Marine traffic and potential impacts towards cetaceans within
the Madeira EEZ
INÊS CUNHA1,2, LUÍS FREITAS1, FILIPE ALVES1, ANA DINIS1, CLÁUDIA RIBEIRO1, CÁTIA NICOLAU1, RITA FERREIRA1,
JOSÉ A. GONÇALVES2AND NUNO FORMIGO2
Contact e-mail: email@example.com
Human population growth has resulted in an increase of marine traffic. This has been associated with wildlife disturbance and the effects are
expected to increase with continued traffic expansion. A particularly impacted group is cetaceans, known to play an important role in the sustainability
and regulation of marine ecosystems. An assessment of marine traffic can therefore contribute towards wildlife conservation measures, especially
when evaluated in the context of important areas for cetaceans. The present study took place in Madeira’s Exclusive Economic Zone (EEZ), an
area hosting a high diversity of cetacean species as well as island-associated groups. Automatic Identification System (AIS) data were collected
from a land station between 2008 and 2011 and marine traffic and cetacean visual data collected during shipboard surveys between 2001 and 2011.
Results show that Madeira’s offshore traffic (up to 12 n.miles from the shore) corresponds to approximately 12% and 22% of the traffic observed
in the Baltic and North Sea, respectively. It is mostly composed of cargo ships navigating over fixed routes and using the area as a passage towards
different destinations. Cruise ships intersect the area mainly to reach Funchal’s port. The number of recreational boats in the area was found to be
underestimated since many of them are not equipped with AIS devices. The level of Madeira inshore traffic is harder to evaluate since it is a small
area encompassing a shipping route, yet it may represent 0.8% of the traffic recorded in the Strait of Gibraltar. According to the inshore shipboard
survey data, coastal marine traffic is mainly composed of fishing boats (47%), recreational boats (24%), ships (17%), whalewatching boats (10%)
and big game fishing boats (2%). Most inshore and offshore vessels were found to be navigating at over 10 knots. An inshore ‘higher use corridor’
common to both vessels and cetaceans was identified as a potential danger zone.
KEYWORDS: ATLANTIC OCEAN; SUSTAINABILITY; SURVEY – VESSEL; DISTRIBUTION; NORTHERN HEMISPHERE; FIN WHALE;
SHORT-FINNED PILOT WHALE; BEAKED WHALES; SPERM WHALE; BOTTLENOSE DOLPHIN; ATLANTIC SPOTTED DOLPHIN;
other bodies both intergovernmental and non-governmental
and supporting specialist workshops (IWC, 2016).
Moreover, cetaceans attract a significant interest from the
general public, resulting in significant growth of the
whalewatching industry (Jelinski et al., 2002; Orams, 2000).
This in turn raises the need to monitor the industry and adopt
codes of conduct and regulations in order to minimise its
impact on cetaceans and ensure its sustainability (Ritter,
Shipping traffic appears to be a significant fraction of the
anthropogenic sound input into the marine environment
(Southall, 2005; Weilgart, 2007) and the potential impact of
this on cetaceans has been considered an important issue
(Southall, 2005). Marine mammals rely on hearing as their
main sense. Thus, they are vulnerable to ocean noise
pollution that might be the cause of some strandings and
mortality incidents, among other disturbances, or chronic
effects such as ‘masking’, altered vocal behaviour, hearing
damage, increase in stress levels, habitat displacement and
alterations in migration routes (Weilgart, 2007).
Coastal cetaceans are even more exposed to anthropogenic
disturbances, especially in highly populated areas, where the
effects can be cumulative (Piwetz et al., 2012).
The present study took place in Madeira’s Exclusive
Economic Zone (EEZ) and provides, for the first time,
information regarding traffic distribution patterns within
the study area. The geographical position of Madeira’s
J. CETACEAN RES. MANAGE. 16: 17–28, 2017 17
Marine traffic has been associated with a high potential of
disturbance towards marine species, such as whales and
dolphins, and this negative effect is expected to continue
increasing as a consequence of continued traffic expansion
(Nowacek et al., 2001).
Cetaceans manifest behavioural changes (Piwetz et al.,
2012) that could trigger shifts in habitat use, temporary
displacement and an increase in energy consumption. When
continuously exposing the animals to these pressures long
term consequences, such as changes in survival rates or
population size, might follow (Bejder et al., 2006;
Constantine et al., 2004; Nowacek et al., 2001).
Over recent decades, due to the vast expansion of marine
traffic, cetaceans have also been victims of increased ship
strikes all around the world (Carrillo and Ritter, 2008; Laist
et al., 2001; Silber et al., 2012; Waerebeek et al., 2007).
Marine traffic is acknowledged as a worldwide threat
towards whale and dolphin populations and is being
addressed by various mitigation strategies, some supported
by the International Maritime Organization (IMO),
especially in regions where there are overlapping areas of
busy marine traffic and high cetacean density (IWC, 2011;
Panigada et al., 2006; Ritter, 2007; Silber et al., 2012). The
International Whaling Commission (IWC) is playing a lead
role by proposing mitigation measures and legislation on
ship strikes, creating a ship strikes database, working with
1Madeira Whale Museum, 9200-031 Caniçal, Madeira, Portugal.
2Faculty of Sciences of University of Porto, 4169-007 Porto, Portugal.
archipelago near the shipping corridors connecting Europe
to South America and Africa. Madeira is itself the destination
of many cruise ships, cargo ships and leisure crafts. It is
constantly surrounded and sought out by marine traffic.
These islands, as other islands in the Atlantic, are feeding,
reproductive and breeding grounds for several whale
and dolphin species (Alves et al., 2013; Dinis, 2014; Freitas
et al., 2004b).
In January 2005, the use of Automatic Identification
System (AIS) devices became mandatory for ships with gross
tonnage (GT) equal or superior to 300 and all passenger ships
of every size, following the ruling of IMO’s International
Convention for the Safety Of Life At Sea (SOLAS). AIS is a
ship-to-ship and ship-to-shore message system based on VHF
signals, which provides static and dynamic data related with
each vessel trip (Silber et al., 2012). Later the European
Union, through the Directive 2009/17/EC, established the
mandatory use of AIS devices in fishing vessels and other
vessels over 15m (IWC, 2011), where the Madeira
Autonomous Region is included (Ministério da Agricultura
do Mar do Ambiente e do Ordenamento do Território, 2012).
The previously mentioned ruling promoted the wide use of
AIS that in turn became an important source of data on
marine traffic worldwide.
The main goals of the present study are to: (1) assess the
spatial and temporal distributions of the inshore and offshore
marine traffic of the Madeira archipelago; (2) identify zones
of higher and lower marine traffic within the study area
according to the type of vessel; and (3) identify zones of
overlap between higher marine traffic areas and higher
occurrence of cetaceans.
