Received: 14 July 2017
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Accepted: 29 August 2017
DOI: 10.1111/gcb.13892
PRIMARY RESEARCH ARTICLE
~ o Southern Oscillation influences the abundance and El Nin movements of a marine top predator in coastal waters Kate R. Sprogis1
| Fredrik Christiansen1
| Moritz Wandres2
| Lars Bejder1,3
1
Cetacean Research Unit, School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA, Australia
Abstract ~o Southern Oscillation (ENSO) influence popLarge-scale climate modes such as El Nin
2
School of Civil, Environmental and Mining Engineering and the UWA Oceans Institute, The University of Western Australia, Crawley, WA, Australia 3
Marine Mammal Research Program, Hawaii Institute of Marine Biology, University of Hawaii, Hawaii, HI, USA Correspondence Kate R. Sprogis, Cetacean Research Unit, School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA, Australia. Email:
[email protected] Funding information South West Marine Research Programme; Bemax Cable Sands; BHP Billiton Worsley Alumina Ltd; the Bunbury Dolphin Discovery Centre; Bunbury Port Authority; City of Bunbury; Cristal Global; the Western Australian Department of Parks and Wildlife; Iluka; Millard Marine; Naturaliste Charters; Newmont Boddington Gold; South West Development Commission; WA Plantation Resources
ulation dynamics in many species, including marine top predators. However, few quantitative studies have investigated the influence of large-scale variability on resident marine top predator populations. We examined the effect of climate variability on the abundance and temporary emigration of a resident bottlenose dolphin (Tursiops aduncus) population off Bunbury, Western Australia (WA). This population has been studied intensively over six consecutive years (2007–2013), yielding a robust dataset that captures seasonal variations in both abundance and movement patterns. In WA, ENSO affects the strength of the Leeuwin Current (LC), the dominant oceanographic feature in the region. The strength and variability of the LC affects marine ecosystems and distribution of top predator prey. We investigated the relationship between dolphin abundance and ENSO, Southern Annular Mode, austral season, rainfall, sea surface salinity and sea surface temperature (SST). Linear models indicated that dolphin abundance was significantly affected by ENSO, and that the magnitude of the effect was dependent upon season. Dolphin abundance was lowest during winter 2009, when dolphins had high temporary emigration rates out of the study area. This coincided ~o event that occurred throughout the study period. Coupled with with the single El Nin this event, there was a negative anomaly in SST and an above average rainfall. These conditions may have affected the distribution of dolphin prey, resulting in the temporary emigration of dolphins out of the study area in search of adequate prey. This study demonstrated the local effects of large-scale climatic variations on the shortterm response of a resident, coastal delphinid species. With a projected global increase in frequency and intensity of extreme climatic events, resident marine top predators may not only have to contend with increasing coastal anthropogenic activities, but also have to adapt to large-scale climatic changes. KEYWORDS
~ o Southern Oscillation, La Nin ~a, bottlenose dolphin, climate change, climate variability, El Nin large-scale climate indices, Leeuwin Current, marine mammal
1 | INTRODUCTION
which periodically fluctuate and vary in intensity (Stenseth et al., 2003). Climate variability affects physical oceanic conditions and
~ o Southern Oscillation Large-scale climate phenomena, such as El Nin
influences ecological and biological processes (Barber & Chavez,
(ENSO) and the North Atlantic Oscillation (NAO), are climate modes
1983; Stenseth et al., 2002). Climate variability may influence fauna
Glob Change Biol. 2017;1–12.
wileyonlinelibrary.com/journal/gcb
© 2017 John Wiley & Sons Ltd
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directly by altering their physiology (e.g. metabolic and reproductive
exhibit a high degree of philopatry to a subset of their natal area
processes) or indirectly through changes in predator/prey dynamics
(Tsai & Mann, 2013). Off Bunbury, south-west Australia, dolphins
or presence of competitors (Durant, Hjermann, Ottersen, & Stenseth,
occupy oceanic and inner waters (bay, estuary and river; Figure 1)
2007; Stenseth et al., 2002). Climate variability affects the breeding
re, Kobryn, and experience seasonal shifts in habitat use (Smith, Fre
success, foraging patterns and population dynamics of marine top
& Bejder, 2016). Dolphins that reside in the inner waters have smal-
predators (Bost et al., 2015; Durant, Anker-Nilssen, Hjermann, &
ler home ranges than dolphins in oceanic waters (Sprogis, Raudino,
Stenseth, 2004; Forcada, Trathan, Reid, & Murphy, 2005). An under-
Rankin, MacLeod, & Bejder, 2016). Here, dolphins are opportunist
standing of these effects is becoming increasingly important as cli-
feeders consuming fish and cephalopods (McCluskey, Bejder, & Lon-
mate change is expected to lead to an increase in the frequency and
eragan, 2016; Smith & Sprogis, 2016; Sprogis, Raudino, Hocking, &
intensity of extreme climatic events (Cai et al., 2014).
