ArticlePDF Available

Abstract and Figures

Blue whales are sighted every year around the Azores islands, which apparently provide an important seasonal foraging area. In this paper we aim to characterize habitat preferences and analyze the temporal distribution of blue whales around São Miguel Island. To do so, we applied Generalized Additive Models to an opportunistic cetacean occurrence dataset and remotely sensed environmental data on bathymetry, sea surface temperature, chlorophyll concentration and altimetry. We provide a brief description of the oceanography of the area, emphasizing its high spatio-temporal variability. In order to capture this dynamism, we used environmental data with two different spatial resolutions (low and high) and three different temporal resolutions (daily, weekly and monthly), thus accounting for both long-term oceanographic events such as the spring bloom, and shorter-term features such as eddies or fronts. Our results show that blue whales have a well-defined ecological niche around the Azores. They usually cross the archipelago from March to June and habitat suitability is highest in dynamic areas (with high Eddy Kinetic Energy) characterized by convergence or aggregation zones where productivity is enhanced. Multi-scale studies are useful to understand the ecological niche and habitat requirements of highly mobile species that can easily react to short-term changes in the environment.
Content may be subject to copyright.
RESEARCH ARTICLE
Multi-scale habitat preference analyses for
Azorean blue whales
Laura Gonza
´lez Garcı
´a
1
*, Graham J. Pierce
2,3
, Emmanuelle Autret
4
, Jesu
´s M. Torres-
Palenzuela
1
*
1Applied Physics Department, University of Vigo, Vigo, Spain, 2Instituto de Investigaciones Marinas
(CSIC), Vigo, Spain, 3CESAM & Departamento de Biologia, Universidade de Aveiro, Aveiro, Portugal,
4Laboratoire d’Oce
´anographie Physique et Spatiale, IFREMER, Brest, France
*lauragonzalez@uvigo.es (LG); jesu@uvigo.es (JTP)
Abstract
Blue whales are sighted every year around the Azores islands, which apparently provide an
important seasonal foraging area. In this paper we aim to characterize habitat preferences
and analyze the temporal distribution of blue whales around São Miguel Island. To do so,
we applied Generalized Additive Models to an opportunistic cetacean occurrence dataset
and remotely sensed environmental data on bathymetry, sea surface temperature, chloro-
phyll concentration and altimetry. We provide a brief description of the oceanography of the
area, emphasizing its high spatio-temporal variability. In order to capture this dynamism, we
used environmental data with two different spatial resolutions (low and high) and three differ-
ent temporal resolutions (daily, weekly and monthly), thus accounting for both long-term
oceanographic events such as the spring bloom, and shorter-term features such as eddies
or fronts. Our results show that blue whales have a well-defined ecological niche around the
Azores. They usually cross the archipelago from March to June and habitat suitability is
highest in dynamic areas (with high Eddy Kinetic Energy) characterized by convergence or
aggregation zones where productivity is enhanced. Multi-scale studies are useful to under-
stand the ecological niche and habitat requirements of highly mobile species that can easily
react to short-term changes in the environment.
Introduction
The archipelago of the Azores is like an oasis in the middle of the Atlantic Ocean. It is located
about 2000 km SE of Newfoundland and 1500 km west of continental Portugal. In spite of
being within the boundaries of the oligotrophic North Atlantic Subtropical Gyre [1], these
islands are a key habitat for pelagic organisms. This is due to the presence of oceanographic
features like eddies and fronts and their interaction with the irregular topography, which
enhances biological productivity at different time and spatial scales. This dynamism favours
the development of bottom-up processes across trophic levels and drives biomass aggregation
at convergence zones, thus creating suitable foraging hotspots [25].
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 1 / 25
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Gonza
´lez Garcı
´a L, Pierce GJ, Autret E,
Torres-Palenzuela JM (2018) Multi-scale habitat
preference analyses for Azorean blue whales. PLoS
ONE 13(9): e0201786. https://doi.org/10.1371/
journal.pone.0201786
Editor: João Miguel Dias, Universidade de Aveiro,
PORTUGAL
Received: March 20, 2018
Accepted: July 23, 2018
Published: September 28, 2018
Copyright: ©2018 Gonza
´lez Garcı
´a et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The authors originally
accessed the cetacean occurrence data by
contacting the whale watching company,
Futurismo Azores Adventures Lda (Portas do Mar,
Loja 26, São Miguel Island, Azores, Portugal).
Other researchers may request data access in the
same manner by emailing Futurismo Azores
Adventures Lda at info@futurismo.pt.
Funding: This work was supported by the Applied
Physics Department of the Universidad de Vigo
(Spain); a predoctoral grant from Consellerı
´a de
Cultura, Educacio
´n e Ordenacio
´n Universitaria,
Cetacean habitat is often identified in relation to certain water depths or sea bed slope e.g.
[6,7] but habitat preferences are better described by also taking into account oceanographic
characteristics such as productivity or temperature. Dynamic variables are frequently used as
proxies for prey availability or abundance, notwithstanding the temporal lags and spatial dis-
placement that may occur between physical processes and the biological responses [8].
Around the Azores, 28 different species of whales and dolphins have been reported [9].
Seven of these are baleen whales, including the blue whale (B.musculus), which is listed by the
IUCN as an endangered species since its population has decreased by at least 70%, and possibly
90% over the last three generations, assuming a 31-year average generation time [10]. There
are four recognized subspecies, of which only one is found in our study area, the Northern
blue whale (B.m.musculus) [10]. Blue whale populations have been extensively studied in the
eastern Pacific [11,12], Antarctic [1315] and in some Atlantic areas such as the Gulf of
St. Lawrence [1618] and the central and NE North Atlantic [19,20]. Some individuals under-
take annual migrations between the low latitude breeding areas and the north Atlantic feeding
grounds, while others are thought to be resident in high productivity habitats [10]. Although
they are seen in spring in mid-Atlantic waters around the Azores, apparently while on their
northward journey [21], studies about them in the region are still scarce. This is partly due to
the high logistic complexity and economic cost of dedicated sea surveys, which usually con-
strains the data available spatially, temporally or to a small sample size, not only for blue whales
but also for other cetaceans [2224]. Therefore, the use of opportunistic data sources such as
whaling, stranding records, observations from fishing vessels, or more recently, sightings from
whale watching boats, becomes increasingly important to complement and improve the exist-
ing knowledge of cetaceans in the region [9,21,25,26].
Whale watching activities started in the main Azorean islands in the early 1990’s, and offer
a cost-effective method to obtain highly valuable data on cetacean distribution and behaviour.
Such opportunistic data collection has been proven to be a useful source of information to bet-
ter understand the occurrence and habitat preferences of cetaceans worldwide and to obtain
data that are otherwise inaccessible, especially where there is a lack of baseline data [2729].
Even with some limitations, such as poorly quantified sampling effort or logistical constraints
imposed by the nature and primary purpose of the platform (tourism), there are clear advan-
tages such as inexpensiveness and good spatial and temporal coverage on a regular basis
[8,29], especially regarding rare species [30]. Hence, knowing the strengths and weaknesses of
each dataset beforehand is a key point in order to maximise its use while avoiding false conclu-
sions or misconceptions.
Previous research on baleen whales in the Azores examined the timing of their arrival in
relation to the spring bloom and their foraging behaviour around the archipelago before they
continue to travel further north [21,24]. Their distribution has been studied in relation to sea
depth [9] and several oceanographic variables with monthly resolution [31,32]. So far, other
temporal scales have not been adequately investigated, although some recent studies suggest
that cetaceans, as highly mobile species, and particularly in very dynamic environments, may
be affected by both short- and long-term ocean variability [3335].
As a migratory species, blue whales may be attracted to the Azores by persistent oceano-
graphic events (e.g. spring bloom) which are usually quite predictable and can be detected
based on coarse (temporal and spatial) scale oceanographic data. Monthly composites of satel-
lite variables can provide the necessary evidence, and can help to reduce data loss apparent at
finer scales caused by the great abundance of clouds in the area. However, when investigating
habitat preferences of highly mobile marine species such as blue whales, capture of finer-scale
oceanographic processes turns out important, as the animals can easily react to short-term
environmental changes in order to better exploit the available resources. Thus, finer scales are
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 2 / 25
Xunta de Galicia [Modalidade B] (PRE/2013/409)
for the Marine Science, Technology and
Management (DOMAR) PhD Program (www.edu.
xunta.gal/); and a grant from LabexMER action for
young researchers in 2016 (https://www.labexmer.
eu/). The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
known to improve model performance [36,37], so consideration of weekly and/or daily values
is highly recommendable.
Identifying areas of likely occurrence of blue whales and other cetaceans is a necessary first
step to correctly identify Important Marine Mammal Areas (IMMA) [38] or Marine Protected
Areas important for cetaceans [39,40] to achieve conservation and management goals. Fur-
thermore, the Marine Strategy Framework Directive (MSFD) requires for every EU Member
State to assess its current level of environmental monitoring in order to improve its conserva-
tion measures, which usually implies collection of data on the status of various ecosystem com-
ponents, including cetaceans [41]. New trends towards dynamic ocean management are
currently under discussion in order to better adjust conservation measures to the highly
mobile nature of marine life and its rapidly changing environment [42,43].
In this paper we analyze temporal distribution and habitat preferences for blue whales off
São Miguel (Azores). We use sightings collected between 2008 and 2014 from whale watching
vessels and a set of bathymetric and oceanographic remotely sensed variables. We apply Gen-
eralized Additive Models (GAMs) to investigate relationships between whale presence and
environmental conditions and thus to make inferences about their habitat preferences. GAMs
are a non-parametric tool used to quantify linear or non-linear relationships between an inde-
pendent variable and one or more explanatory variables [44,45]. GAMs have been widely
employed to model cetacean distribution worldwide e.g.[4651]. There have been few studies
of blue whale habitat use in the Azores and these have used presence-only modelling tech-
niques, such as Maximum Entropy (MaxEnt) [31,32]. In this study we modelled blue whale
habitat use in the Azores at different temporal (monthly, weekly and daily) and spatial resolu-
tions (low and high) in order to provide a deeper understanding of blue whale habitat ecology,
getting the most from the different resolutions of the environmental data available.
Methods
Study area
The Azores archipelago is formed by nine volcanic islands located at 24–32˚W and 36–41˚N.
They emerge abruptly from the deep mid-Atlantic waters (~4000 m deep), spreading along
630 km WNW-ESE and crossing the Mid-Atlantic Ridge. Our sea surveys were conducted off
the south coast of São Miguel, the eastern-most island of the archipelago, located at 25–
26.2˚W and 37.3–38.1˚N (Fig 1). Required licenses and permissions to operate the whale
watching tours were provided by “Direc¸ão Regional dos Transportes” (Decreto Legislativo
Regional 23/2007/A) and “Direc¸ão Regional de Turismo” (DLR 10/2003/A). The study area
includes a great variety of marine habitats, from shallow waters over the very narrow continen-
tal shelf to deep waters, with depths of more than 2000 m relatively close to the shore. Sea sur-
face temperature ranges from approximately 15˚C in winter to 25˚C in late summer. The Gulf
Stream directly influences the archipelago with its cold branch, the North Atlantic Current
(NAC), flowing south-eastward between 45 and 48˚N. The Azores Front/Current System (AF/
AC), characterized by strong salinity and temperature cross-gradients, passes south of the
archipelago (between 32 and 37˚N) forming the northern boundary of the North Atlantic sub-
tropical gyre [52]. Both NAC and AF/AC are major sources of mesoscale oceanographic fea-
tures [53] which favour the aggregation of marine life [5,8,54,55].
Sightings
Cetacean sightings were recorded during the commercial trips of a whale watching company
with base port in Ponta Delgada (São Miguel, Azores). Unlike many other opportunistic data
sources, data were gathered throughout the year, in so far as the weather, the sea state and the
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 3 / 25
number of tourists were good enough to conduct the trips. For each trip, experienced observ-
ers with binoculars (Steiner 20x80 mm), stationed at strategic points on land, typically started
searching for cetaceans around one hour prior to launch and would continue observing and
relaying this information to the boat, until cetaceans are sighted from the boat. Thus, cetaceans
were located first from land and then boats were piloted directly towards the location of the
animals, observing current legislation about approaching free-living cetaceans (Decreto Legis-
lativo Regional n˚ 10/2003/A and DLR 13/2004/A). Once there, biologists on board registered
the location (GPS) and time of the sighting, as well as species, group size and composition,
behaviour and other observations. As research was not the main purpose of the activity, collec-
tion of standardized data, always registered manually, was restricted so as to interfere as little
as possible with the main goal, tourism. Therefore, routes were not recorded, but starting and
finishing times of each trip were recorded.