The research focused on the Madeira EEZ (Fig. 1), including
the inshore waters around Madeira, Desertas and Porto Santo
Islands. These volcanic islands are located in the Atlantic
Ocean at an average latitude and longitude of 32° 46’N and
16° 46’W and 635km from Africa’s West coast. Madeira
stands isolated from the closest mainland and nearby
archipelagos by depths greater than 4,000m. The archipelago
main islands are surrounded by several steep submarine
canyons, with a small continental shelf, and often influenced
by the Gulf Stream current, thus presenting favourable
conditions to hold a substantial level of marine biodiversity
(Aguin-Pombo and Carvalho, 2009).
The offshore study area comprises the entire Madeira
EEZ, an area of approximately 454,479km2(VLIZ, 2014).
The inshore study area is about 4,500km2, which includes
the coastal waters from shore up to 12 n.miles, divided into
eight survey sectors, covering depths from 0 to –2,000m
In order to characterise the vessels’ temporal and spatial
distribution in the offshore and inshore waters of the Madeira
Archipelago, two different types of data were used: (1)
records collected during shipboard surveys carried out by the
Madeira Whale Museum (MWM) research team from 2001–
12, in the context of different projects (2001–02 – Project
Cetáceos Madeira; 2007–08 – Project Emacetus; 2010–12 –
Project Cetáceos Madeira II); (2) AIS data supplied by
Administração dos Portos da Região Autónoma da Madeira
(APRAM) recorded between 2008 and 2011.
The first type of data was used to build inshore ‘traffic
sighting rates distribution maps’ to compare with ‘cetacean
sighting rate distribution maps’ obtained from data gathered
by the same platforms and within the same time period. This
gives an overview of the regional marine traffic patterns,
allowing identification of higher and lower inshore vessel
traffic zones and the detection of possible areas of potential
conflict between cetaceans and vessels.
The second type of data was used for: (1) preliminary
characterisation of the Madeira offshore traffic; and (2)
corroboration of the inshore traffic sighting rates distribution
maps (previously described).
Both spatial and temporal analyses were run, pooling the
data by boat type and season. The summer and winter
18 CUNHA et al.: MARINE TRAFFIC AND CETACEANS IN MADEIRA
Fig. 1. Study area.
seasons were defined as the periods between June–October
and November–May, respectively.
Among the several AIS ship report parameters, only the
equipment ID, position, date, Speed Over Ground (SOG) and
type of boat attributes were used in this paper.
The data gathered were displayed and validated using
ArcGIS 10.1 (ESRI, Redlands, CA, USA). The number of
the vessels’ position tracks was reduced to one point of
coordinates (and related parameters) for each thousand
degree cell within the polygon that limits Madeira EEZ, thus
reducing the number of coordinates to be handled. This
procedure enabled reconstruction of routes, although
possibly with some sample bias in areas of heavy route
Mapping vessels positions tracks
In order to keep the maps presenting the vessels tracks
perceptible, a seven day period of vessels’ tracks was
considered enough to illustrate the traffic scenario (Eiden
and Martinsen, 2010). The first week from each month was
chosen, except for March from which the last week was used
(the only available data).
Vessel types from the AIS data files were organised into
four categories: cargo ships (cargo vessels or cisterns); cruise
ships (passenger ships); recreational boats (wing-in-ground-
effect craft, high speed craft or practical dinghy); and other
type of vessels (tugboat, vessel or no classification). When
counting the number of vessels per week, each vessel was
considered only once.
Speed grid maps
Vessel speed is one of the important factors which might
determine the severity of a ship collision with a whale.
A raster file (in ASCII format) was generated, where the
vessels medium SOG value was associated to each pixel,
creating speed grid maps. The speed grid maps for the
offshore traffic area are represented in a grid of 10 × 10
n.miles (18.52 ×18.52km), while the speed grid maps for the
inshore traffic area are represented in a grid of 2 × 2 n.miles
(3.704 × 3.704km). All vessel types were considered in the
same text file.
The probability of a whale being lethally injured by a ship
strike is > 50% if the vessel is navigating at speeds over
10 knots, but if the incident occurs while the ship is moving
> 15 knots, the chances of a lethal injury increases from
80% to 100% (Vanderlaan and Taggart, 2007). For this study,
vessels travelling at ≤ 10 knots were considered as ‘low’
speed while vessels > 10 knots were considered as ‘high’
Shipboard survey collected data
Field work methods
The sampling methods and protocol for cetacean sightings
and traffic data collection remained unchanged in its critical
aspects over the years.
The shipboard surveys were carried out in Beaufort Sea
state ≤ 3. Two research vessels were used during the study
period, R/V Calcamar for the 2001–02 surveys and the R/V
Ziphius from 2004 onwards. The first vessel is a 12m open
deck wooden fishing boat with an average surveying speed
of 5.5 knots and with the observing points at an average
height of 3m. The second vessel is a steel motor sail boat
with an average survey speed of 6.5 knots and two dedicated
observation platforms, one placed ahead midship (one
observer) and the other astern (two observers), both at an
average height of 4.5m. The field work aimed primarily at
surveying cetaceans and was done according to distance
sampling methodology (single platform). Each sector of the
study area was sampled on average twice every three
months, with randomly placed zig-zag transects.
A minimum of three observers scanned the sea
continuously looking for animals and every hour all the
marine traffic observed up to the horizon, 360° around the
vessel, was recorded in a computer using Logger 2000
software, along with the observation effort.
Traffic data recorded
The traffic data recorded included: sighting time; observed
vessel(s) type(s) (recreational boat – private vessel with
less than 24m in length; fishing boat – commercial fishing
boat of any size; big game fishing boat; ships – private or
commercial vessel with more than 24m in length;
whalewatching boats – vessels with less than 24m in length);
number of boats of each type; estimated visibility recorded
in classes (visibility ≤ 1 n.miles; 1 < visibility ≤ 3 n.miles;
3 < visibility ≤ 5 n.miles; visibility > 5 n.miles); and the
identification of the data recorder.
Every observation was tagged with the research vessel’s
GPS position at the moment of the sighting, i.e. the longitude
and latitude recorded do not correspond to the precise
position of the observed vessels. Consequently the records
of the vessel sightings have an associated position with an
error up to 15 n.miles (≈ 22km) (roughly the maximum
distance at which a vessel would be identified taking into
account the observation platform height) and vary also
according to the observed vessel’s type/height (smaller boats
are detected at much closer ranges and with a smaller
associated position error in relation to the research vessel
GPS position). One pair of coordinates might correspond to
more than one vessel, according to the number of boats
spotted at a particular time. The vessel estimated distance
and direction was not taken into account. Furthermore
(considering that the traffic data was collected every hour)
it is possible that a few vessels might have been registered
more than once, depending on their trajectory, change in
trajectory and speed in relation to the research vessel and
The data collected were separated into two periods
according to the vessels’ classification: (1) 2001–09: vessels
were classified only as ships, recreational boats or fishing
boats; (2) 2010–12: whalewatching and big game fishing
were added to the vessel classification list.