Bejder, 2017). The recruitment and distribution of potential dolphin
Cetaceans (whales and dolphins) are long-lived, marine top
prey is influenced by the strength of the Leeuwin Current (LC),
predators that are influenced by climate variability, including effects
which is the dominant oceanographic feature off WA that transports
on their social structure (Lusseau et al., 2004), distribution (Salvadeo,
warm, oligotrophic, low salinity waters southwards (Caputi, Fletcher,
mez-Gallardo, Najera-Caballero, Urban-Ramirez, & Lluch-Belda, Go
Pearce, & Chubb, 1996; Lenanton, Joll, Penn, & Jones, 1991; Pearce
2015), breeding success (Greene & Pershing, 2004; Leaper et al.,
& Griffiths, 1991). The strength of the LC is affected by ENSO con-
2006), abundance (Tershy, Breese, & Alvarezborrego, 1991) and sur-
~a events when easterly ditions, and is strengthened during La Nin
vival (Urb an & Ludwig, 2003). Long-term datasets are required to
trade winds pile up warm water on the western side of the Pacific
detect biological responses to anomalous climate conditions. Long-
Ocean, resulting in a westerly flow through the Indonesian Through-
term datasets on highly mobile delphinids indicate that inter-annual
flow (Feng, Meyers, Pearce, & Wijffels, 2003; Pattiaratchi & Buchan,
variability in their distribution is linked to the ENSO cycle. For exam-
~o events when 1991). In contrast, the LC is weakened during El Nin
ple, a 15-year study in the Eastern Tropical Pacific, documented that
trade winds weaken or reverse and the pool of warm water gathers
the distribution of spinner dolphins (Stenella longirostris) and common
on the eastern side of the Pacific Ocean, resulting in a weaker
dolphins (Delphinus delphis) is related to ENSO which affects their
Indonesian Throughflow (Feng et al., 2003; Pattiaratchi & Buchan,
preferred oceanic habitat (Fiedler & Reilly, 1994; Reilly & Fiedler,
1991). The strength and variability of the LC correlated with ENSO
1994). Likewise, during a 27-month ship transect study off Monterey
conditions affects species biology and ecology within WA waters. In
Bay, USA, sighting rates of common dolphins (Delphinus spp.)
this study, we use a 6-year capture–recapture dataset to investigate
~o event, suggesting that the prey base increased during an El Nin
the effects of ENSO on the abundance and temporary emigration of
may have changed to include species that were not otherwise avail-
the resident, near-shore dolphin population off Bunbury.
able (Benson, Croll, Marinovic, Chavez, & Harvey, 2002). Similarly, teeth from toothed whales reflect long-term information on the environment of the host animal, and from these, climate variability can be traced (Dellabianca et al., 2012). Teeth from dusky dolphins (Lagenorhynchus obscurus) off Peru showed an anomalous dentine ~o event relating to the collapse of layer that coincided with an El Nin
2 | MATERIALS AND METHODS 2.1 | Capture–recapture data collection for dolphin abundance and temporary emigration estimations
their primary prey, anchovies (Engraulis ringens; Manzanilla, 1989).
Boat-based, photo-identification surveys for estimating seasonal dol-
Long-term datasets are thus of key importance to assess extreme cli-
phin abundance and movement patterns were conducted off Bunbury,
matic events and potential impacts of climate change, but few are
south-west Australia (Figure 1). Details of field surveys are presented
available because of logistical difficulties and financial costs (Bost
in Sprogis, Pollock, et al. (2016). In brief, data were collected continu-
et al., 2015; Hughes et al., 2017; Simmonds & Isaac, 2007).
ously across austral seasons from March 2007 to April 2013 in coastal
Research into the effects of large-scale climate variability is lim-
and estuarine waters (Figure 1). Surveys were systematic and followed
ited for resident, near-shore dolphin species that exhibit smaller
predetermined zig-zag transect lines, totalling 417 surveys across
home ranges than highly mobile oceanic dolphin species. One study
383 days. Upon a dolphin group sighting, a photograph of every dol-
from a 9-year dataset on resident killer whales (Orcinus orca),
phin’s dorsal fin was aimed to be captured, and the Global Positioning
Canada, and an 11-year dataset on resident bottlenose dolphins (Tur-
System (GPS) location, time and group composition were recorded.
siops truncatus), Scotland, highlights that climate variability influences
Data were available on individual dolphins (including sex and age class)
grouping patterns (Lusseau et al., 2004). Specifically, Lusseau et al.
