Predicting climate warming effects on green turtlehatchling viability and dispersal performanceCatherine Cavallo*,1, Tim Dempster1, Michael R. Kearney1, Ella Kelly1, David Booth2, KateM. Hadden3 and Tim S. Jessop1

1Department of Zoology, University of Melbourne, Parkville, Victoria 3010, Australia; 2School of Biological Sciences,University of Queensland, St. Lucia, Queensland 4067, Australia; and 3Tiwi Land Council, PO Box 38545, Winnellie,Northern Territory 0821, Australia

Summary

1. Ectotherms are taxa considered highly sensitive to rapid climate warming. This is because

body temperature profoundly governs their performance, fitness and life history. Yet, while

several modelling approaches currently predict thermal effects on some aspects of life history

and demography, they do not consider how temperature simultaneously affects developmental

success and offspring phenotypic performance, two additional key attributes that are needed to

comprehensively understand species responses to climate warming.

2. Here, we developed a stepwise, individual-level modelling approach linking biophysical and

developmental models with empirically derived performance functions to predict the effects of

temperature-induced changes to offspring viability, phenotype and performance, using green

sea turtle hatchlings as an ectotherm model. Climate warming is expected to particularly threa-

ten sea turtles, as their life-history traits may preclude them from rapid adaptation.

3. Under conservative and extreme warming, our model predicted large effects on performance

attributes key to dispersal, as well as a reduction in offspring viability. Forecast sand tempera-

tures produced smaller, weaker hatchlings, which were up to 40% slower than at present,

albeit with increased energy stores. Conversely, increases in sea surface temperatures aided

swimming performance.

4. Our exploratory study points to the need for further development of integrative individual-

based modelling frameworks to better understand the complex outcomes of climate change for

ectotherm species. Such advances could better serve ecologists to highlight the most vulnerable

species and populations, encouraging prioritization of conservation effort to the most threa-

tened systems.

Key-words: environmental change, developmental trade-offs, ectotherm, organismal perfor-

mance, climate warming, complex life history

Introduction

Ectotherms that function close to their thermal maximum,

including many tropical species, face an amplified threat

from climate change (Deutsch et al. 2008). With average

near-surface air temperatures estimated to rise between 1.0

and 3.8 °C by 2100 (Stocker et al. 2013), understanding

how temperature affects species at the individual level is

essential to managing at risk populations. Species that are

already at risk due to current threatening processes, or

which have low capacity for adaptation, are particularly

vulnerable (Deutsch et al. 2008; Poloczanska, Limpus &

Hays 2009).

Environmental temperatures affect ectotherms in diverse

and complicated ways (Angilletta, Niewiarowski & Navas

2002). As many ectotherms are oviparous, the develop-

mental thermal environment to which their embryos are

exposed can have profound effects on lifetime fitness (Jan-

zen 1995). Incubation temperature governs developmental

success (Ackerman 1997), hatchling phenotypes (Booth

2006) and hatchling phenotypic performance [e.g.

behaviour (O’Steen 1998) and sprint speed (Shine & Har-

low 1996)], all of which are key processes that determine

how early life phase survival influences population dynam-

ics (Schwanz et al. 2010). Environmental temperatures*Correspondence author. E-mail: ccavallo89@gmail.com

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society

Functional Ecology 2014 doi: 10.1111/1365-2435.12389

subsequent to hatching strongly affect early survival rates

via impacts on performance (e.g. swimming ability (Booth

& Evans 2011).

Sea turtles are predicted to be severely threatened by

rapid climate warming, due to temperature-dependent

early life history and life-history attributes that may reduce

their adaptive capacity (Poloczanska, Limpus & Hays

2009). Despite persisting though gradual historic climate

changes, sea turtles may be maladapted to keep pace with

rapid climate change due to attributes which include high

nesting site fidelity and long generation times (Polo-

czanska, Limpus & Hays 2009). In addition, sea turtles are

vulnerable to climate change during development, because

of the consequences of increased nest temperatures (Booth

& Evans 2011; Lalo€e et al., 2014).

