Changes of Oceanic Conditions Drive Chagos Whale Migration Patterns in the Central Indian Ocean

Abstract

Marine ecosystems are experiencing rapid shifts under climate change scenarios and baleen whales are vulnerable to environmental change, although not all impacts are yet clear. We identify how the migration behaviour of the Chagos whale, likely a pygmy blue whale (Balaenoptera musculus brevicauda), has changed in association with shifts in environmental factors. We used up to 18 years of continuous underwater acoustic recordings to analyse the relationships between whale acoustic presence and sea surface temperature (SST), chlorophyll-a concentration, El-Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). We compared these relationships between two independent sites Diego Garcia southeast (DGS) and Diego Garcia northwest (DGN) where Chagos whales are detected and are suspected to move interannually across the Chagos-Laccadive ridge. We showed that the number of whale songs detected increased on average by 7.7% and 12.6% annually at DGS and DGN respectively. At the DGS site, Chagos whales shifted their arrival time earlier by 4.2 ± 2.0 days/year ± SE and were detected for a longer period by 7.3 ± 1.2 days/year ± SE across 18 years. A larger number of songs were detected during periods of higher chlorophyll-a concentration, and with positive IOD phases. At the DGN site, we did not see an earlier shift in arrival and songs were not detected for a longer period across the 13 years. Whale presence at DGN had a weaker but opposite relationship with chlorophyll-a and IOD. The oceanic conditions in the Indian Ocean are predicted to change under future climate scenarios and this will likely influence Chagos whale migratory behaviour. Understanding how environmental factors influence whale movement patterns can help predict how whales may respond to future environmental change. We demonstrate the value of long-term acoustic monitoring of marine fauna to determine how they may be affected by changing environmental conditions.

Full text

Changes of Oceanic Conditions Drive
Chagos Whale Migration Patterns in
the Central Indian Ocean
Junlin Lyra Huang
1,2
, Emmanuelle C. Leroy
3
, Gary Truong
1,2
and Tracey L. Rogers
1,2
*
1
Centre for Marine Science and Innovation, School of Biological, Earth and Environmental Sciences, University of New South
Wales, Sydney, NSW, Australia,
2
Evolution and Ecology Research Centre, School of Biological, Earth and Environmental
Sciences, University of New South Wales, Sydney, NSW, Australia,
3
Globice Re
´union, Saint Pierre, France
Marine ecosystems are experiencing rapid shifts under climate change scenarios and
baleen whales are vulnerable to environmental change, although not all impacts are yet
clear. We identify how the migration behaviour of the Chagos whale, likely a pygmy blue
whale (Balaenoptera musculus brevicauda), has changed in association with shifts in
environmental factors. We used up to 18 years of continuous underwater acoustic
recordings to analyse the relationships between whale acoustic presence and sea
surface temperature (SST), chlorophyll-a concentration, El-Niño Southern Oscillation
(ENSO) and the Indian Ocean Dipole (IOD). We compared these relationships between
two independent sites Diego Garcia southeast (DGS) and Diego Garcia northwest (DGN)
where Chagos whales are detected and are suspected to move interannually across the
Chagos-Laccadive ridge. We showed that the number of whale songs detected increased
on average by 7.7% and 12.6% annually at DGS and DGN respectively. At the DGS site,
Chagos whales shifted their arrival time earlier by 4.2 ± 2.0 days/year ± SE and were
detected for a longer period by 7.3 ± 1.2 days/year ± SE across 18 years. A larger number of
songs were detected during periods of higher chlorophyll-a concentration, and with positive
IOD phases. At the DGN site, we did not see an earlier shift in arrival and songs were not
detected for a longer period across the 13 years. Whale presence at DGN had a weaker but
opposite relationship with chlorophyll-a and IOD. The oceanic conditions in the Indian
Ocean are predicted to change under future climate scenarios and this will likely influence
Chagos whale migratory behaviour. Understanding how environmental factors influence
whale movement patterns can help predict how whales may respond to future
environmental change. We demonstrate the value of long-term acoustic monitoring of
marine fauna to determine how they may be affected by changing environmental conditions.
Keywords: baleen whale, long-term change, climate change, interannual variability, environmental drivers, Indian
Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO), sea surface temperatures
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8438751
Edited by:
Olaf Meynecke,
Griffith University, Australia
Reviewed by:
Jennifer Allen,
Griffith University, Australia
Peter Corkeron,
New England Aquarium, United States
*Correspondence:
Tracey L. Rogers
t.rogers@unsw.edu.au
Specialty section:
This article was submitted to
Marine Megafauna,
a section of the journal
Frontiers in Marine Science
Received: 27 December 2021
Accepted: 26 May 2022
Published: 23 June 2022
Citation:
Huang JL, Leroy EC, Truong G
and Rogers TL (2022) Changes of
Oceanic Conditions Drive Chagos
Whale Migration Patterns in the
Central Indian Ocean.