from a long-term research programme (2007 onwards) focussed on
(2004) showed that these delphinids live in smaller groups when
the Bunbury dolphin population (Smith, Pollock, Waples, Bradley, &
their preferred prey is not highly available which occurs after a lower
Bejder, 2013; Sprogis, Pollock et al., 2016).
phase of the NAO and Pacific Decadal Oscillation. To date, the
Details of capture–recapture modelling methods for obtaining
effects of ENSO events on the abundance of resident, near-shore
abundance estimates and temporary emigration rates are presented
dolphins are not known.
in Sprogis, Pollock, et al. (2016). In short, photographic images of
Resident, near-shore Indo-Pacific bottlenose dolphins (Tursiops
dorsal fins were used to identify individuals and subsequent recap-
aduncus) inhabit the waters off Western Australia (WA), where they
€rsig & Wu €rsig, tures of individuals by unique nicks and notches (Wu
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F I G U R E 1 Location of the 120 km2 study area off Bunbury, Western Australia, where dolphin photo-identification surveys were conducted from 2007 to 2013. Boatbased transects were conducted for abundance estimates in the Inner waters (bay, inlet, estuary and river), Back Beach and Buffalo Beach (zig-zag lines). The southward flowing Leeuwin Current is the main oceanographic feature off Western Australia (designated by the arrow in the insert)
1977). Photographs were graded for image quality and fin distinc-
total ) for each The total abundance of adult and juvenile dolphins (N
tiveness (Rosel et al., 2011). Only good and excellent quality pho-
primary period (season) was calculated. Temporary emigration is the
tographs and moderate and highly distinctive fins were used to
probability of being temporarily out of the study area if the individ-
create capture histories of individual dolphins for capture–recapture
ual was present (c″) or absent (c0 ) in the previous primary period. In
modelling. Pollock’s Robust Design capture–recapture models (Pol-
the current study, we analysed these abundance estimates and tem-
lock, 1982) were used to calculate abundance and temporary emigra-
porary emigration rates to investigate whether large-scale climate
tion rates using the program MARK (White & Burnham, 1999). The
variability had an influence on the population dynamics of the resi-
Robust Design incorporates both open and closed population models
dent dolphin population.
and is structured to have open sampling events (termed “primary periods”), within which are multiple closed events (termed “secondary periods”). In our study, primary periods were based on austral
2.2 | Environmental data
seasons (i.e. four primary periods per year, equating to 25 primary
To characterize the climate conditions, data on ENSO, Southern
periods over the study duration), whereas secondary periods were
Annular Mode (SAM), rainfall, sea surface temperature (SST) and sea
based on the number of days it took to complete the three transects
surface salinity (SSS) were obtained. ENSO is a result of complex
within the study area (Figure 1; equating to 139 secondary periods).
interactions between the ocean and atmosphere in the tropical
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Pacific Ocean, and is associated with climatic anomalies globally
Seasonal abundance of adult and juvenile dolphins (combined)
(Wang, Deser, Yu, DiNezio, & Clement, 2017; Wang & Fiedler,
was modelled as the response variable. Environmental variables
~a, Neutral and 2006). ENSO fluctuates between three phases: La Nin
~a, Neutral, El included in the full model were (i) ENSO event (La Nin
~ o. The Southern Oscillation Index (SOI) is a measure of El Nin
~ o), (ii) austral season (summer = December, January, February; Nin
strength of these ENSO phases, and is the normalized difference in
autumn = March,
surface atmospheric pressure between Darwin, Australia and Tahiti
spring = September, October, November), (iii) seasonal average of
(Trenberth, 1997). Monthly SOI values were obtained from the
SSS and (iv) time (a continuous variable representing the sequence
April,
May;
winter = June,
July,
August;
Bureau of Meteorology (2014e). Sustained SOI values +8 indicate La Nin ~a events El Nin
Autumn 2007 and 25 corresponding to Autumn 2013). For each aus-
(Bureau of Meteorology, 2014a). In the Southern Hemisphere, the
tral season, an ENSO event was assigned (based on monthly SOI val-
dominant mode of atmospheric circulation variability is the SAM,
~a, ues), with events being modelled as a categorical variable (La Nin
which is a driver for climate variability (Gong & Wang, 1999; Thomp-
~ o). Seasonal phases of ENSO events were used so Neutral, El Nin
son & Wallace, 2000). Monthly SAM data were extracted from the
they could be related to the seasonal estimates of dolphin abun-
Natural Environment Research Council (NERC) of the British Antarc-
~ o, La Nin ~a and Neutral events were assigned to a seadance. El Nin
tic Survey (Marshall, 2003; NERC, 2015). In a positive SAM event, a
son if the events covered at least two of the 3 months of that
belt of strong westerly wind contracts towards Antarctica. Con-
season (details in Table S1).
