Review of methods for estimating cetacean density from passive
acoustics
Len Thomas and Tiago MarquesSIO Symposium: Estimating cetacean density
from Passive Acoustics16th July 2009
www.creem.st-and.ac.uk/decaf/ www.voicesinthesea.org
• 3-year project: May 2007-2010• Objectives:
1. Develop methods for estimating the density of cetaceans from fixed passive acoustic devices. Methods should be applicable to a wide range of scenarios.
2. Demonstrate methods by implementing on a set of case studies
3. Promote adoption of methods in the marine mammal research community.
• University of St Andrews (UStAnd) - Len Thomas; Tiago Marques; David Borchers; Catriona Harris
• Naval Undersea Warfare Center (NUWC) - Dave Moretti; Jessica Ward; Nancy DiMarzio; Ron Morrissey; Susan Jarvis; Paul Baggenstoss.
• Space and Naval Warfare Systems Command (SPAWAR) - Steve Martin
• Oregon State University (OSU) - Dave Mellinger; Elizabeth Kusel
• Woods Hole Oceanographic Institution (WHOI) - Peter Tyack
• Steering group: Steve Buckland, Jay Barlow and Walter Zimmer.
Outline
• Part I: Review of density estimation in cetaceans, and passive acoustic methods
• Part II: Decision tree for fixed passive acoustic methods, with examples
Part I: Review
Goal
• Estimate population size/density of cetacean species
• Problems:– Many species occur at very low density over
very large areas– Many of these areas are hard (expensive) to
access– Most spend almost all their time underwater
Standard methods
• Mark recapture– Photo-ID– Tagging studies
• Visual line transect surveys
www.topp.org
Steve Dawson
Tim Gerrodette
Mick Baines
Rob Williams
a type of “distance sampling” method
Strip transects
anD =
Densitynumber seen
area of surveyed region
Line transects
0 20 40 60 80 100
0
10
20
30
40
50
0 20 40 60 80 100
0
10
20
30
40
50
0 20 40 60 80 100
0
10
20
30
40
50
0 20 40 60 80 100
0
10
20
30
40
50
apnD =
Densitynumber seen
area of surveyed region
proportion of animals in surveyed region detected,
“average probability of detection”
Estimating p
these animals are estimated to have been
missed!
assume see everything at zero distance
frequ
ency
perpendicular distance from line, x 2
12
if you saw everything at all distances, on average the histogram bars should be here
=p̂ area under curvearea under rectangle
Estimating p
0 2 4 6 8 10
0.00
0.04
0.08
x
π(x)
True distribution of animals
0 2 4 6 8 10
0.0
0.2
0.4
0.6
0.8
1.0
x
g(x)
Detection function, g(x)
Observed distribution, f(x)
0 2 4 6 8 10
0.00
0.05
0.10
0.15
0.20
x
f(x)
∫=w
dxxgxp0
)(ˆ)(ˆ π
Animals in groups
apsEnD s )(
=
number of groups seen
area of surveyed regionaverage probability of detecting a group
• Measure distance to group, and size of group
average group size in population
Cue counting in visual line transect surveys
• For species with long dives, you cannot be sure to see every animal on the transect line – i.e., g(0)<1
• One solution is cue counting: record radial distance to cues (e.g., whale blows) in a sector ahead of the boat. Assume you see all cues at zero distance.
φ
xObserver travels along a line
radial distance to detected cue is recorded if cue in sector
rpTnD
ˆˆˆ
φ=
sector searched
time spent searching
average cue detection probability
average cue production rate
Point transects
Area and hence number of animals increase linearly with
distance:
12
34
ππ3
π5
π7
0 1 2 3 4
1020
3040
5060
distance
n.de
tect
ed
0 1 2 3 4
1020
3040
5060
distance
n.de
tect
ed
0 1 2 3 4
1020
3040
5060
distance
true
n
Estimating p for point transects and cue counts
12
34
ππ3
π5
π7
0 1 2 3 40.0
0.2
0.4
0.6
0.8
1.0
distance
p(de
tect
)
Estimating p
0 2 4 6 8 10
0.00
0.04
0.08
x
π(x)
True distribution of animals
0 2 4 6 8 10
0.0
0.2
0.4
0.6
0.8
1.0
x
g(x)
Detection function, g(x)
Observed distribution, f(x)
0 2 4 6 8 10
0.00
0.05
0.10
0.15
0.20
x
f(x)
∫=w
dxxgxp0
)(ˆ)(ˆ π
0 2 4 6 8 10
0.00
0.05
0.10
0.15
0.20
x
π(x)
0 2 4 6 8 10
0.0
0.2
0.4
0.6
0.8
1.0
x
g(x)
0 2 4 6 8 100.
000.
050.
100.