Front. Mar. Sci. 9:843875.
doi: 10.3389/fmars.2022.843875
ORIGINAL RESEARCH
published: 23 June 2022
doi: 10.3389/fmars.2022.843875

1 INTRODUCTION
Marine ecosystems are experiencing rapid shifts in structure and
function globally (Belkin, 2009;Bryndum-Buchholz et al., 2019),
which challenges the ability of marine wildlife to adapt
(Poloczanska et al., 2013;Miller et al., 2018). Ocean warming
and acidification, resulting from anthropogenic climate change,
pose a threat to the marine environment and biodiversity (Miller
et al., 2018). For example, increasing sea temperature has been
associated with changes in biodiversity of the deep-sea fish
community (Yasuhara and Danovaro, 2016). Global
environmental changes such as heat shocks and ocean
acidification also have negative impacts on the temperate
seagrass ecosystem (Perry et al., 2019). Current predictions
indicate that global warming will continue, with or without
mitigation (IPCC, 2021). Furthermore, greater variability such
as more frequent and more extreme events (i.e., such as
heatwaves, tropical cyclones, and flooding) as well as changes
to the phases of natural climate phenomena (i.e., ENSO, the El
Niño Southern Oscillation and IOD, the Indian Ocean Dipole)
are predicted with increased global warming (Cai et al., 2014;Cai
et al., 2015). The large-scale climate phenomena ENSO and IOD
affect weather globally, and the frequency of extreme ENSO and
IOD conditions is likely to increase under increased greenhouse
warming (Cai et al., 2014;Cai et al., 2015). Marine ecosystems are
experiencing fluctuations due to this large-scale environmental
variability, such as an increase in sea surface temperature (SST),
changes to currents and upwelling systems, and changes to
primary productivity. The potential effects of these changes
may require marine species to adapt their behaviour (Gibson
et al., 2007). Therefore, we need to better understand how marine
wildlife responds to large-scale environmental changes.
Measuring the changes in the timing of migration is an
excellent way to study the response of fauna to environmental
variability particularly where important life history events are
tied to specific timing (i.e., breeding or foraging is tied to times of
high-resource availability). The responses of marine animals to
environmental change vary across individuals, populations, and
communities (Miller et al., 2018). For example, phenological
changes, such as the variation in the timing of migration, will
affect essential components of a species’life history (Forrest and
Miller-Rushing, 2010). Understanding potential drivers of
mammal migration will help us predict how they will respond
to future warming scenarios (Gnanadesikan et al., 2017). The
drivers of migration vary across species and ecosystems. Round-
trip migration patterns are driven by the need for refuge (to
avoid unfavourable conditions), to breed (to reproduce) and to
forage (to increase access to food) (Shaw, 2016). Many marine
species migrate to breed (Shaw, 2016). For example, the cuttlefish
Sepia officinalis, the squid Loligo gahi, and the bivalve Macoma
balthica are proposed to have latitudinal or longitudinal
migration for spawning seasons in different ocean basins
(Hiddink, 2003;Arkhipkin et al., 2004;Keller et al., 2014;
Shaw, 2016). On the other hand, mammals display diverse
migration types (Gnanadesikan et al., 2017). Large baleen
whales tend to be long-distance migrants, moving poleward in
summer to the feeding areas and migrating towards lower
latitudes in winter to breed and calve (Horton et al., 2011).
Migration timing between feeding and breeding grounds may be
influenced by species-specific environmental shifts and complex
internal and external mechanisms (Dingle and Drake, 2007).
Global climate change is considered greater driver of change in
the distribution and phenology of marine compared to terrestrial
animals (Poloczanska et al., 2013). Studying the shifts of
migration timing could be an ideal approach to gain insight
into how marine wildlife responds to environmental variability.
Large baleen whales are potentially vulnerable to
environmental changes. Many baleen whale species and
populations are endangered, following the 20
th
century
industrial whaling. For instance, as the largest animals in the
world, blue whales (Balaenoptera musculus) were targeted by
whalers, with only 0.15% of the Southern Hemisphere
population surviving commercial whaling (Branch et al., 2007;
Thomas et al., 2016). Large baleen whales such as blue whales
have few natural predators and feed on prey at low trophic levels,
occupying a rare niche with few other species. Thus, if global
change that leads to their loss occurs, it is unlikely that they could
be easily replaced within the food web. Furthermore, their long-
distance migration behaviour requires considerable energy
(Branch et al., 2007). Thus, large baleen whales require
predictable and high-energy food sources for the few months
they feed. This makes them vulnerable and less able to adapt
when climate change and other anthropogenic-related
conditions reduce food availability and predictability. Using a
decade of acoustic data, a previous study showed that the
distributions and movement patterns of six baleen whale
species were shifting in the western Atlantic (Davis et al.,
2020). Moreover, previous studies have shown the
relationships between baleen whales phenology and
environmental factors. For example, Derville et al. (2019)
assessed humpback whales’habitat shifting under the impact
of ocean warming in Oceania breeding grounds using 19 years of
survey data. Charif et al. (2020) used a 6-year dataset to analyse
the phenological changes of North Atlantic right whales as
environmental conditions change. An eight-year humpback
whale acoustic dataset was used to study the whale presence
and climate oscillations (Schall et al., 2021).
The Indian Ocean is an ideal location to identify how
environmental changes affect whale behaviour because of its
high biodiversity. It is a complex marine system with nutrient-
poor water, but diverse marine fauna species (Anderson et al.,
2012). It is the warmest of the ocean basins, with the southern
parts of the Indian Ocean experiencing higher rates of warming
compared to the northern areas (Han et al., 2014). Variant blue
whale species are recorded acoustically within this region
including Antarctic blue whale (B.m. intermedia); and four
acoustic populations of the pygmy blue whale: Sri Lankan;
Madagascan; Australian and Oman whales (Stafford et al.,
2004;Stafford et al., 2011;Samaran et al., 2013;Double et al.,
2014;Leroy et al., 2016;Leroy et al., 2018;Cerchio et al., 2020).
