estimating latitudinal variability in extreme heat stress on rocky intertidal shores

ORIGINALARTICLE

Estimating latitudinal variabilityin extreme heat stress on rockyintertidal shoresJustin A. Lathlean*, David J. Ayre and Todd E. Minchinton

Institute for Conservation Biology and

Environmental Management & School of

Biological Sciences, University of Wollongong,

Wollongong NSW 2522, Australia

*Correspondence: Justin A. Lathlean, Institute

for Conservation Biology and Environmental

Management & School of Biological Sciences,

University of Wollongong, NSW 2522,

Australia

E-mail: [email protected]

ABSTRACT

Aim Broad-scale patterns of heat stress play an important role in shaping the

geographical distributions of many species and may differ from large-scale

changes in average temperatures. For species living on rocky intertidal shores

extreme heat stress occurs when hot dry aerial conditions coincide with midday

low tides. We used empirical and modelled temperature data, and estimates of

cumulative aerial exposure and solar radiation, in order to test the hypothesis

that heat stress on Australian rocky intertidal shores decreases with increasing

latitude.

Location Rocky intertidal shores of south-eastern Australia spanning

> 1500 km and 13° of latitude (26°24023″ S to 39°07047″ S).

Methods In situ temperature measurements, hourly tidal elevations and daily

solar radiation taken over three consecutive summers (December 2009–Febru-

ary 2012) were used to quantify latitudinal variability in extreme heat stress,

cumulative aerial exposure and solar radiation, respectively. Comparisons

between hourly in situ temperatures and meteorological data were used to pro-

duce a large-scale statistical model capable of estimating intertidal substratum

temperatures during daytime low-tides, which was then extrapolated across 22

locations.

Results Heat stress estimated using in situ loggers deployed across five east

coast locations typically did not decline with increasing latitude and neither

did midday exposure or solar radiation. The meteorological model proved to

be a successful method for estimating rocky shore heat stress and in contrast

to the empirical data displayed strong latitudinal trends in mean daily maxima

and cumulative heat stress. Modelled acute heat stress (i.e. summer maxima),

however, did not decline with increasing latitude, as there was greater thermal

variability at higher latitudes.

Main conclusions The meteorological model developed in this study repre-

sents a useful approach for estimating broad-scale patterns of heat stress on

rocky intertidal shores. Results also indicate that latitudinal patterns of acute

and chronic heat stress may differ from average temperatures, which are com-

monly assumed to decline with increasing latitude. Such broad-scale patterns

of thermal stress as described in this study will significantly contribute to our

ability to understand the impact of climate change on vulnerable rocky inter-

tidal communities.

Keywords

Biogeography, climate change, climatology, ecological forecasting, heat stress,

intertidal zone, south-eastern Australia, temperature.

1478 http://wileyonlinelibrary.com/journal/jbi ª 2014 John Wiley & Sons Ltddoi:10.1111/jbi.12311

Journal of Biogeography (J. Biogeogr.) (2014) 41, 1478–1491

INTRODUCTION

Temperature has long been recognized as a major factor influ-

encing broad-scale patterns of distribution and abundance

(Shelford, 1911; Grinnell, 1917; Orton, 1920) because it affects

virtually all physiological processes (Helmuth, 2009).

Although ecologists have typically been concerned with quan-

tifying large-scale changes in average temperatures and the

associated effects on biological communities, more recent

attention has been given to understanding whether infrequent

extreme heat events produce longer lasting impacts than con-

tinual gradual change (Gaines & Denny, 1993; Denny et al.,

2009; Harley & Paine, 2009; Wethey et al., 2011b). This ques-

tion has become increasingly important, as global climate

change is expected to increase the number and frequency of

extreme weather events, reducing the time ecosystems will

have to recover from such disturbances (Bertness et al., 2002).

Despite this, very few studies have attempted to quantify

broad-scale patterns of extreme heat stress to determine the

relative importance of local and regional influences in struc-

turing geographical patterns of temperature variation.

Rocky intertidal ecosystems have emerged as excellent

study systems in which to investigate the effects of extreme

heat events on biological populations and communities (Hel-

muth et al., 2006b; Denny et al., 2009; Harley & Paine, 2009;

Wethey et al., 2011b; Lathlean et al., 2012). Living at the

interface between the marine and terrestrial environment,

rocky intertidal organisms experience dramatic fluctuations

in daily temperature, at times in excess of 30 °C in less than

12 h (Firth & Williams, 2009; Lathlean et al., 2011). These

daily fluctuations in temperature are primarily driven by the

tidal cycle (Helmuth et al., 2006b). During aerial exposure

an organism’s body temperature will be the product of sev-

eral climatic and non-climatic factors, including most nota-

bly solar elevation and intensity, air temperature, wind

speed, wave height and humidity (Helmuth et al., 2011).

These factors can vary over biogeographical scales to produce

complex spatial and temporal patterns in thermal variability.

For example, Helmuth et al. (2002, 2006a) demonstrated

along the west coast of the USA that, as a result of spatial

variability in the number of midday low tides in summer,

body temperatures of the mussel Mytilus californianus were

unexpectedly higher in cooler northern locations than in war-

mer southern locations. Likewise, using only variability in tidal

patterns and solar elevation, Mislan et al. (2009) produced a

simple model which predicted that thermal stress on rocky

intertidal shores along the west coast of the USA would vary

independently of latitude in the coming decade. Such studies

contradict the widely reported inverse relationship between tem-

perature and latitude (Sorte & Hofmann, 2004; Schoch et al.,

2006; Jones et al., 2010). They also challenge the common bio-

geographical assumption that across a species’ distribution indi-

viduals situated at higher latitudes will experience lower

temperatures and reduced heat stress (Sagarin & Gaines, 2002).

Sea-surface temperatures along the east coast of Australia

typically decline with increasing latitude due to the weaken-

ing East Australia Current (EAC) as it flows from the Coral

Sea in Queensland to the Tasman Sea in New South Wales

and Victoria (Lough, 2009). It remains unclear, however,

whether such a latitudinal gradient in sea temperatures man-

ifests on rocky intertidal seashores. Indeed, preliminary

research within this region reveals that rocky intertidal shores

separated by approximately 400 km can experience equiva-

lent maximum temperatures during low tide (Lathlean et al.,

2011). Such geographical differences in extreme heat stress

may be a result of variability in the number of midday low

tides and solar radiation (Mislan et al., 2009). If so, the

future response of rocky intertidal communities in this

region to climate change might be substantially different

from that of their subtidal counterparts.

Data from meteorological stations located close to rocky

shores are often used to predict the thermal variability experi-

enced by rocky intertidal organisms (Helmuth et al., 2011; Mi-

slan & Wethey, 2011; Wethey et al., 2011a; Denny & Dowd,

2012). However, our research in south-eastern Australia has

shown that simply using air temperatures recorded by coastal

weather stations is a poor predictor of extreme thermal stress

on rocky intertidal shores (Lathlean et al., 2011). More accurate

predictions generally incorporate several additional parameters

such as relative humidity, wind speed, precipitation and solar

radiation (see Wethey et al., 2011a). Such meteorological data

are often readily available because they are used for weather

forecasting, and this is particularly true along the south-eastern

coast of Australia where hundreds of weather stations are situ-

ated within 5 km of the coast and have been in operation for

more than 20 years (Australian Bureau of Meteorology, 2012).

