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

REFERENCES Anderson, D. J., Kobryn, H. T., Norman, B. M., Bejder, L., Tyne, J. A., & Loneragan, N. R. (2014). Spatial and temporal patterns of naturebased tourism interactions with whale sharks (Rhincodon typus) at Ningaloo Reef, Western Australia. Estuarine, Coastal and Shelf Science, 148, 109–119. Arcangeli, A., & Crosti, R. (2009). The short-term impact of dolphin-watching on the behavior of bottlenose dolphins (Tursiops truncatus) in Western Australia. Journal of Marine Animals and their Ecology, 2, 3–9. ~o. Barber, R. T., & Chavez, F. P. (1983). Biological consequences of El Nin Science, 222, 1203–1210. Benson, S. R., Croll, D. A., Marinovic, B. B., Chavez, F. P., & Harvey, J. T. (2002). Changes in the cetacean assemblage of a coastal upwelling ~o 1997-98 and La Nin ~a 1999. Progress in ecosystem during El Nin Oceanography, 54, 279–291. Bond, A. L., & Lavers, J. L. (2014). Climate change alters the trophic niche of a declining apex marine predator. Global Change Biology, 20, 2100– 2107. , C., Terray, P., Barbraud, C., Bon, C., Delord, K., . . . Bost, C. A., Cotte Weimerskirch, H. (2015). Large-scale climatic anomalies affect marine predator foraging behaviour and demography. Nature Communications, 6, 8220. https://doi.org/10.1038/ncomms9220 Bureau of Meteorology (2014a). Climate glossary: Southern Oscillation Index (SOI). Melbourne, Vic., Australia: Australian Bureau of Meteorology. Retrieved from http://www.bom.gov.au/climate/glossary/soi. shtml (accessed 20 November 2014). ~o - detailed Australian analysis. Bureau of Meteorology (2014b). El Nin Melbourne, Vic., Australia: Australian Bureau of Meteorology.

9

Retrieved from http://www.bom.gov.au/climate/enso/enlist/ (accessed 20 November 2014). ~a – detailed Australian analysis. Bureau of Meteorology (2014c). La Nin Melbourne, Vic., Australia: Australian Bureau of Meteorology. Retrieved from http://www.bom.gov.au/climate/enso/lnlist/ (accessed 20 November 2014). Bureau of Meteorology (2014d). Monthly rainfall – Bunbury. Melbourne, Vic., Australia: Australian Bureau of Meteorology. Retrieved from http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccOb sCode=139&p_display_type=dataFile&p_startYear=&p_c=20046657&p_stn_num=0099 65 (accessed 20 November 2014). Bureau of Meteorology (2014e). S.O.I. (Southern Oscillation Index) archives 1876 to present. Melbourne, Vic., Australia: Australian Bureau of Meteorology. Retrieved from http://www.bom.gov.au/climate/curre nt/soihtm1.shtml (accessed 20 November 2014). Burnham, K. P., & Anderson, D. R. (2002). Model selection and multi-model inference: A practical information-theoretic approach. New York, NY: Springer. Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference – understanding AIC and BIC in model selection. Sociological Methods and Research, 33, 261–304. Cai, W., Borlace, S., Lengaigne, M., van Rensch, P., Collins, M., Vecchi, G., ~o events . . . Fei-Fei, J. (2014). Increasing frequency of extreme El Nin due to greenhouse warming. Nature Climate Change, 4, 111–116. Caputi, N., De Lestang, S., Feng, M., & Pearce, A. (2009). Seasonal variation in the long-term warming trend in water temperature off the Western Australian coast. Marine and Freshwater Research, 60, 129– 139. Caputi, N., Fletcher, W. J., Pearce, A., & Chubb, C. F. (1996). Effect of the Leeuwin Current on the recruitment of fish and invertebrates along the Western Australian coast. Marine and Freshwater Research, 47, 147–155. Caputi, N., Melville-Smith, R., de Lestang, S., Pearce, A., & Feng, M. (2010). The effect of climate change on the western rock lobster (Panulirus cygnus) fishery of Western Australia. Canadian Journal of Fisheries and Aquatic Sciences, 67, 85–96. Cheung, W. W. L., Meeuwig, J. J., Feng, M., Harvey, E., Lam, V. W. Y., Langlois, T., . . . Pauly, D. (2012). Climate-change induced tropicalisation of marine communities in Western Australia. Marine and Freshwater Research, 63, 415–427. Cullen, L. E., & Grierson, P. F. (2009). Multi-decadal scale variability in autumn-winter rainfall in south-western Australia since 1655 AD as reconstructed from tree rings of Callitris columellaris. Climate Dynamics, 33, 433–444. Dellabianca, N. A., Hohn, A. A., Goodall, R. N. P., Pousa, J. L., MacLeod, C. D., & Lima, M. (2012). Influence of climate oscillations on dentinal deposition in teeth of Commerson’s dolphin. Global Change Biology, 18, 2477–2486. R Development Core Team (2011). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Durant, J. M., Anker-Nilssen, T., Hjermann, D. Ø., & Stenseth, N. C. (2004). Regime shifts in the breeding of an Atlantic puffin population. Ecology Letters, 7, 388–394. Durant, J. M., Hjermann, D. Ø., Ottersen, G., & Stenseth, N. C. (2007). Climate and the match or mismatch between predator requirements and resource availability. Climate Research, 33, 271–283. Elliser, C. R., & Herzing, D. L. (2011). Replacement dolphins? Social restructuring of a resident pod of Atlantic bottlenose dolphins, Tursiops truncatus, after two major hurricanes. Marine Mammal Science, 27, 39–59. Evans, P. G., Pierce, G. J., & Panigada, S. (2010). Climate change and marine mammals. Journal of the Marine Biological Association of the United Kingdom, 90, 1483–1487.