Blue whales produce stereotypical calls, repeated over days,
months, and years. Each blue whale population has a distinct
Huang et al. Oceanic Conditions Drive Chagos Whales
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8438752

vocal characteristic, which is used to identify their ‘acoustic
populations’(McDonald et al., 2006). The Chagos whales are
possibly a new acoustic population of blue whales (B.m.
brevicauda spp.)(Leroy et al., 2021). Chagos songs were
initially considered as a variant of the Madagascan pygmy blue
whale song (McDonald et al., 2006). Preliminary reports have
shown that Chagos whales have acoustically presented all year at
the southern end of the Seychelle-Chagos thermocline ridge
(Sousa and Harris, 2015;Leroy et al., 2021). The area of the
Seychelle-Chagos thermocline ridge is a region of low sea surface
temperature and high nutrients (Jayakumar et al., 2011). Yet we
know little about the ecology and migration patterns of the
Chagos whales, especially how their movement are linked with
the environmental shifting in the nutrient-rich tropical region.
This study aims to give an insight into how marine species
respond to environmental shifts. The detections of the Chagos
whale songs in almost two decades of continuous acoustic data
recorded in either side of the Chagos-Laccadive ridge are used
first to quantify inter- and intra-annual variations in the acoustic
presence and then, to identify if acoustic presence changes with
shifts in environmental conditions such as sea surface
temperature (SST), primary production (chlorophyll-a
concentration), Southern Oscillation Index (SOI) and Indian
Ocean Dipole (IOD). The hypothesis for the environmental
factors impacting Chagos whale presence is as follows: if the
sea surface temperature (SST) is low and if primary production is
high then more Chagos whales will be present. We aim to give an
insight on how climate change may affect the migration
phenology of the Chagos whale in the tropical central
Indian Ocean.
2 MATERIALS AND METHODS
2.1 Study Area
Underwater acoustic data was obtained from the international
data system of the Comprehensive Nuclear Test-Ban Treaty
Organisation (CTBTO). Two hydrophone arrays were located
220 km apart on either side of Diego Garcia Island: one on the
northwest side (Diego Garcia North - DGN), and the other on
the southeast side (Diego Garcia South –DGS) (Figure 1).
Within each array, a set of three hydrophones was moored in
the sound fixing and ranging (SOFAR) channel, where sound
achieves the maximum speed (Hanson, 2001).
The CTBTO hydroacoustic stations continuously monitor
underwater sound waves with a sampling frequency of 250 Hz.
For the DGN site, the data recorded between January 2002 and
February 2014 by the hydrophone number H08N1 (6.34°S,
71.01°E) were used, and for DGS the data recorded between
January 2001 and December 2019 by the instrument number
H08S1 (7.65°S, 72.47°E) were used. The instrument depths were
respectively 1248 and 1413 m (see Leroy et al., 2021 for more
details). The recording sites are separated by the Chagos bank
(Pulli and Upton, 2001). Thus, we used the DGN and DGS sites
to represent the soundscapes northwest and southeast of Diego
Garcia respectively.
FIGURE 1 | Map of the study area in the central Indian Ocean. Red dots indicate the locations of the hydrophone stations: north-western site off Diego Garcia (referred
to as DGN), and south-eastern site off Diego Garcia (referred to as DGS). The dotted rectangle shows the location of the Chagos-Laccadive Ridge. B This figure is
modified from Leroy et al., 2021.
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2.2 Whale Acoustic Presence Data
Hourly presence or absence of Chagos whale songs was inferred
from automated detections of the signal as described in Leroy
et al. (2021). All data from the DGN site and data prior to
February 2018 from the DGS site were from Leroy et al., 2021,
however, data after February 2018 to the end of 2019 from the
DGS site were obtained and tested for this study using the same
approach as described in Leroy et al. (2021).
The individual detections were logged into Matlab matrices
along with the information related to each individual song, such
as the exact date-time of the detected event. The data from each
site were divided into eight-day blocks (192 hours), consistent
with the local environmental data (see 2.3 Environmental Data,
below). Whale acoustic presence is defined as the proportion of
hours in the eight-day block in which whale songs were detected.
In cases where acoustic recording had been interrupted, and if
the recorded hours were less than 50% of the time block, these
blocks were considered to have insufficient data and were
excluded from the analysis. In 13 years of acoustic data, 82.3%
(498 eight-day blocks) of DGN recordings and in 18 years 91.7%
(760 eight-day blocks) of DGS data had sufficiently complete
data to be included in the analysis. Acoustic presence at both sites
was pooled to analyse seasonal variability (Similar method to
aggregate presence data for right whale see (Similar method to
aggregate presence data for right whale see Charif et al., 2020).
Pooled data was divided into months for comparison to oceanic
data (see Environmental Data, below).
We used the eight-day grouped whale acoustic presence data
to analyse the peak and extended peak seasons. To identify the
peak and the extended peak seasons, the following algorithms
were applied: firstly, we found the eight-day time blocks when
whale acoustic presence was greater than 90% in the 192-hour
block throughout the entire time series, then we chose the peak
to be eight-day period with the highest whale acoustic presence
among consecutive periods containing data. When there was
more than one period satisfying the criteria, for example,
multiple consecutive 100% whale acoustic presence periods, all
100% eight-day periods were defined as peaks. Next, the growth
rate of the whale acoustic presence was calculated as the
difference between the two adjacent periods divided by
the whale acoustic presence of the preceding period. The
maximum rate (defined as the start date of the extended peak)
and minimum rate (defined as the end date of extended the peak)
are the ones with the most rapid change at the local range. Lastly,
the time between the start date and the end date containing the
peak whale acoustic presence was defined as the extended peak
season. Similar methods to define extended peak seasons were
applied in Charif et al. (2020), where they used the smoothed
eight-period differences.