Only those sightings for which the species identification was confirmed (genus accepted for
beaked whales) and a reliable location recorded (longitude, latitude) were considered for the
analyses (Fig 2). Following this process, around 93% of the sightings data were retained for
analysis. The study period ran from May 2008 to December 2014.
Effort
As animals were spotted firstly from land and sightings at sea are thus not associated with the
amount of boat-based search effort during a trip, it is not possible to calculate real search effort
or blue whale sightings per unit effort for this dataset. However, we recorded the number of
trips, trip duration and time spent at sea each season and year as a rough indicator of the sea-
sonal and year-to-year distribution of effort. As described below, we used sightings of other
cetacean species as “absences” for blue whales.
Environmental variables
The main environmental variables used for the analyses are summarized in Table 1. We
obtained depth data from the General Bathymetric Chart of the Oceans (GEBCO, 2012) with a
Fig 1. Azores archipelago with its nine islands and São Miguel area enlarged with bathymetry data. Depth
contours each 500 m. Notice the three different areas considered for the environmental variables processing: area
Azores (bottom left corner); Area São Miguel (grey square marked in Azores area and enlarged map); and area south
São Miguel (dashed square).
https://doi.org/10.1371/journal.pone.0201786.g001
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 4 / 25
resolution of 30 arc-seconds. Slope was derived in degrees from the depth raster using the Sur-
face Analysis tool from IDRISI Kilimanjaro. Each sighting was linked to its corresponding
depth and slope values using Matlab R2012a. Distance to the coast from each sighting was cal-
culated with ArcGIS 9.3, using as a reference high resolution coastline data provided by the
Instituto Hidrográfico de Portugal in 2011.
Fig 2. Map of the study area with all the sightings registered between May 2008 and December 2014. Black points
are blue whale sightings (n = 89) and grey points correspond to the other species (n = 7711). Ponta Delgada is marked
in the south coast as the base port.
https://doi.org/10.1371/journal.pone.0201786.g002
Table 1. Main environmental variables from which the others were derived.
VARIABLES UNITS SPATIAL RESOLUTION TEMPORAL
RESOLUTION
SOURCE
PHYSIOGRAPHIC Depth m 30 arc-seconds(~1 km) static GEBCO-08
Distance to the
coast
m 30 arc-seconds(~1 km) - Instituto Hidrogra
´fico Portugal high resolution
coast line
OCEANOGRAPHIC SST K (˚C) 0.05˚ grid (~6 km)(10–100 km
effective)
Daily OSTIA
SST K (˚C) 1 km 3–5 images/day MetOp
CHL A mg/m
3
1 km Daily, 8-days GlobColour
MSLA-UV m/s 0.25˚ Daily AVISO
WIND m/s 0.5˚ grid (~54 km)(79 km
effective)
6 h ECMWF
SST: Sea Surface Temperature. CHL A: chlorophyll a concentration. MSLA-UV: Mean Sea Level Geostrophic Velocity Anomalies. GEBCO-08: General Bathymetric
Chart of the Oceans. OSTIA: Operational SST and Sea Ice Analysis. MetOp: Advanced Very High Resolution Radiometer (AVHRR) on board the Meteorological
Operational satellite. GlobColour: European Node for Global Ocean Colour. AVISO: satellite altimetry data. ECMWF: European Centre for Medium-Range Weather
Forecasts.
https://doi.org/10.1371/journal.pone.0201786.t001
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 5 / 25
The distribution of a given species usually depends on the distribution of its prey. Oceano-
graphic variables such as temperature or chlorophyll can act as proxies for primary production
and prey availability or distribution. The oceanographic dynamic variables considered were
prepared at three different temporal scales: daily, weekly and monthly. The products were cho-
sen according to the availability of homogeneous data over the studied period. All the dynamic
variables were processed with Matlab R2012a.
Data on Sea Surface Temperature (SST) were gathered from two different products, one
with high spatial resolution to get a detailed view of the study area and better account for the
local oceanographic features (Fig 3), and one with coarser resolution that simplifies data pro-
cessing and reduces data loss. The latter was derived from Operational SST and Sea Ice Analy-
sis (OSTIA), which is run at the UK Meteorological Office on a daily basis. It combines high
resolution products from infrared and microwave satellite instruments and in situ SST data,
resulting in a foundation SST which avoids diurnal variability. Although interpolated onto a
high spatial resolution grid (0.05˚), this SST product exhibits a spatial resolution close to 50
km [56]. The high resolution product comes from the Advanced Very High Resolution Radi-
ometer (AVHRR) on board the Meteorological Operational satellite (MetOp), which provides
several daily images of SST with an effective resolution of 1 km (EUMETSAT/OSI-SAF, 2008).
Each MetOp SST value has a quality index assigned, allowing us to exclude, for the purposes of
the present analysis, the “unprocessed”, “not useable” and “bad” data. For both products, we
extracted a value for each sighting location and day and also an average and standard deviation
for São Miguel (37–38.5˚N, 26.5–24.5˚W) and for the Azores archipelago (35–42˚N, 33–
23˚W). Weekly values were calculated as a 7 day moving average (±3 days from the sighting
date). A climatological mean (daily, weekly and monthly mean) was also calculated over the
area for the 7 years of study to account for seasonality. A SST anomaly was calculated as the
difference between the SST at the sighting location and the corresponding climatological mean
for each observation. This allows us to account for local spatial or temporal variations. Further-
more, location of thermal fronts were calculated from SST images (data gaps interpolated)
using a Canny edge detection function [57] with the upper threshold set at 1e-5˚C/m (~1˚C/
100 km). Distance, gradient and SST of the nearest front point were calculated for every
sighting.
Fig 3. High resolution sea surface temperature daily map from MetOp data (only values with quality Index >2 were considered).
Colorbar for temperature (˚C). Blank spaces are missing values or bad quality values masked. Sharp gradients can be seen in the area,
some with more than 2˚C of difference in less than 20 km. A) Image from the 20
th
of June 2010. It is clearly visible the cold water patch on
the south coast of the island and a tongue of warm water retained by the platform on the south west. B) Image from the 9
th
of July 2010.
Cold water in the west and north side of the island, retained as well in the SW due to the interaction with the bathymetry.
https://doi.org/10.1371/journal.pone.0201786.g003
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 6 / 25
Chlorophyll-a concentration is considered as a proxy for primary production. As blue
whales feed mainly on krill, primary production should directly affect their prey availability
with a relatively short time-lag. Krill development varies with environmental conditions, but
juvenile stages can be reached in less than three (or even two) months after spawning, which
usually happens after the phytoplankton bloom [58]. Chlorophyll data were obtained from
GlobColour (http://globcolour.info) L3 products that combine, when available, data from Sea-
WIFS, MODIS, MERIS and VIIRS under a weighted average merging method, with weightings
based on the sensor/product characterisation. As chlorophyll concentration is a dynamic vari-
able, and its distribution can change in a short period of time, we decided to use daily, 8-day
and monthly (processed from the 8-day ones) composites with 1km resolution. For each tem-
poral resolution, we extracted a value for the sighting location, and a mean value and standard
deviation over the Azores archipelago (35–42˚N, 33–23˚W), São Miguel (37–38.5˚N, 26.5–
24.5˚W) and south of São Miguel (37–37.7˚N, 26–25˚W) (Fig 1). The latter was done in order
to capture the effects of local upwelling, which has been detected on one side or the other of
the island, based on the daily maps (Fig 4). The chlorophyll concentration of previous weeks
(up to 17 weeks) and months (up to 4), i.e. before the sighting, was calculated to take into
account the possible time lag between the phytoplankton bloom and the development of krill.
Furthermore, three different chlorophyll indices were calculated as a ratio between the highest
values of chlorophyll in a small area and the average chlorophyll concentration over a bigger
area: chlorophyll Index 1 as the ratio between coastal São Miguel (37.65–37.75˚N, 25.8–
25.3˚W) and São Miguel area (37–38.5˚N, 26.5–24.5˚W); chlorophyll Index 2 as the ratio
between the south of São Miguel (37–38˚N, 26–25˚W) and a bigger area south of the archipel-
ago (30–38˚N, 32–22˚W); and chlorophyll Index 3 as the ratio between the Azores area (35–
42˚N, 33–25˚W) and a bigger Atlantic area surrounding the archipelago (30–48˚N, 38–15˚W).
As blue whales are migratory, the different concentrations of chlorophyll close to the island
compared with oceanic waters can help us to understand when or why the whales approach
the studied area.
To account for the mesoscale variability of ocean dynamics in the area we calculated the
Eddy Kinetic Energy (EKE) from altimetry data [55]. Gridded geostrophic velocity anomalies
(Mean Sea Level Anomalies: MSLA-UV) computed with respect to a 20-year mean were
Fig 4. GlobColour images for chlorophyll a concentration. Warm colours represent higher concentrations of chlorophyll a. Blank
spaces are missing values. Colorbar with log(chl a). A) Image from the 19
th
of December 2010. Chlorophyll concentrates on the north side
of the island, especially N of João Bom (NW) and off Praia da Viola (NNE). Chlorophyll concentration seems to expand further NE. B)
Image from the 10
th
of June of 2011. In this case, a local upwelling seems to occur in the W and SW of the island. It reaches quite high
concentrations, but in a limited area.
https://doi.org/10.1371/journal.pone.0201786.g004
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 7 / 25
produced by Ssalto/Duacs and distributed by AVISO (http://www.aviso.altimetry.fr/duacs/,
accessed online on 6/07/2016, and now accessible at http://marine.copernicus.eu/). The data
used were “Delayed Time” “all satellite merged” global daily maps on a 1/4˚ grid. The “All sat-
ellite merged” option was chosen because, during the entire study period, at least 3 satellites
were available at any moment and, thus, the quality of the retrieved velocity field is enhanced
with respect to the reference configuration merging [59], which uses observations from only 2
satellites. Horizontal and vertical components of the geostrophic velocity anomalies were used
to calculate the EKE, which is defined as: 1/2((u-u
mean
)
2
+(v-v
mean
)
2
), where u
mean
and v
mean
are the temporal means of each velocity component over the whole study period [53,55]. In
this case we consider monthly and seasonal means over the 7-year period, defining winter as
December-January-February, spring as March-April-May, summer as June-July-August and
autumn as September-October-November.
Wind data were obtained from the European Centre for Medium-Range Weather Forecasts
(ECMWF) ERA-Interim 0’5 6-hourly surface analysis, which combines an assimilation system,
constrained by the available in situ observations, and atmospheric modelling re-analysis. Its
gridded resolution is 0.5˚ (~54 km) but the effective resolution is around 79 km [60]. Wind
data were obtained only for the day and location of the sighting, in order to avoid misleading
temporal and spatial averages.
Variable selection
After preparing all the variables for the different spatial and temporal scales we had six main
datasets: low spatial resolution monthly (47 variables), weekly (57) and daily (59), and high
spatial resolution monthly (41 variables), weekly (60) and daily (60).
A detailed data exploration was carried out to assess collinearity among explanatory vari-
ables. We computed Pearson correlation matrices for each dataset in order to investigate the
linear dependence between the predictors. These were visualized as dendrograms to better
illustrate the similarity among the variables. The bigger the vertical distance between two vari-
ables, the more independent they are. We retained all the variables with a distance bigger than
0.8 from other variables [48,61]. The variables with distances under this value were selected
firstly according to their horizontal distribution, retaining at least one of each cluster of corre-
lated variables. When two highly collinear variables had similar ecological meanings, the one
with most missing values was removed. For daily datasets, chlorophyll mean and standard
deviation values from south of São Miguel were kept instead of those from around São Miguel
to better account for local events. After this first selection, collinearity was assessed again based
on the Variance Inflation Factor (VIF), removing all the variables whose VIF was higher than
5, one by one, starting with the highest value and recalculating VIFs at each step [62]. Finally,
some additional variables were excluded from the high resolution selections (according to the
dendrograms) due to greatly reducing (by hundreds and even thousands) the sample size avail-
able. After this process our datasets comprised of 19 (low spatial resolution daily), 18 (low spa-
tial resolution weekly), 17 (low spatial resolution monthly), 17 (high spatial resolution daily),
19 (high spatial resolution weekly) and 14 (high spatial resolution monthly) explanatory
variables.