10

|

Feng, J., Li, J., Yun, L., Zhu, J., & Xie, F. (2015). Relationships among the monsoon-like southwest Australian circulation, the Southern Annular Mode, and winter rainfall over southwest Western Australia. Advances in Atmospheric Sciences, 32, 1063–1076. ~a forces Feng, M., McPhaden, M. J., Xie, S.-P., & Hafner, J. (2013). La Nin unprecedented Leeuwin Current warming in 2011. Scientific Reports, 3, 1277. https://doi.org/10.1038/srep01277 Feng, M., Meyers, G., Pearce, A., & Wijffels, S. (2003). Annual and interannual variations of the Leeuwin Current at 32° S. Journal of Geophysical Research-Oceans, 108, 3355. https://doi.org/10.1029/2002jc 001763 Feng, M., Waite, A. M., & Thompson, P. A. (2009). Climate variability and ocean production in the Leeuwin Current system off the west coast of Western Australia. Journal of the Royal Society of Western Australia, 92, 67–81. Feng, M., Weller, E., & Hill, K. (2009). The Leeuwin Current. In: E. S. Poloczanska, A. J. Hobday, & A. J. Richardson (Eds.), A marine climate change impacts and adaptation report card for Australia 2009 (pp. 1– 11). Wembley, WA: NCCARF Publication 05/09. ISBN 978-1921609-03-9. Fiedler, P. C., & Reilly, S. B. (1994). Interannual variability of dolphin habitats in the eastern tropical Pacific. II: Effects on abundances estimated from tuna vessel sightings, 1975–1990. Fishery Bulletin, 92, 451–463. Forcada, J., Trathan, P. N., Reid, K., & Murphy, E. J. (2005). The effects of global climate variability in pup production of Antarctic fur seals. Ecology, 86, 2408–2417. Fury, C. A., & Harrison, P. L. (2011). Impact of flood events on dolphin occupancy patterns. Marine Mammal Science, 27, E185–E205. Fury, C. A., & Reif, J. S. (2012). Incidence of poxvirus-like lesions in two estuarine dolphin populations in Australia: Links to flood events. Science of the Total Environment, 416, 536–540. Gillanders, B. M., Elsdon, T. S., Halliday, I. A., Jenkins, G. P., Robins, J. B., & Valesini, F. J. (2011). Potential effects of climate change on Australian estuaries and fish utilising estuaries: A review. Marine and Freshwater Research, 62, 1115–1131. Gillett, N. P., Kell, T. D., & Jones, P. D. (2006). Regional climate impacts of the Southern Annular Mode. Geophysical Research Letters, 33, L23704. Gong, D. Y., & Wang, S. W. (1999). Definition of Antarctic Oscillation Index. Geophysical Research Letters, 26, 459–462. Greene, C. H., & Pershing, A. J. (2004). Climate and the conservation biology of North Atlantic right whales: The right whale at the wrong time? Frontiers in Ecology and the Environment, 2, 29–34. Groom, C., & Coughran, D. (2012). Three decades of cetacean strandings in Western Australia: 1981 to 2010. Journal of the Royal Society of Western Australia, 95, 63–76. Hanson, C. E., Pattiaratchi, C. B., & Waite, A. M. (2005). Seasonal production regimes off south-western Australia: Influence of the Capes and Leeuwin Currents on phytoplankton dynamics. Marine and Freshwater Research, 56, 1011–1026. H€ aussermann, V., Gutstein, C. S., Bedington, M., Cassis, D., Olavarria, C., € rsterra, G. (2017). Largest baleen whale mass morDale, A. C., . . . Fo ~o event is likely related to harmful toxic tality during strong El Nin algal bloom. PeerJ, 5, e3123. Hobday, A. J., & Lough, J. M. (2011). Projected climate change in Australian marine and freshwater environments. Marine and Freshwater Research, 62, 1000–1014. Hoegh-Guldberg, O., & Bruno, J. F. (2010). The impact of climate change on the world’s marine ecosystems. Science, 328, 1523–1528. Holyoake, C., Finn, H., Stephens, N., Duignan, P., Salgado, C., Smith, H., . . . McElligott, D. (2010). Technical report on the bottlenose dolphin (Tursiops aduncus) unusual mortality event within the Swan Canning Riverpark, June-October 2009 (pp. 190). Murdoch: Murdoch University. Hughes, B. B., Beas-Luna, R., Barner, A. K., Brewitt, K., Brumbaugh, D. R., Cerny-Chipman, E. B., . . . Carr, M. H. (2017). Long-term studies