2.3 Environmental Data
Two local drivers and two oceanic drivers were considered in this
study. Data for the two local drivers, sea surface temperature
(SST) and chlorophyll-a (as an indicator of phytoplankton
abundance), were downloaded from the NOAA ocean watch
website (https://oceanwatch.pifsc.noaa.gov/doc.html#currents).
The seasonal oceanic data including Dipole Mode Index (DMI)
representing the event of Indian Ocean Dipole (IOD) and
Southern Oscillation Index (SOI) representing El-Nino
Southern Oscillation (ENSO) were extracted from the
Australian Bureau of Meteorology (BOM) (http://www.bom.
gov.au/climate/enso/soi/) and the NOAA Physical Science
Laboratory of Global Climate Observing System (https://psl.
noaa.gov/gcos_wgsp/Timeseries/DMI/).
SST was downloaded through the CoralTemp dataset of the
NOAA website. The analysed data were continuously recorded
(24 hours per day) throughout the study period. The mean SST
was computed (In total four data spots with two units at each
side, spatial resolution for one unit is approximately 5 km) in the
approximately 100 km2 encompassing the hydrophone stations.
Then the mean SST was calculated for the corresponding eight-
day period. Mean SST and minimum SST were calculated for
each year and for two seasons: December to May and June
to November.
Chlorophyll-a was used as a proxy of phytoplankton biomass.
Blue whales almost feed exclusively on krill (Euphausiid spp.).
Krill information in the Indian Ocean is limited. We will
therefore use the concentration of chlorophyll-a (as an
indication of phytoplankton biomass) as blue whale food
approximation (Allen, 1971). Chlorophyll-a data were acquired
from the MODIS-Aqua 8-daily dataset. Like SST, we computed
the mean chlorophyll-a (In total six data spots with two units at
one side, three units at the other side, spatial resolution is
approximately 4 km per unit) containing the hydrophone
stations of 96 km
2
. Mean chlorophyll-a was calculated for each
year and both seasons (December to May; June to November).
One potential caveat is that the measurement of the chlorophyll-
a was from the water surface, hence the data may not reflect krill
density in deep water (Branch et al., 2007).
SOI was downloaded from the BOM website where it was
calculated using the pressure difference between Tahiti and
Darwin. DMI was extracted from the NOAA Physical Sciences
website, which was based on the HadISST1.1 SST dataset. Both
oceanic variables were recorded on a monthly scale.
2.4 Statistical Analysis
2.4.1 Overall Trends and Interannual Difference in
the Number of Chagos Whale Songs
To identify if there were trends in the number of Chagos whale
songs detected we computed the correlation of annual average
whale songs per day (continuous variable) versus year (ordinal
variable) for both sites. Spearman rank coefficients were used to
represent the correlation and the corresponding p-values
were calculated.
Linear models were used to analyse the trend of average songs
per day of the Chagos whales versus year for both sites. To
compute the percentage of increasing rate, we used the linear
model of the logarithm of the average songs per day versus year.
To quantify the shift of the start date of the extended peak
seasons, linear models were used.
To test whether whale acoustic presence at the DGS and DGN
was significantly different, we used the Wilcoxon rank sum test.
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2.4.2 Correlation Between Whale Acoustic Presence
and Environmental Factors
To assess the correlation of whale acoustic presence and
environmental factors at each site we used SST and
chlorophyll-a (continuous variables) versus year (ordinal
variable) using spearman rank coefficients and calculated the
corresponding p-values.
Generalised linear models were used to interpret the
relationship between whale acoustic presence (proportion of
hours where whale songs were detected) and environmental
factors. Whale acoustic presence was paired with SST,
chlorophyll-a, DMI and SOI at each site with a binomial
family (weight = number of recording hours in the
corresponding eight-day period). The effect of year was
considered by using ‘glm.er’function (Bates et al., 2021)
in RStudio.
Generalised linear models (binomial) were chosen because 1)
They fit the analysis we made with the acoustic whale song data.
Since we aggregated the binary whale songs into 8 days (192
hours), and generalized linear models probabilities, the model
can indicate the correlations between the independent and
dependent variables. 2) They provide a multiple-period view.
Both whale acoustic data and the environmental data for DGN
and DGS were continuous and spanning for years, thus binomial
models were suitable.
2.4.3 Extended Peak Seasons of the Chagos
Whale Songs
For all extended peak seasons detected at both sites, the
correlations between the start date (Julian day) with
environmental variables (yearly mean SST, mean chlorophyll-a
concentration, the corresponding mean and minimum SST and
chlorophyll-a concentration) were calculated using Spearman
rank coefficients. The correlations between the length of
extended peak seasons with environmental variables were
calculated using Pearson coefficients.
We identified the first extended peak seasons of each year
across 18-year at the DGS site and 13-ear at the DGN site. The
spearman rank correlations between the start date (Julian day)
with year and the environmental variables were calculated.
The years with unclear start dates due to missing data were
considered insufficient for analysis. For those years with clear
start dates of extended seasons, we calculated the total length
of extended peak seasons and calculated the Pearson
correlations with the environmental variables. We also
calculated the spearman rank coefficient between the total
length and year.