Cetacean information records
Data on cetacean sightings included: the date and initial time
of the sighting; vessels position at the initial time of the
sighting; estimated radial distance and angle to the bow at
the initial sighting time; species; minimum, average and
maximum group size; and the number of calves.
J. CETACEAN RES. MANAGE. 16: 17–28, 2017 19
Mapping vessels estimated locations
The vessels’ locations were plotted in a vector environment
over a grid with resolution of 2 × 2 n.miles (3.704 × 3.704km),
covering the inshore study area. The Madeira Archipelago
coastlines and pre-defined inshore survey sectors were also
overlaid within the same range.
The observation effort value of each grid cell was
represented through a colour gradient in order to show the
areas that were more intensely surveyed. The effort was
measured as the sum of kilometres of the research vessel’s
trackline in each cell.
Subsequently, the results were represented on three types
of maps: (1) types of vessels distribution maps – pie chart
maps, where the proportion of every type of boat was
represented per cell; (2) plot of research vessel’s locations
projected over the effort grid where the sightings points were
represented with variable diameters according to the number
of boat detections associated with each pair of coordinates;
and (3) traffic sighting rates distribution maps – each grid
cell’s vessel sighting rate, calculated by dividing the number
of boats sighted in each cell by the respective survey effort
and represented through a colour gradient over the grid.
To avoid misleadingly high sighting rate values in
surveyed cells with very low effort, any grid cell with survey
effort less than 5km (grid cell diagonal length) (Fortuna,
2006) was filtered out and not quantified in either of the
resulting GIS maps. This procedure was systematically
applied to all the maps, according to vessel type and
seasonality. A seasonal analysis was only possible for traffic
sighting rate distributions for vessel types recorded during
the whole sampling period (2001–12), i.e. ships, recreational
boats and fishing boats.
Mapping cetacean estimated locations
Cetacean sightings data were displayed in the same vector
environment as the traffic data.
Following the same procedure, two types of maps were
created: (1) cetacean species’ distribution maps: pie chart
maps, where the proportion of the more relevant species of
cetaceans was represented per cell, i.e. by dividing the
estimated number of sighted animals of a certain species in
an encounter by the total number of animals of all species
sighted in the same cell; and (2) cetaceans’ sighting rate
distribution maps: each grid cell corresponds to the cetacean
sighting rate, calculated by dividing the number of cetacean
sightings for each cell by the respective survey effort, and
represented through a colour gradient over the grid. An
additional map was made showing the sighting rates of
cetacean groups with calves, i.e. the number of cetacean
groups sighted with calves in each cell (disregarding the
number of calves) divided by the corresponding survey effort
and represented through a colour gradient over the grid.
Likewise, for the marine traffic maps, only cells with a
minimum value of 5km of survey effort were displayed.
The normality of the AIS traffic data, as well as the shipboard
surveys traffic and cetaceans’ data were tested using Shapiro-
Wilk and Kolmogorov-Smirnov tests. A t-test was applied to
the AIS data to check for the existence of a significant
difference between winter and summer season distributions.
A Kruskal-Wallis test was run with both general and
seasonal traffic sighting rates distributions from the
shipboard surveys to check for the existence of significant
differences between the sectors. If one distribution was found
to be heterogeneously distributed across the area (p< 0.05),
a Mann-Whitney test was applied to find out which sectors
were statistically different from each other and to detect
significant seasonal variations in the same sector. The same
procedure was applied to the cetacean distributions.
The statistical tests were run in IBM SPSS Statistics 19
Offshore traffic distribution
The number of vessels crossing the Madeira EEZ varied
between 100 and 300 vessels per week (Fig. 2a). The highest
traffic peaks are in the summer months (August 2008
and September 2009) and the lowest in the winter season
(February 2009 and May 2011). Nevertheless, no significant
differences were found (t-test results p> 0.05) between
The Madeira EEZ was found to be intersected by an
average of 188 vessels per week, i.e. 0.0004 vessels per km2.
20 CUNHA et al.: MARINE TRAFFIC AND CETACEANS IN MADEIRA
Fig. 2. Number of vessels per week and the percentage of vessel type
incidence per each representative month during the Winter (November–
May) and Summer (June–October), between 2008 and 2011: (a) offshore
traffic; (b) inshore traffic.
Traffic composition does not vary considerably, with cargo
ships representing at least 70% of all traffic, followed by either
unclassified vessels or cruises/ferries, in variable proportions
(Fig. 2a). May 2008 stands out as an outlier from the remaining
sampled months, with the ‘other type of vessels’ category
representing a greater proportion of the traffic composition.
It was possible to identify five recurrent shipping lanes
(see Fig. 3), common to all 20 AIS traffic monthly maps
(only July is presented for 2008): three lanes presenting a
NE–SW orientation, one at the West side and the other two
at the East side of the islands; two orientated on an E–W axe:
one to the South and the other to the North of the islands.
The NE–SW orientated shipping lane, located further East
from the Madeira Island seems to be more intensively used.
All these shipping lanes are mostly used by cargo ships.
Inshore traffic distribution
The number of vessels registered by the AIS system varied
between 27 and 42 boats per week, year round. This inshore
area was also mainly intersected by cargo ships (Fig. 2b and
Fig. 3b), many of them navigating closer to Madeira.
The inshore traffic area was intersected by an average of
34 vessels per week, i.e. 0.008 vessels per km2.
Though all vessel types were considered, the speed values are
mostly from ships (including cargos and cruises), accounting
for at least 70% in both offshore and inshore traffic
composition of one week of each sampled month (Fig. 4).
In general, both inshore and offshore cells present on
average ‘high’ speed traffic.
Traffic data collected in inshore shipboard surveys
A total of 830 vessels were sighted, 401 between 2001 and
2009 (54% fishing boats, 27% recreational boats and 15%
ships) and 429 between 2010 and 2012 (47% fishing boats,
24% recreational boats, 17% ships, 10% whalewatching
boats and 2% big game fishing boats).
All types of vessels’ sighting rates revealed a non-normal
distribution (p< 0.05).
The descriptive statistics data (mean and standard
deviation) for the general and seasonal vessels’ sighting rate
distribution, for each type of vessel and for all sectors, are
presented in Table 1.
The Mann-Whitney test results are presented in Fig. 5,
according to the type and season of vessels’ sighting rate
According to Fig. 5 and Figs 6a and 6b, Sector 3 is significantly
different (Mann-Whitney test significant results for p< 0.05)
from all the remaining sectors and holds the highest average
J. CETACEAN RES. MANAGE. 16: 17–28, 2017 21
Fig. 3. Vessel position tracks projected over the Madeira EEZ area. Some
areas show higher track density shipping lanes. Only one representative
map (July 2008) is displayed among the 20 representative months used
in the present study: (a) offshore traffic; (b) inshore traffic.
Fig. 4. Speed grid cell maps displayed within the Madeira ZEE area. Each
pixel represents the vessels Speed Over Ground (SOG) average value.
