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Spatial and seasonal variability in cetacean distribution in the
fjords of northern Patagonia, Chile
Francisco A. Viddi, Rodrigo Hucke-Gaete, Juan P. Torres-Florez, and Sandra Ribeiro
Viddi, F. A., Hucke-Gaete, R., Torres-Florez, J. P., and Ribeiro, S. 2010. Spatial and seasonal variability in cetacean distribution in the fjords of
northern Patagonia, Chile. – ICES Journal of Marine Science, 67: 959 –970.
Compared with other Chilean coastal areas, little is known about the diversity and distribution of cetaceans in northern Patagonian
fjords. Between December 2000 and November 2001, surveys on platforms of opportunity were undertaken in southern Chile to evalu-
ate species richness and the spatial and seasonal distribution of cetaceans. Nine species were recorded, blue, humpback, and minke
whales, Peale’s dolphin, Chilean dolphin, killer whale, false killer whale, bottlenose dolphin, and Cuvier’s beaked whale. The pattern of
cetacean distribution displayed significant seasonal differences, with most baleen whales (mysticetes) observed during late summer
and autumn, and toothed cetaceans (odontocetes) mostly during spring. Generalized additive models, used to assess the spatial dis-
tribution of cetaceans, showed that mysticetes were distributed disproportionately along a north– south gradient, in open gulfs with
oceanic influence, and close to shore. In contrast, odontocetes were observed mainly within narrow channels, areas with complex
coastal morphology, peaking at different water depths. These findings, although from a single year of data, increase our understanding
of habitat determinants of cetacean distribution in southern Chile. The results have the potential to be applied to coastal conservation
and management in the region.
Keywords: Balaenoptera musculus,Cephalorhynchus eutropia, cetacean distribution, Chilean fjords, generalized additive models,
Lagenorhynchus australis, spatial distribution.
Received 4 June 2009; accepted 14 November 2009; advance access publication 10 January 2010.
F. A. Viddi: Marine Mammal Research Group, Graduate School of the Environment, Macquarie University, Sydney, NSW 2109, Australia. R. Hucke-
Gaete and J. P. Torres-Florez: Marine Mammal Ecology Laboratory, Centro Ballena Azul, Instituto de Ecologı
´a y Evolucio
´n, Universidad Austral de
Chile, Casilla 567, Valdivia, Chile, and Centro de Investigacio
´n en Ecosistemas de la Patagonia, Francisco Bilbao 449, Coihaique, Chile. S. Ribeiro:
Instituto Estadual de Meio Ambiente e Recursos Hı
´dricos, Projeto corredores Ecolo
´gicos, BR 262, Cariacica, Espirito Santo, Brazil. Correspondence to
F. A. Viddi: tel: þ61 2 98507980; fax: þ61 2 98507972; e-mail: fviddi@gse.mq.edu.au.
Introduction
Waters off the Chilean coast are recognized as holding some of the
greatest biological productivity in the world (Daneri et al., 2000),
and the extraordinary productivity appears to be influential in the
distribution and abundance of several species, including whales
and dolphins. Of the 90 cetacean species described worldwide,
almost half have been recorded in Chilean waters (Aguayo-Lobo
et al., 1998). Nevertheless, despite this large number of species,
little is known about their ecology, especially in the fjord region
of southern Chile.
The northern Patagonian fjords comprise a complex oceano-
graphic environment (Silva et al., 1997;Silva et al., 1998), and
recently became known as a vast estuarine system, where research
has focused mainly on oceanography and the fisheries (Pickard,
1971;Balbontin and Bravo, 1993;Silva et al., 1995;Balbontin
and Bernal, 1997;Fierro et al., 2000). However, although marine
mammals are a conspicuous biological component of the area,
their distribution in the fjord region between 41 and 488S has
not been described.
Most information available in the literature about the diversity
and distribution of cetaceans in Chilean waters is derived from
whaling data, opportunistic sightings, strandings, updates of dis-
tributional ranges, and osteological material, dispersed mostly in
unpublished technical reports, conference proceedings, and a
few scientific publications (Aguayo-Lobo et al., 1998). There
seems to be a knowledge gap in northern Patagonian fjords, in par-
ticular between Puerto Montt and Golfo de Penas (418300and
488S). This gap is punctuated by only a few localized systematic
studies, mostly on Chilean (Cephalorhynchus eutropia) and
Peale’s dolphins (Lagenorhynchus australis;Heinrich, 2006), a
rediscovery of a feeding ground of blue whales (Balaenoptera mus-
culus;Hucke-Gaete et al., 2003), and a recent descriptive study of
marine mammals in the area (Aguayo-Lobo et al., 2006).
Therefore, to our knowledge, no detailed study has been made
at the mesoscale regarding the ecological determinants of cetacean
distribution in the area.