Temporal distribution
To assess the temporal distribution of whale sightings, taking into account the survey effort,
we calculated an Encounter Rate (ER) as the number of sightings of blue whales divided by the
total number of trips over each month or season. There were normally two trips per day
(morning and afternoon). We consider the season as the period that included 99% of the blue
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 8 / 25
whale sightings, hence March to June every year. We did not use data from 2008 in seasonal
averages since data onboard were recorded only in surveys conducted during six months in
2008: from May to September and December.
Habitat preferences analysis
Descriptive analyses were carried out in order to visualize available habitat conditions within
the study area and understand distribution of the blue whale sightings in relation to habitat
variables. The available area is considered as the Minimum Convex Polygon containing 100%
of the sightings calculated with the “sp” and “adehabitatHR” packages for R [63,64,65]. It
accounts for a total extension of 5780 km
2
. We determined the range of available values for the
static variables in the estimated available area, although it should be noted that the true sur-
veyed area is probably slightly larger.
To model species distribution, available environmental conditions need to be represented
in the analysis [66]. Usually these “background” data are chosen at random to represent the
range of environmental conditions in the study area, but this process can fail to account for
sample bias, as sightings are generally recorded in more accessible areas [67]. Furthermore, the
changing marine environment can present different conditions in the exactly same location at
different times, therefore creating a different habitat. We decided to use a pseudo-absence
approach in order to have effort-related ‘non-occurrence points’ and to reduce possible spatial
bias [66,68]. To do so, we use the presence of the other cetaceans recorded during the study
period as pseudo-absence points for blue whales [69], making sure to exclude occasions when
blue whales were recorded at the same time as the other species. We thus ensured that those
locations were sampled and that blue whales were not seen there at the given time, while
accounting for, or at least reducing, sampling bias. The approach used here could be consid-
ered conceptually closer to the background sampling approach, as the number of “pseudo-
absences” is, in this study, much bigger than the number of presences and the pseudo-absence
dataset well characterizes the environmental conditions present across the study region [66].
Nevertheless, environmental conditions that were unsuitable for launching the boat (more
prevalent in winter) would not have been included in the dataset or, therefore, our models.
Statistical analyses were carried out with the open-source software R 2.15.0 (R Development
Core Team, 2012) and the mgcv library [70]. We applied Generalized Additive Models with a
binomial distribution and a logit link function to each of the six datasets previously prepared
in order to investigate habitat preferences of blue whales. The number of splines (k-knots
which measure the complexity of the fitted curve-) was set to 4 for all explanatory variables to
prevent overfitting and avoid unrealistically complex relationships. We used a backwards
stepwise procedure to select the best model for each dataset, with removal of variables at each
step being based on individual significance of explanatory variables and on the overall good-
ness of fit as measured by the Akaike Information Criterion (AIC) value. Starting from the
first model with all possible variables, the least significant one (normally with highest p-value)
was dropped out each time. If the resulting model had a lower AIC or a bigger sample size
(and in the latter case was not a markedly poorer model according to the AIC value), it was
retained. In some cases confidence bands were rather wide, even in the model with the lowest
AIC, so extra model selections steps were needed [71]. In order to avoid a number of predic-
tors higher than m/10, where m is the number of observations of the least represented category
(here m = 89) [72], we retained in the final model only the variables significant at p<0.01. The
best model for each dataset was achieved and the deviance explained was noted.
We applied a temporal k-fold cross-validation for each of the resulting models [73], using
sightings from six of the seven available years as the training dataset, and the remaining one
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 9 / 25
for evaluation (all but 2008 because no blue whales were recorded that year during the six
months of data available). Then we compared the performance of each final model based on
the Area Under the Curve (AUC) of the Receiving Operating Characteristic plot computed
with pROC package for R [74]. AUC indicates how well the model adjusts to our presence/
absence distribution without accounting for overfitting, so that more complex models usually
have better AUC [75]. Hence, as described above, we attempted to avoid very complex models
to carry out validation. We obtained an AUC for each year (i.e. for each option for the testing
dataset) and an average AUC value and its corresponding standard deviation for each model,
allowing us to compare the different models. We consider that a random model has a 50%
chance to correctly distinguish between presence and non-presence locations, so we assume
that any model with AUC higher than 0.5 should be better than random.
Results
Sea surveys
In total, 7711 sightings of twenty different species of cetaceans were recorded during the 2364
trips completed over 1386 non-consecutive days between May 2008 and December 2014 (Fig
2). Both resident and migratory species were sighted. Out of these records, 89 (1.2%) were blue
whales (B.musculus), registered during 85 trips over 70 days across the study period. There
were 124 individuals recorded, mostly observed alone (71.9% of times) and with a maximum
of three blue whales together in the same sighting. In three of the sightings, blue whales were
observed together with common dolphins (Delphinus delphis), while on six occasions they
were observed together with fin whales (Balaenoptera physalus), once reaching a total of at
least ten whales in the same group.
Effort
The average number of trips per year (excluding 2008, when there were only 6 months of sight-
ings) is 338, although in both 2009 and 2014 there were more than 400 trips, and in 2011 there
were only 269. Accordingly, the average time recorded at sea each year (excluding 2008) is 949
h, being highest in 2014 with 1134 h, and lowest in 2011 with 804 h (Fig 5).
The period of maximum effort corresponds to summer (June, July and August) when the
45.5% of the trips of the year were realized. The blue whale season, i.e. March to June every
year, accounted for a total of 881 trips and 2462 h at sea. The number of trips recorded per
year in this period ranged from 117 to 159, only higher in 2014, when 210 trips were carried
out (mean 146.8 ±34.4). On average, 380 h (±96 h) were spent at sea per blue whale season,
Fig 5. Time (hours) spent at sea per year and month. Rough approximation of the effort carried out over the seven
years of study.
https://doi.org/10.1371/journal.pone.0201786.g005
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 10 / 25
although in 2014 there were 563 h. Thus, effort coverage is reasonably consistent throughout
the study period; and variation between months and years affects presences and pseudo-
absences equally.
Environmental variables
Bathymetry data are consistent with the volcanic nature of these islands that abruptly emerge
from the bottom surfacing almost without any surrounding continent shelf and resulting in
the presence of very deep waters very close to the shore (Figs 1and 6). Two deep oceanic
trenches both reach depths of more than 3200 m at the NW (Fossa do Hirondelle) and SE
(Bacia de São Miguel) of the study area. At the SW tip of the island, a shallower platform
extends 60 km further south with an average depth of 500 m and a surrounding bottom of
2000 m. Considering the minimum convex polygon containing 100% of the sightings as the
sampled area, the available sampled depths range from 0 up to 3237 m. Most of the sampled
area (~60%) falls between 1500 and 2500 m depth, with ~13% between 2100 and 2300 m. The
slope of the bottom ranges from 0 to 46.4˚. 80% of the sampled area has a slope of <10˚, while
the highest slope values are found adjacent to the island and the submarine mounts and banks,
and the smallest slope values (almost no slope) occur where the deepest waters are found.
Every year sea surface temperature reaches its minimum value in winter; minima during
the study period range from 15.2˚C in 2013 to 16.75˚C in 2012. SST starts to progressively
increase in April, and achieves its highest values (up to 24.7˚C) in late summer (end of August,
beginning of September). During the study period we noticed clear inter-annual differences.
Fig 6. 3D bathymetric map of the oriental group of the Azores. We can appreciate a more confined area in the south of São Miguel, enclosed
by the shallower platform on the west, and submarine mounts and Santa Maria to the south. Latitude and longitude (degrees) represented on the
x and y axis, and depth (m) in the z. The colour bar represents depth, going from dark blue as maximum depth and increasing up to red-
brownish which represents land (above 0m).
https://doi.org/10.1371/journal.pone.0201786.g006
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 11 / 25
2008 was warmer than the average, especially in summer. Winter 2012 was warmest, almost
2˚C higher than the coldest year, 2010. In winter and spring of 2010, 2011 and 2014, the water
was colder than in other years. Around the studied area it is not uncommon to find a surface
temperature difference of 2˚C between coastal and offshore waters (40 km from land), as well
as sharp changes over short periods of time, as in September 2014, when SST dropped more
than 2˚C in less than two weeks.
Chlorophyll concentration is not particularly high in the Azores. Daily means around São
Miguel ranged from 0.029 mg/m
3
in late September 2011 up to 2.007 mg/m
3
in April 2010.
The concentration was usually below 1mg/m
3
, especially in summer when it rarely exceeded
0.2 mg/m
3
. In late autumn it starts to increase, achieving maximum values every year in spring,
mainly in April, when the well known “spring bloom” takes place [24].
Looking at high resolution images for sea surface temperature and chlorophyll, although
there were many cells with missing data due to the abundance of clouds, we detected local fea-
tures on one side or other of the island, i.e. colder waters (Fig 3) or higher concentrations of
chlorophyll close to the islands (Fig 4).
Eddy Kinetic Energy becomes higher in late spring-summer, with the Azores Front/Current
System south of the archipelago (32–34˚N) clearly visible in the seasonal maps. Winds around
the archipelago are highly variable in strength and direction, but westerly winds seem to be
generally strongest, which was particularly noticeable in winter 2009 and winter-spring 2010.
Temporal distribution of blue whales
Blue whales were sighted regularly every year (except in 2008) between March and June, with
only one sighting outside this period (a whale recorded in September 2012) (Fig 7). The overall
seasonal ER (excluding 2008) was 0.10 sightings per trip, but ER was particularly high in 2010
(0.16) and 2012 (0.23), the latter being the year in which the highest number of blue whale
sightings was recorded (n = 29). The highest monthly ER was in April (0.19), when 42.9% of
the sightings were registered. April 2014 was by far the month with most blue whale sightings
(18% of the total) within the study period. Inter-annual variation was also found in the number
of sightings of blue whales recorded, with most sightings (29) in 2012.
Habitat preferences
Descriptive analyses. Blue whales were sighted in waters of a wide range of depths (126–
3000 m), but 83.2% of them were in waters between 500 and 1500 m depth (median: 987.6m),
although this depth occurred in only 33% of the sampled area (Fig 8a). They were mostly
Fig 7. Temporal distribution of blue whales between May 2008 and December 2014. Dashed line shows the effort
recorded, thus, hours spent at sea (left axis) each month along the 7 years of study. On the right of the graph, the year
with the corresponding number of blue whale sightings in brackets is indicated. Sightings made in 2008 are grey, due
to the late beginning of the data collection.
https://doi.org/10.1371/journal.pone.0201786.g007
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 12 / 25
(86.5%) sighted over slopes of less than 7.5˚ (range: 1–20.55˚, median: 3.99˚). Regarding sea
surface temperature at the sighting location, the whales were mostly seen in cold waters (75.3%
of them between 15 and 17˚C), although they were sighted in waters with SST ranging from
14.4˚ up to 22.1˚C (median: 16.31˚C). Despite the general low chlorophyll concentrations in
the study area (66.3% of the daily values are below 0.2 mg/m
3
), most of the blue whales
(90.6%) were found when concentrations were relatively high (>0.1 mg/m3) around São
Miguel (Fig 8b).
GAM results. The three low spatial resolution models, daily, weekly and monthly, per-
formed reasonably well in terms of deviance explained (33.7%, 38.7% and 40.7% respectively)
and AUC (0.913, 0.918 and 0.936). They all show a small between-year standard deviation in
AUC (0.031, 0.036 and 0.025), thus fitting reasonably well for all the studied years (Table 2).
We found bigger differences among the three high spatial resolution models, the weekly
model being the best model of this group. Deviance explained was reasonably good for the
three models (daily 30.6%, weekly 38.2% and monthly 40.3%), but when applying the k-fold
cross-validation, the monthly model showed a much poorer performance. Its AUC was the
smallest of all the implemented models (0.88) and the AUC standard deviation the highest
(0.100) (Table 2), which means that for some years this monthly model fits quite well, while for
others it fits poorly, leading to substantial differences in AUC among the years considered
within the model.