If shown to be an effective surrogate, such meteorological data

could be used to estimate large-scale thermal variability of rocky

intertidal shores along south-eastern Australia and identify

rocky shore communities vulnerable to climate change.

The aim of this study was to use a combination of empirical

and modelled temperature data, along with estimates of midday

exposure and solar radiation, to quantify latitudinal variability in

heat stress on rocky intertidal shores of south-eastern Australia.

First, we analysed in situ temperature measurements for five

locations spanning more than 1200 km of coastline, in order to

test the hypothesis that rocky intertidal heat stress decreases with

increasing latitude. Second, we quantified temporal and latitudi-

nal variability in midday exposure and solar radiation to test

whether patterns of heat stress reflect the coincidence of high lev-

els of solar radiation during midday low tides. We then tested

the capacity of a meteorological model to predict rocky intertidal

heat stress and extrapolate to other sites along the shore.

MATERIALS AND METHODS

Quantifying latitudinal patterns of heat stress

In situ temperature measurements

To assess latitudinal variability in rocky intertidal heat stress,

two TidbiT� v2 Temp data loggers (Onset Stowaway logger,

Journal of Biogeography 41, 1478–1491ª 2014 John Wiley & Sons Ltd

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Latitudinal variability in heat stress

model UTBI-001, accuracy � 0.2 °C) were deployed 10 to

15 m apart within the mid-intertidal zone (0.8–1 m above

mean low water mark) across five locations spanning

1200 km and 9° of latitude: Cape Byron (28°38012″ S,

153°38023″ E) (site no. 10), Port Macquarie (31°27055″ S,

152°56018″ E) (site no. 20), Garie Beach (34°11019″ S,

151°03059″ E) (site no. 27), Bermagui (36°25022″ S,

150°04056″ E) (site no. 36), and Mallacoota (37°34026″ S,

149°45056″ E) (site no. 42; Fig. 1). These five locations are

linearly arrayed along the east coast of Australia and were

selected as a test for latitudinal variation. Loggers were also

deployed across two additional locations in southern Victo-

ria: Cape Paterson (38°40028″ S, 145°38000″ E) (site no. 51)

and Kilcunda (38°33003″ S, 145°28008″ E) (site no. 52).

These were included to examine differences in heat stress

along a different coastline at approximately the same latitude

(Fig. 1). From November 2009 to March 2012 each logger

continuously recorded ambient temperatures at 10-min

intervals and was attached to horizontal emergent rocky sub-

strata to reduce temperature variability caused by small-scale

topographic heterogeneity (Harley, 2008; Denny et al., 2011).

Consequently, temperatures recorded by these data loggers

most likely reflect that of the underlying rocky substratum

(hereafter referred to as ‘substratum temperature’). Whilst

these substratum temperatures may not directly represent the

internal body temperatures of any specific species, they can

broadly be seen as an approximate estimate for numerous

species that are usually in direct contact with the primary

substrata (e.g. barnacles, limpets, chitons, whelks). Our pre-

vious comparisons using both biomimetic loggers and infra-

red imagery confirm that substratum temperatures, measured

by unmodified Tidbit� loggers, significantly influence the

body temperatures of a range of both adult invertebrates and

early settlers (see Lathlean et al., 2012, 2013). Substratum

temperatures recorded by the two loggers at each location

were shown to typically vary by less than 1 °C and were

therefore averaged to produce a single value for each 10-min

interval.

We focused our analyses on summer months (December

to February) because they constitute the period with the

highest probability of rocky intertidal shores in this region

experiencing extreme heat events (Lathlean et al., 2011).

Therefore, characterization of latitudinal substratum temper-

ature variability consisted of equivalent sampling intervals

over 3 years: (1) 1 December 2009 to 28 February 2010; (2)

1 December 2010 to 28 February 2011; and (3) 1 December

2011 to 28 February 2012. The 29 February 2012 was

excluded from analyses to standardize the number of days

across the three sampling intervals. For each of these inter-

vals, we compared acute heat stress (i.e. the absolute maxi-

mum substratum temperature), chronic heat stress (i.e. mean

daily maximum substratum temperature) and cumulative

heat stress at each location. Cumulative heat stress was

defined as the number of hours spent above 30 °C or 35 °C.The selection of these temperature thresholds was somewhat

arbitrary because no information exists on the physiological

tolerances of rocky intertidal organisms in south-eastern

Australia. Nevertheless, previous studies undertaken at simi-

lar latitudes in the Northern Hemisphere have shown such

temperatures to represent thermally stressful conditions for a

range of intertidal species (Helmuth, 1998; Denny et al.,

2006). Owing to damaged or malfunctioning loggers no data

could be acquired for Bermagui during the summer of 2009

and 2010 or for Mallacoota during the summer of 2011 and

2012. For each of the three separate sampling intervals Spear-

man rank correlations were used to test the hypothesis that

rocky intertidal heat stress decreases with increasing latitude.

Cumulative aerial exposure and solar radiation

With the seven locations being spread across a broad geo-

graphical region (> 1500 km), we expected temperature dif-

ferences among rocky intertidal shores to reflect underlying

latitudinal patterns in the tidal cycle and or levels of solar

radiation (Helmuth et al., 2002). To separately test whether

aerial exposure of low intertidal regions and solar radiation

vary with latitude we used tidal data from 15 tidal gauges

and daily solar radiation data from 17 weather stations rang-

Figure 1 Map of the study region in south-eastern Australia,illustrating the location of numerous temperature data loggers,

weather stations and tidal gauges used to characterizebiogeographical variability in rocky intertidal temperatures. See

Appendix S1 for the name of the location associated with eachnumber.


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J. A. Lathlean et al.

ing from Noosa Heads in southern Queensland to Point

Lonsdale in southern Victoria (Fig. 1, and see Appendix S1

in Supporting Information). Previous studies have primarily

focused on characterizing midday exposure (Helmuth et al.,

2002; Finke et al., 2007; Mislan et al., 2009) as this is consid-

ered the most thermally stressful time of day. Whilst preli-

minary analyses confirmed in situ temperatures were

typically greater during the middle of the day we character-

ized latitudinal variability in aerial exposure for three distinct

times of the day: morning (09:00 h to 11:00 h), midday

(11:00 h to 13:00 h) and afternoon (13:00 h to 15:00 h),

because of the potential bias against certain locations that

may experience more stressful mid-morning and mid-after-

noon low tides. Similar to Helmuth et al. (2002) and Finke

et al. (2007), we characterized latitudinal variability in aerial

exposure as the cumulative number of hours over which

low- and mid-intertidal regions of rocky intertidal shores are

exposed to aerial conditions. We defined periods of aerial

exposure for low intertidal regions as periods when the

height of the tide was within the lower 25% of the tidal

range. Likewise, periods of aerial exposure within mid-inter-

tidal regions were identified when the height of the tide was

within the lower 50% of the tidal range. This approach is

particularly useful for linking tidal patterns with biological

processes because low tides during summer represent times

of greatest risk from extreme heat events for intertidal organ-

isms (Mislan et al., 2009; Lathlean et al., 2011).

We quantified latitudinal patterns of solar radiation by

comparing summer maximum and mean daily radiation mea-

sured by the 17 weather stations for each of the three consecu-

tive summers separately (n = 90 days). In the majority of

cases, daily solar radiation was measured from weather stations

situated within 1 km from the coastline except for solar radia-

tion captured by weather stations at Pambula (3.2 km; site no.