Cetaceans, and mobile animals in general, exploit the environ-
ment disproportionately, and their distribution varies temporally
and spatially (Samuel et al., 1985;Stevick et al., 2002). Studies
have described cetacean distribution and habitat preference by
linking their presence to different habitat variables. Most cetacean
studies propose that habitat selection and use patterns are princi-
pally a function of distribution, movement, and the abundance of
prey (Ballance, 1992;Karczmarski et al., 2000;Stevick et al., 2002)
and as a means of finding refuge from predators (Heithaus
and Dill, 2002). However, the specific environmental conditions
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selected by cetaceans may be proxies for the characteristics of the
environment that relate more directly to prey concentrations.
Bottom depth, water clarity, sea surface temperature, primary
productivity, proximity to estuaries and rivers, bottom type,
tidal currents, and/or frontal systems have been used as proxies
to assess, describe, and predict cetacean habitat association in
studies in the North Atlantic (Gowans and Whitehead, 1995;
Hastie et al., 2005), New Zealand (Bra
¨ger et al., 2003), the Gulf
of Mexico (Baumgartner et al., 2001), the Mediterranean Sea
(Can
˜adas et al., 2005;Panigada et al., 2008), and the North Sea
(Mendes et al., 2002;Skov and Thomsen, 2008).
Cetaceans are difficult to study because they spend most of
their time out of sight beneath the surface and most species live
far from shore where direct observations are complex or logisti-
cally prohibitive. Such constraints render cetacean research
costly in terms of carrying out regular surveys (Lopez et al.,
2004;Kiszka et al., 2007). When data collection is required and
funding is limited, platforms of opportunity, such as fishing
vessels, tourist ships, or cargo ferries, may provide a means of gath-
ering crucial information on cetacean distribution. The routes for
such ships are not determined by a research design, but rather by
needs such as safer, more efficient, or scenic routes. Therefore,
these survey track lines fail to provide equal coverage probability
(either systematic or random sampled), and basic statistical
design is compromised. Therefore, although the data generated
by platforms of opportunity are useful, they should be taken
merely as initial insights into cetacean distribution and as impor-
tant starting points for designing systematic surveys.
Considering the need to understand the ecology of many ceta-
cean species and the rapid expansion of coastal development in
southern Chile, such as for aquaculture, this study aimed to
examine the spatial and seasonal distribution of cetaceans in
northern Chilean Patagonia using platforms of opportunity. The
information gathered will support the efforts made in terms of
developing conservation and management policies at local and
national levels and will form a baseline from which future systema-
tic surveys can be designed.
Material and methods
The study was undertaken between 418300and 488000S in southern
Chile (Figure 1). The area consists of an intricate array of inner
passages, archipelagos, channels, and fjords, stretching along ca.
900 km of linear coastline and enclosing 12 000 km of convo-
luted and protected shoreline.
The general oceanographic conditions affecting this area are
under the direct influence of the West Wind Drift (WWD). The
bulk of the oceanic west-driven currents encounter the South
American continent at about latitude 418S, one of the major
fjord regions of the world, and the origin of a northbound
current characterized by a north-flowing branch, the Humboldt
Current, which in turn splits into coastal and oceanic, and a south-
bound current represented by a poleward-flowing branch, the
Cape Horn Current (Longhurst, 1998). The interaction between
the WWD, Subantarctic waters, fjord freshwater (coastal run-off
from glacier melt, river drainage, and copious precipitation),
and tidal currents defines a strong vertical and horizontal salinity
gradient (Davila et al., 2002;Palma and Silva, 2004), which in turn
supports phytoplankton and primary productivity (Iriarte et al.,
2007). Freshwater run-off and glacier meltdown in some areas
can cause anomalies in water salinity, density, and temperature
inshore (such as in Laguna San Rafael, where glaciers reach the
sea). In addition, river discharge brings sediments and terrigenous
material, which in combination influence the dynamics of coastal
circulation (Silva et al., 1998).
Data collection
Data on cetacean distribution were collected between December
2000 and November 2001. Vessel-based observations were made
in the channels and fjords of southern Chile (from Puerto
Montt to south of Taitao Peninsula) aboard platforms of opportu-
nity, which included tour and cargo ferries that traverse the fjords
on fixed routes. Daily observations were made by 2– 4 observers
during 14 cruises on board the MV “Evangelistas” and the MV
“Puerto Eden” from the highest vantage points aboard (12
and 10 m, respectively).
Trained observers searched for cetaceans 50% by eye and 50%
using 7 50 binoculars, covering a strip of 5 km looking ahead
to 90
o
on each side (2.5 km to each side). Search effort was
occasionally interrupted while observers attempted to determine
species positively. Data on effort included date, start/end time,
geographic position (using a hand-held GPS), weather, and sea
state (Beaufort scale).