3 RESULTS
3.1 Overall Trends and Interannual
Difference in the Number of Chagos
Whale Songs
We assessed the number of Chagos whale songs detected within a
total of 92,180.8 hours of underwater hydroacoustic data from
the DGN site and 143,406.7 hours from the DGS site, over 598
and 828 eight-day periods, spanning across 13 and 18 years,
respectively. In total, 486,320 Chagos songs were detected at the
DGN site over 13 years and 837,640 Chagos songs were detected
at the DGS site during the 18-year period. The average number of
songs per hour was higher at the DGS site (5.84 per hour) than at
the DGN site (5.27 per hour). Chagos song occurrence increased
over time for both the DGN and DGS sites (Figure 2), although
the rate was higher at the DGN compared to the DGS site. The
lowest number of Chagos songs recorded was in 2008 at both
sites, whereas the greatest number of songs was recorded in 2012
at the DGN site. The greatest number of songs recorded at the
FIGURE 2 | Annual change in the average number of Chagos whale songs detected per day off Diego Garcia/Chagos Archipelago at the north-western (DGN) site
where the linear trend has a slope of 15.1 songs/day/year (12.6%/year) with corresponding r= 0.56 and p-value of 0.06, and the south-eastern (DGS) site where
the linear trend has a slope of 8.8 songs/day/year (7.7%/year) with corresponding rof 0.79 and p-value < 0.001. ris the Spearman rank coefficient of the average
number of songs per day with year.
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DGS site was in 2017, although hydroacoustic data was not
available for the DGN site this year.
Seasonal variabilities were found at both sites in the 46 eight-
day periods across the study (18 years at the DGS and 13 years at
the DGN) (Figure 3). The whale acoustic presence was different
on the DGS and DGN sites throughout the mutual study period
that whale songs were recorded at both sites between 2002 and
2014 (Wilcoxon rank sum test, test statistics = 122688, p-value =
0.033). There was a seasonal influence in the number of whale
song detections, as more whales were found at the DGN site in
the warmer months (May to October), whereas in the cooler
months, November to April, there were more whales at the
DGS site.
3.2 Correlation Between the
Whale Acoustic Presence and
Environmental Factors
The mean concentration of chlorophyll-a was relatively stable
with only a slightly decreasing trend at both Diego Garcia sites
over 13 and 18 years respectively (Table 1). However, the average
SST and minimum SST increased overall at both sites (Table 1).
The increasing rates of both mean SST and minimum SST at the
DGS site were higher than those at the DGN site.
There was a seasonal effect observed on the number of whale
songs detected at both sites (Figure 4;Table 2). At the DGS site,
SST was negatively correlated with the whale acoustic presence,
whereas at the DGN site, SST and whale acoustic presence had a
positive relationship.
The relationship between chlorophyll-a concentration and
whale acoustic presence was negatively correlated at the DGN
site and positively correlated at the DGS site. When the
chlorophyll-a concentration is higher, we observed more whale
songs at the DGN site and less whale songs at the DGS site. At
the DGN, the negative correlation between chlorophyll-a and
whale acoustic presence was weaker, whereas at the DGS site the
positive correlation between chlorophyll-a and whale acoustic
presence was stronger (Table 2).
El Niño Southern Oscillation (measured by Southern Oscillation
Index, SOI) and Indian Ocean Dipole (measured by Dipole Mode
Index, DMI) both had critical relationships with the whale acoustic
presence (Table 2). DMI had a stronger correlation with whale
acoustic presence compared to SOI. In the DGS, DMI and whale
acoustic presence were positively correlated, while SOI and
whaleacousticpresencewerenegativelycorrelated.WhenDMIis
higher or ENSO index is lower, we observed more whale presence.
In the DGN, DMI was negatively correlated with whale acoustic
presence and SOI was positively correlated with whale acoustic
presence.WeobservedlesswhalesongswhenDMIislowerorwhen
ENSO index is higher. The correlation between both SOI and DMI
and whale presence was stronger in the DGS compared to in
the DGN.
3.3 Extended Peak Seasons of the Chagos
Whale Songs
At the DGS site, 30 extended peak seasons were identified over 18
years (Figures 5A,6). The mean duration of the extended peak
FIGURE 3 | Mean Chagos whale acoustic presence (%) + Standard error (defined as the percentage of hours when whale songs were detected, see Material and
Methods) on the DGS and DGN site for each eight-day period (46 bins for each site) during the study period (2002- 2014 on the DGN site: N = 9 years for 2 of the
46 bins, N = 10 for 17 bins, N = 11 for 20 bins, N = 12 for 7 bins; 2002- 2019 on the DGS site: N = 16 for 22 bins, N = 17 for 24 bins). Vertical bars (blue bars for
SE site, red bars for DGN site) represent the average whale acoustic presence (%).
TABLE 1 | Linear model coefficients of mean sea surface temperature (SST);
minimum SST; mean chlorophyll-a concentration versus year at the two oceanic
sites off Diego Garcia, DGN refers to the north-western site (n = 13 years) and
DGS to south-eastern site (n = 18 years).
DGN DGS
coefficients p-value coefficients p-value
Mean SST 0.0154 0.1934 0.0270 0.0224
Minimum SST 0.0183 0.5669 0.0491 0.0693
Mean chlorophyll-a-0.0025 0.0747 -0.0013 0.0812
Bold fonts are relationships that corresponding p-values are less than 0.05.
Huang et al. Oceanic Conditions Drive Chagos Whales
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seasons was 54 days. Unlike the DGN site, most extended peak
seasons in the DGS were in cooler periods between May and
December. Most extended peaks started between May and August.