38), Bass (6.5 km; site no. 50) and Wonthaggi (6.0 km; site

no. 49) (Fig. 1, Appendix S1). However, these differences are

unlikely to substantially affect latitudinal patterns of solar radi-

ation because the two main processes influencing geographical

variability in solar radiation are cloud cover and solar eleva-

tion. Pearson correlation analysis was used to test whether

both periods of midday exposure and solar radiation varied

with latitude during each of the three consecutive summers.

Modelling latitudinal patterns of heat stress

In situ measurements and meteorological data from four

locations (i.e. Cape Byron, Port Macquarie, Mallacoota and

Cape Paterson) were used to develop a predictive model of

the relationship between various meteorological parameters

and rocky intertidal substratum temperatures (Fig. 1, Appen-

dix S1). These four locations span the full extent of the study

region (> 1500 km) and were included within a single multi-

ple linear regression to produce a standardized equation

capable of estimating rocky intertidal temperatures across a

broad geographical area. Of the seven locations where in situ

temperatures were recorded, these four were chosen because

of their close proximity to coastal weather stations. The stan-

dardized equation more accurately predicted in situ tempera-

tures at Cape Byron, Port Macquarie and Mallacoota when

meteorological data from Cape Paterson were excluded from

the multiple linear regression. This modified model was sub-

sequently used to estimate rocky intertidal temperatures for

all locations from Noosaville in southern Queensland to Wil-

sons Promontory in eastern Victoria, whilst a similar model

was produced for Cape Paterson using meteorological data

derived from a single nearby weather station.

Meteorological parameters initially included as model pre-

dictors were hourly (1) air temperature, (2) wind speed, (3)

wind direction (calculated as the average speed/direction

during the 10 minutes prior to the time of observation), (4)

percentage relative humidity, (5) precipitation (mm), (6)

tidal height (m), and (7) solar elevation (i.e. variation in the

angle of the sun at different times of the day and at different

latitudes – provided by Geosciences Australia, http://www.ga.

gov.au/geodesy/astro/smpos.jsp) – and daily solar radiation

(MJ/m2). Preliminary forward stepwise regressions (SPSS Sta-

tistical Software package) revealed that wind speed, wind

direction and precipitation did not significantly increase the

explanatory power of the model and were therefore omitted

from all subsequent analyses. Thus, the fitted regression line

took the following form:

RSi ¼ b0 þ b1Ai þ b2THi þ b3RHi þ b4DSRi þ b5SEi þ ei

where RSi is the value of rocky shore temperatures measured

by in situ data loggers at time i; b0 is the y-intercept; b1 to b5represents the partial regression coefficients for each of the

meteorological parameters; Ai represents air temperature

recorded by weather station at time i; THi is the height of the

tide in metres above a reference datum point at time i; RHi is

percentage relative humidity at time i; DSRi is the level of

daily solar radiation at time i; SEi represents solar elevation in

altitude degrees at time i; and ɛi represents the unexplained

error associated with the i-th observation. Hourly DSR values

in equation 1 were assigned a single daily value because daily

solar radiation was calculated as the total solar energy falling

on a horizontal surface over a 24-h period. Analyses were re-

run using arcsine transformations of solar elevation and rela-

tive humidity in an attempt to improve the model but such

modifications did not alter the fit of the model to the data.

To check for collinearity between meteorological parameters

we examined the tolerance levels associated with each predic-

tor within the model. Tolerance levels for all predictors were

found to be greater than 0.6, indicating weak collinearity.

Additional analyses that included interactive effects of two or

more predictors provided only a very slight improvement to

the power of the model (i.e. R2 value increased from 0.6 to

0.63) and therefore were not necessary to accurately estimate

substratum temperatures.

We were primarily concerned with temperatures recorded

during daytime low tides as most extreme heating events on

rocky intertidal shores occur during times of aerial exposure


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in the middle of the day (Denny et al., 2009; Mislan et al.,

2009). Therefore, we only considered data recorded between

06:00 and 18:00 h and when substrata within the upper two-

thirds of a location’s tidal range was exposed. To identify

periods of daytime aerial exposure we used tidal data col-

lected from 15 tidal gauges ranging from Tweed Heads in

northern New South Wales (28°11000″ S, 153°33000″ E) to

Point Lonsdale in southern Victoria (38°16000″ S,

144°36000″ E) (Fig. 1, Appendix S1). Tidal data collected

from gauges reported tidal height above a reference datum

point every 6 to 15 minutes (depending on the location) and

was obtained via either the Manly Hydraulics Laboratory

(http://www.mhl.nsw.gov.au/) or the National Tidal Centre

(http://www.bom.gov.au/oceanography/projects/ntc/). Rocky

intertidal shores within this region are influence by mixed

semi-diurnal tides and thus locations experience at least one

daytime low-tide each 24 h.

Accuracy of the model

To test the accuracy of the meteorological model in estimat-

ing extreme heat stress we undertook linear regressions com-

paring daily maximum substratum temperatures recorded by

in situ loggers to those estimated using the meteorological

parameters at each of the four locations. We also used linear

regressions to compare daily cumulative heat stress (i.e.

hours spent over 30 °C) calculated using in situ loggers and the

meteorological model. In addition, we assessed the ability of the

meteorological model to accurately predict extreme heat events

by calculating the percentage of extreme heat events recorded by

in situ loggers that were accurately predicted by the model

across the four locations during each sampling interval. An

extreme heat event was defined as any period where tempera-

tures remained above 30 °C for 2 h or more.

Extrapolation using the model

Using the meteorological model, and meteorological data

from 22 coastal weather stations spanning more than

1500 km, we estimated latitudinal variability in maximum

temperatures, mean daily maximum temperatures and cumu-

lative heat stress during summer low tides from the Sunshine

Coast in southern Queensland (26°38059″ S, 153°05059″ E)

to Cape Paterson in southern Victoria (38°40028″ S,

145°38000″ E) (Fig. 1). For each sampling interval Pearson

correlation analysis was used to test the relationship between

the various measures of heat stress (i.e. absolute maxima,

mean daily maxima and cumulative heat stress) and latitude.

RESULTS

Latitudinal heat stress – in situ temperatures

Substratum temperatures recorded by in situ loggers located

along the five east coast locations typically did not display

significant large-scale patterns of heat stress consistent with

the expected latitudinal decline (Fig. 2, Table 1). The only

exception was cumulative time spent over 30 °C which

declined significantly with latitude during the summer

months of 2010–11 and 2011–12 (Table 1). Interestingly,

maximum temperatures and cumulative thermal stress at

Cape Paterson and Kilcunda, the two additional south-wes-

tern locations, were equivalent to and sometimes greater

than maximum temperatures and cumulative thermal stress

at Cape Byron and Port Macquarie, locations situated more

than 1500 km further north (Fig. 2). This suggests that

regions along the south-west coast of Victoria may experi-

ence considerably different environmental conditions than

their east coast counterparts.

Latitudinal patterns of cumulative midday exposure

and solar radiation

Cumulative midday aerial exposure of intertidal regions

generally did not vary with latitude except during the sum-

mer months of 2011–12 when both low- and mid-intertidal

regions within southern locations experienced greater

cumulative exposure than equivalent regions further north

(Fig. 3, Table 2). Southern locations also experienced greater

solar radiation during the summer months of 2011–12

with mean daily solar radiation increasing with increasing

latitude (Fig. 4, Table 3). Coincidently, Cape Paterson and

Kilcunda, the two southernmost locations, experienced their

greatest levels of heat stress during the summer months of

2011–12 (Fig. 2). This suggests that the unusually high

summer temperatures at these two locations in 2011–12

were the result of both greater midday exposure and solar

radiation. In contrast to cumulative midday exposure, after-

noon aerial exposure decreased with increasing latitude

during the summer months of 2009–10 and 2010–11

(Fig. 3). Cumulative morning aerial exposure did not

vary with latitude during any of the three consecutive

summers.