Data on cetacean sightings included ship’s position, time, angle
and approximate distance to the sighting (using rangefinder reticle
binoculars), species, and group size. As the encounter rate was of
interest, a precise estimate of distance was not necessary. A ceta-
cean sighting was defined as a single animal, or a group of
animals of one species within a radius of 100 m for dolphins or
of 1 km for whales (although no association between individuals
in a group was inferred). Maximum, minimum, and best estimates
of group size were recorded for each sighting. As ferries would not
change their routes to approach a sighting, the time spent collect-
ing data relating to a group produced just a species identification
and a record of the number of animals. Searching was only per-
formed under Beaufort state 3. Search effort ceased when visi-
bility was too poor, possibly precluding sightings as a
consequence of weather conditions, i.e. strong winds, heavy rain,
fog, and/or high sea state.
Data analysis with GIS
Observation effort was neither spatially nor seasonally distributed
evenly, but was only during daylight and when sea and weather
conditions permitted. Periods of effort (time spent on active
visual survey) were plotted as lines into ArcGIS (version 9.2;
Environmental Systems Research Institute, Redlands, CA, USA),
from which a raster layer of density of track lines was created.
Here, the track density was generated by calculating the total
length of track portions that fell within a radius of 5000 m in
the neighbourhood of each output raster cell of 1 1 km. Each
grid cell would receive a value for density of effort of total distance
covered in each cell (metres covered per unit area; Figure 1). These
values were used as weights for later analysis to adjust sightings in
relation to effort and distance to track line. All geographic data
were recorded and downloaded as a Projected Coordinate
System WGS 1984 UTM Zone 18S.
All sightings were plotted in ArcGIS after correcting for angle
and distance. For each sighting, a value of the inverse track
density was extracted from GIS (used as weight in data modelling)
and used along with physiographic features such as depth, channel
width, distance to coast, and coast complexity. Latitude was used
as UTM Northing to account for cetacean spatial distribution,
given that the ferry route had a north–south –north direction.
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Raw data for depth (as latitude, longitude, and z-values) were
obtained from the Chilean Navy (Servicio Hidrogra
´fico y
Oceanogra
´fico de la Armada de Chile), from which a Triangular
Irregular Network model was created using three-dimensional
analyst in ArcGIS. Width of channel for each sighting was esti-
mated by measuring the distance between the two coastlines, per-
pendicular to the track line followed by the ferry. For those
sightings made outside the inner fjords (along the open coast),
an arbitrary value of 140 km for channel width was given, 20 km
more than the widest distance measured in the inner passages.
Distance to coast and coastline complexity were generated and
subsequently extracted for each sighting and absence point in
GIS. Coastline complexity is a measure of concentration of
islands, convoluted bays, and narrow channels measured as a
density of coastline. This variable was calculated by estimating
the total length of coastline (in km) falling in 1 km 1 km cell
within a searching radius of 10 km (values of zero mean that
there was no coast within a radius of 10 km).
Finally, to use the actual locations for each sighting and their
derived environmental features for statistical analysis, random
points were generated using the extension Hawth’s tools for
ArcGIS (Beyer, 2004). In all, 170 random points were generated,
slightly more than the actual number of sightings (Goetz et al.,
2007). These random points then represented an absence of sight-
ings. Each random point was generated taking into consideration
survey effort (Torres et al., 2008). For this, an algorithm was used
in ArcGIS to create random points considering weightings for
track density (i.e. more random points were generated in those
Figure 1. The study area in southern Chile and the density of the tracks of cargo ferries (areas in darker grey had greater effort in terms of
total distance covered per unit area).
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areas with greater effort). For each random point generated, all the
variables mentioned above were extracted using GIS.
Seasonal and spatial analysis
In general, data were analysed by pooling data for mysticetes and
odontocetes separately. Additional assessments of Chilean and
Peale’s dolphins were made because those two species had suffi-
cient sightings for analysis.
For seasonal statistical analysis, four seasons were considered:
summer (12 December 2000–27 February 2001), autumn (12
April–17 June 2001), winter (28 July– 5 September 2001), and
spring (4 October –1 November 2001). A Chi-squared test of inde-
pendence was used to assess the relationship between cetacean
encounter and season. A post hoc Pearson residuals analysis was
then carried out for Chi-squared significant values to determine
which seasons explained the lack of independence. When sample
size was small, a p-value approximation procedure was computed
from a Monte Carlo test with 10 000 replicates within the
Chi-squared protocol (Hope, 1968). A Chi-squared analysis
assumes equal effort among categories, in this case seasons. We
adjusted the counts for all taxa and species by estimating the sight-
ing rate per season, then multiplying each seasonal rate by a con-
stant effort of 79. We then rounded the numbers to derive
standardized counts. Analyses were carried out in R 2.9.1
(R Development Core Team, 2009).
Generalized additive models (GAMs) were used to examine the
role of spatial and environmental variables on the sighting
locations of cetaceans. GAMs allow a data-driven approach by
fitting smoothed non-linear functions of explanatory variables
without imposing parametric constraints (Hastie and Tibshirani,
1990). Smoother terms were derived using thin-plate regression
splines implemented under the “mgcv” package in R 2.9.2
(Wood, 2006). A binomial distribution (presence/absence)
family and a logit link function were used. Presence/absence was
used instead of a density value because we decided that there
were too few sightings to calculate a density (sightings per area
or per time) sufficiently meaningful to develop the models.