versely, a negative SAM event reflects an expansion of the belt of
The most parsimonious model (i.e. model with the lowest AIC
strong westerly winds towards the equator. The strengthening of
and the fewest number of parameters) was selected using manual
the SAM is associated with a significant cooling over much of Aus-
forwards stepwise selection based on minimization of the Akaike
tralia (Gillett, Kell, & Jones, 2006), as well as an increase in rainfall
information criterion (AIC; Burnham & Anderson, 2002, 2004). Mod-
over south-western Australia (Raut, Jakob, & Reeder, 2014). Monthly
els with DAIC ≤2 of the most parsimonious model were considered
rainfall rates were obtained for the Bunbury region from the Bureau
to have comparable support, thus Akaike weights (wi) were calcu-
of Meteorology (2014d). Rainfall in the south-west is generally low-
lated to provide a measure of strength for each model (Burnham &
est during warmer months (i.e. December to February) and highest
Anderson, 2002). Variables and interactions between variables were
during cooler months (May to October; Feng, Li, Yun, Zhu, & Xie,
added sequentially to the null model based on biological explanation,
2015) and is strongly connected to SST, with a decrease in SST
and the F-statistic from the ANOVA F-test was estimated for each
resulting in higher rainfall during winter (Samuel, Verdon, Sivapalan,
variable in the model. Model validation tests were run to identify
& Franks, 2006). Daily SST and SSS data were extracted from the
potential violations of the assumptions of the model. To test the
high-resolution (1/12°) HYCOM and NCODA global reanalysis data-
assumption of homogeneity in the model, scatter plots of residuals
set (GLBu0.08, tds.hycom.org/thredds/GLBu0.08) run by the Naval
vs. fitted values and residuals against each explanatory variable were
Research Laboratory of the U.S. Navy (location: 115.52°E and
created. Normality of residuals was investigated using Quantile–
33.31°S, off Bunbury at the 20 m depth contour, Figure 1). Sea sur-
Quantile plots and from residual histograms. Outliers and influential
face temperature anomalies were calculated as the deviation of the
points were investigated using leverage and Cook’s distance. Tempo-
monthly means from 2007 to 2013.
ral auto-correlation between data points was investigated using the auto-correlation function.
2.3 | Statistical analyses To investigate the effects of climatic variability on seasonal dolphin abundance, linear models were developed in R v3.0.3 software (R Development Core Team, 2011). Prior to model selection, collinearity between variables was investigated using variance inflation factors (VIF), multi-panel scatterplots and Pearson correlation. Initial data
3 | RESULTS 3.1 | Summary of ENSO events, rainfall, SST anomalies and dolphin abundance and temporary emigration
exploration indicated that mean seasonal rainfall and SST were colli-
~a events (SOI >+8) occurred during the study; (i) a modThree La Nin
near with season (VIFrainfall = 7.17, VIFSST = 11.98); thus, rainfall and
erate event from June 2007 to February 2008, (ii) a weak event
SST were removed, as season was the variable of interest that
from August 2008 to April 2009 and (iii) a moderate–strong event
encapsulates these environmental variables. Furthermore, dolphin
from April 2010 to March 2012 (Figure 2a; Bureau of Meteorology,
abundance, temporary emigration, sighting rates (Smith et al., 2013;
~a event experienced the lowest aver2014c). The 2008–2009 La Nin
Sprogis, Pollock et al., 2016), space use (Smith et al., 2016), prey
age rainfall in summer 2009 (8.07 mm, Figure 2b). The 2010–2012
selection (Smith & Sprogis, 2016; Sprogis et al., 2017) and prey avail-
~ a event consisted of two positive SOI peaks across the La Nin
ability (McCluskey et al., 2016) vary seasonally in the study area.
2 years (Figure 2a). In the first peak (the summer of 2010/2011),
ENSO events were based on monthly SOI values, which was colli-
there was an extreme warming trend in SST (Figure 3) with high
near with SAM (SAM~SOI, slope = 0.05, p = .03); therefore, SAM
summer (23.42°C) and autumn (23.66°C) water temperatures
was removed as a variable from the final model.