Only one representative map (July 2008) is displayed among the 20
representative months used in the present study. The ‘low’ speed cells are
coloured in green and yellow and the ‘high’ speed cells are coloured in
orange and red: (a) offshore traffic; (b) inshore traffic.
22 CUNHA et al.: MARINE TRAFFIC AND CETACEANS IN MADEIRA
Mean and standard deviation (SD) of the traffic sighting rates (number of vessels per 10km) distribution maps according to boat per vessel type and
Total area Sector 1 Sector 2 Sector 3 Sector 4 Sector 5 Sector 6 Sector 7 Sector 8
type Season Mean SD Mean
SD Mean SD Mean
SD Mean SD Mean SD
S0 0.66 1.22 0.42 0.69 0.41 0.86 1.59 2.16 0.30 0.57 0.41 0.57 0.56 0.72 0.31 0.34 0.66 0.95
AV S1 0.69 1.53 0.53 1.36 0.62 1.29 1.46 2.59 0.49 1.23 0.48 0.88 0.50 0.76 0.32 0.49 0.55 0.90
S2 0.58 1.42 0.34 0.76 0.32 1.01 1.35 2.43 0.26 1.01 0.39 0.60 0.67 1.23 0.27 0.45 0.53 1.40
S0 0.14 0.42 0.03 0.11 0.14 0.59 0.37 0.68 0.06 0.16 0.08 0.20 0.15 0.31 0.07 0.13 0.06 0.19
S S1 0.14 0.41 0.05 0.16 0.19 0.69 0.32 0.59 0.12 0.32 0.06 0.24 0.13 0.28 0.08 0.20 0.04 0.23
S2 0.13 0.47 0.02 0.09 0.12 0.69 0.34 0.74 0.05 0.19 0.11 0.29 0.17 0.42 0.05 0.13 0.05 0.20
S0 0.18 0.46 0.09 0.34 0.02 0.10 0.46 0.78 0.11 0.31 0.12 0.33 0.20 0.44 0.13 0.20 0.12 0.30
RB S1 0.17 0.57 0.18 0.77 0.06 0.19 0.38 0.89 0.11 0.44 0.10 0.33 0.14 0.32 0.13 0.28 0.08 0.25
S2 0.19 0.69 0.03 0.17 0.00 0.00 0.55 1.23 0.05 0.16 0.07 0.17 0.34 0.93 0.12 0.29 0.13 0.53
S0 0.29 0.54 0.29 0.46 0.23 0.59 0.52 0.77 0.10 0.21 0.22 0.27 0.21 0.35 0.11 0.18 0.48 0.75
FB S1 0.33 0.81 0.31 0.70 0.36 1.01 0.49 1.21 0.26 0.65 0.32 0.50 0.23 0.43 0.11 0.24 0.43 0.86
S2 0.24 0.63 0.29 0.73 0.22 0.72 0.44 0.78 0.04 0.17 0.21 0.37 0.16 0.54 0.10 0.23 0.32 0.87
WWB S0 0.04 0.30 … … … … … … … … … … … … … … … …
BGF S0 0.007 0.70 … … … … … … … … … … … … … … … …
Key: Vessel type: AV = all vessels; S = ships; RB = recreational boats; FB = fishing boats; WWB = whalewatching boats; BGF = big game fishing boats.
Season: S0 = general distribution; S1 = winter distribution; S2 = summer distribution.
Fig. 5. Results of the Mann-Whitney tests for the different types of vessels and seasonality (winter and summer). Matrix comparing
vessel sighting rates distribution across the sectors of the study area. Key: Black circles P ≤ 0.05; White circles P > 0.05.
vessels’ sighting rates, standing out as the inshore traffic ‘busy
zone’. Sectors 2 and 4 are the less intensive traffic areas. Sector
2 is significantly different from every other sector except 4 and
7. However, apart from Sector 3, only Sectors 5 and 6 are also
significantly different from Sector 4. Thus, Sectors 3, 5 and 6
present higher traffic activity. During the winter, Sector 3 is the
more intensively used sector, significantly different from all
the remaining sectors, except Sector 8. During the summer
season, Sector 3 is significantly different from all sectors except
5 and 6, indicating a higher traffic activity in those areas.
However, no significant seasonal variations were found in
any of the sectors.
In both general and winter ship traffic distributions, Sector 3
had the higher activity and is significantly different from the
remaining sectors. Sector 6 has a higher level of ship traffic,
being also significantly different from the Sectors 1 and 8,
with the lowest general and winter ship traffic distributions,
respectively. During the summer season, Sector 3 is no longer
significantly different from Sectors 5 and 6, indicating an
increase in ship traffic in those areas. However, no significant
seasonal variations were found in any of the sectors.
Recreational boats, whalewatching boats and big game
Though recreational boats are spread throughout all sectors,
Sectors 3, 6 and 7 present a higher activity (Table 1 and Fig.
5), particularly in Sector 3 that is significantly different from
all sectors, except 6 and 7, for both winter and summer
seasons. Sector 2 is more active during the winter (Mann-
Whitney test significant results for p< 0.05).
All the whalewatching boats’ sightings positions are located
in Sector 3, except for one sighting location in Sector 4.
Big game fishing boats use mainly Sector 3.
Considering the fishing boats’ general distribution, Sector 3
is the most used, followed by Sectors 5 and 8. Sector 2 is the
least used sector, followed by Sector 1 (Table 1 and Fig. 5).
There were found no significant differences among the
sectors during the winter season. Traffic activity seems to
drop in Sector 4 during the summer season (Mann-Whitney
test significant results for p< 0.05).
Based on these results it is possible to identify a ‘higher
use corridor’ composed by Sectors 3 and 6, both sectors of
higher activity in most traffic distributions across the inshore
study area, as further described below.
Cetaceans data collected during shipboard surveys
The descriptive statistics data (mean and standard deviation)
for the general sighting rate distribution, for all cetaceans
and cetaceans groups with calves, for all sectors, are
presented in Table 2.
J. CETACEAN RES. MANAGE. 16: 17–28, 2017 23
Fig. 6. Inshore traffic distribution maps: (a) plot of all vessel locations over
the effort grid. Each coordinate point may correspond to more than one
vessel sighting, represented through a variable circle diameter. Each effort
grid cell presents a different gradient depending how much it was
surveyed; (b) general traffic sightings rates distribution map (each cell
value corresponds to the number of vessels per 100km of effort); (c)
inshore vessel type distribution represented through a pie chart per grid
cell representing the proportion of each type of vessel sighted in that
location for the period 2010–12.
Mean and standard deviation (SD) of the general sighting rates
distribution maps, for overall cetaceans and cetacean groups with calves,
according to sectors.
Cetaceans Cetacean groups with calves
Mean SD Mean SD
Sector 1 0.39 0.40 0.07 0.15
Sector 2 0.25 0.45 0.07 0.26
Sector 3 0.46 0.47 0.12 0.19
Sector 4 0.26 0.29 0.02 0.08
Sector 5 0.32 0.33 0.08 0.15
Sector 6 0.36 0.38 0.09 0.18
Sector 7 0.31 0.46 0.06 0.13
Sector 8 0.34 0.38 0.09 0.20
According to the Mann-Whitney test results (Table 4),
Sectors 2 and 4 presented the lowest cetacean presence.