Working with density values would have required a grid system
in which the values for the covariates for each cell in the grid
were obtained from the mean values within each cell, so losing
covariate detail with the spatial scale of some channels.
Backward selection, beginning from a fully saturated model,
was used to obtain the best-fitting models based on their general-
ized cross-validation scores (GCV). The GCV can be viewed as the
criterion that selects the effective degrees of freedom of a model
where the scale parameter is unknown and is therefore estimated
by the model. The GCV operates by performing smoothing par-
ameter selection wherein the GCV essentially finds an appropriate
smoother for each covariate (Wood, 2006). A lower GCV score
indicates a better-fitting GAM. GCV is known to tend to overfit
on occasions, so a gamma of 1.4 was specified, effectively
correcting the overfit without compromising model fit (Kim and
Gu, 2004;Wood, 2006). Additionally, because the scale parameter
is unknown and the data likely to be overdispersed, the “gam”
function was forced to estimate the scale parameter by specifying
the scale as 21(Wood, 2006).
From the initial full model, the covariate with the highest non-
significant p-value was removed and refitted to the reduced model.
If that model resulted in a lower GCV score, it was retained and
again the covariate with the highest p-value was removed. The pro-
cedure was repeated until the removal of any covariate resulted in
a higher GCV score. If at any stage removing the least significant
covariate resulted in an increased GCV score, then the next least
significant term was removed, etc., until no further reduction in
GCV could be obtained. Diagnostic plots were also made to deter-
mine the fit effectiveness of the models (Wood, 2006). If the model
reduced the smoothing spline to an estimated degree of freedom
approximating to 1 and there was no apparent pattern in the
residuals, then the smoother function was replaced by a linear
term. A weight vector was included in the model process, corre-
sponding to the inverse value of density of the survey track lines.
This vector was included in the GAMs to account for the different
effort made at spatial scales, noting that models not including
effort data are one of the major factors causing erroneous analysis
in cetacean literature (Redfern et al., 2006). Four models were
built, for five predictive variables. The first two described the
global spatial distribution of mysticetes and odontocetes over
the whole year, and the other two highlighted specific habitats of
Peale’s and Chilean dolphins. We chose GAMs because they gen-
erate smoothed curves representing the relationship between the
response and each predictor variable in the model. GAMs are par-
ticularly good at identifying and describing non-linear relation-
ships that are more typical than linear relationships in ecology
(Oksanen and Minchin, 2002).
Results
The overall observation effort was 315 h (during 47 days and 14
ferry cruises) accomplished during the four seasons, representing
almost 8000 km of coverage. There were 129 cetacean sightings
(Figure 2), comprising nine different cetacean species, along the
channels and fjords south of Puerto Montt. There were three
species of baleen whale, blue (B. musculus), humpback
(Megaptera novaeangliae), and minke (Balaenoptera bonaerensis),
and six of odontocete, Peale’s dolphin (L. australis), Chilean
dolphin (C. eutropia), killer whale (Orcinus orca), false killer
whale (Pseudorca crassidens), bottlenose dolphin (Tursiops trunca-
tus), and Cuvier’s beaked whale (Ziphius cavirostris). Peale’s
dolphin was by far the most frequently observed, followed by the
Chilean dolphin. Bottlenose dolphins were, however, the most
abundant cetacean owing to the large size of the groups observed
(Table 1).
Seasonal and spatial distribution
Seasonally, the pattern of cetacean distribution was significantly
different in each season (
x
2
¼37.05, p,0.001). Mysticetes were
mostly recorded during autumn and never in winter (
x
2
¼
33.38, p,0.001), whereas odontocetes were observed more
often during spring and less frequently during summer (
x
2
¼
18, d.f. ¼3, p,0.001; Figure 3). Peale’s dolphins showed a sig-
nificant seasonal pattern (
x
2
¼14.59, d.f. ¼3, p¼0.002), with
more sightings in spring, whereas Chilean dolphins had no seaso-
nal pattern, with a similar sighting distribution during all seasons
(
x
2
¼1.29, p¼0.756; Table 2).
The GAM revealed that the overall spatial distribution of mys-
ticetes was not even, with peaks in the number of sightings along a
north–south gradient. The models also retained two other spatial
covariates among the five tested: width of channel and distance to
the coast. Overall, the best model for mysticete data explained
58.9% of the deviance (Table 3). Baleen whales selected areas of
wide channels and close to the shore (Figure 4).
The GAM results for odontocete presence indicated that the
best model included the effect of coast complexity (as a linear
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term), UTM Northing, distance to coast, and depth, and explained
48.8% of the deviance (Table 3). Odontocetes in general had a stra-
tified distribution along the north–south gradient, with peaks of
sightings at different latitudes. They identified areas closer to
shore, depths .200 m, and areas with complex coastal mor-
phology (Figure 5).