The starting time of the extended peak seasons shifted earlier
throughout the study period (Spearman rank correlation = -0.53;
p-value = 0.05; Supplementary Table 1). The average shift of
arrival time is 4.2 ± 2.0 days/year ± SE. The probability of having
two peaks instead of one was higher after 2011. Also, consecutive
peaks (more than 90% of whale acoustic presence) were more
frequent in later years. The total length of extended period
increased by 7.3 ± 1.2 days/year ± SE across 18 years. At the
DGN site (Figures 5B,6), we detected 15 extended peak seasons
over 13 years and the average period length was 50 days. Most
extended peaks started between October and December and
finished in January or February. Extra extended peaks were
found in 2005, 2006, 2009, and 2013. Extended peak seasons
were similar in pattern between 2002 and 2004. There were more
and longer extended peak seasons in 2005. However, information
was limited in 2006 and 2007 due to missing data. There was no
peak found in 2008. In 2009 and 2010, extended peaks were
similar to the previous pattern between 2002 and 2004. Longer and
stronger extended peak seasons were observed from 2011 and
2013. The extended peak season analysis was not complete in 2014
due to missing data and therefore the end date of the last extended
peak could not be determined. There was no strong correlation
between the start time of the extended peak season and the year
(Spearman rank correlation=-0.45;p-value=0.31;
Supplementary Table 1).
While the mean annual chlorophyll-a concentration was
similar between DGN (0.14 mg/m
3
) and DGS (0.13 mg/m
3
),
there was a difference between sites in the interannual change in
chlorophyll-a concentration (Figure 7). At the DGS site, there
were higher average chlorophyll-a concentrations in peak
seasons than non-peak seasons however there was little
seasonal difference at the DGN site.
4 DISCUSSION
This study provides insights into the association between
environmental variation and long-term patterns of Chagos
whale phenology. Whale presence data are almost continuous
TABLE 2 | GLM model coefficients for the south-eastern (DGS) site and north-
western (DGN) site.
DGS DGN
Variable coefficient p-value coefficient p-value
8-day
SST -1.173 <0.001 0.279 <0.001
chlorophyll-a16.954 <0.001 -1.782 <0.001
Monthly
SOI -0.016 <0.001 0.005 <0.001
DMI 1.299 <0.001 -0.190 <0.001
For each coefficient, it is the coefficient for the GLM (family = binomial, weights = number of
hours recorded) between the whale acoustic presence (%) and the corresponding sea
surface temperature (SST) and chlorophyll-a concentration (N = 498 for DGN; N = 760 for
DGS); Southern Oscillation Index (SOI) and Dipole Mode Index (DMI) (N = 134 for DGN;
N = 205 for DGS).
FIGURE 4 | Histograms of the mean Chagos whale acoustic presence (%) (defined as the percentage of hours when whale calls were detected, see Material and Methods)
during each eight-day period and their corresponding a) mean SST (°C), indicated by orange line chart; and 2) chlorophyll-a concentration (mg/m
3
) indicated by green line
chart; for 2002 –2014 on the DGN site (red bars; N = 16 for 22 bins, N = 17 for 24 bins) and for 2002 -1 2019 on the DGS site (blue bars; N = 9 years for 2 bins, N = 10 for
17 bins, N = 11 for 20 bins, N = 12 for 7 bins).
Huang et al. Oceanic Conditions Drive Chagos Whales
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at the two acoustically independent sites over 18 years (January
2002 –December 2019) and 13 years (January 2002 –February
2014), which covers episodes of rapid ocean warming
throughout the Southern Hemisphere oceans. We found that
the number of songs per day had increasing trends at both sites
throughout the years. This study also suggests that Chagos
whales may be feeding at the DGS site, in association with
nutrients provided by the upwelling effect in the positive IOD
phase. In addition, we found that the Chagos whales were
arriving at their potential feeding ground earlier in the year in
recent times (Figure 8).
4.1 Overall Trends and Interannual
Difference in the Number of Chagos
Whale Songs
The Chagos whales are present near Diego Garcia each year
across the entire study period, with over five songs per hour
detected at both sites, as also shown in Leroy et al. (2021). Peaks
in different seasons were identified at both sites for almost every
year (except for 2008, where the least number of Chagos songs
were detected at each station). This suggests that the tropical
Indian Ocean is a crucial habitat for this population of whales.
The central Indian Ocean is a complex marine system with rich
cetacean fauna (Anderson et al., 2012). Distinct blue whale
acoustic groups are observed and produce more song types
than in any other oceanic region (McDonald et al., 2006;
Samaran et al., 2013). At Diego Garcia, besides the best-
described Antarctic blue whale (McDonald et al., 2006), there
have been at least four other pygmy blue whale populations (B.
m. brevicauda or B. m. indica) detected from acoustic recordings,
including the Sri Lankan/Northern Indian Ocean, the
Madagascan/Central Indian Ocean, the Australian/
Southeastern Indian Ocean and the Arabian sea/Northwestern
Indian Ocean (NWIO) (Stafford et al., 2004;Stafford et al., 2011;
A
B
FIGURE 5 | Long-term whale acoustic presence (defined as the percentage of hours when whale songs were detected, see Material and Methods)at(A) DGS
(from 2002 to 2019) and (B) DGN (from 2002 to 2014). Vertical bars indicate the proportion of hours that one or more Chagos whale songs were detected in each
eight-day period. Grey/white stars indicate peak time when there were more than 90% of the hours with Chagos song detections. Darker bars are the extended
peak seasons that contain (as defined in the Methods section). Diagonal crosshatch-filled shades were unavailable data (See Material and Methods for the definition
of the extended peak season).