(Fig. S1; see details in Pearce & Feng, 2013). Rainfall during the
SPROGIS
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5
(a)
(b)
(c)
F I G U R E 2 Time-series data for the duration of the study for (a) monthly Southern Oscillation Index (SOI) values with a 3-month running mean (black line). ~o event (May 2009–March 2010) El Nin ~a events (June 2007–February and La Nin 2008, August 2008–April 2009, April 2010–March 2012) demarcated, (b) monthly rainfall (mm), (c) seasonal temporary emigration for adult and juvenile dolphins; the probability of being out of the study area if the individual was present (c″) in the previous secondary period (note the peak in temporary emigration from autumn to winter 2009), total for (d) seasonal abundance estimates N adult and juvenile dolphins. Vertical lines show 95% confidence intervals. Lines between data points are for illustrative purposes only; continuity of values is not implied
(d)
~a event consisted of below average winter rain2010–2012 La Nin
Meteorology, 2014b). A negative anomaly in SST resulted in a cool-
falls (2010 = 87.73 and 2011 = 128 mm; Figure 2c).
ing trend in SST off south-western Australia (Figure 3), with SST at
~o event was sustained from May A weak to moderate El Nin
its lowest in winter (18.62°C) and spring (18.45°C; Fig. S1). Corre-
2009 to March 2010 (Figure 2a; Bureau of Meteorology, 2014b).
~o, the rainfall was above sponding with the initial phase of El Nin
~o had the largest negative effect The initial phase of the El Nin
average in winter 2009 (mean = 158.48 mm). During this time, June
across Australia, when the SOI signal was weak (Bureau of
had a high of 224 mm rainfall (Figure 2c).
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F I G U R E 3 Sea surface temperature (SST) anomalies for south-west Australia. Darker red indicates a positive anomaly, with an increase in ~o in SST. Darker blue indicates a negative anomaly, with a decrease in SST. Note the extremes: the negative anomaly for the moderate El Nin ~a in summer/autumn 2011 (red rectangle) winter/spring 2009 (blue rectangle), and the positive anomaly for the strong La Nin ~ o event, the estimated During the initial phase of the 2009 El Nin
4 |
DISCUSSION
abundance of dolphins in winter declined to its lowest record during the study period (76.23 7.32 SE, CI 67.99–85.48; Figure 2e). The
We investigated the relationship between climate variability (ENSO)
same trend was documented for adult males and adult females in a
and other environmental variables on the abundance of Indo-Pacific
sex-specific analyses (Sprogis, Pollock et al., 2016). At the corre-
bottlenose dolphins off south-west Australia, from 2007 to 2013.
sponding time, there was an unparalleled peak in temporary emigra-
Dolphin abundance was significantly affected by ENSO. Further-
tion of dolphins out of the study area (c″) from autumn to winter
more, the direction (positive or negative) and the magnitude of the
2009 (0.57 0.05 SE, Figure 2d).
effect depended on the season at which the ENSO event occurred. Dolphin abundance was significantly influenced by season with
3.2 | The effects of climatic events (ENSO) on dolphin abundance
general lows in winter or spring, and highs in summer or autumn (Sprogis, Pollock et al., 2016). Seasonal dolphin abundance is influenced by seasonal prey changes in the oceanic waters (McCluskey
The most parsimonious model explaining the abundance of adult and
et al., 2016), bay (Potter, Tiivel, Valesini, & Hyndes, 1997) and estu-
juvenile dolphins from 2007 to 2013 included the variables season
ary (Veale, Tweedley, Clarke, Hallett, & Potter, 2014) within the
and ENSO event as an interaction term (model 9 in Table 1). The
study area. The distinct seasonal trends of the LC also affect the
model explained 81% of the total variance (R2) in the observed dol-
recruitment and distribution of potential dolphin prey (Caputi et al.,
phin abundance. Seasonal dolphin abundance was significantly
1996; Lenanton et al., 1991). In winter, the LC is strong with the
affected by season (analysis of variance [ANOVA]: F3,14 = 8.549,
meandering flow flooding the continental shelf (Hanson, Pattiaratchi,
p = .002), ENSO event (ANOVA: F2,14 = 7.215, p = .007), and the
& Waite, 2005; Pearce & Griffiths, 1991). During this time, the abun-
interaction
(ANOVA:
dance and biomass of prey is low (McCluskey et al., 2016); thus, dol-
F5,14 = 4.159, p = .016; Table 1). Dolphin abundance was lowest
phins may have to search further afield for prey. This appears to be
~o event, and highest during winter 2009 with a corresponding El Nin
the case in Bunbury, as in winter/spring dolphins are observed in
generally across summer and autumn varying by ENSO event (Fig-
deeper offshore waters (up to ~20 m depth, ~10 km from the coast),
ure 2). There was no collinearity between these variables or tempo-
where they are not observed during summer (Sprogis, 2015). In con-
ral auto-correlation between data points.
trast, in summer, the LC is opposed by the Capes Current, a seasonal
between
season
and
ENSO
event
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7
T A B L E 1 Akaike information criterion (AIC) ranked model selection results of the linear models to explain the effects of ENSO events (La ~a, El Nin ~o, Neutral), austral season, sea surface salinity (SSS) and time (primary periods across seasons) on dolphin abundance (A = seasonal Nin abundance) Model No.