To date, there has been an almost singular focus on

assessing the impact of temperature on sex ratios to predict

the vulnerability of sea turtles to climate change (Polo-

czanska, Limpus & Hays 2009). However, sex determina-

tion is not the only process rising temperatures will impact

– two other key processes that may have major bearing on

the viability of sea turtle populations will likely be dis-

rupted. The most pressing of these concerns is the risk that

rising incubation temperatures will reduce developmental

success, particularly if sea turtles are unable to adapt phe-

nology or nest site choice (Pike 2014). Extreme environ-

mental temperatures pose a serious threat to incubating

eggs; the immobility of embryos and reduced thermal tol-

erance range relative to adults mean they cannot avoid

unfavourable conditions. Although buffered to an extent

by burial 30–65 cm below the surface (Limpus 2009a), sea

turtle embryos are sensitive to overheating, and offspring

viability is expected to decrease rapidly above 33–35 °C(Ackerman 1997). Just 3 days above 34 can be lethal

(Maulany, Booth & Baxter 2012), and extended periods at

sublethal temperatures as low as 32–33 °C have been

shown to produce impaired locomotion of hatchlings caus-

ing emergence failure from their nests (Segura & Cajade

2010). In one species, temperatures of only 30–32 °Creduced hatching success and nest emergence (Santidrian

Tomillo et al. 2014).

Another important threat from climate warming may

result from the thermosensitivity of embryonic develop-

ment rate. Among ectotherms, higher developmental tem-

peratures often result in smaller offspring with larger

residual energy stores (Whitehead, Webb & Seymour 1990;

Booth & Astill 2001). Temperature may mediate this

through differential effects on rates of development

(increase in stage) and growth (increase in size) (Jong et al.

2009), leading to extensive variation in phenotype and per-

formance that can persist across the life of individuals

(Warner & Shine 2008). For sea turtles, high incubation

temperatures produce phenotypes of small body size that

are physically weaker but with greater yolk energy reserves

(Fig. 1, (Booth & Evans 2011). Since the rate at which

incubating turtles develop influences phenotypic traits, it

alters dispersal attributes in the early life-history stage,

with important consequences for population dynamics

(Booth & Evans 2011). A trade-off is induced between

morphological and energetic traits. During their intensive

and acute dispersal phase through potentially risky near-

shore waters, smaller, weaker hatchlings might experience

higher predation risk (Gyuris 2000), but their greater

energy stores may increase resistance to starvation relative

Fig. 1. Details of the effects of the develop-

mental and dispersal environment on sea

turtle hatchling phenotypes and swimming

performance. Long incubations associated

with cool temperatures allow greater con-

version of egg nutrients to hatchling tissue,

creating larger hatchlings with smaller

energy reserves. The reverse is true of warm

incubation conditions, where development

is more rapid. Temperatures in the marine

environment then act on these pheno-

types to facilitate or inhibit swimming

performance.

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

2 C. Cavallo et al.

to larger, faster hatchlings (Kraemer & Bennett 1981).

Complicating matters, realized locomotive performance is

determined by the interaction of these nest temperature-

induced phenotypes with the temperature of the dispersal

environment (sand and sea surface temperatures); warm

temperatures enhance locomotive performance (Booth &

Evans 2011). Given that irruptive natal dispersal is a cana-

lized life-history event in most sea turtles, changes in natal

dispersal performance through swimming or energetic

attributes could have important consequences for popula-

tion dynamics by constraining recruitment, especially in

those populations already subjected to anthropogenic

impacts at other life stages (Heppell, Snover & Crowder

2003).

The temperature sensitivity of ectotherm physiology

implies that offspring viability and phenotypes are likely

to be significantly affected by rising temperatures in both

the terrestrial developmental and marine dispersal envi-

ronments (Walters, Blanckenhorn & Berger 2012), poten-

tially impacting population dynamics and viability

(Mitchell, Allendorf & Keall 2010; Boyle et al. 2014).

Our current incapacity to predict the extent and conse-

quence of these effects presents a major gap in how we

forecast the fate of sea turtles and ectotherms generally.

Recently, a modelling framework that combines mecha-

nistic microclimate and developmental models was devel-

oped to predict hatchling sex ratios in species with

temperature-dependent sex determination (Mitchell et al.

2008). The next step is to develop a model framework

that also predicts viability and phenotypic consequences

of nest and dispersal environment temperature. Here, we

develop a stepwise modelling framework, which incorpo-

rates environmental and developmental processes to pre-

dict temperature effects on development and dispersal.