Sector 2 is significantly different (p< 0.05) from all sectors,
except Sectors 4, 7 and 8. Sector 3 has the highest cetacean
presence, being significantly different from Sectors 2 and 4,
followed by Sectors 1, 6 and 5.
Cetacean groups with calves
According to the Mann-Whitney test results (Fig. 7), Sectors
2 and 4 have the lowest presence of groups with calves,
while Sectors 3, 5 and 1 have the highest, followed by Sector
6. In Fig. 8b it is possible to see that most cells in Sector 5
marked with cetacean presence are in the upper part of that
sector, between Sectors 3 and 6.
Even though some cargo ships are headed to Caniçal or
Funchal, the study area seems to be mostly crossed by cargo
ships heading to different destinations such as the North
Sea, Middle East, the North and South America or the
Mediterranean region. Cruises/ferries on the other hand cross
the area usually to reach Funchal Port, one of the traditional
cruise ship stops in this region of the Atlantic.
An exceptional event or combination of events, that the
authors could not identify may have led to a much higher
than normal traffic of vessels of the ‘other type of vessels’
category in May 2008.
Madeira EEZ maritime traffic level, though less intensive,
is still considerable when compared with some of the busiest
waterways in the world. The Baltic Sea, with a surface area
of approximately 392,978km2(Lepparanta and Myrberg,
2009), corresponding to approximately 86% of Madeira
EEZ, is crossed by an average of 1,319 vessels per seven day
period (0.0034vessels km–2) (Eiden and Martinsen, 2010)
and the Madeira EEZ corresponds to approximately 12% of
its traffic. The North Sea, with approximately 750,000km2
(Lepparanta and Myrberg, 2009), is crossed by an average
of 1,335 per seven days period (0.0018vessels/km2) (Eiden
and Martinsen, 2010). Madeira EEZ corresponds to 61% of
the North Sea area and to 22% of its traffic. However, Eiden
and Martinsen (2010) reported that the presented number of
vessels crossing the North Sea is underestimated.
To put this into perspective, the inshore study area should
be compared with other coastal areas with similar surface
areas. The Strait of Gibraltar is located between the southern
coast of Spain and the northern coast of Morocco (58km long
and approximately 13km at shorter distance between shores)
and is an important shipping route since it is the only
connection between the Atlantic Ocean and the Mediterranean
Sea. Its surface area is roughly 1,914km2(considering the
strait’s western extreme 43km wide, between Barbate and
Tanger and the eastern extreme 23km wide, between Rock of
Gibraltar and Ceuta Canyon) corresponding approximately to
43% of the Madeira inshore study area and is crossed by an
average of 1,975 vessels per week (IWC, 2011), i.e.
approximately 1.03 vessels km–2. The inshore traffic of the
study area is about 0.8 % of the Strait of Gibraltar’s traffic.
24 CUNHA et al.: MARINE TRAFFIC AND CETACEANS IN MADEIRA
Fig. 7. Results of the Mann-Whitney tests for the cetacean distributions.
Matrix comparing cetacean sighting rates distributions (overall and of
groups with calves) across the sectors of the study area. Key: Black
circles P ≤ 0.05; White circles P > 0.05.
Fig. 8. Inshore cetacean sighting rates distribution maps: (a) general
cetacean sighting rates distribution map (each cell value corresponds to
the number of sightings per 100km of effort); (b) cetacean groups with
calves sighting rates distribution map (each cell value corresponds to the
number of sightings where calves were present per 100km of effort).
A closer look at the average traffic speed in inshore cells
indicates that ‘low’ speeds are common (green and yellow
coloured cells) near Madeira and Porto Santo Islands ports
(Funchal, Caniçal and Porto Santo ports), which are mostly
associated with vessels approaching and leaving harbours or
mooring places (Sectors 3 and 7). However, ‘high’ speeds
cells are the most common in these two sectors (Fig. 4b). In
Sectors 4 and 5 (East and West Desertas) the ‘low’ speed
cells closest to the shore are from small vessels, as ships
would not be able to moor there. Desertas Islands are
frequently sought out for touristic purposes, by local
recreational and commercial boats and foreign recreational
boats. Cargo and cruise ships also use these sectors, passing
farther away from the coast to different destinations at
cruising speed. The same happens in Sector 6 (the
passageway between Madeira and Porto Santo island) and
Sector 2 (West of Madeira), where ‘low’ speeds are rare
AIS data limitations
Though this kind of data has revealed itself to be very useful
and accurate, it has some limitations. The AIS transmission
range is limited and can vary depending on the transmitting
and receiving aerial heights as well as meteorological
conditions that can affect the spatial coverage (dependent on
the VHF signal range from the coast). This is particularly
true for the greater distances from Funchal (Eiden and
Martinsen, 2010; Mou et al., 2010), as shown in Fig. 3 where
large gaps can be found between points from the same route.
The original database files were large and hard to manage,
so file converters were used to reduce their sizes. The
discontinuity and heterogeneity of AIS data available for
each of the sampling units throughout the study period was
also a problem. Data was unavailable for some months of a
particular year or some months were integrally represented
while others only had data covering a few days.
Even though AIS covers a great variety of vessel types,
smaller recreational boats may not have such devices (Eiden
and Martinsen, 2010; Evans et al., 2011; IWC, 2011; Mou
et al., 2010). Nevertheless, the percentage of smaller
recreational boats should still be smaller than cargo or cruise
ships, as they tend to be either local boats navigating mostly
inshore waters or they are sailing boats crossing the Atlantic
or passing through Madeira on their way to the Canary
Islands or the Caribbean. This type of traffic is more frequent
during the autumn season (October to December), when they
can take advantage of the trade winds.
The number of vessels given here is also certainly
underestimated as no AIS data on fishing vessels was
Traffic data collected in inshore shipboard surveys
Considering the inshore traffic composition (Fig. 6c), Sector
3 stands out as the zone with the higher traffic level, used by
every type of vessel, as expected. The south of Madeira
Island, with calmer waters sheltered from the trade winds
(NE), is the most populated coast and is where most of the
small harbours and main ports are located in the archipelago.
These characteristics justify why this is the sector with higher
traffic both in summer and winter. Sector 6 follows, with an
important amount of movement between the two main
islands, frequently done by recreational boats, fishing boats
and ships, especially in summer time.
Though Sectors 3, 5 and 6 appear to be the most frequently
used sectors, when crossing these data with the available AIS
data for the same area (Fig. 3b), mainly composed of ships,
Sector 5 is rarely crossed. This may be justified by the
associated discrepancy of some recorded positions of
observed bigger vessels, as explained in the methodology
The Madeira fishing fleet is 89% composed of vessels
less than 12m in length (Direcção-Geral das Pescas e
Aquacultura, 2007), carrying out demersal fishing and
operating near the harbours. The most profitable are the tuna
and black scabbard fishing fleets, which together accounted
for 84% of the total landings and 87% of 2012 economic
revenue of the fishing activity in the region (Instituto
Nacional de Estatistica, 2013), also operate offshore, away
from the area covered by the inshore shipboard surveys.