Variables
F
1
A~1
NA
2
A~time
0.008
df (within)
p
R2
0
24
NA
NA
1
23
.929
0.000
df (among)
AIC
DAIC
wi
w1/wj
1
237.81
21.92
0.00
>50
2
239.80
23.91
0.00
>50
K
3
A~ENSO
3.012
2
22
.070
0.215
3
235.76
19.87
0.00
1.44
4
A~Season
3.647
3
21
.029
0.342
4
233.33
17.44
0.00
0.07
5
A~SSS
0.463
1
23
.503
0.020
5
239.31
23.42
0.00
>50
6
A~Season + time
2.661
4
20
.063
0.347
5
235.14
19.25
0.00
>50
7
A~Season 9 time
1.915
7
17
.130
0.441
8
237.28
21.39
0.00
>50
8
A~Season + ENSO
4.377
5
19
.008
0.535
6
228.65
12.76
0.00
>50
9
A~Season 9 ENSO
6.088
10
14
.001
0.813
12
215.89
0.0
0.29
10
A~Season + SSS
2.697
4
20
.060
0.350
5
235.03
19.14
0.00
1.00 >50
11
A~Season 9 SSS
1.700
7
17
.176
0.412
8
238.54
22.65
0.00
5.81
12
A~Season 9 ENSO + time
5.274
11
13
.003
0.817
13
217.36
1.47
0.14
0.00
13
A~Season 9 ENSO 9 time
2.865
16
8
.067
0.851
24
222.15
6.26
0.01
10.94
14
A~Season 9 ENSO + SSS
5.607
11
13
.002
0.826
13
216.10
0.21
0.26
0.05
15
A~Season 9 ENSO 9 SSS
3.588
16
8
.036
0.878
24
217.28
1.39
0.14
1.80
16
A~Season 9 ENSO + SSS + time
4.833
12
12
.005
0.829
14
217.72
1.83
0.11
1.25
17
A~Season 9 ENSO + SSS 9 time
4.135
13
11
.012
0.830
15
219.49
3.6
0.05
2.42
DAIC, difference in AIC values compared to model 9; K, number of parameters; wi, Akaike weight; w1/wj, evidence ratio where w1 is the most parsimonious model and j indexes the remaining models. The most parsimonious model was model 9 (bold).
wind-driven current that flows near-shore and causes localized
~a appear negatively affected by the 2011 heat wave or other La Nin
upwelling, which brings temperate, nutrient-rich waters into the area
events (three occurred during our study), and dolphin abundance
(Hanson et al., 2005; Pearce & Pattiaratchi, 1999). The abundance
remained stable.
and biomass of potential dolphin prey increases in summer (McClus-
~o events, when the LC is weakened, the climatic During El Nin
key et al., 2016), resulting in higher abundances of dolphins in near-
conditions negatively influence the settlement of rock lobsters (Pan-
shore waters (Sprogis, Pollock et al., 2016). A high density of dol-
ulirus cygnus; Caputi, Melville-Smith, de Lestang, Pearce, & Feng,
phins in the study area during summer/autumn may also be influ-
2010; Pearce & Phillips, 1988), and the breeding and foraging suc-
enced by their social dynamics and the peak in the breeding/calving
cess of shearwaters (Puffinus spp.), noddies (Anous spp.) and terns
season (further explained in Smith et al., 2016; Sprogis, Pollock et al.,
(Sterna fuscata; Bond & Lavers, 2014; Surman & Nicholson, 2009).
2016).
~ o event spanning from During our 6-year study, there was one El Nin
~a events, the LC is strengthened, and during El During La Nin
May 2009 to March 2010, with the initial phase having the largest
~o events the current is weakened (Feng et al., 2003; Pattiaratchi Nin
negative impact across Australia, when the SOI signal was weak
& Buchan, 1991). The correlation between ENSO and the LC
(Bureau of Meteorology, 2014b). During this initial phase (winter
strength affects species biology and ecology within WA waters. For
2009), there was an unprecedented decline in dolphin abundance
~a events have positive impacts on growth rates of example, La Nin
and an unparalleled peak in dolphins temporarily emigrating out of
fish (Nguyen et al., 2015; Ong et al., 2015) and corals (Ong et al.,
~o, when the LC is weakened, there is the study area. During El Nin
2016) and altered the distribution patterns of aggregating whale
also cooler than normal SSTs (Feng, Waite, & Thompson, 2009). This
sharks (Rhincodon typus; Anderson et al., 2014; Wilson, Taylor, &
was documented in our study, as there was a cooling trend in SST
~ a event in summer 2011, Pearce, 2001). During the strong La Nin
off south-western Australia, and these conditions result in reduced
there was an extreme warming of SST with anomalies of 2–4°C per-
primary production (Feng, Waite et al., 2009) and changes in the dis-
sisting for several months (Pearce & Feng, 2013), during which
tribution of potential dolphin prey (Caputi et al., 1996; Lenanton
ecosystems and marine species were affected (Feng, McPhaden, Xie,
~o et al., 1991). Thus, it is possible that the interaction of an El Nin
& Hafner, 2013; Smale & Wernberg, 2012; Wernberg et al., 2013).