Huang et al. Oceanic Conditions Drive Chagos Whales
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8438758

Samaran et al., 2013;Double et al., 2014;Leroy et al., 2016;Leroy
et al., 2018;Cerchio et al., 2020). Cerchio et al. (2020) recorded
NWIO songs at the DGS and DGN sites over the 2010 –2013
period. They observed considerably fewer songs and peak
seasonsthanChagossongsidentified in this study; for
instance, in 2010 NWIO songs were only recorded in May at
the DGS site while we recorded Chagos song all year with two
extended peak seasons at the same site. This provides further
evidence that the central Indian Ocean is a crucial habitat of the
Chagos whale population and Chagos whales are likely one of the
most abundant populations in this area.
This study shows that the number of Chagos whales’songs
has increased over time. A 7.8% increase in song numbers was
observed on the southeastern side of the Chagos-Laccadive
Ridge, while songs increased by 12.6% on the northwestern
side. Changes in acoustic presence could, to some extent,
reflect changes in the number of whales in a given area (Charif
et al., 2020). The values are within the range of population trends
in other blue whale species (i.e., Antarctic blue whales 7%,
Branch et al., 2004) and other baleen whales (i.e., 12%
humpback whales, Wedekin et al., 2017; 4-5% eastern North
Pacificfin whales, Zerbini et al., 2006), and are much higher than
North Atlantic right whale (i.e., 2.8% from 1980 to 2010, decline
post-2010, Pace et al., 2017). Acoustic data is not a reliable source
to estimate the abundance of whale population because of the
unknown distance that whale songs travel, but relative densities
from the number of songs recorded can be inferred (Branch
et al., 2007).
4.2 Correlation Between the Number of
Whale Songs Detected and
Environmental Factors
Although the DGN and DGS sites are close, they are acoustically
independent due to the existence of Chagos-Laccadive Ridge.
The Chagos Bank acts as a natural acoustic barrier and sounds
produced on either side of the Chagos Bank are unlikely to be
detected on the other side (Pulli and Upton, 2001). Clear
seasonal patterns in Chagos whale presence were identified
throughout the entire study period (Leroy et al., 2021). The
relationship we predicted matches with the condition presented
by the 18-year data from the DGS site as there were more whale
songs detected when there were more food and lower
temperature; while at the DGN site, more whales were present
when there were less food and higher temperature. Chagos
whales were more frequently detected at the DGN site between
December and May (average SST: DGN site: 28.85°C; DGS site:
28.92°C), while most extended peak seasons of whale presence at
the SE site were between June and November, in the cooler
season (average SST: DGN site: 28.01°C; DGS site: 27.53°C). The
mean and minimum SST tended to increase at both sites,
although it increased at higher rates in the DGS site compared
to in the DGN site.
Chlorophyll-a (a proxy for krill, the whales’main prey) has an
influence on whale presence. As we found that Chagos whales were
present at the DGS site at times with higher chlorophyll-a
concentrations,itispossiblethatChagoswhalesusethisregion
seasonally to feed. The correlation between chlorophyll-a and whale
acoustic presence is stronger at the DGS site than at the DGN site.
It is therefore likely that whales are feeding in the vicinity of
the DGS site at the Chagos-Laccadive ridge because chlorophyll-
a and whale acoustic presence correlated positively. Chagos
whale presence is negatively correlated with chlorophyll-a in
the DGN, suggesting they are not feeding substantially at or near
the DGN site.
Indian Ocean Dipole (IOD) was correlated with Chagos
whalepresence.AttheDGSsite,IODandChagoswhale
acoustic presence were correlated positively, whereas at the
DGNsite,IODandChagoswhaleacousticpresencewere
FIGURE 6 | Heatmaps of Chagos whale acoustic presence at DGS (left, blue colour, from 2002 to 2019) and at DGN (right, red colour, from 2002 to 2014). Grey
bars are missing data.
Huang et al. Oceanic Conditions Drive Chagos Whales
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8438759

correlated negatively. The existence of Chagos-Laccadive ridge
makes the two sites acoustically independent, and the effect of
IOD may be different between sites. Chagos whales were more
likely to be detected at the DGS site during positive IOD phases.
During positive IOD phases, cooler SST is recorded at the DGS
site and upwelling events may occur (Saji et al., 1999). It is likely
that the cold upwelling water brings nutrients that may influence
the presence of Chagos whales. The negative correlation between
IOD and Chagos whale presence at the DGN site suggests a
higher whale abundance during the negative IOD phase, in
association with cooler water and upwelling (Figure 9). Similar
patterns were found in Sri Lankan pygmy blue whales where they
fed in the Arabia Sea off the coast of Somalia during the period of
intense upwelling of the monsoon season (Anderson et al., 2012).
In addition, ENSO was correlated to whale songs detected,
but the influence was not as strong as the IOD. It may be because
FIGURE 7 | Boxplot of the mean chlorophyll-a concentration(mg/m
3
) for DGS (n = 13 from 2002 to 2014) and DGN (n = 18 from 2002 to 2019) during the
corresponding peak seasons and non-peak seasons. Boxes represent interquartile ranges (IQR); solid white lines represent medians; black lines encompass data
range 1.5*IQR below and above IQR; dots represent potential outliers.
FIGURE 8 | Annual change in the start julian day of the first extended peak season per year off Diego Garcia at the DGN site where the linear trend has a slope of -1.6 days/
year (sample size is too small to calculate a p-value) and DGS site where the linear trend has a slope of -4.2 days/year with corresponding rof -0.53 and p -value of 0.05. ris
the spearman coefficient of the start julian day of the first peak season with year.