15x
f(x)
Line transectPoint transect and cue counting
Outstanding issues with visual surveys
• Some species do not make obvious, discrete cues– g(0)<1 even for cues– detection ranges short (so very low sample
sizes)– weather dependent
• Visual surveys can only operate in the day (in good conditions)
• Vessel-based surveys can be expensive, or impossible in some places/seasons
The potential of passive acoustics
• Some species that are hard to see are very easy to hear
• E.g., sperm whale – towed passive acoustic line transect surveys
Survey vessel
2D acoustic array
Advantages of passive acoustics
• Can work at night, and less weather dependent
• Sounds can be recorded onto hard drives, so may not require trained marine mammal observers on boat (but require much more processing afterwards)
• For some species, sample sizes much larger
Issues with passive acoustics
• Will only work for some species– Animals have to breathe but they do not have
to vocalize!• Ecolocation clicks associated with foraging• Social sounds (breeding, contact, etc)
• Post-processing recorded sounds raises issues usually ignored with visual surveys:– automated detection and classification
systems make mistakes– localization measurement error
Issues with passive acoustics II
• Even with human operators, we know much less about what animals sound like than what they look like
• Focus on – verifying sounds; associating with sightings– development of reliable automated detection
and classification systems
Towed passive acoustics
• Standard line transect methods apply• Can work really well• E.g., Barlow and Taylor (2005):
– simple towed array to get distances– visuals to get group sizes– 45 groups detected over 14,500km effort– compare with visual alone: 8 detections over
8,000km effortBarlow, J. and B.L. Taylor. 2005. Estimates of sperm whale abundance in the northeastern temperate pacific from a combined acoustic and visual survey. Marine Mammal Science 21: 429-445
Fixed passive acoustic detectors
• Advantages over towed systems:– Often cheaper to deploy (although gliders)– Can make use of existing systems– Better temporal coverage (although worse
spatial)• Disadvantages over towed systems:
– Possibly poor spatial coverage– Need to account for animal movement– More difficult to do ranging
Types of fixed passive system• Capability of sensors:
– frequencies sensed– ability to sense direction (multiple linked
sensors close to one another, or directional hydrophones)
• Autonomous vs cabled• Bottom mounted vs surface
mounted/floating• Single sensor or
sparse array vs dense array (will depend on species)
Cornell Laboratory of Ornithology
Scripps Institute of Oceanograpy
Part II: Methods for fixed passive acoustics
Recall earlier slide
• Disadvantages over towed systems:1. Possibly poor spatial coverage2. Need to account for animal movement3. More difficult to do ranging
• Possible consequences for estimating density:1. Can’t assume true density gradient around detector
is known Don’t have enough samples to extrapolate from surveyed area to study area
2. Otherwise will overestimate density.3. Without distances, can’t do distance sampling!
Can’t work out p, so can’t get density.
Early thoughts on possible
methods
Poster presented at ISEC 2009 conference; available from DECAF web site
Update
What can you identify acoustically?
Cues (e.g. whale clicks, dive starts)
Groups of animals
Individual animals
Yes1
NoCan you get cue rate?
Can you get mean group size?
Is detection certain within some defined area, and can you exclude all detections from outside that area?
No
YesStrip transect methods
Examples:
1. strip transect (animal based)
2. dive counting (cue based, see beaked whale case study)
Can you estimate distance to sound source?
No
YesDo you know (relative) density gradient 5?
NoYes
Is g(0) known2?
NoYes
Is π(r) a triangular distribution?
Can you associate sounds across hydrophones?
No
Yes
Can you get bearing?
NoYes
Is sound propagation modeling possible?
No
Yes
Is g(0) known2?
Yes
Simultaneous spatial density surface and detection function modeling3
Do you know (relative) density gradient 5?
Yes No
Can you estimate g(r) any other way4?
No
Yes
No
Yes
Standard point transect snapshot methods or cue counting
Possible analysis methods for fixed passive acoustics (∼ point transect data):
Mark-recapture point transects with hydrophones as capture occasions and distance as a covariate (SECR)
Horvitz Thomson type estimator
∑=
=k
i k
knk
D1
2
1ˆπρ
Horvitz Thomson type estimator
where A(p>0) is a defined area where g(r)>0
∑= >
=k
i ip pAD
1 )0(
1ˆ
Can you associate sounds across hydrophones?
Yes
No
What can you identify?
What can you identify acoustically?
Cues (e.g. whale clicks, dive starts)
Groups of animals
Individual animals
Yes1
NoCan you get cue rate?
Can you get mean group size?
Certain detection
Is detection certain within some defined area, and can you exclude all detections from outside that area?
No
YesStrip transect methods
Examples:
1. strip transect (animal based)
2. dive counting (cue based)
• Identify: time and approximate location of start of a group dive
• Detectability: assume certain• Measuring range: can estimate whether inside or out of survey area• Assumption about true distribution: not required
Fig.