We aimed to forecast temperature effects on offspring

viability, hatchling phenotypes and dispersal performance

using the green turtle (Chelonia mydas) as a model ecto-

therm. We predicted that rising beach temperatures

would approach and eventually exceed the upper thermal

limits for sea turtle incubation, potentially decreasing off-

spring viability and reducing annual cohort size. We also

predicted that rising temperatures in both the terrestrial

and marine environments would lead to synergistic effects

on hatchling phenotypes and dispersal performance.

Materials and methods

We developed a stepwise, individual-level modelling approach to

forecast changes in sea turtle hatchling viability, offspring pheno-

types and swimming trait performance during natal dispersal

under conservative and extreme emissions scenarios (Stocker et al.

2013) (Fig. 2). The framework first obtains predicted outputs for

the thermal incubation environment (i.e. nest in sand) in which

sea turtle eggs develop using a biophysical model implemented in

NicheMapR (Kearney & Porter 2009). These predicted tempera-

tures are then used to estimate the development rate using the

mechanistic model DEVARA (Dallwitz & Higgins 2003). Predicted

nest temperatures, developmental rates and predicted sea surface

temperatures are then fitted to a series of linear relationships

derived from published hatchling data (e.g. Booth & Astill 2001;

Booth & Evans 2011) to predict developmental, phenotypic and

dispersal performance attributes of hatchling green turtles using

linked linear regression equations in Microsoft Excel. Finally, pre-

dicted outputs were regressed against time using general additive

models to evaluate statistical inference in package mgcv (Wood,

2010) in R.

STUDY SPEC IES AND SYSTEM

To predict the effects of climate change on green turtle develop-

ment and dispersal performance, we modelled hatchlings pro-

duced from an example nesting population from Cape Van

Diemen beach (11.16S, 130.37E), Melville Island, Australia. This

location is typical of multispecies (green, olive ridley and flat-

back; Limpus 2009a,b) rookeries in northern Australia. No sea-

sonality has been recorded for nesting green sea turtles at Cape

van Diemen (Whiting et al. 2007). Hence, we modelled the incu-

bation and dispersal of a theoretical turtle (egg-hatchling) laid on

each day of the calendar year (e.g. Fig. S1 in Supporting infor-

mation). Modelled turtles were pooled within each year to pro-

duce an annual mean result for each trait measured. To explore

directional responses to climate warming, we have explicitly mod-

elled variation pertaining to different nest and sea surface tem-

peratures. For simplicity, we controlled temperature variation

across the beach as well as nest depth. Temperature records at

nest depth collected during our fieldwork indicated a negligible

level of temperature variation across the beach. More variation

is shown down the beach profile; however, green turtle nests are

typically located in the upper part of the beach (Hays, Mackay

& Adams 1995) and therefore there should be little variation

associated with nest location.

INPUTS

Step one: climatic variables

Sand and sea surface temperatures were required to model the

effects of the incubation and dispersal environment on viability,

phenotype and performance. Hourly sand temperatures at nest

depth (50 cm: (Limpus 2009a,b) were predicted using the microcli-

mate model NicheMapR (Kearney & Porter 2009; Kearney et al.

2013; Kearney, Isaac & Porter 2014). NicheMapR is a mechanistic

model which solves mass balance equations using sand properties,

microclimate variables and climate/weather variables, which were

obtained from the Australian Water Availability Project; (AWAP:

(Raupach et al. 2009, 2012). To simulate climate warming, mean

monthly minimum and maximum air temperatures for the study

area were increased using projected surface warming from two

recent IPCC climate models (conservative and extreme (Meehl

et al. 2007); Table S1, Supporting information). To validate the

accuracy of NicheMapR-modelled sand temperatures, we assessed

the correlation between reconstructed and observed sand tempera-

tures (recorded from temperature loggers Thermocron iButton;

Thermodata Pty Ltd; DS1921H) for 2012 (Fig. S2).

Sea surface temperatures for the dispersal zone were obtained

from the International Oceans and Atmosphere Data set (ICO-

ADS; http://www.cdc.noaa.gov/coads/) for all historic scenarios.