Huang et al. Oceanic Conditions Drive Chagos Whales
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 84387510

Diego Garcia is in the central Indian Ocean while ENSO has a
stronger influence in the Pacific Ocean.
4.3 Extended Peak Seasons of the Chagos
Whale Songs
This study shows that Chagos whales stay near the southeastern
side of Diego Garcia, potentially to feed in the vicinity of the
highly productive zones, during the cooler seasons (June to
November). Pygmy blue whale song detection has interannual
differences, peaking in activity between August and October at
DGS and between November and February at DGN, suggesting
the area may represent a natural migratory corridor between the
north and south of Diego Garcia. After March, they may disperse
as far north as the Laccadive Sea off Sri Lanka or as far south as
Kimberley region of Australia (Leroy et al., 2021). The migration
of Chagos whales was proposed to be in a clockwise pattern
across the equatorial Indian Ocean (Leroy et al., 2021). Unlike
the Chagos blue whales, other blue whales migrate north-south
so that their distribution spans across latitudes (Branch et al.,
2007). For example, Antarctic blue whales are frequently found
south of 70°S in the austral summer where they feed, while in the
winter they migrate to low latitudes further north to calve and
mate (Double et al., 2014;Balcazar et al., 2015). This study
showed that Chagos whales are most frequently present from
September to November at the DGS site, and they move to DGN
site and stay there from December to February. In line with
preliminary reports of occurrence of the Chagos whale songs
(Sousa and Harris, 2015;Leroy et al., 2021), this study suggest
Chagos whales may have a different migration pattern, rather
than moving north-south, as the other blue whales do, that
instead they migrate from east-to-west.
This study shows that Chagos whales are recorded in the tropical
central Indian Ocean year-round. Not all whales migrate, as S

irovic
et al. (2004) recorded Antarctic blue whales songs year-round in the
Southern Ocean, near the Antarctic Peninsula. Tripovich et al.
(2015) also found blue whale songs all year round in southern
Australian waters off Portland, South Australia. The proportion of
the blue whale population that migrates remains unclear. We found
that Chagos whales were present year-round however their
occurrence was highly seasonal. Further study of the acoustic
presence of Chagos whales in further north or south (e.g., Sri
Lanka and Kimberley) could reveal if Chagos whales have long-term
habitats other than central Indian Ocean.
Chagos whales tended to arrive earlier at the DGS site each
year in the study. The extended peak season at the DGS site
starting time shifted from around September to May (Except for
the year 2002, the starting time of the extended peak season was
May on the DGS). Former studies suggest that blue whales are
arriving earlier at their feeding area, and the starting time of the
peak season is correlated with colder SST anomalies of the
previous seasons in Southern California (Szesciorka et al.,
2020). Ramp et al. (2015) show that the humpback whales and
FIGURE 9 | Conceptual figure of the relationship between Indian Ocean Dipole (IOD) and whale song detected at DGN and DGS. Positive and negative IOD phases
influence in the Indian Ocean were described by Saji et al., 1999.
Huang et al. Oceanic Conditions Drive Chagos Whales
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 84387511

fin whales were arriving at their feeding ground earlier from 1984
to 2010. Our analysis reveals that the Chagos whales are likely to
arrive at their feeding ground earlier.
It is important we understand how the long-term warming of
the tropical Indian Ocean (Levitus et al., 2009;Xue et al., 2012)is
influencing its marine life; given this region has warmed faster
than the equivalent tropical Pacific and Atlantic and that the
warming is accelerating (Rayner, 2003). Although it is difficult to
sustain long-term monitoring studies in remote regions, like the
central tropical Indian Ocean, this study demonstrated how
continuous multiyear acoustic surveillance reveals that Chagos
whales change the timing of their migration from year to year,
and that local and oceanic environmental conditions may be
implicated. The migration pattern of the pygmy blue whales is
poorlyknownintheIndianOcean(Branch et al., 2007).
Understanding the migration pattern helps model the
distribution of these whales and assists the prediction of whale
presences into the future. It can also benefitconservation
managers to develop conservation strategies for wildlife
protection (i.e., establishment of marine protected area).
Further study of the Chagos whales’movement will provide
insights on how environmental changes influence marine life.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
Ethical review and approval was not required for the animal
study because the acoustic data was used.
AUTHOR CONTRIBUTIONS
JH and TR conceived the project and designed methodology. EL
provided acoustic detection data. JH and TR analysed the data;
JH and TR led the writing and GT and EL contributed to editing
and gave final approval for publication.
FUNDING
Funding was provided by the Winnifred Scott Foundation.
ACKNOWLEDGMENTS
Hydroacoustic data was made available via a Virtual Data
Exploitation Centre contract with the Comprehensive Test Ban
Treaty Organization (CTBTO). We thank Anant Mathur for his
suggestion on statistics. We would also like to thank mammal
lab, especially Anna Lewis for providing comments on the
draft manuscript.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/fmars.2022.843875/
full#supplementary-material
Supplementary Figure 1 | Scatter plots of the Chagos whale acoustic presence
(%) (defined as the percentage of hours when whale songs were detected, see
Material and Methods section) during each eight-day period and their
corresponding environmental factors: chlorophyll-a concentration (mg/m
3
); SST
(°C); SOI; and DMI for the south-eastern (DGS) site (red) and north-western (DGN)
site (blue). The red and blue lines indicate their binomial relationships.
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Huang et al. Oceanic Conditions Drive Chagos Whales
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 84387513

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Huang et al. Oceanic Conditions Drive Chagos Whales
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 84387514