.291 D=0.1502 n=41 (39 Grays)
Spring H=0 p=0.285 D=0.1958 n=24
Mysticetes Distance to Fronts: 2 Sample K-S Test
0 20 40 60 80 Distance (km)
0 20 40 60 80 100 120 140 Distance (km)
0 50 100 150 Distance (km)
0 20 40 60 Distance (km)
Thermal Fronts: Observations
• Seasonal differences in front activity
• Zone of high front activity located on the continental shelf
• Mysticetes are more tightly associated with fronts than odontecetes
Habitat
Modelling
Distance
Sampling
Density
Surface
Modelling
Visual
Sightings
Visual
Sightings
Visual
Sightings
Thermal
Fronts
Detection
Functions
Distance
Sampling
Habitat
Predictions
Density &
Abundance
Density
Predictions
Distance Sampling
r
x
d
Southern CalCOFI Study Area
San Diego
Point Conception
Strip Transects
L
w
Density = (# animals seen) / (area searched)
D = n · S
2 · w · L n = # sightings
S = mean group size
Abundance
N = A · D
A = study area
Line Transects
Density
D = n · S
2 · ESW · L
ESW = effective strip width
Distance Sampling – Density & Abundance
0
0.2
0.4
0.6
0.8
1
200 400 600 800 1000 1200 1400 1600
Distance from Trackline (m)
De
tec
tio
n P
rob
ab
ilit
y
r d
d = r · sin x
x trackline
Blue Whales
Humpback
Whales
Fin
Whales
Abundance Estimation – Baleen Whales
Species
n
N
95% CI
Density
1000
km2
Blue 37 191 104-325 1.06
Fin 67 338 190-528 1.87
Humpback 68 426 179-792 2.35
Species n N n N n N n N
Blue 6 151 8 171 30 491 23 491
Fin 5 158 13 338 35 690 15 387
Probability of detection with
distance from track-line.
Truncation Distance: 2 km
Abundance/Density Estimate: Pooled 2004-2009
Abundance Estimates: Stratified by Season 2004-2009
n=number of observations; N=abundance estimate
n=number of observations; N=abundance estimate
winter spring summer fall
Habitat
Modelling
Distance
Sampling
Density
Surface
Modelling
Visual
Sightings
Visual
Sightings
Visual
Sightings
Thermal
Fronts
Detection
Functions
Distance
Sampling
Habitat
Predictions
Density &
Abundance
Density
Predictions
Density Surface Modelling
r
x
d
Density Surface Modelling Common Dolphin Example
G. Campbell SIO A. Cummins SIO
Short-beaked Long-beaked
Average Density per Year
tt tD 10ˆ
Nu
mb
er
of
do
lph
ins p
er
km
2
Year
Density
Density Surface Modelling
2 km
Density Surface Modelling
a
nD t
t ˆ
tt oceoD )exp(ˆ0
ESWLa **2
tt aoceon ))log(exp( 0
Density Predictions
Density Surface Modelling
1. Estimate effective strip half width: ESW
2. Estimate density of groups at the segment
Using oceanographic covariates
3. Estimate average group size
Scale up to density of individual animals
a = 2 * L *ESW
Hedley and Buckland 2004 Thomas et al. 2004
Variable Trends at Different Sites
Study area
Nu
mb
er
of
do
lph
ins p
er
km
2
Year
Density
tDt 10ˆ
Group Density
Fixed effects: Random Effects: Year Year (Intercept and slope) Season Depth SST SAL Chlorophyll a Oxygen Thermocline
Variables Retained in the Model
Variables Excluded from the Model
PO4 Concentration Phaeopigment Concentration Distance to Mainland Distance to Channel Islands
Year Effect
• Coefficient value for fixed effect Year:
-0.097 p < 0.001
Year 2004: exp( -0.097 * 1) = 0.91
• Suggests common dolphin densities in the CalCOFI
study area are dropping by about 9% annually.
Applications
Photo: Cascadia Research Collective
• Identify biologically important areas
• Predictive modelling
• Aid management / policy decisions
• Mitigate anthropogenic impacts
Naval Mitigation:
optimal time windows for naval training exercises
http://upload.wikimedia.org/wikipedia/commons/f/fa/USS_Hayler_DD-997.jpg
Modify shipping lane routes or identify speed reduction zones
Los Angeles
Point Conception
Courtesy of Megan McKenna March 10-13th2010 ship activity Source: Automatic Identification System (AIS) Note: Color represent direction of travel
• Expand habitat modelling analysis through
integration of larger and more diverse
sample of oceanographic data.
Habitat
Modelling
• Produce density and abundance estimates
for nine cetacean species with covariates.
Distance
Sampling
Next Steps
• Test less abundant species.
• Include variation in group size to assess
spatial and temporal trends.
Density
Surface
Modelling
Acknowledgements
• Observers and Acousticians: Robin Baird, Jessica Burtenshaw, Annie
Douglas, E. Elizabeth Henderson, Veronica Iriarte, Autumn Miller, Laura Morse, Erin Oleson Nadia Rubio Michael Smith, Melissa Soldevilla, Ernesto Vasquez, Katherine Whitaker, Suzanne Yin and Stephen Claussen.
• SIO & SWFSC CalCOFI: Dave Wolgast, Jen Wolgast, Jim Wilkinson,
Dave Faber, Amy Hays, Dave Griffith, Grant Susner
• St. Andrews University: Len Thomas, Steve Buckland
• CNO N45 and NPS Frank Stone and Curt Collins