These fleets may be underrepresented in the traffic sighting
rates maps, especially during the summer period. The black
scabbard fishery usually runs May–December and the tuna
fishery usually runs between April and October.
Potential impact towards cetaceans
Comparing the distributions of traffic and cetaceans (all
groups and groups with calves) with the inshore study area
it can be seen that the traffic ‘higher use corridor’ (including
Sectors 3 and 6) overlaps a substantial part of the cetaceans’
preferential distribution area, where the encounter rates are
higher. Therefore, this corridor can be considered a ‘potential
danger zone’. According to previous studies, Sectors 3 and
6 include a critical area for cetaceans in general, where these
are more frequently sighted (Freitas et al, 2014).
Cetaceans may be disturbed by vessels in different ways,
such as (eco)tourism, ship strikes or water noise and
pollution produced by boats (Bejder and Samuels, 2003;
Laist et al., 2001; Weilgart, 2007). However, some types of
vessels are associated with specific cetacean interactions that
should be considered independently. Likewise, some species
are more prone to traffic interactions, than others.
The interactions between cetaceans and whalewatching
vessels have been previously investigated in the study area
when short term effects were observed among the Delphinidae
(Ferreira, 2007). Stress responses have also been reported for
short-finned pilot whales when followed by whalewatching
boats, especially when these encounters were not conducted
following the voluntary guidelines (Freitas et al., 2004a).
Unfortunately, there are no data available on underwater
noise or water pollution in the Madeira archipelago.
Therefore, the following discussion will be mainly focused
on ship strikes.
Potential ship strike risk in inshore waters
The incidences of ship strikes in a certain area are not easy
to quantify. Their probability depends on different variables
such as the level of traffic activity, the number of cetaceans
and their behaviour within that area. The amount of time
whales spend underwater away from watercraft and their
ability to detect and consequently avoid them are related to
the probability of ship strikes within a certain area.
J. CETACEAN RES. MANAGE. 16: 17–28, 2017 25
Every type and size of vessel can strike whales, but the
more serious or lethal cases registered occurred with ships
with a length of 80m or more, travelling at speeds over
14 knots (Evans et al., 2011; Laist et al., 2001), usually
ferries, cargo and cruise ships. In previous studies focused
on the collisions between vessels and Mediterranean fin
whales (Panigada et al., 2006), ferries and cargo ships were
the type of vessels with the highest number of strikes,
accounting for 62.5% and 16.7% of cases, respectively.
The probability of a ship strike being fatal increases from
20% to 80% as ship speed increases from 8.6 to 15knots. At
speeds below 11.8 knots, the likelihood of lethal injury is
less than 50%, while at speeds over 15 knots, the probability
rises from 80% to 100% (Vanderlaan and Taggart, 2007).
Even though in Sector 3 ships tend to reduce speed or
slowly pick up speed as they get close to or away from the
ports, temporarily giving time and space for whales to avoid
them, cells with average speed over 15 knots are still present
(Fig. 4), specially away from the shore and at higher depths
where the presence of whales is more likely. In Sector 6, also
part of the ‘higher use corridor’, speeds over 10 knots are
the most frequent. This means that if a ship strike takes place
within this area there is a high probability it will be fatal.
Fin whales (Balaenoptera physalus), short-finned pilot
whales (Globicephala macrorhynchus), Cuvier’s beaked
whales (Ziphius cavirostris) and sperm whales (Physeter
macrocephalus) are the species present in the study area
(Fig. 9) and are among the species known to be more
frequently involved in ship strikes (Carrillo and Ritter, 2008;
Laist et al., 2001; Panigada et al., 2006).
All the species mentioned above are present in Sectors 3
and 6 (Figs. 9 and 10). The short-finned pilot whale stands
out from the remaining species due to its localised
distribution (adults and calves), which overlaps the ‘higher
use corridor’, making it potentially more vulnerable to ship
strikes (Fig. 8). Baleen whales, sperm whales and beaked
whales were sighted in the same area, especially across
Sector 6 (Freitas et al., 2004b; Freitas et al., 2014).
Vessel strikes involving small cetaceans are more
frequently associated with small vessels and in many cases
the animals show evidence of vessel propeller cuts
(Waerebeek et al., 2007). ‘Small vessels are here defined as
fast small to medium size planing craft powered by inboard
or outboard engines, where most of the recreational,
whalewatching and big game fishing boats are included.
Considering these three vessel types all together, they
represent almost 40% of the Madeira inshore traffic fleet.
Recent reports also refer to cases of vessel collisions with
cetaceans caused by sailing boats (Ritter, 2012).
Bottlenose dolphins (Tursiops truncatus), Atlantic spotted
dolphins (Stenella frontalis) and common dolphins
(Delphinus delphis) are some of the species globally reported
26 CUNHA et al.: MARINE TRAFFIC AND CETACEANS IN MADEIRA
Fig. 10. Distribution of cetacean species presence represented through a pie
chart where each pie represents the proportion of each species sightings
with calves within each grid cell through the period 2001 until 2012: (a)
dolphin presence distribution pattern; (b) whale presence distribution
Fig. 9. Distribution of cetacean species presence represented through a
pie chart where each pie represents the proportion of each species
sighted within each grid cell through the period 2001 until 2012: (a)
dolphin presence distribution pattern; (b) whale presence distribution
as casualties of small vessel strikes. Both adults and calves
are present in the sectors most intensively used by
recreational boats (Sectors 3, 6 and 7), whalewatching boats
(Sector 3) and big game fishing boats (Sector 3), and thus
subject to its potential impact.
In the MWM strandings database (1986–2012) there are
three deaths associated with ships strikes out of the 136
stranded animals recorded: a possible ship strike with
Gervais’ beaked whale (Mesoplodon europaeus) and two
confirmed ship strikes, one with a Cuvier’s beaked whale
(one out of five recorded standings of this species) and
another with a common dolphin. These species were
previously reported as more vulnerable to traffic incidents
(Laist et al., 2001; Waerebeek et. al., 2007).
Although not many strandings associated with vessel
strikes have been reported so far in the Madeira islands, these
might have been overlooked, since most carcasses usually
sink to deep waters before stranding or refloating due to
decomposition. Some animals may be hit in the open ocean
or may drift away from the islands’ coast never to be detected
(Laist et al., 2001; Silber et al., 2012; Weilgart, 2007). Also,
carcasses in advanced states of decomposition may mask
signs of possible causes of death (Laist et al., 2001; Silber
et al., 2012) and blunt trauma impacts may not show any
external signs (Evans et al., 2011; Silber et al., 2012).