Modelling latitudinal patterns of heat stress

Accuracy of the model

Multiple regression analysis revealed that the cumulative

effect of local air temperature, tidal height, relative humidity,

solar elevation and solar radiation can explain 59.6% of the

variation in rocky intertidal substratum temperatures across

the three eastern locations (Cape Byron, Port Macquarie and

Mallacoota) and up to 60.5% for the separate model under-

taken for Cape Paterson (Table 4). In all cases, air tempera-

ture was the first parameter to be entered into the model,

and therefore explained the greatest amount of variation in

in situ temperatures, followed by solar elevation, solar expo-

sure, tidal height and relative humidity, respectively

(Table 4). Comparisons between substratum temperatures

calculated by the meteorological model, produced as a result

of multiple regression analysis, and those measured by in situ


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loggers revealed that our meteorological model was effective

at predicting maximum daily temperatures across all four

locations (Fig. 5). The meteorological model also accurately

estimated cumulative heat stress across all four locations with

the number of estimated hours spent above 30 °C being

highly correlated with in situ measurements (CB: r2 = 0.522,

P < 0.001, n = 258; PM: r2 = 0.363, P < 0.001, n = 262; M:

r2 = 0.500, P < 0.001, n = 156; CP: r2 = 0.518, P < 0.001,

n = 254). Furthermore, the meteorological model was effec-

tive at predicting both the number and timing of extreme

heat events with the model predicting on average 73% of

extreme heat events across all four locations during each of

the three sampling intervals (Fig. 6). The meteorological

model therefore represents an effective method for estimating

extreme heat stress on rocky intertidal shores of south-east-

ern Australia.

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Figure 2 Latitudinal temperature variabilityamong seven rocky intertidal shores along

south-eastern Australia over threeconsecutive summers. Maximum and mean

daily maximum temperatures represent thesummer maximum and average of all daily

maximum temperatures recorded by in situloggers during the three consecutive

summers. Cumulative heat stress representsthe number of hours temperature remained

above 30 °C and 35 °C.

Table 1 Summary of Spearman rank correlations undertaken between latitude and heat stress measured by in situ loggers along the

coast of south-eastern Australia.

Source

Eastern locations only All locations

n r-value P-value n r-value P-value

2009/10

Latitude vs. Max temp. 4 �0.800 0.200 6 �0.486 0.329

Latitude vs. Mean daily max temp. 4 �0.800 0.200 6 �0.086 0.872

Latitude vs. > 30 °C 4 �0.800 0.200 6 �0.486 0.329

Latitude vs. > 35 °C 4 �0.800 0.200 6 �0.143 0.787

2010/11

Latitude vs. Max temp. 5 �0.600 0.285 7 �0.143 0.760

Latitude vs. Mean daily max temp. 5 �0.600 0.285 7 �0.143 0.760

Latitude vs. > 30 °C 5 �0.900 0.037 7 �0.679 0.094

Latitude vs. > 35 °C 5 �0.800 0.104 7 �0.286 0.535

2011/12

Latitude vs. Max temp. 4 �0.600 0.400 6 0.543 0.266

Latitude vs. Mean daily max temp. 4 �0.800 0.200 6 0.143 0.787

Latitude vs. > 30 °C 4 1.000 < 0.001 6 �0.257 0.623

Latitude vs. > 35 °C 4 �0.800 0.200 6 0.257 0.623


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Extrapolation using the model

Rocky intertidal temperatures modelled using meteorological

data from 22 coastal weather stations reveal that chronic heat

stress (i.e. mean daily maximum temperature) and cumula-

tive time spent above 30 °C declines with increasing latitude

(Fig. 7, Table 5). This partially supports patterns produced

by our in situ measurements which indicate a latitudinal gra-

dient exists for cumulative time spent above 30 °C for rocky

intertidal shores along the east coast of Australia. In contrast

to this strong latitudinal decline in chronic thermal stress,

acute thermal stress (i.e. absolute summer maxima) did not

decline with increasing latitude during any of the three sam-

pling intervals. As a result, rocky intertidal shores in south-

ern New South Wales and Victoria experience greater

thermal variability than their counterparts in northern New

South Wales and southern Queensland (Fig. 7). Similar to in

situ measurements, modelled temperatures for the two

south-western locations, Cape Paterson and Wilsons Prom-

ontory, were at times greater than east coast locations

(Fig. 7). This was most obvious during the summer of 2011–12

when maximum modelled temperatures at Cape Paterson

and Wilsons Promontory reached 44.9 °C and 40.6 °C,respectively, whilst modelled temperatures at Mallacoota and

Green Cape, two east coast locations at similar latitudes, only

reached 37.1 °C and 35.1 °C, respectively (Fig. 7).

DISCUSSION

Our predictive model shows that, in line with expectations,

heat stress along more than 1500 km of the temperate

south-east coast of Australia decreases with increasing lati-

tude, although not as a simple function of variation in tidal

exposure or solar radiation. By contrast our empirical results

failed to detect several of these latitudinal gradients, poten-

tially due to limited latitudinal extent and replication. This

illustrates the usefulness of such meteorological models to

provide abiotic measurements in the absence of empirical

data. The latitudinal patterns displayed by locations on the

south-east coast provide an important opportunity to begin

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40

80

120

160

27 29 31 33 35 37 390

40

80

120

160

27 29 31 33 35 37 39

)srh(erusopxelairea

evit alumuC

Latitude (°S) Latitude (°S) Latitude (°S)

MORNING (09:00-11:00)

MIDDAY (11:00-13:00)

AFTERNOON (13:00-15:00)

Figure 3 Latitudinal variation in cumulative morning (left), midday (centre) and afternoon (right) aerial exposure calculated as the

total time low (white diamonds) and mid (black squares) intertidal regions along the coast of south-eastern Australia were exposed toaerial conditions between 09:00 h and 11:00 h, 11:00 h and 13:00 h, and 13:00 h and 15:00 h, respectively, during the three consecutive

summers. Lines are presented in cases where Pearson correlations were significant.


1484


assessing current broad-scale patterns of thermal stress on

rocky intertidal communities in this region. Indeed this coast

is expected to suffer relatively severe impacts of climate

change including the strengthening impact of the East Aus-

tralian Current (EAC) at higher latitudes (Cai et al., 2005).

Overall, our data provide evidence of decreased heat stress at

higher latitudes as measured by acute and chronic maximum

temperatures and cumulative time spent over 30 °C and

35 °C. However, our data, together with the absence of

equivalent thermal gradients on the west coast of the USA

(Helmuth et al., 2002, 2006a), indicate the importance of

applying region-specific modelling.