Fig 8. Histograms of environmental values recorded in the study area against the ones associated with blue whale
sightings. A) Depth. B) Daily chlorophyll around São Miguel. It is noticeable how blue whales occur across a much
smaller range of conditions than the available in the habitat.
https://doi.org/10.1371/journal.pone.0201786.g008
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 13 / 25
Distance to the coast was selected in all models, both high and low resolution, always with
the same shape of the fitted curve, which indicates a preference for waters further than 10km
from the coast (Fig 9). To put this in context, observers on land could detect whale blows up to
around 30 km offshore but all sightings used in the analysis were from the whale watching
boats, which normally travelled up to 30 km from the shore, recording most of the sightings
(77.7%) within 15 km from the coast (Fig 10). Therefore, although data might present bias
towards shorter distances from the coast due to the initial searching for animals being done
from land, blue whales sightings are among those recorded furthest away from land. Seasonal
EKE around the archipelago was retained in all the models too. Habitat suitability for blue
whales seems to be slightly higher when medium EKE values (60–70 cm
2
s
-2
) are found around
the Azores (Fig 11). However, in the vicinity of São Miguel island, the highest available values
(>45 cm
2
s
-2
) were preferred.
Sea surface temperature gradient was selected for weekly and monthly low resolution mod-
els (Fig 12c, 12d, 12g and 12h respectively) and monthly high resolution (Fig 13e). Habitat
suitability is increased when low gradient values are found around the archipelago, while
stronger gradients are preferred around São Miguel. Climatological SST means were included
in three of the final models (low spatial resolution daily and weekly (Fig 12a and 12e), and
high spatial resolution weekly (Fig 13c)), with the shapes of the smoothers indicating a prefer-
ence for waters colder than 19.5˚C.
Chlorophyll concentration around the Azores was selected for both weekly models (Figs
12f and 13d). From the range of chlorophyll values available, blue whales prefer medium values
around the Azores (1–1.5 mg/m
3
). In the low spatial resolution monthly model, high SD of the
chlorophyll concentrations was preferred.
Table 2. Summary of the GAM results for the six obtained models.
LOW SPATIAL RESOLUTION HIGH SPATIAL RESOLUTION
DAILY AUC 0.913 0.905
SD 0.031 0.033
DEV (%) 33.7 30.6
n7647 7522
Explanatory
Variables
Distance to the coast, SST climatic daily mean, N wind in the
sighting location, EKE seasonal Azores
Distance to the coast, chlorophyll week 4, EKE seasonal São Miguel,
EKE seasonal Azores
WEEKLY AUC 0.918 0.921
SD 0.036 0.018
DEV (%) 38.7 38.2
n7633 7633
Explanatory
Variables
Distance to the coast, SST gradient Azores, SST gradient São
Miguel, SST climatic weekly mean, chlorophyll mean Azores,
EKE seasonal Azores
Distance to the coast, SST climatic weekly mean, chlorophyll mean
Azores, EKE seasonal Azores
MONTHLY AUC 0.936 0.88
SD 0.025 0.100
DEV (%) 40.7 40.3
n7647 7647
Explanatory
Variables
Distance to the coast, SST gradient São Miguel, SST gradient
Azores, chlorophyll SD Azores, EKE seasonal São Miguel,
EKE seasonal Azores
Distance to the coast, SST gradient São Miguel, SST gradient SD São
Miguel, Chlorophyll Index 3, chlorophyll month 2 and month 4, EKE
seasonal São Miguel, EKE seasonal Azores
AUC: Area Under the Curve of the Receiving Operating Characteristic plot. SD: Standard Deviation of the AUC. DEV(%): Percentage of deviance explained in the
model. n: total number of cetacean records (including presence and pseudo-absence) used in the model. SST: Sea Surface Temperature. EKE: Eddy Kinetic Energy.
https://doi.org/10.1371/journal.pone.0201786.t002
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 14 / 25
Fig 9. Smoothers showing the effect of distance from the coast on blue whale presence. LD = low spatial daily, LW = low spatial weekly, LM = low spatial monthly,
HD = high spatial daily, HW = high spatial weekly and HM = high spatial monthly. Black marks on the x-axis indicate the distribution of our observations.
https://doi.org/10.1371/journal.pone.0201786.g009
Fig 10. Distance to the coast of the recorded sightings. Most of the sightings were done within 30 km from the coast (98.9%); 77.7% within
15 km from the coast.
https://doi.org/10.1371/journal.pone.0201786.g010
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 15 / 25
Discussion
Modelling results can be evaluated from two main perspectives: statistical and ecological per-
formance. The former seeks a robust empirical description of the existence, strength and form
of relationships between the response variable (whale presence) and the explanatory variables
(environmental conditions); the latter looks for the ecological explanations underlying the
observed empirical relationships. Ideally, and as is the case here, statistical and biological per-
formance is linked, because putative explanatory variables are selected on the basis of hypothe-
sised (or at least plausible) relationships between whales and their environment, especially in
relation to the distribution of feeding opportunities.
Arguably the biggest challenge relates to the opportunistic nature of the dataset. Initial land
search for the animals could bias sightings towards shorter distances from the shore. However,
this bias would affect both blue whale sightings and pseudo-absences (sightings of other spe-
cies), which will tend to counteract the original bias. What is likely is that the number of data
points will decline with increasing distance from the shore, resulting in wider confidence limits
and a reduced ability to detect preferences. Finally, as our models have a descriptive purpose
(and not predictive), we analyze habitat preferences of blue whale in a certain study area, lim-
ited in this case by the distance observed from the coast.
The lack of well-quantified effort information directly affects abundance estimations and
hampers calculation of encounter rates per unit of area or time. We calculated sightings per
number of trips to provide a rough overall abundance index, but did not use this in the models.
Pseudo-absences for analyzing habitat preferences can be obtained as random points inside
the study area, along the survey route, or using the locations for presence of other species e.g.
Fig 11. Smoothers showing the effect of seasonal EKE in the Azores archipelago for each of the six final models on blue whale presence. LD = low spatial daily,
LW = low spatial weekly, LM = low spatial monthly, HD = high spatial daily, HW = high spatial weekly and HM = high spatial monthly. Black marks on the x-axis
indicate the distribution of our observations.
https://doi.org/10.1371/journal.pone.0201786.g011
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 16 / 25
[67,69,76]. All these methods may present limitations [77], but they allow us to apply model-
ling techniques (e.g. GAMs) that (evidently) would not be possible without access to absence
data. Consequently, we decided to use the sightings of the other species as pseudo-absences of
blue whales as done by Esteban et al. (2013) [69]. In this way we should minimise biases due to
spatial and temporal variation in effort.
Multiple sightings of the same individual are likely to occur. If they happen in a short period
of time and within a short distance from each other, over-sampling of the same environmental
conditions may occur and true sample sizes (numbers of independent sightings) will be over-
estimated (i.e. there will be pseudo-replication). For this reason, we deleted all the sightings
Fig 12. Smoothers of the selected variables in the low spatial resolution final models. All final models include distance to the coast and seasonal EKE for the Azores
shown in Figs 9and 11, apart from the variables here represented. A-B) Low spatial daily. C-E) Low spatial weekly. F-H) Low spatial monthly. Black marks on the x-
axis indicate the distribution of our observations.
https://doi.org/10.1371/journal.pone.0201786.g012
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 17 / 25
Fig 13. Smoothers of the selected variables in the high spatial resolution final models. All final models include distance to the coast and seasonal EKE
for the Azores apart from the variables here represented. A-B) High spatial daily. C-D) High spatial weekly. E-I) High spatial monthly. Black marks on the
x-axis indicate the distribution of our observations.
https://doi.org/10.1371/journal.pone.0201786.g013
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 18 / 25
recorded within 1 hour in near locations, unless different individuals were confirmed with
photo-identification. Nevertheless, blue whales can remain around the same area for several
days [21] and their behaviour and habitat choice may change during this time. Therefore, one
sighting of each whale does not necessarily cover all the habitat conditions it requires.
Abundance of blue whales in the Central and Northeast Atlantic has been estimated at less
than 1000 individuals [20], while in the Northwest Atlantic more than 400 blue whales have
been identified [78]. In this context, the number of sightings (not to be confused with the
number of individuals) used in this study (89) was remarkably high compared with the 17
used by Prieto et al. [31] and Tobeñaet al. [32] or 35 by Visser et al. [24] in the same archipel-
ago in previous studies. Furthermore search effort was carried out all year long, with blue
whales mostly sighted in spring time, a peak which does not coincide with the period of maxi-
mum effort of the year, summer. This implies that this strong seasonality in blue whale sight-
ings around the Azores is not an artefact of the seasonal pattern of search effort.
Another important issue relates to the highly dynamic nature of the marine environment in
the study area. The interaction between the dynamic oceanographic conditions and the wide
range of sea depths available creates a constantly changing environment that provides a great
variety of living conditions throughout the year. As such, the ecological niche of the blue whale
encompasses numerous different combinations of conditions, each defining a part of the niche
space that is constantly shifting in space and time and which may never co-occur with some
other parts of the niche space. In this context of very dynamic domains and highly mobile spe-
cies which can easily react to those environmental changes, the use of different temporal and
spatial scales provides relevant information about the different components of the blue whale
niche that otherwise could be overlooked [4,33,35].
In this study low spatial resolution models and the high spatial resolution weekly model
performed better than the high spatial resolution daily and monthly ones. In general, high spa-
tial resolution models were less consistent, and indeed following an objective model selection
process became more difficult for the high spatial resolution data sets, due to several different
models having similar goodness of fit. Although this could indicate that the model is failing to
capture the underlying ecological processes, we suspect that an important issue is the quality of
the remote-sensing data, which presented many missing values caused by the frequent cloud
coverage in the Azores. This issue implied a considerable decrease of the number of sightings
for which environmental values were available (95.35% of the sightings lost for the high resolu-
tion daily model). This argument is supported by some other recent studies in the Azores
[31,32], which avoided the use of high resolution satellite-derived variables due to these practi-
cal limitations. Nevertheless, we decided to consider two different spatial resolutions for sea
surface temperature, which we expected to be an important variable affecting blue whale distri-
bution. High spatial resolution data allow us to recognize meso- and submesoscale oceano-
graphic features (with dimensions of dozens to hundreds of km) known to affect our study
area [53,55]. With coarser scales, they could be overlooked. Those features sometimes last just
for a few days (or even hours) but, even if ephemeral, they can have a profound effect on the
ocean productivity, and thus on species distribution. For this reason we argue in favour of try-
ing daily variables, as blue whales are highly mobile species able to easily react to short-term
changes in the environment.
Ecological meaning
Only two of the explanatory variables were included in all the final models: distance to the
coast (Fig 8) and seasonal EKE for the Azores (Fig 10). In all models blue whales show a prefer-
ence for waters further than 10 km from the coast, which are usually deep waters. EKE
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 19 / 25
represents a good proxy for mesoscale eddy activity [55], so the preference for medium to high
EKE values around the archipelago leads us to think about the power of eddies to aggregate
plankton and potentially enhance food availability.
In weekly models, medium values of chlorophyll in the archipelago were preferred. High
chlorophyll concentrations are generally related with the phytoplankton bloom and, thus, the
first link of the trophic chain. Blue whales feed almost exclusively on krill (Euphausiacea)
[10,79], and this zooplankton family, the distribution of which is directly affected by the ocean-
ographic dynamism of the area, needs some time to develop after the bloom leading to a notice-
able reduction of the chlorophyll concentration in the area at the time the whales are present.
This would explain the apparent preference of the whales for locations with medium values of
chlorophyll concentration instead of higher ones. Furthermore, blue whales are known to feed
not only on the surface, but also at depths of up to almost 300 m [80]. Chlorophyll distribution
in the water column depends on local oceanography and its interaction with the bathymetry,
and the concentration at depth will not always agree with its surface signature [81,82].
In the low spatial resolution monthly model, there was a preference for high values of chlo-
rophyll SD, meaning areas where chlorophyll is patchily distributed. Supporting this idea, the
preference for quite strong SST gradients around São Miguel (low spatial resolution monthly
model), together with the high EKE around the island (high spatial resolution monthly
model), suggests that dynamic environments, thus with the presence of oceanographic features
like eddies (normally linked with EKE), filaments or fronts (normally linked with high SST
gradients), could result in more suitable habitats for blue whales. Those features can be very
relevant when or where oligotrophic conditions are present, implying convergence or aggrega-
tion zones which can increase food availability [4,83].