Peale’s dolphins showed a non-linear relationship with water
depth, peaking at depths of 50, 250, and 400 m. They
were also more frequent in areas with high coastal complexity
and with uneven distribution along the north–south gradient
(Figure 6). Overall, the best model explained 59.4% of the
deviance. For Chilean dolphins, the model retained just depth
and distance to the coast as linear terms, and explained 64.6%
Figure 2. Cetacean distribution in the northern Patagonian fjords, southern Chile. (a) Mysticetes and (b) odontocetes.
Table 1. Summary of cetacean species, number of sightings,
number of animals, and group size average and range observed
during 14 surveys between December 2000 and November 2001.
Cetacean
Number of
sightings
Number of
animals
Group size
Average Range
Peale’s dolphin 42 182 4.3 1–15
Chilean dolphin 32 109 3.3 1–15
Bottlenose
dolphin
8 273 34.1 4–
100
Killer whale 4 8 2 1–3
False killer whale 2 10 5 3–7
Blue whale 7 14 2 1–3
Humpback whale 6 17 2.8 1–5
Minke whale 5 7 1.4 1– 3
Cuvier’s beaked
whale
11––
Unidentified
mysticetes
10 13 – –
Unidentified
odontocetes
12 35 – –
All species 129 669 – –
Figure 3. Seasonal sighting rate and effort for cetaceans in the
northern Patagonian fjords, southern Chile, for all taxa pooled, for
odontocetes, and for mysticetes.
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of the deviance (Table 3). Chilean dolphins were mostly found
near the shore in shallow water (Figure 7).
Discussion
Before this study, little was known about the species richness and
distribution of cetaceans in the fjords and inner seas of northern
Patagonia, Chile. During the systematic assessment described
here, nine species were recorded in 1000 km of linear surveys
south of Puerto Montt (between 418300and 488000S).
Although all species sighted during the study have been
reported previously for Chilean waters, no sightings for this par-
ticular study area had been documented for false killer whales
and Cuvier’s beaked whales. Those two species, along with killer
whales, were the least frequent of the species recorded in the
study area. Capella et al. (1999) suggested that killer whales
might be rare along the Chilean coast. Nevertheless, more systema-
tic studies covering a larger temporal and spatial range need to be
developed, because several new records have been documented
(FAV, unpublished data).
Consistent with earlier reports, Peale’s dolphins appear to be the
most common cetacean in the area (33% of the sightings), followed
by Chilean dolphins (25% of the sightings) (Goodall et al., 1988,
1997a;Goodall, 1994). Nevertheless, bottlenose dolphins were the
most gregarious and the most numerous cetacean observed, repre-
senting 40% of the total number of animals recorded. The Moraleda
channel and associated fjords and channels, especially those close to
the Aysen fjord, seem to be a hotspot for odontocetes because all
toothed cetaceans recorded during this study were sighted there.
Compared with other fjord systems in the world, the number of
species observed here is comparable with results from BC, Canada
(Williams and Thomas, 2007), and Fjordland, New Zealand
(Lusseau and Slooten, 2002), where seven and nine species have
been reported, respectively. However, the greater systematic
effort, both temporally and spatially, allocated by those authors
contrast with our 1-year surveys on platforms of opportunity.
Williams and Thomas (2007) developed systematic stratified
surveys covering a great proportion of the fjord area of British
Columbia, and Lusseau and Slooten (2002) used sightings data
from tourist operators from 1996 to 1999. Hence, we believe that
Table 2. Post hoc Pearson’s residuals from Chi-squared analysis for
cetacean sightings by season.
Season Mysticetes Odontocetes
Peale’s
dolphin
Chilean
dolphin
Summer 20.981 22.263 22.313 20.514
Autumn 4.903 21.720 20.953 20.514
Winter 22.550 0.996 0.408 0.857
Spring 21.373 2.988 2.858 0.171
High negative and positive values indicate degrees of negative or positive
association, respectively.
Table 3. Results of GAMs for cetacean sightings in Chile’s northern Patagonian fjords, including the covariates selected by the models.