Latitudinal thermal variability

The majority of studies indicate that rocky intertidal shores,

and their biological communities, typically experience large-

scale patterns of thermal stress that are inversely related to

latitude (Sorte & Hofmann, 2004; Schoch et al., 2006; Jones

et al., 2010; Monaco et al., 2010). For example, Sorte & Hof-

mann (2004) found that individuals of the intertidal gastro-

pod Nucella canaliculata at lower latitudes produce greater

amounts of heat-shock proteins compared with individuals

at higher latitudes. Survival of the intertidal mussel Mytilus

edulis increases with increasing latitude along the Atlantic

coast of the USA (Jones et al., 2010). Whilst in Chile the size

and reproductive output of the intertidal porcelain crab Petr-

olisthes granulosus increases with increasing latitude (Monaco

et al., 2010). In recent years, however, there have been a few

exceptions to this generic trend. Helmuth et al. (2002,

2006a) found that body temperatures of the intertidal mussel

Mytilus californianus were greater at higher latitude locations

in Washington State in comparison to lower latitude loca-

tions in California. On the east coast of the USA, Leonard

(2000) found that rocky intertidal temperatures were at times

greater in Maine than in Rhode Island located approximately

400 km further south. Likewise, body temperatures of inter-

tidal limpets from the genus Patella appear to vary indepen-

dently of latitude along the Atlantic coast of the Iberian

Peninsula (Seabra et al., 2011). In many of these exceptions

local variability in thermal stress has overridden latitudinal

effects resulting in unexpected patterns of large-scale thermal

stress. Helmuth et al. (2002, 2006a) found that it was local

Table 2 Summary of Pearson correlations undertaken

between latitude cumulative morning (09:00–11:00 h), midday(11:00–13:00 h) and afternoon (13:00–15:00 h) exposure

within low- and mid-shore regions at 15 sites along thecoast of south-eastern Australia.

Source r-value P-value

2009/10

Morning

Latitude vs. Aerial exposure (low) 0.466 0.080

Latitude vs. Aerial exposure (mid) 0.050 0.859

Midday


Latitude vs. Aerial exposure (mid) �0.497 0.059

Afternoon

Latitude vs. Aerial exposure (low) �0.792 < 0.001

Latitude vs. Aerial exposure (mid) �0.925 < 0.001

2010/11

Morning



Midday



Afternoon

Latitude vs. Aerial exposure (low) �0.536 0.040


2011/12

Morning



Midday



Afternoon

Latitude vs. Aerial exposure (low) �0.136 0.629


20

25

30

35

40 Dec 2009 - Feb 2010MaxMean

20

25

30

35

40 Dec 2010 - Feb 2011

20

25

30

35

40

25 27 29 31 33 35 37 39

Dec 2011 - Feb 2012

Latitude (°S)

m/JM(

noitaidarralosyl ia

D2 )

Figure 4 Latitudinal variation in daily solar radiation along the

coast of south-eastern Australia during the three consecutivesummers. Max and mean solar radiation represent the summer

maximum and mean daily solar radiation at each location. Linesare presented in cases where Pearson correlations were

significant.


1485


variability in the timing of midday low-tides that produced

counter-intuitive patterns of mussel body temperatures. In

the present study, however, cumulative midday exposure

increased only slightly with increases in latitude and only

during the summer of 2011 and 2012. Therefore, differences

in the timing of midday low tides along the south-east coast

of Australia are unlikely to be a major contributing factor

responsible for the geographical variability in rocky intertidal

heat stress in this region. Denny & Paine (1998) showed,

however, that aerial exposure of intertidal organisms, and

thus the temperatures they experience, oscillates on an 18+year cycle due to changes in lunar inclination. Consequently,

whilst no geographical pattern in tidal exposure was detected

during the present study period, such patterns could change

over time. The unusually high temperatures detected at Cape

Paterson and Kilcunda, the two additional south-western

locations, may reflect differences in large-scale environmental

conditions between south-west Victoria and the east coast of

Australia. In addition to the slight increases in midday expo-

sure and solar radiation, 30-year averages indicate that

regions in southern Victoria experience (1) lower summer

humidity than north-eastern Victoria and New South Wales,

and (2) predominantly northerly offshore summer winds

compared with the north-easterly to southerly onshore sum-

mer winds along the New South Wales coast (Australian

Bureau of Meteorology, 2012). Such lower humidity and off-

shore desert winds may be the driving factors responsible for

the observed differences in rocky intertidal heat stress

between southern Victoria and the east coast of Australia.

Modelling rocky intertidal heat stress

The meteorological model developed in this study represents

a powerful tool for quantifying heat stress on rocky intertidal

shores and provides the first comprehensive large-scale

assessment of thermal variation along these shores. Biophysi-

cal models are becoming increasing popular and sophisti-

cated within the rocky intertidal literature (see Helmuth,

1999; Denny et al., 2006; Gilman et al., 2006; Helmuth et al.,

2011; Wethey et al., 2011a; Kearney et al., 2012). For exam-

ple, Wethey et al. (2011a) successfully modified a meteoro-

logical land surface model to simulate the body temperatures

of the intertidal mussel Mytilus californianus among six loca-

tions along the west coast of the USA. Similarly, Helmuth

et al. (2011) used wind speed, air temperature and solar

radiation to simulate the number of thermally stressful days

experienced by M. californianus at a single location in central

Table 3 Summary of Pearson correlations undertaken between

latitude and summer maxima and mean daily solar radiation at17 sites along the coast of south-eastern Australia.

Source r-value P-value

2009/10

Latitude vs. Max solar radiation 0.972 < 0.001

Latitude vs. Mean solar radiation 0.155 0.552

2010/11

Latitude vs. Max solar radiation 0.840 < 0.001


2011/12

Latitude vs. Max solar radiation 0.675 0.003


Table 4 Summary of step-wise multiple regression analyses undertaken using multiple meteorological parameters and in situtemperatures recorded by Tidbit� data loggers along the coast of south-eastern Australia. Three separate analyses were undertaken using

(1) data from all four locations (Cape Byron, Port Macquarie, Mallacoota and Cape Paterson), (2) data from three eastern locations(Cape Byron, Port Macquarie and Mallacoota), and (3) data from Cape Paterson only. Included predictors represent the number and

order of predictors entered into the step-wise multiple regressions. In all cases wind speed was omitted as a non-influential predictor ofin situ temperature.

Source d.f. F-ratio P-value R2 Incl. predictors b-value SE P-value Tolerance

All locations (n = 4) 4,7133 2542.83 < 0.001 0.588 Intercept 0.931 0.384 0.015

Air temperature 0.860 0.013 < 0.001 0.926

Solar elevation 0.098 0.003 < 0.001 0.955

Daily solar exposure 0.257 0.008 < 0.001 0.969

Tidal height �7.903 0.352 < 0.001 0.977

Three eastern locations 5,4996 1476.97 < 0.001 0.596 Intercept �6.647 0.840 < 0.001

Air temperature 1.029 0.019 < 0.001 0.721

Solar elevation 0.087 0.003 < 0.001 0.938


Tidal height �7.392 0.406 < 0.001 0.983

Relative humidity 0.047 0.006 < 0.001 0.615

Cape Paterson [only] 5,2130 654.15 < 0.001 0.605 Intercept 5.731 1.221 < 0.001

Air temperature 0.765 0.026 < 0.001 0.673

Solar elevation 0.119 0.006 < 0.001 0.905

Tidal height �10.109 0.678 < 0.001 0.956


Relative humidity �0.031 0.008 < 0.001 0.639


1486


California. Similar to the present study, Helmuth et al.

(2011) found that heat stress was largely determined by air

temperatures and solar radiation. However, Helmuth et al.