Madeira is expected to have far less ship strikes than
continental coastal areas due to the oceanic nature of most
marine traffic (lower cetacean densities in open ocean) in the
archipelago and the relatively small coastal traffic, namely
ferries, when compared with, for example, the Canary
Islands. Madeira has one ferry (20 knots) connecting the two
main Islands travelling at most twice a day, while the Canary
Islands have several fast ferries (≥30 knots) and regular
ferries connecting all the islands with several trips per day.
The ‘higher used corridor’ thus stands as a potential vessel
strike risk area (Fig. 11) in the context of the Madeira
archipelago marine traffic.
Potential ship strike risk in offshore waters
Unfortunately, there are no data on cetacean distribution (e.g.
sightings rates per cell) in the offshore waters of the Madeira
EEZ making it impossible to compare both cetaceans’ and
vessels’ distribution patterns to identify overlapping areas of
higher cetacean numbers and traffic presence. Nevertheless,
cetaceans’ densities are expected to be lower in oceanic open
waters, both because of the expected lower food availability
and the huge areas involved. However, the months with
higher level of traffic activity correspond to the summer
period, where the presence of calves is more likely,
increasing the possibility then of being hit by a ship.
As expected, there is very little evidence of ship strikes in
Madeira offshore waters, mainly because it is a large area
with little human presence and a relatively small nearby
coast line where carcasses may come ashore. Difficulties in
gathering ship strike evidence have been reported in most
other related studies (IWC, 2011; Laist et al., 2001;
Waerebeek et al., 2007), even in areas where ship strikes are
a serious concern (Carrillo and Ritter, 2008). Some of the
most intensive studies on the subject, have focused not
only on stranding archives but also on historical and
anecdotal records, and still, only a few of the total number
of ship strikes were revealed (IWC, 2011; Laist et al., 2001).
This type of archival data was not collected in the present
The marine traffic in the Madeira EEZ, while not so alarming
as in other areas of higher traffic level, is still a concern and
may have an important impact in the surrounding
environment that should not be ignored.
A ‘higher use corridor’ in Madeira inshore waters used by
both vessels and cetaceans was identified, standing as a
potential ship strike risk zone. Even so, based on the
available evidence, the marine traffic impacts are not
apparently high and the animals continue to use the area,
indicating that at the present impact level it is, at least,
It is important that studies of the spatial and temporal
characterisation of the maritime traffic in Madeira EEZ
continue, in order to identify specific routes and produce
traffic density maps for this area. To obtain real positions of
sighted vessels a radar should be used during the inshore
shipboard surveys run by the MWM around Madeira inshore
The potential impact regarding ship strikes and water
noise on cetaceans should be quantified for the present study
In order to infer the probability of a ship strike in the
study area, some of the ASCOBANS (IWC, 2011)
recommendations on the subject could be followed. Among
other measures, a dedicated trained observer should be
placed on board cargo and cruise ships to register cetacean
presence and interactions/behaviour towards marine traffic
in the vicinity (Correia et al., 2015). The available species
photo-identification catalogues should also be used to detect
possible signs of blunt trauma, such as propeller cuts, in
either adults or young cetaceans.
It is recommended that fast ferries in the Madeira EEZ
should not be permitted as it has already been proven that
these are responsible for several ship strike incidents with
cetaceans elsewhere (Carillo and Ritter, 2008).
J. CETACEAN RES. MANAGE. 16: 17–28, 2017 27
Fig. 11. The ’potential danger zone’. The darker cells represent the cetacean
index presence that intersected Sectors 3 and 6, the ‘higher use corridor’,
a preferential area for both cetaceans and ships, where the latter often
cross the area moving at speeds over 10 knots , i.e. a potential ship strike
It is too soon to understand the real impact of marine
traffic in the Madeira EEZ based on these initial results. It is
important to keep track of the traffic expansion and ascertain
how it is impacting cetacean populations so that, if required,
mitigation measures may be implemented in time.
Thanks are due to João Viveiros, Miguel Silva, Hugo Vieira,
Filipe Nóbrega, Ricardo Antunes, Carla Freitas, Nuno
Marques, Marianne Böhm-Beck, Jonatan Svensson, Virginie
Wyss, Daniel Martins, Mafalda Ferro and other volunteers
for ﬁeld work assistance. Thanks also to APRAM for the AIS
data supply and technical support. Financial support:
Machico Municipality, LIFE and FEDER/INTERREG III-B
EU programs for funding the data collection throughout the
projects CETACEOS MADEIRA (LIFE99 NAT/P/006432),
EMECETUS (05/MAC/4.2/M10) and CETACEOS
MADEIRA II (LIFE+ NAT/P/ 000646) which were carried
out by the Madeira Whale Museum.
Aguin-Pombo, D. and Carvalho, M.A.A.P. 2009. Madeira Archipelago.
582–85pp. In: D.A. Clague and R.G. Gillespie (eds.) Encyclopedia of
Islands. University of California Press, Berkley. 1,111pp.
Alves, F., Quérouil, S., Dinis, A., Nicolau, C., Ribeiro, C., Freitas, L.,
Kaufmann, M. and Fortuna, C. 2013. Population structure of short-finned
pilot whales in the oceanic archipelago of Madeira based on photo-
identification and genetic analyses: implications for conservation. Aquatic
Conserv. Mar. Freshw. Ecosyst. 23: 758–76.
Bejder, L. and Samuels, A. 2003. Evaluating impacts of nature-based
tourism on cetaceans. pp. 229–56. In: N. Gales, M. Hindell, and R.
Kirkwood (eds.) Marine Mammals: Fisheries, Tourism and Management
Issues. CSIRO, Collingwood, Victoria. 451pp.
Bejder, L., Samuels, A., Whitehead, H., Gales, N., Mann, J., Connor, R.,
Heithaus, M., Watson-Capps, J., Flaherty, C. and Krutzen, M. 2006.
Decline in relative abundance of bottlenose dolphins (Tursiops sp.)
exposed to long-term disturbances. Conserv. Biol. 20: 1,791–8.
Carrillo, M., and Ritter, F. 2008. Increasing numbers of ship strikes in the
Canary Islands: Proposals for immediate action to reduce risk of vessel
whale collisions. Paper SC/60/BC6 presented to the IWC Scientific
Committee, 2008 (unpublished). 10pp. [Paper available from the Office
of this Journal]
Constantine, R., Brunton, D.H., and Dennis, T. 2004. Dolphin-watching tour
boats change bottlenose dolphin (Tursiops truncatus) behaviour. Biol.
Conserv. 117: 299–307.
Correia, A.M., Tepsich, P., Rosso, M., Caldeira, R. and Sousa-Pinto, I. 2015.
Cetacean occurrence and spatial distribution: Habitat modelling for
offshore waters in the Portuguese EEZ (NE Atlantic). J. Mar. Sys.143.
Dinis, A. 2014. Ecology and conservation of bottlenose dolphins in Madeira
Archipelago, Portugal. Doctoral Thesis, University of Madeira. 157pp.