Taxon and
parameter Estimate e.d.f. s.e. t-value F-value p-value
Deviance
explained
(%) r
2
GCV
score n
Mysticetes
Intercept 28.74 3.73 22.34 0.020
Smoother terms
UTM Northing 5.18 3.31 ,0.001
Channel width 8.03 4.69 ,0.001
Distance to coast 2.23 9.98 ,0.001
Best final model Myss(UTM Northing) þs(channel width) þs(distance to coast) 58.9 0.91 2.49 201
Odontocetes
Intercept 22.71 0.73 23.7 ,0.001
Smoother terms
UTM Northing 8.86 6.85 ,0.001
Distance to coast 4.40 4.6 ,0.001
Depth 2.54 6.91 ,0.001
Linear term
Coast complexity 13.39 2.95 0.004
Best final model Odos(UTM Northing) þs(distance to coast) þs(depth) þcoast
complexity
48.8 0.81 2.27 266
Peale’s dolphin
Intercept 21.32 0.36 23.70 ,0.001
Smoother terms
UTM Northing 7.6 2.89 0.005
Depth 6.99 3.89 0.001
Coast complexity 1.44 5.12 0.008
Best final model L.a.s(UTM Northing) þs(depth) þs(coast complexity) 59.4 0.89 1.85 141
Chilean dolphin
Intercept 2.60 0.37 7.08 ,0.001
Linear terms
Distance to coast 20.001 23.74 ,0.001
Depth 20.06 25.50 ,0.001
Best final model C.e.distance to coast þdepth 64.6 0.76 0.69 181
Mys, Mysticete; Odo, Odontocete; L.a., Peale’s dolphin; C.e., Chilean dolphin; e.d.f., effective degrees of freedom.
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with increased spatial and temporal systematic survey coverage
within our region, other species might be sighted, especially those
known to frequent the area. These include Commerson’s dolphins
(Cephalorhynchus commersonii;Capella and Gibbons, 1991), dusky
dolphins (Lagenorhynchus obscurus;Pitman and Balance, 1995),
Burmeister’s porpoises (Phocoena spinipinnis;Heinrich, 2006),
and sei whales (Balaenoptera borealis; RH-G, unpublished data).
Spatial and seasonal distribution of mysticetes
Baleen whale species registered in this study were mostly observed
during autumn, in open areas (wide channels) close to shore.
Although most baleen whales were observed in the open sea,
there is evidence that whales transit through narrow channels in
the southern part of the study area not covered by ferries,
especially blue and humpback whales, as reported by
Hucke-Gaete (2004). Although many studies on baleen whales
show some type of association with water depth (Williams et al.,
2006;Ingram et al., 2007;Panigada et al., 2008), the whales in
our study did not show any relationship with this particular vari-
able. Although both blue and humpback whales were sighted at
practically the same frequency, blue whales are more common in
the Chiloe–Corcovado Gulf (Hucke-Gaete et al., 2003).
The seasonal and spatial distribution of whales, in particular in
the Corcovado Gulf, might be explained by the seasonal contrast
between high levels of primary productivity (phytoplankton)
reported during spring and summer compared with those of
winter (Hucke-Gaete, 2004;Delgado and Marin, 2006),
particularly that observed in 2001 (Iriarte et al., 2007).
Mesoscale physical processes such as eddies, fronts, and plumes
would enhance the collection and retention of phytoplankton
within the area (Hucke-Gaete, 2004). These seasonal phytoplank-
ton blooms in turn favour lagged formation of large zooplankton
swarms (secondary production), such as krill, the essential food of
several larger species, particularly blue whales, during late summer
and autumn. In fact, Chile’s austral region south of 418S is one of
the planet’s most complex systems of fjords and channels, consti-
tuting some of the largest estuarine systems of the world and
characterized by high levels of habitat heterogeneity, biodiversity,
and productivity (Silva et al., 1998;Davila et al., 2002;Palma
and Silva, 2004). Indeed, mesoscale oceanographic conditions,
especially those with cyclic temporal patterns, seem to be a
major factor in whale distribution in southern Chile. Certainly,
primary productivity, tidal fronts, and other oceanographic fea-
tures are crucial in producing the predictable high concentrations
of prey that shape the seasonal movement and habitat use patterns
for many whale species (Davis et al., 2002;Croll et al., 2005;
Johnston et al., 2005;Donoil-Valcroze et al., 2007). However,
this is not a general pattern for all whales. Fin whales
(Balaenoptera physalus), for example, are resident year-round in
Baja California because of the persistent high productivity there.
Spatial and seasonal distribution of odontocetes
Most odontocete sightings were made during spring and least
during summer, particularly so for Peale’s dolphins. However,
Figure 4. GAM-predicted smooth splines of the response variable presence/absence of mysticetes as a function of the explanatory variables
UTM Northing, distance to the coast, and channel width. The degrees of freedom for non-linear fits are in parenthesis on the y-axis. Tick
marks above the x-axis indicate the distribution of observations (with and without sightings). Dotted lines represent the 95% confidence
intervals of the smooth spline functions.
Variability in cetacean distribution in northern Patagonian fjords of Chile 965
at Macquarie University on July 21, 2010 http://icesjms.oxfordjournals.orgDownloaded from
no evidence of seasonal migration has been found for that species.
Chilean dolphin sightings, on the other hand, revealed no seasonal
variation, suggesting year-round residence at least to some extent
in the area surveyed, consistent with the findings of Heinrich
(2006) for Chilean dolphins in southern Chiloe Island. However,
this is not a general pattern for all species of Cephalorhynchus,
because Hector’s dolphin (C. hectori) is known to show seasonal
shifts in the patterns of movement and distribution in New
Zealand (Dawson and Slooten, 1988).