SPROGIS

ET AL.

contribute disproportionately to ecology and policy. BioScience, 67, 271–281. IPCC (2014). Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. In Core Writing Team RKPaLaME (Ed.), Report of the Intergovernmental Panel on Climate Change (pp. 151). Geneva: IPCC. Jensen, F. H., Bejder, L., Wahlberg, M., Soto, N. A., Johnson, M., & Madsen, P. T. (2009). Vessel noise effects on delphinid communication. Marine Ecology Progress Series, 395, 161–175. Jentsch, A., Kreyling, J., & Beierkuhnlein, C. (2007). A new generation of climate-change experiments: Events, not trends. Frontiers in Ecology and the Environment, 5, 365–374. Kerr, R. A. (2011). Humans are driving extreme weather; time to prepare. Science, 334, 1040–1040. Leaper, R., Cooke, J., Trathan, P., Reid, K., Rowntree, V., & Payne, R. (2006). Global climate drives southern right whale (Eubalaena australis) population dynamics. Biology Letters, 2, 289–292. Learmonth, J. A., MacLeod, C. D., Santos, M. B., Pierce, G. J., Crick, H. Q. P., & Robinson, R. A. (2006). Potential effects of climate change on marine mammals. Oceanography and Marine Biology: An Annual Review, 44, 431–464. Lenanton, R. C., Joll, L., Penn, J., & Jones, K. (1991). The influence of the Leeuwin Current on coastal fisheries of Western Australia. Journal of the Royal Society of Western Australia, 74, 101–114. Lusseau, D., Williams, R., Wilson, B., Grellier, K., Barton, T. R., Hammond, P. S., & Thompson, P. M. (2004). Parallel influence of climate on the behaviour of Pacific killer whales and Atlantic bottlenose dolphins. Ecology Letters, 7, 1068–1076. €tzen, Manlik, O., McDonald, J. A., Mann, J., Smith, H. C., Bejder, L., Kru M., . . . Sherwin, W. B. (2016). The relative importance of reproduction and survival for the conservation of two dolphin populations. Ecology and Evolution, 6, 3496–3512. ~o event recorded in dentiManzanilla, S. R. (1989). The 1982–1983 El Nin nal growth layers in teeth of Peruvian dusky dolphins (Lagenorhynchus obscurus). Canadian Journal of Zoology, 67, 2120–2125. Marshall, G. J. (2003). Trends in the Southern Annular Mode from observations and reanalyses. Journal of Climate, 16, 4134–4143. McCluskey, S. M., Bejder, L., & Loneragan, N. R. (2016). Dolphin prey availability and calorific value in an estuarine and coastal environment. Frontiers in Marine Science, 3(30). https://doi.org/10.3389/fma rs.2016.00030 Miller, L. J., Mackey, A. D., Hoffland, T., Solangi, M., & Kuczaj, S. A. II (2010). Potential effects of a major hurricane on Atlantic bottlenose dolphin (Tursiops truncatus) reproduction in the Mississippi Sound. Marine Mammal Science, 26, 707–715. NERC (2015). An observation-based Southern Hemisphere Annular Mode Index. Retrieved from http://www.nerc-bas.ac.uk/icd/gjma/sam.html Neumann, D. R. (2001). Seasonal movements of short-beaked common dolphins (Delphinus delphis) in the north-western Bay of Plenty, New ~o/La Nin ~a. Zealand: Influence of sea surface temperature and El Nin New Zealand Journal of Marine and Freshwater Research, 35, 371– 374. Nguyen, H. M., Rountrey, A. N., Meeuwig, J. J., Coulson, P. G., Feng, M., Newman, S. J., . . . Meekan, M. G. (2015). Growth of a deep-water, predatory fish is influenced by the productivity of a boundary current system. Scientific Reports, 5, 9044. http://www.nature.com/srep/ 2015/150312/srep09044/abs/srep09044.html - supplementaryinformation Ong, J. J. L., Rountrey, A. N., Meeuwig, J., Newman, S., Zinke, J., & Meekan, M. (2015). Contrasting environmental drivers of adult and juvenile growth in a marine fish: Implications for the effects of climate change. Scientific Reports, 5, 10859. Ong, J. J. L., Rountrey, A. N., Zinke, J., Meeuwig, J. J., Grierson, P. F., O’Donnell, A. J., . . . Meekan, M. G. (2016). Evidence for climate-