We agree with Ferna
´ndez [34], who suggested the use of weekly or daily scales in order to
acquire useful results for highly mobile species in dynamic environments. However, in our
study the monthly low resolution model also seems to perform well for blue whales. This could
be caused by the location of our study area, in the eastern group of the Azores, which is partic-
ularly influenced by a westward flow of eddies derived from the Azores Current with a life
span of more than six months [55] and a noteworthy aggregation power [53,84]. Therefore, a
great part of this variability, and hence, of its effects on whale distribution, may be captured by
monthly variables for this region.
Temporal distribution
We confirmed the presence of blue whales every year around the Azores in spring time, agree-
ing with previous studies in the area [21,24,31]. During spring, it is known that at least some
blue whales interrupt their migration towards the North Atlantic feeding grounds to forage
around the archipelago before continuing their journey [21]. Our results support this hypothe-
sis, highlighting, in all the final habitat preference models the importance of dynamic variables
responsible for the availability and distribution of whales prey. These habitat preferences can-
not be therefore generalized to those favoured by the species while travelling [54].
After summer, blue whales are supposed to migrate to their low latitude breeding grounds,
but as sightings in autumn are extremely rare in the Azores (only 1 sighting in September
2012) they probably follow different routes, as suggested by the recent findings off NW Iberia,
where several blue whales have been sighted in September and October 2017 [85].
Conclusions
Our results suggest that multiscale studies are very useful in species distribution modelling for
highly mobile species which exploit dynamic habitats. With the use of several temporal and
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 20 / 25
spatial scales, we increase chances of capturing relevant oceanographic structures or environ-
mental changes likely to affect whale distribution, both persistent and larger events (e.g. spring
bloom) and more localized and short-term ones (e.g. mesoscale eddies or local upwelling). We
compared daily, weekly and monthly resolutions with two different spatial scales. Weekly
composites of satellite-derived oceanographic variables turned out to be a valid option for
both spatial scales. Daily data were useful as well for both spatial scales, as final models didn’t
retain chlorophyll or SST variables, the ones that considerably reduce sample size due to the
inherent data gaps in the environmental products.
Blue whales have a well-defined ecological niche around the Azores. They prefer deep
waters further from the coast (>10 km) within cold temperatures (typical in spring time). The
importance of dynamic areas, which can enhance productivity and promote convergence
zones, is revealed by medium to high EKE values included in all the final models. All the
models obtained within this study show a good statistical performance: reasonably high devi-
ance explained (between 31.0 and 41.6%) and reasonably good AUC (from 0.822 ±0.024 to
0.931 ±0.024); and generally made sense ecologically.
Acknowledgments
We thank all the biologists and crew onboard the whale watching boats of Futurismo Azores
Adventures, particularly Miranda van der Linde for her efforts to ensure the dataset effective
and useful; Roberto Soares, who spent very long hours looking for the animals. We also
warmly thank Marc Ferna
´ndez Morro
´n for his advice on the model evaluation and Cristina
Gonza
´lez Haro, for her support on the final manuscript.
Author Contributions
Conceptualization: Laura Gonza
´lez Garcı
´a, Graham J. Pierce, Jesu
´s M. Torres-Palenzuela.
Data curation: Laura Gonza
´lez Garcı
´a, Emmanuelle Autret.
Formal analysis: Laura Gonza
´lez Garcı
´a.
Funding acquisition: Laura Gonza
´lez Garcı
´a, Jesu
´s M. Torres-Palenzuela.
Investigation: Laura Gonza
´lez Garcı
´a.
Methodology: Laura Gonza
´lez Garcı
´a, Graham J. Pierce, Emmanuelle Autret, Jesu
´s M. Tor-
res-Palenzuela.
Project administration: Jesu
´s M. Torres-Palenzuela.
Resources: Laura Gonza
´lez Garcı
´a, Emmanuelle Autret, Jesu
´s M. Torres-Palenzuela.
Supervision: Jesu
´s M. Torres-Palenzuela.
Visualization: Laura Gonza
´lez Garcı
´a.
Writing – original draft: Laura Gonza
´lez Garcı
´a, Graham J. Pierce, Emmanuelle Autret.
Writing – review & editing: Laura Gonza
´lez Garcı
´a, Graham J. Pierce.
References
1. Dave AC, Barton AD, Lozier MS, McKinley GA. What drives seasonal change in oligotrophic area in the
subtropical North Atlantic? J Geophys Res Oceans. 2015; 120: 7722–7751.
2. Olson DB, Backus RF. The concentrating of organisms at fronts: a cold-water fish and a warm-core
Gulf Stream ring. Journal of Marine Research. 1985; 43: 113–137.
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 21 / 25
3. Bailleul F, Cotte
´C, Guinet C. Mesoscale eddies as foraging area of a deep-diving predator, the southern
elephant seal. Mar. Ecol. Prog. Ser. 2010; 408: 251–264. https://doi.org/10.3354/meps08560
4. Cotte
´C, D’Ovidio F, Chaigneau A, Le
´vy M, Taupier-Letage I, Mate B, et al. Scale-dependent interac-
tions of Mediterranean whales with marine dynamics. Limnol Oceanogr. 2011; 56: 219–232. https://doi.
org/10.4319/lo.2011.56.1.0219
5. GodøOR, Samuelsen A, Macaulay GJ, Patel R, Hjøllo SS, Horne J, et al. Mesoscale Eddies Are Oases
for Higher Trophic Marine Life. 2012; PLoS ONE 7(1): e30161. https://doi.org/10.1371/journal.pone.
0030161.
6. Panigada S, Notarbartolo di Sciara G, Panigada MZ, Airoldi S, Borsani JF, Jahoda M. Fin whales sum-
mering in the Ligurian Sea: distribution, encounter rate, mean group size and relation to physiographic
variables. J Cetacean Res Manage. 2005. 7: 137–145.
7. Ingram SN, Walshe L, Johnston D, Rogan E. Habitat partitioning and the influence of benthic topogra-
phy and oceanography on the distribution of fin and minke whales in the Bay of Fundy, Canada. J Mar
Biol Ass UK. 2007; 87: 149–156. https://doi.org/10.1017/S0025315407054884
8. Redfern JV, Ferguson MC, Becker EA, Hyrenbach KD, Good C, Barlow J, et al. Techniques for ceta-
cean-habitat modeling. Mar Ecol Prog Ser. 2006; 310: 271–295.
9. Silva MA, Prieto R, Cascão I, Seabra MI, Machete M, Baumgartner MF, et al. Spatial and temporal dis-
tribution of cetaceans in the mid-Atlantic waters around the Azores. Marine Biology Research. 2014;
10(2): 123–137. https://doi.org/10.1080/17451000.2013.793814
10. Reilly SB, Bannister JL, Best PB, Brown M, Brownell Jr RL, Butterworth DS, et al. Balaenoptera muscu-
lus. The IUCN Red List of Threatened Species 2008: e.T2477A9447146. 2008; http://dx.doi.org/10.
2305/IUCN.UK.2008.RLTS.T2477A9447146.en
11. Bailey H, Mate BR, Palacios DM, Irvine L, Bograd SJ, Costa DP. Behavioural estimation of blue whale
movements in the Northeast Pacific from state-space model analysis of satellite tracks. Endang Species
Res. 2009; 10: 93–106. https://doi.org/10.3354/esr00239
12. Irvine LM, Mate BR, Winsor MH, Palacios DM, Bograd SJ, Costa DP, et al. Spatial and temporal occur-
rence of blue whales off the US West Coast, with implications for management. PloS ONE. 2014; 9(7):
e102959. https://doi.org/10.1371/journal.pone.0102959 PMID: 25054829
13. Branch TA, Stafford KM, Palacios DM, Allison C, Bannister JL, Burton CLK, et al. Past and present dis-
tribution, densities and movements of blue whales Balaenoptera musculus in the Southern Hemisphere
and northern Indian Ocean. Mammal Review. 2007; 37(2): 116–175. https://doi.org/10.1111/j.1365-
2907.2007.00106.x
14. Olson PA. Antarctic blue whale photo-identification catalogue summary. 2012; SC/64/SH8 Paper pre-
sented to the IWC Scientific Committee.
15. Miller BS, Barlow J, Calderan S, Collins K, Leaper R, Olson P, et al. Validating the reliability of passive
acoustic localisation: a novel method for encountering rare and remote Antarctic blue whales. Endanger
Species Res. 2015; 26: 257–269. https://doi.org/10.3354/esr00642
16. Sears R, Calambokidis J. Update COSEWIC status report on the Blue Whale Balaenoptera musculus
in Canda, in COSEWIC assessment and update status report on the Blue Whale (Balaenoptera muscu-
lus) in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. 2002. 132 pp.
17. Ramp C, Be
´rube
´M, Hagen W, Sears R. Survival of adult blue whales Balaenoptera musculus in the
Gulf of St. Lawrence, Canada. Mar Ecol Prog Ser. 2006; 319: 287–295.
18. Doniol-Valcroze T, Lesage V, Giard J, Michaud R. Challenges in marine mammal habitat modelling: evi-
dence of multiple foraging habitats from the identification of feeding events in blue whales. Endang spe-
cies res. 2012; 17(3): 255–268. https://doi.org/10.3354/esr00427
19. Sigurjo
´nsson J, Gunnlaugsson T. Recent trends in abundance of blue (Balaenoptera musculus) and
humpback whales (Megaptera novaeangliae) off west and southwest Iceland, with a note on occurrence
of other cetacean species. Rep. int. Whal. Commn. 1990; 40: 537–551.
20. Pike DG, Vı
´kingsson GA, Gunnlaugsson T, Øien N. A note on the distribution and abundance of blue
whales (Balaenoptera musculus) in the Central and Northeast North Atlantic. NAMMCO Sci. Publ.
2009; 7: 19–29.
21. Silva MA, Prieto R, Jonsen I, Baumgartner MF, Santos RS. North Atlantic Blue and Fin Whales Sus-
pended Their Spring Migration to Forage in Middle Latitudes: Building up Energy Reserves for the Jour-
ney? PLoS ONE. 2013; 8(10): e76507. https://doi.org/10.1371/journal.pone.0076507 PMID: 24116112
22. Silva MA, Prieto R, Magalhães S, Cabecinhas R, Cruz A, Gonc¸alves JM, et al. Occurrence and distribu-
tion of cetaceans in the waters around the Azores (Portugal), Summer and Autumn 1999–2000. Aquatic
Mammals. 2003; 29: 77–83.
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 22 / 25
23. Olsen E, Budgell P, Head E, Kleivane L, Nøttestad L, Prieto R, et al. First Satellite-tracked long distance
movement of a sei whale (Balaenoptera borealis) in the North Atlantic. Aquatic Mammals. 2009; 35(3):
313–318.
24. Visser F, Hartman KL, Pierce GJ, Valavanis VD, Huisman J. Timing of migratory baleen whales at the
Azores in relation to the North Atlantic spring bloom. Mar. Ecol. Prog. Ser. 2011; 440: 267–279.
25. Clarke R. Whales and dolphins of the Azores and their exploitation. Reports of the International Whaling
Commission. 1981; 31: 607–615.
26. Gonc¸alves JM, Barreiros JP, Azevedo J, Norberto R. Cetaceans stranded in the Azores during 1992–
1996. ARQUIPE
´LAGO Life and Marine Sciences. 1996; 14A: 57–65.
27. Hauser D. Evaluating the use of whalewatch data in determining killer whale (Orcinus orca) distribution
patterns. J. Cetacean Res. Manage. 2006; 8(3): 273–281.
28. Tepsich P, Rosso M, Halpin PN, Moulins A. Habitat preferences of two deep-diving cetacean species in
the northern Ligurian Sea. Mar Ecol Prog Ser. 2014; 508: 247–260. https://doi.org/10.3354/
meps10851
29. Hupman K, Visser IN, Martinez E, Stockin KA. Using platforms of opportunity to determine the occur-
rence and group characteristics of orca (Orcinus orca) in the Hauraki Gulf, New Zealand. New Zealand
Journal of Marine and Freshwater Research. 2014; 49(1): 132–149. https://doi.org/10.1080/00288330.