In general, odontocete distribution was stratified along a north–
south gradient, with different patterns of distance to shore, and a
preference for shallow water and depths of .200 m. These patterns
probably result from pooling the data from all toothed cetaceans.
Looking in detail at the distribution of the most common
dolphin species in our area, Peale’s dolphins did show a spatial
pattern, driven by latitudinal stratification, with different depths
seemingly preferred in different areas. Peale’s dolphins also selected
areas of coastal complexity, perhaps preferring those habitats with a
more convoluted and protected shoreline, as well as a high density
of islands and channels. Peale’s dolphins were found both close and
far from shore, a flexibility also documented by others (Goodall
et al., 1997a). This is probably one of the major differences
between Peale’s and Chilean dolphins, with the latter preferring to
be close to shore and in shallow water, confirming the results of
other work which showed that these dolphins are seldom found
far from shore or in deep water (Goodall et al., 1988;Goodall,
1994;Heinrich, 2006;Ribeiro et al., 2007). It seems reasonable to
suggest, therefore, that water depth might be the factor limiting
the inshore distribution of Chilean dolphins, showing less plasticity
than for other small cetaceans such as the sympatric Peale’s dolphin.
Indeed, depth has been given as the limiting factor for many small
cetaceans (Bejder and Dawson, 2000;Karczmarski et al., 2000;
Allen et al., 2001;Bra
¨ger et al., 2003). Our results suggest some seg-
regation between Chilean and Peale’s dolphins, in particular as a
response to depth. Segregation patterns between the two species
have also been documented by Heinrich (2006) at Chiloe Island.
In all the areas surveyed, Chilean dolphins were observed year-
round in Laguna San Rafael, highlighting the importance of that
glacier-influenced marine ecosystem to the species. In fact, fresh-
water run-offs, including estuaries, rivers, and creeks, appear to
be important to Chilean dolphins (Goodall, 1994;Heinrich,
2006;Perez-Alvarez et al., 2007;Ribeiro et al., 2007). Until now,
freshwater run-off has not been regarded as an important factor
for Peale’s dolphin habitat preference (Heinrich, 2006). Its selec-
tion of inshore waters may relate to the presence of kelp beds
(Macrocystis pyrifera), habitats they exploit heavily, and mainly
found in the Magellan Strait and off Argentina (de Haro and
In
˜iguez, 1997;Goodall et al., 1997a,b;Lescrauwaet, 1997;
Schiavini et al., 1997;Viddi and Lescrauwaet, 2005). Although
Peale’s dolphins have been sighted opportunistically in deep
water far from shore (Goodall et al., 1997a), we sighted them fre-
quently in such waters. We suggest three possible ecological expla-
nations for this that need to be tested in future studies. First, they
may opportunistically exploit offshore habitats; second, they may
Figure 5. GAM-predicted smooth splines of the response variable presence/absence of odontocetes as a function of the explanatory variables
UTM Northing, distance to the coast, depth, and coast complexity (as linear predictor). The degrees of freedom for non-linear fits are in
parenthesis on the y-axis. Tick marks above the x-axis indicate the distribution of observations (with and without sightings). Dotted lines
represent the 95% confidence intervals of the smooth spline functions.
966 F. A. Viddi et al.
at Macquarie University on July 21, 2010 http://icesjms.oxfordjournals.orgDownloaded from
have an offshore ecotype restricted to deeper water; third, they may
cross deep water while migrating, e.g. the Moraleda Channel or the
Corcovado Gulf.
Final considerations
The association between cetacean distribution patterns and
environmental features of the habitats in which they live has
been the focus of many studies (Gowans and Whitehead, 1995;
Can
˜adas et al., 2005;Ferguson et al., 2006;Panigada et al.,
2008). Modelling animal distributions is a valuable tool for conser-
vation, in particular in terms of predictive power. Assuming that
the distribution of cetaceans is non-random relative to environ-
mental variability, models of cetacean distribution can identify
ecological relationships between the environment and species
habitat selection (Torres et al., 2008). In this study, models were
used as an explanatory tool and as a step towards determining
Figure 6. GAM-predicted smooth splines of the response variable presence/absence of Peale’s dolphins as a function of the explanatory
variables UTM Northing, depth, and coast complexity. The degrees of freedom for non-linear fits are in parenthesis on the y-axis. Tick marks
above the x-axis indicate the distribution of observations (with and without sightings). Dotted lines represent the 95% confidence intervals of
the smooth spline functions.
Figure 7. GAM-predicted smooth splines of the response variable presence/absence of Chilean dolphins as a function of the explanatory
variables depth and distance to coast (both as linear terms). Tick marks above the x-axis indicate the distribution of observations (with and
without sightings). Dotted lines represent the 95% confidence intervals of the smooth spline functions.
Variability in cetacean distribution in northern Patagonian fjords of Chile 967
at Macquarie University on July 21, 2010 http://icesjms.oxfordjournals.orgDownloaded from
the features that drive the process of cetacean distribution. Abiotic
variables may be correlated with the distribution of cetaceans, but
such variables often have little direct influence on the actual selec-
tion of habitats by the animals. In reality, abiotic features are
proxies for prey distribution.