SPROGIS

ET AL.

driven synchrony of marine and terrestrial ecosystems in northwest Australia. Global Change Biology, 22, 2776–2786. Parmesan, C., & Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37–42. http://www.nature.com/nature/journal/v421/n6918/suppinfo/nature 01286_S1.html Pattiaratchi, C. B., & Buchan, S. J. (1991). Implications of long-term climate change for the Leeuwin Current. Journal of the Royal Society of Western Australia, 74, 133–140. Pearce, A., & Feng, M. (2007). Observations of warming on the Western Australian continental shelf. Marine and Freshwater Research, 58, 914–920. Pearce, A. F., & Feng, M. (2013). The rise and fall of the “marine heat wave” off Western Australia during the summer of 2010/2011. Journal of Marine Systems, 111–112, 139–156. Pearce, A. F., & Griffiths, R. W. (1991). The mesoscale structure of the Leeuwin Current - a comparison of laboratory models and satellite imagery. Journal of Geophysical Research – Oceans, 96, 16739–16757. Pearce, A., & Pattiaratchi, C. (1999). The Capes Current: A summer countercurrent flowing past Cape Leeuwin and Cape Naturaliste, Western Australia. Continental Shelf Research, 19, 401–420. Pearce, A. F., & Phillips, B. F. (1988). ENSO events, the Leeuwin Current, and larval recruitment of the western rock lobster. Journal Du Conseil, 45, 13–21. Pollock, K. (1982). A capture-recapture design robust to unequal probability of capture. Journal of Wildlife Management, 46, 752–757. Potter, I. C., Tiivel, D., Valesini, F. J., & Hyndes, G. A. (1997). Comparisons between the ichthyofaunas of a temperate lagoonal-like estuary and the embayment into which that estuary discharges. International Journal of Salt Lake Research, 5, 337–358. Raut, B. A., Jakob, C., & Reeder, M. J. (2014). Rainfall changes over southwestern Australia and their relationship to the Southern Annular Mode and ENSO. Journal of Climate, 27, 5801–5814. Reilly, S. B., & Fiedler, P. C. (1994). Interannual variability of dolphin habitats in the eastern tropical pacific.1. Research vessel surveys, 1986– 1990. Fishery Bulletin, 92, 434–450. Rosel, P. E., Mullin, K. D., Garrison, L., Schwacke, L., Adams, J., Balmer, B., . . . Zolman, E. (2011). Photo-identification capture-mark-recapture. In Southeast Fisheries Science Center (U.S.) (Ed.), Techniques for estimating abundance of bay, sound and estuary populations of bottlenose dolphins along the U.S. east coast and Gulf of Mexico: A workshop report (pp. 30). La Jolla, CA: NOAA Technical Memorandum.  mez-Gallardo, U. A., Najera-Caballero, M., UrbanSalvadeo, C. J., Go Ramirez, J., & Lluch-Belda, D. (2015). The effect of climate variability on gray whales (Eschrichtius robustus) within their wintering areas. PLoS One, 10, e0134655. Samuel, J. M., Verdon, D. C., Sivapalan, M., & Franks, S. W. (2006). Influence of Indian Ocean sea surface temperature variability on southwest Western Australian winter rainfall. Water Resources Research, 42, W08402. https://doi.org/10.1029/2005WR004672 Schumann, N., Gales, N. J., Harcourt, R. G., & Arnould, J. P. Y. (2013). Impacts of climate change on Australian marine mammals. Australian Journal of Zoology, 61, 146–159. Simmonds, M. P., & Isaac, S. J. (2007). The impacts of climate change on marine mammals: Early signs of significant problems. Oryx, 41, 19–26. Smale, D. A., & Wernberg, T. (2012). Ecological observations associated with an anomalous warming event at the Houtman Abrolhos Islands, Western Australia. Coral Reefs, 31, 441–441. Smith, M. D. (2011). An ecological perspective on extreme climatic events: A synthetic definition and framework to guide future research. Journal of Ecology, 99, 656–663. Smith, H. C. (2012). Population dynamics and habitat use of bottlenose dolphins (Tursiops aduncus), Bunbury, Western Australia. Ph.D. thesis, Murdoch University, Perth, WA, 180 pp.