Direcção-Geral das Pescas e Aqualcultura. 2007. Plano Estratégico Nacional
para a Pesca 2007–2013. Lisbon. 84pp.[In Portuguese].
Eiden, G. and Martinsen, T. 2010. Marine traffic density – results of PASTA
MARE project. Technical Note 4.1 Vessel Density Mapping. Issue 4:
1–30. [Available at: https://webgate.ec.europa.eu/maritimeforum/en/
Evans, P.G.H., Baines, M.E. and Anderwald, P. 2011. Risk assessment of
potential conflicts between shipping and cetaceans in the ASCOBANS
Region. Paper AC18/Doc.6-04(S)rev.1 presented to the 18th ASCOBANS
Advisory Committee Meeting, May 2011 (unpublished). 32pp.
Ferreira, R.B. 2007. Monitorização da actividade de observação de cetáceos
no Arquipelago da Madeira, Portugal. Master Thesis, University of
Lisbon. 62pp. [In Portuguese].
Fortuna, C.M. 2006. Ecology and Conservation of the Bottlenose dolphin
(Tursiops truncatus) in the North Eastern Adriatic Sea. Doctoral Thesis,
University of St. Andrews. 275pp.
Freitas, L., Dinis, A., Alves, F. and Nóbrega, F. 2004a. Cetácos no
Arquipelago da Madeira. Museu da Baleia da Madeira, Machico.108pp.
Freitas, L., Dinis, A., Alves, F., Quaresma, I., Antunes, R., Freitas, C., and
Nóbrega, F. 2004b. Projeto para a Conservação dos Cetáceos no
Arquipelago da Madeira – Relatório dos Resultados Cientificos. Museu
da Baleia da Madeira, Caniçal. 139pp. [In Portuguese].
Freitas L, Alves F, Ribeiro C, Dinis A, Nicolau C. and Carvalho A. 2014.
Estudo técnico-científico de suporte à proposta de criação de áreas de
operação para a actividade de whalewatching e respectiva capacidade de
carga. Relatório técnico do Projecto CETACEOSMADEIRA II (LIFE07
NAT/P/000646). Museu da Baleia da Madeira, Caniçal. 87pp. [In
Instituto Nacional de Estatistica. 2013. Estatistica das Pescas 2012. Lisbon.
133pp. [In Portuguese].
International Whaling Commission. 2011. Report of the Joint
IWC/ACCOBAMS Workshop on Reducing Risk of Collisions between
Vessels and Cetaceans, 21–24 September 2010, Beulieu-Sur-Mer, France.
Paper IWC/63/CC8 presented to the IWC Conservation Committee, July
2011, Jersey, Channel Islands, Uk. 41pp. [Paper available from the Office
of this Journal].
International Whaling Commission. 2016. Chair’s Report of the 65th
Meeting of the International Whaling Commission. Annex G. Report
of the Conservation Committee. Report of the 65th Meeting of the
International Whaling Commission 2014: 66–78.
Jelinski, D.E., Krueger, C.C., and Duffus, D.A. 2002. Geostatistical analyses
of interactions between killer whales (Orcinus orca) and recreational
whale-watching boats. Appl. Geogr. 22: 393–411.
Laist, D.W., Knowlton, A.R., Mead, J.G., Collet, A.S. and Podesta, M. 2001.
Collisions between ships and whales. Mar. Mammal Sci. 17: 35–75.
Lepparanta, M. and Myrberg, K. 2009. Physical Oceanography of the Baltic
Sea. Springer, Berlin, Heidelberg, New York. 333pp.
Ministério da Agricultura do Mar do Ambiente e do Ordenamento do
Território. 2012. Decreto lei no. 52/2012. Diário da República 48. 23pp.
Mou, J.M., Tak, C. and Ligteringen, H. 2010. Study on collision avoidance
in busy waterways by using AIS data. Ocean Eng. 37: 483–90.
Nowacek, S.M., Wells, R.S. and Solow, A.R. 2001. Short-term effects of
boat traffic on bottlenose dolphins, Tursiops truncatus, in Sarasota Bay,
Florida. Mar. Mamm. Sci. 17: 673–88.
Orams, M.B. 2000. Tourists getting close to whales, is it what whale-
watching is all about? Tourism Manage. 21(6): 561–69.
Panigada, S., Pesante, G., Zanardelli, M., Capoulade, F., Gannier, A., and
Weinrich, M.T. 2006. Mediterranean fin whales at risk from fatal ship
strikes. Mar. Pollut. Bull. 52: 1,287–98.
Piwetz, S., Hung S., Wang J., Lundquist D. and Würsig B. 2012. Indo-
Pacific humpback dolphin (Sousa chinensis) movements off Lantau
Island, Hong Kong: Influences of vessel traffic. Aquat. Mam. 38: 325–
Ritter, F. 2003. Interactions of Cetaceans with Whale Watching Boats –
Implications for the Management of Whale Watching Tourism. MEER
eV, Berlin. 91 pp. [Available at: http://www.m-e-e-r.de/wissenschaft.
Ritter, F. 2007. A quantification of ferry traffic in the Canary Islands (Spain)
and its significance for collisions with cetaceans. Paper SC/59/BC7
presented to the IWC Scientific Committee (unpublished). 12 pp.
[Available from the Office of this Journal].
Ritter, F. 2012. Collisions of sailing vessels with cetaceans worldwide: First
insights into a seemingly growing problem. J. Cetacean Res. Manage.
Silber, G. K., Vanderlaan, A.S.M., Acerdillo, A.T., Johnson, L., Taggart,
C.T., Brown, W.M, Bettridge, S. and Sagarminaga, R., 2012. The role
of the International Maritime Organization in reducing vessel threat
to whales: Process, options, action and effectiveness. Mar. Policy 36:
Southall, B.L. 2005. Shipping Noise and Marine Mammals: A Forum for
Science, Management, and Technology. Final report of the NOAA
International Symposium, Arlington, Virginia. 40pp.
Vanderlaan, A.S.M. and Taggart, C.T. 2007. Vessel collisions with whales:
The probability of lethal injury based on vessel speed. Mar. Mammal Sci.
Vlaams Institut voor de Zee 2014. Maritime Boundaries Geodatabase,
version 8. [Available at: http://www.marineregions.org. Consulted on
Waerebeek, K.V., Baker, A.N., Félix, F., Gedamke, J., Iñiguez, M., Sanino,
G.P., Secchi, E., Sutaria, D., Helden, A.V. and Wang, Y. 2007. Vessel
collisions with small cetaceans worldwide and with large whales in the
Southern Hemisphere, an initial assessment. Lat. Am. J. Aquat. Mammals
6 (1): 43–69.
Weilgart, L.S. 2007. The impacts of anthropogenic ocean noise on cetaceans
and implications for management. Can. J. Zool. 85 (11): 1,091–116.
28 CUNHA et al.: MARINE TRAFFIC AND CETACEANS IN MADEIRA