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Image: Diane Claridge
Strip transect example: Dive counting for beaked whales at AUTEC
Strip transect example: Dive counting for beaked whales at AUTEC
• Problems:– false positives– hard to automate– groups close together
in space and time
aTrnsD =
number of dive startsaverage group size
study area
survey period
average dive rate
Can you estimate distance to sound source?
YesDo you know (relative) density gradient 5?
Yes
Is g(0) known2?
Yes
Is π(r) a triangular distribution?
Yes
Standard point transect snapshot methods or cue counting
Uncertain detection
North pacific right whales in the Bering Sea: single sensor cue counting with distances
Collaboration with John Hildebrand and Lisa Munger, Scripps Institution of OceanographyTruncation <15km and >75km
aTprnD =
• Identify: cue (call)• Detectability: assume g(0)=1; otherwise estimate from distances• Measuring range: can do via modal separation• Assumption about true distribution: assume triangular distribution
Standard cue counting methods:
2km1000/whales015.0ˆ =D)]13610 CI %95( 36ˆ[ −=N
Fin whales in the Gulf of Cadiz: sparse array point transect with distances
• Identify: repetitive song pulses allow tracking of individuals to a few km• Detectability: assume g(0)=1; otherwise estimate from distances• Measuring range: can do for each point as each has 3 sensors• Assumption about true distribution: assume triangular distribution, due
to design (many points located systematically)
Collaboration with Luis Matias, University of Lisbon, and Danielle Harris
apnD =
Ability to track individuals allows for standard snapshot point transect methods:
Will need (somehow) to account for non-vocal individuals
Known, but non-triangular density gradients
Is π(r) a triangular distribution?
No
Yes
Standard point transect snapshot methods or cue counting
Horvitz Thomson type estimator ∑
=
=k
i k
knk
D1
2
1ˆπρ
Unknown density gradientsDo you know (relative) density gradient 5?
No
Can you get bearing?
NoYes
Is sound propagation modeling possible?
Horvitz Thomson type estimator
where A(p>0) is a defined area where g(r)>0
∑= >
=k
i ip pAD
1 )0(
1ˆ
Yes
?
Unknown density gradients, no sound propagation
Is sound propagation modeling possible?
No
Is g(0) known2?
Yes
Simultaneous spatial density surface and detection function modeling3
Potential example of simulatneous density surface and detectio modelling:
SOSUS
Source: Charif, Clapham & Clark. 2001. MMS 17:751-68.
No distances, associationCan you estimate distance to sound source?
No
Do you know (relative) density gradient 5?
Yes No
Mark-recapture point transects with hydrophones as capture occasions and distance as a covariate (SECR)
Can you associate sounds across hydrophones?
Yes
Minke whales at PMRF: dense array spatially-explicit capture recapture (SECR)
• Identify: minke whale “boings” over a season• Detectability: estimate via SECR methods.• Measuring range: can associate calls received at multiple
hydrophones• Assumption about true distribution: homogeneous Poisson
• SECR methods: Likelihood based (Efford/Borchers); Bayesian (Royle+others)
• Issue in this study:– Incorrect association – equivalent to mark uncertainty in capture-
recapture
Image: Reefteach
these detections hard to localize
No distances, no association
Can you estimate g(r) any other way4?
Yes
Horvitz Thomson type estimator ∑
=
=k
i k
knk
D1
2
1ˆπρ
Can you associate sounds across hydrophones?
No
Beaked whales at AUTEC via sparse array cue counting with auxiliary tag data
• Identify: echolocation clicks per minute per hydrophone over 6 day test dataset
• Detectability: estimate from separate study: 13 tagged whales tracked over 21 dives. Clicks produced associated with clicks received on range hydrophones. Logistic regression.
• Measuring range: not needed• Assumption about true distribution:
assume triangular (by design)
aTprcnD )1( −
= proportion of false positive detections• Issues:
– weather differed between tagging study and main survey, so p may not apply
– bootstrap variance estimation time consuming
Beaked whales at AUTEC via sparse array cue counting with propagation modelling• Same as previous example, except:• Probability of detection estimated from models of
source level characteristics, sound propagation and detector characteristics
Other case studies
• Harbour porpoise in the baltic– Autonomous detectors
• Sperm whales at AUTEC
• Blue whales from SOSUS array– data secrecy issues
• Others to be presented at this meeting
Conclusions• Estimation of whale density from passive
acoustics is a rapidly developing and expanding field
• Often hampered by lack of auxiliary data, e.g., vocalization rates
• The fact that raw data are archived exposes issues such as species identification and localization error to analysis, when these are typically hidden in visual surveys
• For more, stick around today!