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re, C., Kobryn, H., & Bejder, L. (2016). Dolphin sociality, disSmith, H., Fre tribution and calving as important behavioural patterns informing management. Animal Conservation, 19, 462–471. Smith, H. C., Pollock, K., Waples, K., Bradley, S., & Bejder, L. (2013). Use of the robust design to estimate seasonal abundance and demographic parameters of a coastal bottlenose dolphin (Tursiops aduncus) population. PLoS One, 8, e76574. Smith, H. C., & Sprogis, K. R. (2016). Seasonal feeding on giant cuttlefish (Sepia apama) by Indo-Pacific bottlenose dolphins (Tursiops aduncus) in south-western Australia. Australian Journal of Zoology, 64, 8–13. Sprogis, K. R. (2015). Sex-specific patterns in abundance, home ranges and habitat use of Indo-Pacific bottlenose dolphins (Tursiops aduncus) in south-western Australia. Ph.D. thesis, Murdoch University, Perth, Western Australia, 168 pp. Sprogis, K. R., Pollock, K. H., Raudino, H. C., Allen, S. J., Kopps, A. M., Manlik, O., . . . Bejder, L. (2016). Sex-specific patterns in abundance, temporary emigration and survival of Indo-Pacific bottlenose dolphins (Tursiops aduncus) in coastal and estuarine waters. Frontiers in Marine Science, 3(12). https://doi.org/10.3389/fmars.2016.00012 Sprogis, K. R., Raudino, H. C., Hocking, D., & Bejder, L. (2017). Complex prey handling of octopus by bottlenose dolphins (Tursiops aduncus). Marine Mammal Science, 33, 934–945. Sprogis, K. R., Raudino, H. C., Rankin, R., MacLeod, C. D., & Bejder, L. (2016). Home range size of adult Indo-Pacific bottlenose dolphins (Tursiops aduncus) in a coastal and estuarine system is habitat and sex-specific. Marine Mammal Science, 32, 287–308. Stenseth, N. C., Mysterud, A., Ottersen, G., Hurrell, J. W., Chan, K. S., & Lima, M. (2002). Ecological effects of climate fluctuations. Science, 297, 1292–1296. Stenseth, N., Ottersen, G., Hurrell, J. W., Mysterud, A., Lima, M., Chan, K., . . .  Adlandsvik, B. (2003). Review article. Studying climate effects on ecology through the use of climate indices: The North Atlantic ~o Southern Oscillation and beyond. Proceedings of Oscillation, El Nin the Royal Society of London B: Biological Sciences, 270, 2087–2096. Stephens, N., Duignan, P. J., Wang, J., Bingham, J., Finn, H., Bejder, L., . . . Holyoake, C. (2014). Cetacean morbillivirus in coastal Indo-Pacific bottlenose dolphins, Western Australia. Emerging Infectious Diseases, 20, 666–670. Surman, C. A., & Nicholson, L. W. (2009). The good, the bad and the ugly: ENSO driven oceanographic variability and its influence on seabird diet and reproductive performance at the Houtman Abrolhos, Eastern Indian Ocean. Marine Ornithology, 37, 129–138. Tershy, B. R., Breese, D., & Alvarezborrego, S. (1991). Increase in cetacean ~o-Southand seabird numbers in the Canal de Ballenas during an El Nin ern Oscillation event. Marine Ecology Progress Series, 69, 299–302. Thompson, D. W. J., & Wallace, J. M. (2000). Annular modes in the extratropical circulation. Part I: Month-to-month variability. Journal of Climate, 13, 1000–1016. Thomson, J. A., Burkholder, D. A., Heithaus, M. R., Fourqurean, J. W., Fraser, M. W., Statton, J., & Kendrick, G. A. (2014). Extreme temperatures, foundation species, and abrupt ecosystem change: An example from an iconic seagrass ecosystem. Global Change Biology, 21, 1463–1474. ~o. Bulletin of the American Trenberth, K. E. (1997). The definition of El Nin Meteorological Society, 78, 2771–2777. Tsai, Y. J. J., & Mann, J. (2013). Dispersal, philopatry, and the role of fission-fusion dynamics in bottlenose dolphins. Marine Mammal Science, 29, 261–279. Urb an, J., & Ludwig, S. (2003). Abundance and mortality of gray whales ~o and the at Laguna San Ignacio, Mexico, during the 1997–98 El Nin ~a. 1998–99 La Nin Veale, L., Tweedley, J. R., Clarke, K. R., Hallett, C. S., & Potter, I. C. (2014). Characteristics of the ichthyofauna of a temperate microtidal estuary with a reverse salinity gradient, including inter-decadal comparisons. Journal of Fish Biology, 85, 1320–1354.