Although determining cetacean distribution is essential and
indeed critical for crafting and proposing conservation policies,
collecting the data at sea presents many logistic and financial chal-
lenges, in particular in finding suitable seagoing vessels for data
collection (Ingram et al., 2007). Using platforms of opportunity
raises the possibility of collecting data at sea with minor cost.
Nevertheless, the data obtained from such studies have definite
flaws, because of the trade-offs of control over study design, the
non-standardized sampling effort, the limited field time, and the
restrictions of the sampling techniques (Marques, 2001;
Williams et al., 2006;Ingram et al., 2007).
Our data were obtained from non-random surveys on commer-
cial tourist and cargo ferries, but they gave us a cost-effective
opportunity to model cetacean distribution in southern Chile.
However, although we gained important insights into the
ecology of many cetacean species in the area, the conclusions
derived need to be read with caution. The use of platforms of
opportunity may provide scientists a means of collecting data on
a wide range of marine fauna when research funding is limited
(Ingram et al., 2007), but such surveys cannot replace appropri-
ately designed ones. Ferries may have uniform, regular, indeed
unchangeable routes, but they do not have equal area coverage,
so many habitat types can be overlooked and omitted from mod-
elling assessments.
Although marine mammals are found widely across the marine
realm, their distribution is patchy, and some areas are more fre-
quently occupied than others. Those preferred areas are most
likely to be important for their survival and reproduction, and per-
turbations to them will certainly influence the distribution and
abundance of affected species (Harwood, 2001). The richness of
the cetacean fauna in the study area, together with the distribution
patterns, are important signs of the potential ecological role these
animals may be playing in this fragile marine ecosystem. The sea-
sonal variation in whale sightings is an indication of the high
spatial and temporal variability in the physical and biological
oceanography of the Chilean fjord system.
Although predator avoidance, interspecific competition, and
reproductive strategies all play a key role in cetacean distribution
to some extent, energy budget studies indicate that most cetaceans
feed every day or on a highly regular basis (Smith and Gaskin,
1974), so habitat preference is assumed to be determined primarily
by food availability (Stevick et al., 2002). Consequently, the spatial
and seasonal distribution of the species documented here is most
likely linked to dynamic oceanographic variables through physico-
biological interactions and trophic relationships, from primary
productivity (phytoplankton) to the prey species of the cetaceans.
A better understanding of the habitat preferences, and the biologi-
cal and physical variables associated with these processes, will
improve management and conservation efforts by providing a
context for interpreting present and future anthropogenic effects
on cetacean populations and their distribution.
Although the northern Patagonian fjord system is effectively
uninhabited, it is increasingly being exploited by aquaculture, fish-
eries, and associated maritime traffic. Studies have shown that
these anthropogenic activities have negative impacts on marine
mammals (Sullivan Sealey and Bustamante, 1999;Wu
¨rsig and
Gailey, 2002;Hucke-Gaete et al., 2004;Ribeiro et al., 2005;
Heinrich, 2006;Ribeiro et al., 2007). For that reason, we strongly
support systematic studies in the Patagonian fjord system, which
include an assessment of cetacean distribution and abundance in
relation to their environment. Additionally, Wiens (1989)
suggested that studies across broad geographic areas are likely to
overlook important fine-scale details that account for the
dynamics of local populations. Hence, such fine-scale studies
need also be developed to gain more insight of animal response
to changes in the environment at that small scale.
The information generated from this study provides a step
towards understanding the ecology of cetacean species in the
fjords of southern Chile. It also has significant implications for
conservation initiatives, because our data strengthen the ongoing
efforts to propose a marine protected area in the region
(Hucke-Gaete et al., 2006).
Acknowledgements
We are grateful to the Society for Marine Mammalogy and the
Navimag Company for financial and logistical support and to
Servicio Hidrogra
´fico y Oceanogra
´fico de la Armada de Chile
(Chilean Navy) for supporting our work by providing bathymetric
data. We also thank Don Ljunblad for generously contributing
equipment, and the following colleagues and volunteers for con-
tinuous and dedicated assistance: Karin Acun
˜a, Elizabeth
Campos, Victor Castillo, Carla Christie, Vero
´nica Garrido,
Alejandra Henny, Don Ljunjblad, Cristian Peralta, Barbara
Pijanowski, and Maria Paz Villalobos. The project was developed
while RH-G held a Doctoral scholarship from the Comisio
´n
Nacional de Ciencia y Tecnologı
´a (CONICYT). We also thank
the captains and crews of the ferries MV “Evangelistas” and MV
“Puerto Eden”. Rob Harcourt, Iain Field, Panayiota Apostolaki,
Ryan Portner, and two anonymous reviewers gave helpful com-
ments and suggestions on earlier drafts of this manuscript.
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