event and season (winter/spring) compounded effects significantly,
For example, there was an abrupt ecosystem change and dieback of
causing dolphins to search further afield for adequate prey to fulfil
seagrasses which subsequently caused health consequences for her-
their dietary energetic requirements.
bivorous green turtles (Chelonia mydas; Thomson et al., 2014). How-
Large-scale climate variation that affects the abundance and
ever, in Bunbury, dolphin abundance and movement patterns did not
movement patterns of top predators is commonly related indirectly
8
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SPROGIS
ET AL.
to changes in prey availability (Durant et al., 2007). For example, our
the effect of ENSO on a coastal dolphin population. Continued mon-
findings are similar for shearwaters (Puffinus carneipes) from south-
itoring of the Bunbury population is important as climate models
~o resulted in a weaker LC which west WA, in that a stronger El Nin
~o provide evidence for the doubling in frequency of extreme El Nin
changed the distribution of prey which, in turn, negatively impacts
events (from one event every 20 years to one event every 10 years)
the seabirds (Bond & Lavers, 2014). Similarly, off central WA, during
due to global warming (Cai et al., 2014). Additionally, as coastal resi-
~o events, there were reduced volumes of prey species docuEl Nin
dents, individual dolphins are at risk from anthropogenic stressors,
mented in pelagic seabird regurgitates, suggesting a general reduc-
including vessel disturbance, tourism (Arcangeli & Crosti, 2009; Jen-
tion in marine productivity, which ultimately affects the reproductive
sen et al., 2009), entanglement in fishing gear, food provisioning and
~o output of the birds (Surman & Nicholson, 2009). Elsewhere, El Nin
coastal development (Smith, 2012). Recent population viability analy-
events indirectly affect delphinid movement patterns by changing
ses of the Bunbury dolphins are forecasting a 50% population
the distribution of prey, for example in bottlenose dolphins (T. trun-
decline within the next 20 years (Manlik et al., 2016). Therefore,
catus) off California (Wells et al., 1990), and common dolphins (Del-
continued long-term monitoring of the population is of high impor-
phinus delphis) off New Zealand (Neumann, 2001), California (Benson
tance to inform management of observed and predicted trends.
et al., 2002) and the Eastern Tropical Pacific (Reilly & Fiedler, 1994).
Global climate change profoundly alters ecosystems (Hoegh-
~o event in 2009, there was an above In unison with the El Nin
Guldberg & Bruno, 2010; Parmesan & Yohe, 2003; Walther et al.,
average rainfall in Bunbury during winter (June–August), with June
2002). The greatest increase in SST in the Indian Ocean is off south-
having 224 mm of rain (the average winter rainfall during our
west Australia, which is warming up faster than the average global
study = 124 mm). The amount of rainfall in south-western Australia
ocean trend, with ~0.02°C per year during 1951–2004 (Pearce &
is strongly connected to SST, in that when the SST in the Indian
Feng, 2007), particularly from autumn to winter (Caputi, De Lestang,
Ocean decreases the region receives higher rainfall (Cullen & Grier-
Feng, & Pearce, 2009). Additionally, the strength of the LC is pro-
son, 2009; Samuel et al., 2006). Changes in rainfall and terrestrial
jected to weaken under future greenhouse gas concentration scenar-
run-off alter freshwater inputs, changing salinities and impacting the
ios (Feng, Weller, & Hill, 2009; Hobday & Lough, 2011). With the
distribution and abundance of dolphin prey (Gillanders et al., 2011;
region warming and a weakening of the LC, there may be future
Holyoake et al., 2010; Veale et al., 2014). This is particularly true for
changes in fish assemblages (Cheung et al., 2012); however, the
the inner waters in our study area (river, estuary, inlet and bay),
flow-on effects to dolphins and the sensitivity of their ecosystem to
where rapid changes in salinity may affect estuarine fish (Gillanders
climate change are not known.