2014.980278
30. Evans PGH, Hammond PS. Monitoring cetaceans in European waters. Mammal Review. 2004; 34(1):
131–156.
31. Prieto R, Tobeña M, Silva MA. Habitat preferences of baleen whales in a mid-latitude habitat. Deep Sea
Research Part II: Tropical Studies in Oceanography. 2016; 141: 155–167. https://doi.org/10.1016/j.
dsr2.2016.07.015
32. Tobeña M, Prieto R, Machete M, Silva MA. Modelling the Potential Distribution and Richness of Ceta-
ceans in the Azores from Fisheries Observer Program Data. Frontiers in Marine Science. 2016; 3: 202.
https://doi.org/10.3389/fmars.2016.00202
33. Mannocci L, Boustany AM, Roberts JJ, Palacios DM, Dunn DC, Halpin PN, et al. Temporal resolutions
in species distribution models of highly mobile marine animals: Recommendations for ecologists and
managers. Diversity and Distributions. 2017; 23: 1098–1109. https://doi.org/10.1111/ddi.12609
34. Ferna
´ndez M, Yesson C, Gannier A, Miller PI, Azevedo JMN. The importance of temporal resolution for
niche modelling in dynamic marine environments. Journal of Biogeography. 2017; 44(12): 2816–2827.
https://doi.org/10.1111/jbi.13080
35. Scales KL, Hazen EL, Jacox MG, Edwards CA, Boustany AM, Andre M, et al. Scale of inference: on the
sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data.
2017; 40: 210–220. https://doi.org/10.1111/ecog.02272
36. Becker EA, Foley DG, Forney KA, Barlow J, Redfern JV, Gentemann CL. Forecasting cetacean abun-
dance patterns to enhance management decisions. Endanger. Species Res. 2012; 16: 97–112. https://
doi.org/10.3354/esr00390
37. Forney KA, Becker EA, Foley DG, Barlow J, Oleson EM. Habitat-based models of cetacean density and
distribution in the central North Pacific. Endang Species Res. 2015; 27: 1–20. https://doi.org/10.3354/
esr00632
38. Hoyt E, Notarbartolo di Sciara G. Marine Mammal Protected Areas Task Force. https://www.
marinemammalhabitat.org/ accessed in January 2018.
39. Cañadas A, Sagarminaga R, de Stephanis R, Urquiola E, Hammond PS. Habitat selection models as a
conservation tool: proposal of marine protected areas for cetaceans in Southern Spain. Aquatic Con-
serv: Mar Freshw Ecosyst. 2005; 15: 495–521. https://doi.org/10.1002/aqc.689
40. Hoyt E. Marine Protected Areas for Whales, Dolphins and Porpoises: A world handbook for cetacean
habitat conservation. London, Sterling VA. EarthScan. 2005.
41. Santos MB, Pierce GJ. Marine mammals and good environmental status: science, policy and society;
challenges and opportunities. Hydrobiologia. 2015; 750: 13–41. https://doi.org/10.1007/s10750-014-
2164-2
42. Maxwell SM, Hazen EL, Lewison RL, Dunn DC, Bailey H, Bograd SJ, et al. Dynamic ocean manage-
ment: Defining and conceptualizing real-time management of the ocean. Marine Policy. 2015; 58: 42–
50. https://doi.org/10.1016/j.marpol.2015.03.014
43. Becker EA, Forney KA, Fiedler PC, Barlow J, Chivers SJ., Edwards CA, et al. Moving towards dynamic
ocean management: How well do modeled ocean products predict species distributions? Remote Sens.
2016; 8: 149. https://doi.org/10.3390/rs8020149
44. Hastie TJ, Tibshirani RJ. Generalized Additive Models. London, Chapman & Hall. 1990.
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 23 / 25
45. Guisan A, Zimmerman NE. Predictive habitat distribution models in ecology. Ecological Modelling.
2000; 135: 147–186.
46. Panigada S, Zanardelli M, MacKenzie M, Donovan C, Me
´lin F, Hammond PS. Modelling habitat prefer-
ences for fin whales and striped dolphins in the Pelagos Sanctuary (Western Mediterranean Sea) with
physiographic and remote sensing variables. Remote Sensing of Environment. 2008. 112: 3400–3412.
https://doi.org/10.1016/j.rse.2007.11.017
47. Torres LG, Read AJ, Halpin P. Fine-scale habitat modelling of a top marine predator: do prey data
improve predictive capacity? Ecol. Appl. 2008. 18(7): 1702–1717. PMID: 18839765
48. Marubini F, Gimona A, Evans PGH, Wright PJ, Pierce GJ. Habitat preferences and interannual variabil-
ity in occurrence of the harbour porpoise Phocoena phocoena off northwest Scotland. Mar Ecol Prog
Ser. 2009; 381: 297–310. https://doi.org/10.3354/meps07893
49. Spyrakos E, Santos-Diniz TC, Martı
´nez-Iglesias G, Torres-Palenzuela JM, Pierce GJ. Spatiotemporal
patterns of marine mammal distribution in coastal waters of Galicia, NW Spain. Hydrobiologia. 2011;
670: 87–109. https://doi.org/10.1007/s10750-011-0722-4
50. Correia AM, Tepsich P, Rosso M, Caldeira R, Sousa-Pinto I. Cetacean occurrence and spatial distribu-
tion: Habitat modelling for offshore waters in the Portuguese EEZ (NE Atlantic). Journal of Marine Sys-
tems. 2014: 143: 73–85. https://doi.org/10.1016/j.jmarsys.2014.10.016
51. Lambert C, Pettex E, Dore
´mus G, Laran S, Ste
´phan E, Van Canneyt O, et al. How does ocean season-
ality drive habitat preferences of highly mobile top predators? Part II: The eastern North-Atlantic. Deep-
Sea Res II. 2016; 141: 133–154. http://dx.doi.org/10.1016/j.dsr2.2016.06.011.
52. Pe
´rez FF, Gilcoto M, Rı
´os AF. Large and mesoscale variability of the water masses and the deep chlo-
rophyll maximum in the Azores Front. Journal of Geophysical Research: Oceans. 2003; 108(C7):
3215. https://doi.org/10.1029/2000JC000360
53. Caldeira RMA, Reis JC. The Azores Confluence Zone. Frontiers in Marine Science. 2017; 4: 1–14.
https://doi.org/10.3389/fmars.2017.00037
54. Ballance LT, Pitman RL, Fiedler PC. Oceanographic influences on seabirds and cetaceans of the east-
ern tropical Pacific: a review. Prog. Oceanogr. 2006; 69: 360–390.
55. Sala I, Harrison CS, Caldeira RMA. The role of the Azores Archipelago in capturing and retaining incom-
ing particles. Journal of Marine Systems. 2015; 154: 146–156. https://doi.org/10.1016/j.jmarsys.2015.
10.001
56. Reynolds RW, Chelton DB, Roberts-Jones J, Martin MJ, Menemenlis D, Merchant CJ. Objective Deter-
mination of Feature Resolution in Two Sea Surface Temperature Analyses. Journal of Climate. 2013;
26: 2514–2533. https://doi.org/10.1175/JCLI-D-12-00787.1
57. Canny J. A computational approach to edge detection, In Readings in Computer Vision, Morgan Kauf-
mann, San Francisco (CA), 1987. 184–203. https://doi.org/10.1016/B978-0-08-051581-6.50024-6.
58. Tarling GA, Cuzin-Roudy J. Synchronization in the molting and spawning activity of northern krill (Mega-
nyctiphanes norvegica) and its effect on recruitment. Limnology and Oceanography. 2003; 48(5):
2020–2033. https://doi.org/10.4319/lo.2003.48.5.2020
59. Pascual A, Faugère Y, Larnicol G, Le Traon PY. Improved description of the ocean mesoscale variabil-
ity by combining four satellite altimeters. Geophysical Research Letters. 2006; 33(2): L02611. https://
doi.org/10.1029/2005GL024633
60. Dee DP, Uppalaa SM, Simmonsa AJ, Berrisforda P, Polia P, Kobayashi S, et al. The ERA-Interim
reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc.
2011; 137: 553–597. https://doi.org/10.1002/qj.828
61. MacLeod CD, Mandleberg L, Schweder C, Bannon SM, Pierce GJ. A comparison of approaches for
modelling the occurrence of marine animals. Hydrobiologia. 2008; 612: 21–32.
62. Rogerson PA. Statistical methods for geography. London, Sage. 2001. 136 pp.
63. Pebesma EJ, Bivand RS. Classes and methods for spatial data in R. R News 5 (2). 2005; https://cran.r-
project.org/doc/Rnews/
64. Bivand RS, Pebesma E, Gomez-Rubio V. Applied spatial data analysis with R, Second edition.
Springer, NY. 2013; http://www.asdar-book.org/
65. Calenge C. (2006) The package adehabitat for the R software: a tool for the analysis of space and habi-
tat use by animals. Ecological Modelling, 197, 516–519.
66. Peterson AT, Sobero
´n J, Pearson RG, Anderson RP, Martı
´nez-Meyer E, Nakamura M, et al. Ecological
niches and geographic distributions. Princeton University Press, Princeton, USA. 2011. 328 pp.
67. Phillips SJ, Dudı´k M, Elith J, Graham CH, Lehmann A, Leathwick J, et al. Sample selection bias and
presence-only distribution models: implications for background and pseudo-absence data. Ecological
Applications. 2009. 19(1): 181–197. PMID: 19323182
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 24 / 25
68. Elith J, Graham CH, Anderson RP, Dudı
´k M, Ferrier S, Guisan A, et al. Novel methods improve predic-
tion of species’ distributions from occurrence data. Ecography. 2006; 29: 129–151. https://doi.org/10.
1111/j.2006.0906-7590.04596.x
69. Esteban R, Verborgh P, Gauffier P, Gime
´nez J, Afa
´n I, Cañadas A, et al. Identifying key habitat and sea-
sonal patterns of a critically endangered population of killer whales. Journal of the Marine Biological
Association of the United Kingdom. 2013; 94(6): 1317–1325. https://doi.org/10.1017/
S002531541300091X
70. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semipara-
metric generalized linear models. Journal of the Royal Statistical Society (B). 2011; 73(1): 3–36s.
71. Zuur AF, Leno EN, Smith GM. Analysing Ecological Data. Springer. USA. 2007. 680 pp.
72. Harrell FE Jr, Lee KL, Mark DB. Tutorial in Biostatistics Multivariable Prognostic Models: Issues in
Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors. Sta-
tistics in Medicine. 1996; 15: 361–387.
73. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Data Mining, Inference and Pre-
diction. Springer. New York. 2009. 745 pp.
74. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sa
´nchez JC, et al. pROC: an open-source package
for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011; 12:77. https://doi.org/
10.1186/1471-2105-12-77 PMID: 21414208
75. Radosavljevic A, Anderson RP. Making better Maxent models of species distributions: complexity, over-
fitting and evaluation. Journal of Biogeography. 2014; 41: 629–643.
76. Redfern JV, Moore TJ, Fiedler PC, Vos A, Brownell RL, Forney KA, et al. Predicting cetacean distribu-
tions in data-poor marine ecosystems. Diversity and Distributions, 2017; 23: 394–408.
77. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W. Selecting pseudo-absences for species distribution
models: how, where and how many? Methods Ecol Evol. 2012; 3: 327–338.
78. Sears R, Burton CLK, Vikingson G. Review of Blue Whale (Balaenoptera musculus) Photo-identification
Distribution Data in the North Atlantic, Including the First Long-Range Match between Iceland and Mau-
ritania. Poster presented at the Sixteenth Biennial Conference on the Biology of Marine Mammals, San
Diego, California. 2005.
79. Sigurjo
´nsson J, Vikingsson G. 1997. Seasonal abundance of the estimated food consumption by ceta-
ceans in Icelandic and adjacent waters. Journal of Northwest Atlantic Fishery Science. 22: 271–287.
80. Friedlaender AS, Goldbogen JA, Hazen EL, Calambokidis J, Southall BL. Feeding performance by
sympatric blue and fin whales exploiting a common prey resource. Marine Mammal Science. 2015;
31(1): 345–354. https://doi.org/10.1111/mms.12134:
81. Caldeira RMA, Groom S, Miller P, Pilgrim D, Nezlin NP. Sea-surface signatures of the island mass
effect phenomena around Madeira Island, Northeast Atlantic. Remote Sensing of Environment. 2002;
80: 336–360.