(2011) also found that even moderate increases in mean

wind speed could counteract the effects of increases in air

temperature on the number of thermally stressful days. This

differs from the results of the present study which found that

wind speed had no effect on rocky intertidal temperatures.

Indeed air temperature, solar elevation, solar radiation and

relative humidity were all found to have a greater influence

on rocky intertidal heat stress than wind speed. Such differ-

ences are not surprising, however, because Helmuth et al.

(2011) were measuring body temperatures of mussels, which

may be more susceptible to wind speed as they are somewhat

elevated above the substratum. By contrast, wind speed was

not an important parameter in our model because the mea-

surements recorded by our Tidbit loggers were strongly cor-

related to the thermal properties of the substratum.

Implications for predicting the effects of climate

change

Owing to the rapid warming of the Tasman Sea resulting

from further southern penetration of the EAC, coastal

regions of south-eastern Australia have been identified as

being particularly vulnerable to future climate change (Ridg-

way, 2007; Lough, 2009; Wernberg et al., 2011). Indeed, sev-

eral range expansions of intertidal and subtidal invertebrates,

algae and coastal fishes have already been reported (Ling

et al., 2009; Pitt et al., 2010; Stuart-Smith et al., 2010; John-

son et al., 2011; Poloczanska et al., 2011). Consequently, the

geographical patterns of heat stress detected in the present

study may have significant implications for predicting how

the distributions of rocky intertidal invertebrates and algae

will shift in response to future climate change. For example,

if distributions of rocky intertidal organisms are set by upper

air temperatures, and if acute heat stress continues to vary

independently of latitude, then it is unlikely that species

range limits in this region will extend or contract towards

the pole. This may explain recent findings that for 30 com-

mon rocky intertidal species found along the south-east coast

10

20

30

40

50

60 Cape Byron

10

20

30

40

50

60Port Macquarie

10

20

30

40

50

60Mallacoota

10

20

30

40

50

60

10 20 30 40 50

Cape Paterson

P<0.001, n=259)

(r2=0.523,

(r2=0.628,

(r2=0.726,

(r2=0.537,

P<0.001, n=263)

P<0.001, n=157)

P<0.001, n=255)

Model daily maximum substratum temperature (°C)

In si

tu d

aily

max

imum

subs

trat

um te

mpe

ratu

re (°

C)

Figure 5 Relationship between meteorological model and in

situ daily maximum substratum temperatures at four locationsalong the coast of south-eastern Australia. Solid lines represent

linear regressions.

0

20

40

60

80

100 Dec 2009 to Feb 2010

0

20

40

60

80

100 Dec 2010 to Feb 2011

0

20

40

60

80

100

CB PM M CP

Dec 2011 to Feb 2012

foseta

mitsetce rr ocegat necr eP

extr

eme

heat

eve

nts

Location

nd

Figure 6 The percentage of correct estimates of extreme heatevents predicted by the meteorological model at four locations

along the coast of south-eastern Australia (CB, Cape Byron; PM,Port Macquarie; M, Mallacoota; CP, Cape Paterson) across three

consecutive summers. No estimates could be made forMallacoota during the summer months of 2011 and 2012 as a

result of damaged loggers.


1487


of Australia only six were shown to display poleward range

shifts since the 1940s and 1950s (Poloczanska et al., 2011).

This is quite different from the southern range expansions of

several subtidal invertebrates, algae and fishes that have

already been documented within this region (Ling et al.,

2009; Stuart-Smith et al., 2010; Johnson et al., 2011). Such

poleward expansions of subtidal species are consistent with

the strong latitudinal decline in sea-surface temperatures,

and its movement further south, along the east coast of Aus-

tralia (Cai et al., 2005; Lough, 2009; Lathlean et al., 2011).

Whilst the present study focuses on extreme heat stress and

its importance in regulating marine populations and com-

munities it is also worth noting that in recent years extreme

cold stress has been shown to significantly influence many

rocky intertidal communities (Firth et al., 2011; Sousa et al.,

2012). At least for shallow subtidal communities of south-

eastern Australia, however, winter temperatures appear to be

increasing in line with annual trends (Figueira & Booth,

2010), suggesting that species’ tolerances to extreme cold

temperatures are less likely to affect their future geographical

20

25

30

35

40

45

50 Dec 2009 - Feb 2010

Maximum Mean daily maximum0

50

100

150

200

250

300 >30°C

>35°C

20

25

30

35

40

45

50 Dec 2010 - Feb 2011

0

50

100

150

200

250

300

20

25

30

35

40

45

50

26 28 30 32 34 36 38 40

Dec 2011 - Feb 2012

0

50

100

150

200

250

300

26 28 30 32 34 36 38 40

tmutartsbuS

(erutarep

me°C

)

Latitude (°S)

Cum

ulat

ive

heat

str

ess

(hrs

)

Latitude (°S)

Figure 7 Modelled latitudinal variation in

day-time rocky intertidal substratumtemperatures during the summer months of

2009–10, 2010–11 and 2011–12 along thecoast of south-eastern Australia.

Temperatures were calculated usingmeteorological parameters obtained from 22

coastal weather stations and a general linearmodel developed by undertaking multiple

regressions. Maximum and mean dailymaximum temperatures represent the

summer maximum and average of all dailymaximum temperatures recorded by in situ

loggers during the three consecutivesummers. Cumulative thermal stress

represents the number of hours temperatureremained above 30 °C and 35 °C. Circledpoints represent the two south westerlylocations Cape Paterson and Wilsons

Promontory. Lines are presented in caseswhere Pearson correlations were significant.

Table 5 Summary of Pearson correlations undertaken between latitude and heat stress using modelled temperatures derived from

meteorological data (n = 22) collected along the coast of south-eastern Australia.

Source

Eastern locations only All locations

n r-value P-value n r-value P-value

2009/10

Latitude vs. Max temp. 20 0.018 0.938 22 0.136 0.546

Latitude vs. Mean daily max temp. 20 �0.947 < 0.001 22 �0.896 < 0.001

Latitude vs. > 30 °C 20 �0.922 < 0.001 22 �0.887 < 0.001

Latitude vs. > 35 °C 20 �0.880 < 0.001 22 �0.692 < 0.001

2010/11

Latitude vs. Max temp. 20 0.335 0.148 22 0.370 0.090


Latitude vs. > 30 °C 20 �0.879 < 0.001 22 �0.863 < 0.001

Latitude vs. > 35 °C 20 �0.861 < 0.001 22 �0.842 < 0.001

2011/12

Latitude vs. Max temp. 20 �0.772 < 0.001 22 �0.169 0.451


Latitude vs. > 30 °C 20 �0.901 < 0.001 22 �0.753 < 0.001

Latitude vs. > 35 °C 20 �0.795 < 0.001 22 �0.450 0.035


1488


distributions within this region. It should also be noted that

the present study does not investigate small-scale tempera-

ture variability, which on some rocky shores can be as great

as the variation experienced across latitudes (Denny et al.,

2011). This local variability in temperature may lessen the

effects of climate change, allowing individuals within a popu-

lation to evade extreme heat stress. Small-scale variability in

temperature would be expected to be greater on shores with

increased topographic complexity.