12

|

Walther, G., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J. C., . . . Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416, 389–395. ~o Wang, C., Deser, C., Yu, J.-Y., DiNezio, P., & Clement, A. (2017). El Nin and Southern Oscillation (ENSO): A review. In: P. W. Glynn, D. P. Manzello & I. C. Enochs (Eds.), Coral reefs of the eastern tropical pacific: Persistence and loss in a dynamic environment (pp. 85–106). Dordrecht: Springer. Wang, C., & Fiedler, P. C. (2006). ENSO variability and the eastern tropical Pacific: A review. Progress in Oceanography, 69, 239–266. Wells, R. S., Hansen, L. J., Baldridge, A., Dohl, T. P., Kelly, D. L., & Defran, R. H. (1990). Northward expansion of the range of the bottlenose dolphins along the California coast. In S. R. Leatherwood (Eds.), The Bottlenose Dolphin (pp. 421–431). San Diego, CA: Academic Press, Inc. Wernberg, T., Smale, D. A., Tuya, F., Thomsen, M. S., Langlois, T. J., de Bettignies, T., . . . Rousseaux, C. S. (2013). An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nature Climate Change, 3, 78–82. http://www.nature.com/nclimate/ journal/v3/n1/abs/nclimate1627.html - supplementary-information White, G. C., & Burnham, K. P. (1999). Program MARK: Survival estimation from populations of marked animals. Bird Study, 46, 120–139. Wilson, S. G., Taylor, J. G., & Pearce, A. F. (2001). The seasonal aggregation of whale sharks at Ningaloo Reef, Western Australia: Currents, ~o/Southern oscillation. Environmental Biology migrations and the El Nin of Fishes, 61, 1–11.

SPROGIS

ET AL.

€rsig, B., & Wu €rsig, M. (1977). The photographic determination of Wu group size, composition, and stability of coastal porpoises (Tursiops truncatus). Science, 198, 755–756.

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

El Niño Southern Oscillation influences the abundance and movements of a marine top predator in coastal waters.

Large-scale climate modes such as El Niño Southern Oscillation (ENSO) influence population dynamics in many species, including marine top predators. H...
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