82. Hasegawa D, Lewis MR, Gangopadhyay A. How islands cause phytoplankton to bloom in their wakes.
Geophysical Research Letters. 2009; 36: L20605. https://doi.org/10.1029/2009GL039743
83. Le
´vy M. The modulation of biological production by oceanic mesoscale turbulence. Lect. Notes Phys.
2008; 744: 219–261. https://doi.org/10.1007/978-3-540-75215-8_9
84. Bashmachnikov I, Lafon V, Martins A. Sea surface temperature distributions in the Azores region. Part
II: Space-Time variability and underlying mechanisms. Arquipelago. Life and Marine Science. 2004;
21A: 19–32.
85. Martı
´nez A. Avistan una nueva ballena azul frente a Galicia. Faro de Vigo. 10 October 2017. http://
www.farodevigo.es/portada-arousa/2017/10/10/avistan-nueva-ballena-azul-frente/1764937.html
Habitat preference of blue whales
PLOS ONE | https://doi.org/10.1371/journal.pone.0201786 September 28, 2018 25 / 25
... Nevertheless, there are several possible reasons for these baleen whales to occur in the Azores: it may be the location of the archipelago in mid-Atlantic waters that makes it an excellent topographic cue along whales' migration route (e.g., Bauer et al., 2011;Luschi, 2013;Garrigue et al., 2015), or it could be due to the excellent foraging opportunities arising around the islands and/or related to the complex oceanographic regime of the region (Sala et al., 2015;Caldeira and Reis, 2017;González García et al., 2018). Silva et al. (2013) showed that fin whales perform "Area Restricted Search" (indicative of foraging behaviour (MacArthur and Pianka, 1966;Emlen, 1968) during most of their time around the Azores. ...
... It includes two oceanic trenches with more than 3600 m deep (Fossa do Hirondelle and Bacia de São Miguel, NW and SE of the island respectively) and a shallower platform (500-1000 m) that extends 50 km south on the SW of the island (Fig. 1C). This area is also frequently affected by the presence of mesoscale and submesoscale oceanographic features derived from the AF/AC, and its interaction with the surrounding bathymetry (González García et al., 2018). ...
... Although number of trips per year varied considerably (minimum of 227 in the six months surveyed in 2008, maximum of 422 in the entire year 2014), overall, this variation was reasonably consistent throughout the study period, with no significant difference in number of trips across the seven years (Kruskal-Wallis Chi-squared = 4.6437, df = 6, p = 0.5903). Further information about the study area and observation protocols can be found in González García et al. (2018). ...
Article
Fin whales and sei whales are two migratory baleen whale species sighted every year across the waters of the Azores. Improved understanding of the ecological niche and habitat requirements of these baleen whales is needed to identify persistent or predictable oceanographic events that may set the time of their migration, as well as local or ephemeral oceanographic features that may aggregate their prey in a particular area. In dynamic environments such as the open ocean, mesoscale and submesoscale features can become decisive to determine the distributions of highly mobile species such as baleen whales. In this study, we analyse the habitat preferences of fin whales and sei whales around São Miguel Island (Azores) using environmental variables at different temporal and spatial scales. For both species, model results showed a clear influence of variables linked with primary production and therefore, prey availability; as well as a noticeable preference for oceanographically dynamic areas which directly affect distribution and aggregation of prey. Those environmental choices may indicate different levels of foraging habitat use for both species. Differences were found between the species, highlighting preferences for colder waters in fin whales and areas with stronger sea surface temperature gradients in sei whales. Model results obtained for fin whales were similar with those previously published for blue whales, suggesting that both species make similar use of the waters around São Miguel, often foraging during the migration across these waters. Results for sei whale, however, emphasize dynamic variables, indicating that travelling prevails over feeding behaviour during their migration by the Azores.
... Assim, estudos baseados em observações visuais podem fornecer uma noção momentânea sobre a distribuição e uso de habitat da população estudada (Verfuß et al., 2007;Dede et al., 2013). Em contraposição, metodologias de monitoramento que permitem análises em diferentes escalas temporais podem ser muito úteis em estudos de espécies altamente móveis que exploram habitats dinâmicos, como os cetáceos (González-García et al., 2018). ...
... The present study is the first to analyze the distribution of franciscanas throughout the day and to preliminarily identify the main feeding areas in Babitonga Bay, on seasonal and diel scales. Multiscale approaches have been shown to be very useful in studies of distribution of highly mobile species that explore dynamic habitats (González-García et al., 2018), such as the characteristics of the environment and species dealt with here. In particular, the association of foraging with specific environmental characteristics must be considered in the management of anthropic disorders (New et al. 2013;Pirotta et al., 2014). ...
Thesis
The franciscana dolphin (Pontoporia blainvillei) is a small cetacean critically endangered in Brazil, mainly due to the high number of incidental captures in fishing nets (bycatch). In Babitonga Bay, Santa Catarina, there is a resident population which is threatened by habitat degradation. The general objective of the study was to analyze the bioacoustics, behavior, distribution, habitat use and evaluate the effectiveness of an acoustic deterrent device ("pinger") for franciscanas, at different spatial and temporal scales, by means of a passive acoustic monitoring (PAM) device called C-POD (Chelonia Ltd., UK). The acoustic behavior of franciscana was analyzed comparatively in two habitats: estuary (Babitonga Bay: BB) and open sea (Itapirubá Beach: IB). The acoustic parameters of the click trains were analyzed and the minimum inter-click interval criterion <10ms was used as a proxy for foraging/feeding behavior. The main acoustic difference observed between habitats was related to the frequency spectrum, with a bandwidth of 17kHz in BB and 10kHz in IB. Also, the click repetition rate was almost 20% higher in the estuary. Both habitats studied presented a high feeding rate (BB = 68%; IB = 58%), higher in BB (p<0.001) and at night (p<0.001), for both habitats. To analyze the habitat use and distribution of franciscanas in Babitonga Bay, sixty C-PODs stations were implemented between June and December 2018. The generalized additive model selected to describe the relationship between the occurrence of franciscanas and several environmental variables incorporated 51% of the data variation. There is a diel pattern, where franciscanas remain in the areas of high occurrence mainly in the morning. The rest of the day, the population dispersed to other areas with different seasonal patterns. Franciscana avoid areas in periods when the presence of Guiana dolphins (Sotalia guianensis) is very intense and prefer areas with a flat bottom and sandy substrate, but during the evening and dawn they goes into areas of muddy bottom predominantly for feeding. The distribution was predominant in the innermost region of the estuary, without significant use of the bay's inlet channel. The distribution was wider in winter than in spring. The entire central region of the islands, between the north and south margins of the bay, represents an important feeding area. To test the deterrent effect of Banana pinger (Fishtek Marine Ltd, UK), as well as side effects of habituation and habitat exclusion, an exposure-controlled experiment was carried out with 5 C-PODs positioned at different distances from the pinger. The data indicate that the pinger effectively withdraw the franciscanas up to 100m, but not 400m, and therefore has the potential to reduce bycatch. No habituation effects were observed at any distance. There was a gradual decrease in the presence of franciscanas over the days, probably due seasonal variations in the population's habitat use but requires attention in future studies. C-PODs were used in an unprecedented way for the study of franciscanas and showed great potential to monitor the occurrence, behavior, distribution, and habitat use of the species. The results representing an important subsidy for management of the Babitonga Bay population and for the implementation of bycatch mitigation measures for the species in general. Available at: https://repositorio.ufsc.br/handle/123456789/227086?show=full
... Concurrently, the GAM results explained between 34.7 and 41.1% of the total deviance for those species, which is similar to, or better than, the values achieved for cetacean species in many other studies [e.g. 8,35,36]. The MaxEnt models for those three species had moderate discriminatory power with AUC values of 0.60 to 0.69, although those values are lower than those achieved for MaxEnt modelling of cetacean species in some other studies [e.g. 6, 16, 37]. ...
... It may potentially reflect greater prey availability associated with warmer water temperatures, for example the occurrence of Munida swarms higher in the water column during summer compared with winter [44]. Chl-a was also an important PV for SEI-BAL in MaxEnt, which is consistent with other studies indicating that baleen whale distribution in feeding areas is linked to primary productivity, presumably since it drives the occurrence of prey species [12,36,37,45]. ...
Article
Full-text available
Species distribution models (SDMs) are valuable tools for describing the occurrence of species and predicting suitable habitats. This study used generalized additive models (GAMs) and MaxEnt models to predict the relative densities of four cetacean species (sei whale Balaeanoptera borealis , southern right whale Eubalaena australis , Peale’s dolphin Lagenorhynchus australis , and Commerson’s dolphin Cephalorhynchus commersonii ) in neritic waters (≤100 m depth) around the Falkland Islands, using boat survey data collected over three seasons (2017–2019). The model predictor variables (PVs) included remotely sensed environmental variables (sea surface temperature, SST, and chlorophyll- a concentration) and static geographical variables (e.g. water depth, distance to shore, slope). The GAM results explained 35 to 41% of the total deviance for sei whale, combined sei whales and unidentified large baleen whales, and Commerson’s dolphins, but only 17% of the deviance for Peale’s dolphins. The MaxEnt models for all species had low to moderate discriminatory power. The relative density of sei whales increased with SST in both models, and their predicted distribution was widespread across the inner shelf which is consistent with the use of Falklands’ waters as a coastal summer feeding ground. Peale’s dolphins and Commerson’s dolphins were largely sympatric across the study area. However, the relative densities of Commerson’s dolphins were generally predicted to be higher in nearshore, semi-enclosed, waters compared with Peale’s dolphins, suggesting some habitat partitioning. The models for southern right whales performed poorly and the results were not considered meaningful, perhaps due to this species exhibiting fewer strong habitat preferences around the Falklands. The modelling results are applicable to marine spatial planning to identify where the occurrence of cetacean species and anthropogenic activities may most overlap. Additionally, the results can inform the process of delineating a potential Key Biodiversity Area for sei whales in the Falkland Islands.
... Generalized additive models (GAMs) (Hastie and Tibshirani, 1986) were used to predict the persistent hotspots occurrence over the entire Pelagos area, using 2009-2019 persistence index (HI) and the abovementioned environmental variables. GAMS are frequently used to explain cetacean distribution (Redfern et al., 2006;Santora et al., 2014;Correia et al., 2015;García et al., 2018;Schleimer et al., 2019;Becker et al., 2020). ...
Article
Full-text available
The Pelagos Sanctuary is the only pelagic marine protected area in the Mediterranean Sea, instituted for the conservation of cetaceans. Considering the number and size of commercial and touristic ports located along its coasts, this protected area is highly impacted by human activities, and especially marine traffic. Fin whales and sperm whales are regularly sighted in the Pelagos Sanctuary, and ship strikes with large vessels are one of the main threats affecting these two species. Mapping hotspots of distribution along main shipping lanes could be an effective conservation tool, as they directly allow locating high risk areas. In this work, we used data collected during summer from 2009 to 2019, along main marine traffic corridors in the central region of Pelagos Sanctuary. Ship strike hotspots have been identified, considering the persistence of distribution hotspots over the 11 years period. Hotspots occurrence has then been predicted over the entire Pelagos Sanctuary area applying Generalized Additive Models, allowing for ship strike risk assessment over the marine protected area. Our results highlighted the recurrence of important areas for both species along shipping lanes characterized by high vessel traffic, identifying regions where to address conservation measures.
... Primary productivity was included as an additional exposure factor due to its direct effect on the distribution, diversity and abundance of cetacean species in Macaronesia (Correia et al., 2020;García et al., 2018;Tobeña et al., 2016). ...