et al., 2011). Salinity was not selected as a variable in our final
Impacts from gradual climate change on marine top predators in
model; however, changes in prey abundance in the inner waters may
Australia are not well understood (Schumann, Gales, Harcourt, &
have altered the distribution of resident dolphins. Furthermore, dur-
Arnould, 2013), and effects can be direct or indirect (Learmonth
~ o in the Swan River, Perth (~180 km north of Buning the El Nin
et al., 2006). For resident bottlenose dolphins (Tursiops spp.) that rely
bury), resident dolphins developed fatal skin lesions (“tattoo skin
on near-shore habitats, impacts from climate change are unknown;
disease”) that was enhanced by low salinity waters (Holyoake et al.,
nevertheless, behavioural plasticity could assist in adapting to change
2010; Stephens et al., 2014). Tattoo skin disease was also observed
(Evans, Pierce, & Panigada, 2010). However, climate change as
in dolphins in estuaries on the east coast of Australia after flood
extreme discrete events has the capacity to impact the distribution,
events (Fury & Reif, 2012), where dolphins temporarily emigrated
abundance or survival of a species (Jentsch, Kreyling, & Beierkuhn-
out of the river and subsequently delayed their return (Fury & Har-
lein, 2007; Smith, 2011). This is shown for ENSO events (this study),
rison, 2011). In addition, off WA there was a high number of bot-
red tides (H€aussermann et al., 2017), flood events (Fury & Harrison,
tlenose dolphin deaths in 2009 (of which there was a peak in June
2011) and hurricanes (Elliser & Herzing, 2011; Miller, Mackey, Hof-
~o in south-west WA; Fig. S2); during the initial phase of the El Nin
fland, Solangi, & Kuczaj, 2010). There is a projected increase in the
however, the reasoning behind this remains unknown (Groom &
frequency and magnitude of extreme weather events driven by cli-
Coughran, 2012). We suggest that the decline in dolphin abundance
mate change, such as storms, cyclones and flooding under commonly
~o event was temporary, and the dolphins may have during the El Nin
used greenhouse gas concentration trajectories (IPCC, 2014, Kerr,
emigrated out of the study area due to changes in prey availability
2011). These events leave near-shore species particularly vulnerable
and/or potentially unfavourable water quality conditions in certain
(e.g. from flood events, Fury & Harrison, 2011; Fury & Reif, 2012).
areas (e.g. the inner waters).
This research serves as an impetus for long-term studies that aim to better understand and predict effects of the increasing frequency of
4.1 | Importance of long-term monitoring and additional implications for coastal dolphin species These findings emphasize the value of long-term, year-round moni-
extreme climatic events.
ACKNOWLEDGEMENTS
toring to evaluate population trends and identify possible large-scale
Thank you to our numerous research assistants who assisted with
environmental drivers affecting marine top predators. Year-round
fieldwork and data processing. Thank you to Dr Holly Raudino and
monitoring allowed for seasonal trends to be detected, highlighting
to our research associates for collecting field data and/or assistance
SPROGIS
|
ET AL.
with data management; M. Cannon, D. Chabanne, V. Buchanan, K. Nicholson and B. Goguelat. Thank you to the Western Australian Department of Parks and Wildlife for providing bottlenose dolphin stranding data from the strandings database. We are grateful to two anonymous reviewers whose comments greatly improved this manuscript. We thank the funding partners for financial support for longterm research of the South West Marine Research Programme; Bemax Cable Sands, BHP Billiton Worsley Alumina Ltd, the Bunbury Dolphin Discovery Centre, Bunbury Port Authority, City of Bunbury, Cristal Global, the Western Australian Department of Parks and Wildlife, Iluka, Millard Marine, Naturaliste Charters, Newmont Boddington Gold, South West Development Commission and WA Plantation Resources. This study was carried out with approval from the Murdoch University Animal Ethics committee (W2009/06, W2342/ 10) and was licensed by the Department of Parks and Wildlife (SF005811, SF008624). KS conceived the idea. LB conceived and obtained funding for the long-term research programme. KS carried out field work and data processing. MW carried out oceanographic data analysis. FC and KS carried out statistical analyses. KS wrote the paper with editorial input from LB, FC and MW.
ORCID http://orcid.org/0000-0002-9050-3028
Kate R. Sprogis
Fredrik Christiansen Moritz Wandres Lars Bejder
http://orcid.org/0000-0001-9090-8458 http://orcid.org/0000-0002-3063-4448
http://orcid.org/0000-0001-8138-8606
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SUPPORTING INFORMATION Additional Supporting Information may be found online in the supporting information tab for this article.
How to cite this article: Sprogis KR, Christiansen F, Wandres ~ o Southern Oscillation influences the M, Bejder L. El Nin abundance and movements of a marine top predator in coastal waters. Glob Change Biol. 2017;00:1–12. https://doi.org/10.1111/gcb.13892