Recent research suggests that species or populations have a

greater chance of acclimating to changing thermal conditions

if they have been previously exposed to high thermal vari-

ability (Tomanek, 2008). Consequently, because our meteo-

rological model suggests that the thermal variability of rocky

intertidal shores of south-eastern Australia increases with lat-

itude, organisms living at higher latitudes may be more likely

to cope with the expected increases in temperature than their

northern counterparts. However, the unique thermal proper-

ties and thermoregulatory behaviour of individual species

make it difficult to generalize such future responses of multi-

ple species within a community (Somero, 2010). Further

research is required to determine whether or not these large-

scale patterns of heat stress equally reflect the body tempera-

tures of rocky intertidal organisms, and by extension their

physiological processes.

In summary, the results of this study challenge the sim-

plistic view that heat stress varies as a function of latitude

with populations at higher latitudes experiencing less heat

stress than populations at lower latitudes. This study high-

lights that thermal stress on rocky intertidal shores is the

product of several interacting climatic and non-climatic fac-

tors that all need to be considered when estimating extreme

heat stress. Despite their vulnerability to climate change

much work is still required to understand how large-scale

patterns of thermal stress influence benthic communities of

south-eastern Australia.

ACKNOWLEDGEMENTS

We thank Russell McWilliam, Lucia Aguilar and Andrew

Swan for assistance in the field, and Aidan Johnson, Dave

Roberts and Elizabeth Lathlean for constructive comments

on earlier versions of the manuscript. This research was sup-

ported by an Australian Research Council Discovery Project

Grant (Project Number DP0666787) to D.J.A. and T.E.M., a

University of Wollongong post-graduate scholarship to J.A.L.

and by the Institute for Conservation Biology and Environ-

mental Management at the University of Wollongong.

REFERENCES

Australian Bureau of Meteorology (2012) Past weather and

climate data. Bureau of Meteorology, Melbourne. Available

at: http://www.bom.gov.au/climate/data-services/ (accessed

July 2012).

Bertness, M.D., Trussell, G.C., Ewanchuk, P.J. & Silliman,

B.R. (2002) Do alternate stable community states exist in

the Gulf of Maine rocky intertidal zone? Ecology, 83,

3434–3448.

Cai, W., Shi, G., Cowan, T., Bi, D. & Ribbe, J. (2005) The

response of the Southern Annular Mode, the East Austra-

lian Current, and the southern mid-latitude ocean circula-

tion to global warming. Geophysical Research Letters, 32,

1–4.

Denny, M.W. & Dowd, W.W. (2012) Biophysics, environ-

mental stochasticity, and the evolution of thermal safety

margins in intertidal limpets. Journal of Experimental Biol-

ogy, 215, 934–947.

Denny, M.W. & Paine, R.T. (1998) Celestial mechanics, sea-

level changes, and intertidal ecology. Biological Bulletin,

194, 108–115.

Denny, M.W., Miller, L.P. & Harley, C.D.G. (2006) Thermal

stress on intertidal limpets: long-term hindcasts and lethal

limits. Journal of Experimental Biology, 209, 2420–2431.

Denny, M.W., Hunt, L.J.H., Miller, L.P. & Harley, C.D.G.

(2009) On the prediction of extreme ecological events.

Ecological Monographs, 79, 397–421.

Denny, M.W., Dowd, W.W., Bilir, L. & Mach, K.J. (2011)

Spreading the risk: small-scale body temperature variation

among intertidal organisms and its implications for species

persistence. Journal of Experimental Marine Biology and

Ecology, 400, 175–190.

Figueira, W.F. & Booth, D.J. (2010) Increasing ocean tem-

peratures allow tropical fishes to survive overwinter in

temperate waters. Global Change Biology, 16, 506–516.

Finke, G.R., Navarrete, S.A. & Bozinovic, F. (2007) Tidal

regimes of temperate coasts and their influences on aerial

exposure for intertidal organisms. Marine Ecology Progress

Series, 343, 57–62.

Firth, L.B. & Williams, G.A. (2009) The influence of multiple

environmental stressors on the limpet Cellana toreuma dur-

ing the summer monsoon season in Hong Kong. Journal of

Experimental Marine Biology and Ecology, 375, 70–75.

Firth, L.B., Knights, A.M. & Bell, S.S. (2011) Air temperature

and winter mortality: implications for the persistence of

the invasive mussel, Perna viridis in the intertidal zone of

the south-eastern United States. Journal of Experimental

Marine Biology and Ecology, 400, 250–256.

Gaines, S.D. & Denny, M.W. (1993) The largest, smallest,

highest, lowest, longest, and shortest: extremes in ecology.

Ecology, 74, 1677–1692.

Gilman, S.E., Wethey, D.S. & Helmuth, B. (2006) Variation

in the sensitivity of organismal body temperature to cli-

mate change over local and geographic scales. Proceedings

of the National Academy of Sciences USA, 103, 9560–9565.

Grinnell, J. (1917) Field tests of theories concerning distribu-

tion control. The American Naturalist, 51, 115–128.

Harley, C.D.G. (2008) Tidal dynamics, topographic orienta-

tion, and temperature-mediated mass mortalities on rocky

shores. Marine Ecology Progress Series, 371, 37–46.


1489


Harley, C.D.G. & Paine, R.T. (2009) Contingencies and com-

pounded rare perturbations dictate sudden distributional

shifts during periods of gradual climate change. Proceedings

of the National Academy of Sciences USA, 106, 11172–11176.

Helmuth, B. (1998) Intertidal mussel microclimates: predict-

ing the body temperature of a sessile invertebrate. Ecologi-

cal Monographs, 68, 51–74.

Helmuth, B. (1999) Thermal biology of rocky intertidal mus-

sels: quantifying body temperatures using climatological

data. Ecology, 80, 15–34.

Helmuth, B. (2009) From cells to coastlines: how can we use

physiology to forecast the impacts of climate change? Jour-

nal of Experimental Biology, 212, 753–760.

Helmuth, B., Harley, C.D.G., Halpin, P.M., O’Donnell, M.,

Hofmann, G.E. & Blanchette, C.A. (2002) Climate change

and latitudinal patterns of intertidal thermal stress. Science,

298, 1015–1017.

Helmuth, B., Broitman, B.R., Blanchette, C.A., Gilman, S.E.,

Halpin, P.M., Harley, C.D.G., O’Donnell, M., Hoffmann,

A.A., Menge, B.A. & Strickland, D. (2006a) Mosaic pat-

terns of thermal stress in the rocky intertidal zone: impli-

cations for climate change. Ecological Monographs, 76,

461–479.

Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S.J.

(2006b) Living on the edge of two changing worlds: fore-

casting the responses of rocky intertidal ecosystems to cli-

mate change. Annual Review of Ecology, Evolution, and

Systematics, 37, 373–404.

Helmuth, B., Yamane, L., Lalwani, S., Matzelle, A., Tockstein,

A. & Gao, N. (2011) Hidden signals of climate change in

intertidal ecosystems: what (not) to expect when you are

expecting. Journal of Experimental Marine Biology and

Ecology, 400, 191–199.

Johnson, C.R., Banks, S.C., Barrett, N.S. et al. (2011) Climate

change cascades: shifts in oceanography, species’ ranges

and subtidal marine community dynamics in eastern Tas-

mania. Journal of Experimental Marine Biology and Ecology,

400, 17–32.

Jones, S.J., Lima, F.P. & Wethey, D.S. (2010) Rising environ-

mental temperatures and biogeography: poleward range

contraction of the blue mussel, Mytilus edulis L., in the

western Atlantic. Journal of Biogeography, 37, 2243–2259.

Kearney, M.R., Matzelle, A. & Helmuth, B. (2012) Biome-

chanics meets the ecological niche: the importance of tem-

poral data resolution. Journal of Experimental Biology, 215,

922–933.