Article
Over the last decades global warming has caused an increase in ocean temperature, acidification and oxygen loss which has led to changes in nutrient cycling and primary production affecting marine species at multiple trophic levels. While knowledge about the impacts of climate change in cetacean's species is still scarce, practitioners and policymakers need information about the species at risk to guide the implementation of conservation measures. To assess cetacean's vulnerability to climate change in the biogeographic region of Macaronesia, we adapted the Marine Mammal Climate Vulnerability Assessment (MMCVA) method and applied it to 21 species management units using an expert elicitation approach. Results showed that over half (62%) of the units assessed presented Very High (5 units) or High (8 units) vulnerability scores. Very High vulnerability scores were found in archipelago associated units of short-finned pilot whales (Globicephala macrorhynchus) and common bottlenose dolphins (Tursiops truncatus), namely in the Canary Islands and Madeira, as well as Risso's dolphins (Grampus griseus) in the Canary Islands. Overall, certainty scores ranged from Very High to Moderate for 67% of units. Over 50% of units showed a high potential for distribution, abundance and phenology changes as a response to climate change. With this study we target current and future information needs of conservation managers in the region, and guide research and monitoring efforts, while contributing to the improvement and validation of trait-based vulnerability approaches under a changing climate.
... The Azores archipelago is known in the marine biology community as a rich site in terms of cetaceans and represents an important habitat for sperm, fin and blue whales. 1,2 Cetaceans are known to rely on underwater sound to forage, to interact in community, to orientate and to perceive their surrounding environment. 3,4 The Azores archipelago, due to its' geographical location in the center North Atlantic, is in the middle of intense marine traffic routes that connect Central and North America to Europe. ...
Article
Full-text available
The Azores archipelago is an important cetacean habitat spot, registering a very relevant resident and migratory population. Due to its geographical position, it also represents an important commercial crossroad between America and Europe. Vessels represent the major source of underwater noise which may affect cetaceans. Since 2017 acoustic measurements have been performed in the southern side of the channel between Pico-Faial. Although foreground biological signatures are numerous and relatively easy to spot in the data, it is not clear how to separate background abiotic, biotic and man made noise and, therefore, to single out the noise due to shipping. In order to address these questions, a shipping noise prediction tool using a 10-min resolution AIS coverage of the area, together with bathymetric, water column space-time descriptors and surface wind generated noise model, showed a significant noise variability between Pico-Faial islands. This variability was mainly observed in the strait and to the south of it, both along the coast associated with ferries and around offshore banks due to fishing activity. Time series of predicted sound pressure level at three receiver locations favorably compare with it in-situ noise measurements in the 44-177Hz a frequency band.
Chapter
Despite being a very coastal species, knowledge about franciscana dolphins (Pontoporia blainvillei) behavior is very scarce. This chapter aims to summarize information available in the literature as well as unpublished data involving different aspects related to the behavior of the species. Franciscana is a social species, forming aggregations of up to 20–50 animals. The chapter covers aspects related to group behavior, diving and surface behavior, interspecific relationships, reactions to boats and aircraft, movement patterns, and site fidelity, in addition to inferences about the mating system, group structure, and feeding behavior, drawn from different data sources.
Technical Report
Full-text available
Ecological systems usually operate at large temporal scales, which are not always considered in traditional data collection. For instance, some questions, such as climate change or anthropogenic pressure potential effects, require long-term datasets to understand how ecosystems may respond to any disturbances or impacts. In the short run, traditional studies often provide high quality data for a specific space and time. However, long temporal series are needed to identify natural variability and potential trends in the environment and its populations, such as changes in distribution or the ecology of the species. Collecting long-term wildlife occurrence data is challenging and has been often undervalued. Dedicated data collection is generally expensive and limited in space and time for cetaceans. However, opportunistic platforms provide a cost-effective method to obtain information over long periods and defined areas. Nevertheless, challenges are evident for both opportunistic and dedicated programs. Here we summarize critical aspects for long-term initiatives to survive. We account for considerations such as standardization of protocols, long-term planning, specific training, or even robust data validation to guarantee data quality. Additionally, making the data openly available, standardised, providing biases and limitations, or even dedicated consultancy and expertise to avoid misleading conclusions are highly desirable steps towards a FAIR and better-quality scientific output. Finally, long-term initiatives need a long-term engagement. This might be achieved through enthusiastic people or leaders who can keep involved over time with data collectors, third parties interested in data and also the general public. Nowadays, collaboration is vital for highly mobile species such as cetaceans to get information over large areas and long periods. Only by doing so we will obtain the data we require for the studies we need.
Article
Full-text available
Data on species occurrence at the scale of their distributional range and the determination of their habitat use requirements are essential to support conservation and define management plans that account for their habitat requirements. For wide-ranging species, such as cetaceans, especially considering that their marine habitats include offshore areas, collection of such data is challenging. In the absence of dedicated surveys, alternative methodologies are needed, such as the use of data collected from platforms of opportunity and modelling techniques to predict distribution in unsurveyed areas. Using 6 years of cetacean occurrence data collected along cargo ship routes between the Iberian Peninsula, northwestern African coasts and the Macaronesian islands, we developed ecological niche models to assess habitat preferences and predict suitable habitats of the eight most frequently sighted cetacean taxa in the area. Explanatory variables used for model fitting included topographic, oceanographic, detectability, geographic and seasonal features. To provide a robust habitat characterisation, along with predictions of habitat suitability, making best use of occurrence datasets, we applied two modelling techniques, GAM and Maxent, which offer complementary strengths. Coastal areas provide important habitats for common and bottlenose dophins, while other dolphin species (spotted and striped dolphins) have a more oceanic distribution. The predicted niches of Cuvier’s beaked whale and minke whales are mainly in the high seas at northern latitudes. Suitable habitats for sperm whales and pilot whales are mostly in southern areas in continental slope regions. For all the species, models indicated that areas around seamount features offer suitable habitats, likely of high relevance in oligotrophic offshore waters. As such, dedicated survey effort in such areas would facilitate development and implementation of appropriate management plans, which are currently lacking. Our models offer an important contribution to baseline knowledge of cetacean distribution at basin-scale in the region and could support the definition of priority areas, monitoring plans, and conservation measures, essential to comply with the requirements of the EU Marine Strategy Framework Directive.
Article
Full-text available
In 2001 Italy, France, and Principality of Monaco instituted a protected area for marine mammals in northwestern Mediterranean Sea, named the Pelagos Sanctuary. The agreement foresees the commitment by signing parties to manage human activities in the area, with a special mention to whale watching. Whale watching is a form of wildlife tourism which has considerably grown in the last decades. Understanding the profile of whale watchers and their satisfaction toward the activity, is the first step toward a sustainable and effective management of this touristic activity. In this work we provide the first analysis of the whale watching activity in the Pelagos Sanctuary, focusing on commercial whale watching tours departing from Italian harbors in Liguria. We provide a census of the activity and the results of close-ended questionnaires filled by whale watchers during trips in summer 2016 and 2017. The aim of the questionnaires was to understand the level of awareness of experienced and new whale watchers regarding the Pelagos Sanctuary and some conservation initiative going on in the area. Finally, we analyzed the satisfaction level, with the aim of evidencing weakness and strengths of the service offered. Our results evidence a growth in the activity in the last 15 years, with a wider differentiation of offers and impacting a larger area than previously found. Whale watchers in the area come from a variety of countries, demonstrating the importance of the Pelagos as a hot spot for this activity. A high level of satisfaction has been evidenced, with no difference among new and experienced whale watchers. At the same time, more effort is needed to increase awareness of Pelagos and its conservation initiative both at a national and international level. This study provides useful information for the start of an effective management of whale watching in this protected area.
Article
Full-text available
While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time-scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine-scale and ephemeral features are best modelled with instantaneous covariates. Associations with large scale and persistent oceanographic features are best modelled with climatological covariates. Associations with mesoscale features are best modelled with instantaneous or contemporaneous covariates if ephemeral processes are present or interannual variability occurs, and climatological covariates if seasonal processes dominate and interannual variability is weak.
Article
Full-text available
The goal of this study is to characterize the meteorological and oceanographic conditions affecting the Azores Archipelago, and explore their biological implications. The Mid-Atlantic region of the Azores Archipelago is under the permanent influence of the Azores high pressure system, thereby providing sustained Ekman transport that facilitates the convergence in regional oceanography. The west and central island groups are affected by incoming meanders and filaments originating in the Gulf Stream, whereas the east island group is most affected by westward propagating eddies pinching-off from the Azores Current. Output from the European Centre for Medium-Range Weather Forecasts are combined with altimetry data to study the dynamic oceanographic processes affecting the archipelago. Satellite-derived sea surface temperature and sea surface chlorophyll data are used as proxies to examine the biological enrichment processes. Climatological data analysis permits differentiation of the oceanographic systems that reach the west vs. those that affect the east island groups. This is the first study to document the Azores as an oceanic confluence zone and demonstrate the associated biological impacts.
Article
Full-text available
Human activities are creating conservation challenges for cetaceans. Spatially explicit risk assessments can be used to address these challenges, but require species distribution data, which are limited for many cetacean species. This study explores methods to overcome this limitation. Blue whales (Balaenoptera musculus) are used as a case study because they are an example of a species that have well-defined habitat and are subject to anthropogenic threats. Eastern Pacific Ocean, including the California Current (CC) and eastern tropical Pacific (ETP), and northern Indian Ocean (NIO). We used 12 years of survey data (377 blue whale sightings and c. 225,400 km of effort) collected in the CC and ETP to assess the transferability of blue whale habitat models. We used the models built with CC and ETP data to create predictions of blue whale distributions in the data-poor NIO because key aspects of blue whale ecology are expected to be similar in these ecosystems. We found that the ecosystem-specific blue whale models performed well in their respective ecosystems, but were not transferable. For example, models built with CC data could accurately predict distributions in the CC, but could not accurately predict distributions in the ETP. However, the accuracy of models built with combined CC and ETP data was similar to the accuracy of the ecosystem-specific models in both ecosystems. Our predictions of blue whale habitat in the NIO from the models built with combined CC and ETP data compare favourably to hypotheses about NIO blue whale distributions, provide new insights into blue whale habitat, and can be used to prioritize research and monitoring efforts. Predicting cetacean distributions in data-poor ecosystems using habitat models built with data from multiple ecosystems is potentially a powerful marine conservation tool and should be examined for other species and regions.
Article
Full-text available
Marine spatial planning and ecological research call for high-resolution species distribution data. However, those data are still not available for most marine large vertebrates. The dynamic nature of oceanographic processes and the wide-ranging behavior of many marine vertebrates create further difficulties, as distribution data must incorporate both the spatial and temporal dimensions. Cetaceans play an essential role in structuring and maintaining marine ecosystems and face increasing threats from human activities. The Azores holds a high diversity of cetaceans but the information about spatial and temporal patterns of distribution for this marine megafauna group in the region is still very limited. To tackle this issue, we created monthly predictive cetacean distribution maps for spring and summer months, using data collected by the Azores Fisheries Observer Programme between 2004 and 2009. We then combined the individual predictive maps to obtain species richness maps for the same period. Our results reflect a great heterogeneity in distribution among species and within species among different months. This heterogeneity reflects a contrasting influence of oceanographic processes on the distribution of cetacean species. However, some persistent areas of increased species richness could also be identified from our results. We argue that policies aimed at effectively protecting cetaceans and their habitats must include the principle of dynamic ocean management coupled with other area-based management such as marine spatial planning.
Book
This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.
Article
Highly dynamic ocean environments can experience dramatic changes over relatively short timeframes, affecting the spatial distribution of resources and therefore the presence or absence of highly mobile species. We use simulation studies to investigate how different temporal resolutions might affect the results of species distribution models for highly mobile species (e.g. cetaceans) in marine environments. We developed three virtual species with different habitat preferences influenced by (1) only static (topographic), (2) only dynamic (oceanographic), and (3) both dynamic and static variables. Assuming that species would reposition themselves daily according to these preferences (as has been observed for large marine foragers such as cetaceans), we used two different approaches (generalized linear model and generalized boosted model) to test the effect of using daily, weekly and monthly environmental datasets to model distributions. The results showed that the selection of different temporal scales has a very important effect on model predictions. When dynamic variables are important components of habitat preference, models based on daily or weekly timeframes performed best at reconstructing the known niche. It is important that we consider temporal resolution when applying species distribution models. Several factors (e.g. species ecology and oceanographic characteristics of the ecosystem) should be taken into consideration when selecting an adequate temporal scale for niche modelling. For fine scale applications (e.g. dynamic ocean management), highly dynamic ecosystems, and highly mobile species, our results suggest exploring temporal resolution of 7–8 days rather than coarser temporal scales. For some applications annual, seasonal or even monthly averages may produce inferior or inaccurate models.
Article
Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.