Lathlean, J.A., Ayre, D.J. & Minchinton, T.E. (2011) Rocky

intertidal temperature variability along the southeast coast

of Australia: comparing data from in situ loggers, satellite-

derived SST and terrestrial weather stations. Marine Ecol-

ogy Progress Series, 439, 83–95.

Lathlean, J.A., Ayre, D.J. & Minchinton, T.E. (2012) Using

infrared imagery to test for quadrat-level temperature vari-

ation and effects on the early life history of a rocky-shore

barnacle. Limnology and Oceanography, 57, 1279–1291.

Lathlean, J.A., Ayre, D.J. & Minchinton, T.E. (2013) Temper-

ature variability at the larval scale affects early survival and

growth of an intertidal barnacle. Marine Ecology Progress

Series, 475, 155–166.

Leonard, G.H. (2000) Latitudinal variation in species interac-

tions: a test in the New England rocky intertidal commu-

nities. Ecology, 81, 1015–1030.

Ling, S.D., Johnson, C.R., Ridgway, K., Hobday, A.J. & Had-

don, M. (2009) Climate-driven range extension of a sea

urchin: inferring future trends by analysis of recent popu-

lation dynamics. Global Change Biology, 15, 719–731.

Lough, J.M. (2009) Temperature. A Marine Climate Change

Impacts and Adaptation Report Card for Australia 2009 (ed.

by E.S. Poloczanska, A.J. Hobday and A.J. Richardson).

NCCARF Publication 05/09. ISBN 978-1-921609-03-9.

Mislan, K.A.S. & Wethey, D.S. (2011) Gridded meteorologi-

cal data as a resource for mechanistic macroecology in

coastal environments. Ecological Applications, 21, 2678–

2690.

Mislan, K.A.S., Wethey, D.S. & Helmuth, B. (2009) When to

worry about the weather: role of tidal cycle in determining

patterns of risk in intertidal ecosystems. Global Change

Biology, 15, 3056–3065.

Monaco, C.J., Brokordt, K.B. & Gaymer, C.F. (2010) Latitu-

dinal thermal gradient effect on the cost of living of the

intertidal porcelain crab Petrolisthes granulosus. Aquatic

Biology, 9, 23–33.

Orton, J.H. (1920) Sea-temperature, breeding, and distribu-

tion in marine animals. Journal of Marine Biological Associ-

ations, 12, 339–366.

Pitt, N.R., Poloczanska, E.S. & Hobday, A.J. (2010) Climate-

driven range changes in Tasmanian intertidal fauna. Mar-

ine and Freshwater Research, 61, 963–970.

Poloczanska, E.S., Smith, S., Fauconnet, L., Healy, J., Tibb-

etts, I.R., Burrows, M.T. & Richardson, A.J. (2011) Little

change in the distribution of rocky shore faunal communi-

ties on the Australian east coast after 50 years of rapid

warming. Journal of Experimental Marine Biology and Ecol-

ogy, 400, 145–154.

Ridgway, K.R. (2007) Long-term trend and decadal variabil-

ity of the southward penetration of the East Australian

Current. Geophysical Research Letters, 34, L13613.

Sagarin, R.D. & Gaines, S.D. (2002) Geographical abundance

distributions of coastal invertebrates: using one-dimen-

sional ranges to test biogeographic hypotheses. Journal of

Biogeography, 29, 985–997.

Schoch, G.C., Menge, B.A., Allison, G., Kavanaugh, M.,

Thompson, S.A. & Wood, S.A. (2006) Fifteen degrees of

separation: latitudinal gradients of rocky intertidal biota

along the California Current. Limnology and Oceanography,

51, 2564–2585.

Seabra, R., Wethey, D.S., Santos, A.M. & Lima, F.P. (2011)

Side matters: microhabitat influence on intertidal heat

stress over a large geographical scale. Journal of Experimen-

tal Marine Biology and Ecology, 400, 200–208.


1490


Shelford, V.E. (1911) Physiological animal geography. Journal

of Morphology, 22, 551–618.

Somero, G.N. (2010) The physiology of climate change: how

potentials for acclimatization and genetic adaptation will

determine “winners” and “losers”. The Journal of Experi-

mental Biology, 213, 912–920.

Sorte, C.J.B. & Hofmann, G.E. (2004) Changes in latitudes,

changes in aptitudes: Nucella canaliculata (Mollusca: Gas-

tropoda) is more stressed at its range edge. Marine Ecology

Progress Series, 274, 263–268.

Sousa, L.L., Seabra, R., Wethey, D.S., Xavier, R., Queiroz, N.,

Zenboudji, S. & Lima, F.P. (2012) Fate of a climate-driven

colonisation: demography of newly established populations

of the limpet Patella rustica Linnaeus, 1758, in northern

Portugal. Journal of Experimental Marine Biology and Ecol-

ogy, 438, 68–75.

Stuart-Smith, R.D., Barrett, N.S., Stevenson, D.G. & Edgar,

G.J. (2010) Stability in temperate reef communities over a

decadal time scale despite concurrent ocean warming. Glo-

bal Change Biology, 16, 122–134.

Tomanek, L. (2008) The importance of physiological limits

in determining biogeographical range shifts to global

change: the heat-shock response. Physiological and Bio-

chemical Zoology, 81, 709–717.

Wernberg, T., Russell, B.D., Moore, P.J., Ling, S.D., Smale,

D.A., Campbell, A., Coleman, M.A., Steinberg, P.D., Kend-

rick, G.A. & Connell, S.D. (2011) Impacts of climate

change in a global hotspot for temperate marine biodiver-

sity and ocean warming. Journal of Experimental Marine

Biology and Ecology, 400, 7–16.

Wethey, D.S., Brin, L.D., Helmuth, B. & Mislan, K.A.S. (2011a)

Predicting intertidal organism temperatures with modified

land surface models. Ecological Modelling, 222, 3568–3576.

Wethey, D.S., Woodin, S.A., Hilbish, T.J., Jones, S.J., Lima,

F.P. & Brannock, P.M. (2011b) Response of intertidal pop-

ulations to climate: effects of extreme events versus long

term change. Journal of Experimental Marine Biology and

Ecology, 400, 132–144.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the

online version of this article:

Appendix S1 Table listing the detailed site characteristics

(number, latitude/longitude, distance from coast, elevation)

and the recording device employed.

BIOSKETCHES

Justin Lathlean is currently a Postdoctoral Fellow at

Rhodes University, Grahamstown, South Africa. His main

research interests are in the thermal biology of rocky inter-

tidal invertebrates during various stages of their life cycle. He

is particularly interested in understanding how the effects of

climate change will alter the biogeography of marine popula-

tions and communities.

David Ayre is a Professor of Biological Sciences at the Uni-

versity of Wollongong. His interests focus on the evolution

of marine invertebrate life histories and in particular how

selection affects strategies of reproduction, dispersal and

recruitment.

Todd Minchinton is an Associate Professor in Coastal

Ecology at the University of Wollongong. His research

focuses on the importance of dispersal, habitat selection, and

recruitment to the structure and dynamics of marine popula-

tions and communities.

Author contributions: J.L, D.A. and T.M. conceived the ideas;

J.L. collected and analysed the data, and led the writing.

Editor: Melodie McGeoch


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estimating latitudinal variability in extreme heat stress on rocky intertidal shores

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