To simulate climate warming, mean monthly sea surface tempera-

tures averaged for the period 1960–2013 were increased using pro-

jected regional sea surface warming from recent IPCC climate

models [(Whetton et al. 2007); Table S1].

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

Climate effects on ectotherm offspring 3

Step two: incubation temperature and development rate

To model the effect of incubation temperature on turtles, the fluc-

tuating daily temperatures produced by NicheMapR had to be

converted to a constant temperature equivalent (CTE). This was

completed in two steps, using the program DEVARA (Dallwitz &

Higgins 2003) and the constant temperature equivalent method

(Georges, Doody & Beggs 2004). First, hourly development rates

were estimated in Microsoft Excel using linked equations pro-

duced by DEVARA. DEVARA is a purpose built Fortran pro-

gram which estimates development rate as a function of

temperature, using the nonlinear, heuristic function, ra (see

Appendix S1 in Supporting information for a detailed description

of the methods). To produce estimates used in the ra function,

DEVARA used published incubation lengths with their corre-

sponding incubation temperatures (Limpus, Reed & Miller 1985).

The constant temperature equivalent method was then used to

produce a representative constant incubation temperature using

the development rates from DEVARA (Fig. 1b). This method uses

hourly development rate (ra24; Appendix S1) and cumulative

development to determine a ‘developmental median’ (see (Mitchell

et al. 2008). Hereafter, the constant temperature equivalent will be

referred to as ‘incubation temperature’.

Step three: hatchling viability, phenotypes andperformance

Offspring viability was determined by observing whether the

equivalent constant incubation temperature of modelled eggs was

within the published thermal tolerance range of 25–27 °C to 33–35 °C (Ackerman 1997). A range of critical thermal maxima

(CTMax) were used to reflect recorded field thresholds. Eggs were

scored as viable or nonviable depending on whether they avoided

or encountered an incubation period at or above each of these

temperatures. Our results are reported as the number of modelled

nests that incubated below each of these temperatures, for each

modelled year (1992, 2002, 2012, 2030, 2050, 2070) and emissions

scenario (extreme and conservative).

Next, we predicted the effects of temperature, in both the incu-

bation (sand) and dispersal (sea surface) environments, on green

sea turtle hatchling traits relevant to natal dispersal. Hatchling

yolk-free mass (the mass of the hatchling once residual yolk sacs

are removed) was used to give an indication of trends in hatchling

size and amount of egg converted to hatchling tissue. Residual

energy (the joules contained in residual yolk sacs) was measured

to indicate the amount of energy available to hatchlings during

(a)

(bi)

(di)(dii)

(diii)

(div)

(bii)

(c)

Fig. 2. Overview of the stepwise modelling process used to determine the viability, phenotypes, swimming performance and dispersal abil-

ity of modelled green turtle hatchlings. The three major steps in the modelling process are as follows: (a) collation of climate variables,

microclimate properties and incubation estimates to feed external models; (bi) use of external models and programs to reconstruct and

(bii) predict sand and sea surface temperatures and estimate development rate for use in four linear performance equations; and (c) linear

performance functions are then used to estimate four phenotypic outputs: (di) calculation of offspring viability, (dii) phenotypic traits, (diii)

swimming performance and (div) dispersal ability. Note: In online version, red arrows denote influence of nest temperature and blue

arrows denote influence of sea surface temperature.

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

4 C. Cavallo et al.

their first few days of life and subsequently inform maximum dis-

persal ability. Maximum swimming thrust and maximum swim-

ming stroke rate demonstrate the strength and flipper speed of

hatchlings and were later used to inform measures of hatchling

dispersal ability and swimming speed. To inform our predictions,

we used published field and laboratory data that reported the

effects of environmental temperatures on phenotypic performance

in hatchling turtles (e.g. Booth & Astill 2001; Booth & Evans

2011). From this data, we estimated the empirical relationships

between temperature and performance responses for the pheno-

typic traits using linear equations (Appendix S1). These equations

were then fitted with past, current and future temperatures to esti-

mate thermal responses on hatchling performance traits (Appen-

dix S1). Our results are reported as the mean annual phenotype

produced for each modelled year and emissions scenario. For an

example of the within year variation in phenotypes produced by

this model, see Fig. S1.

Several assumptions are implicit in this component of the mod-

elling approach. First, turtles will respond to any predicted tem-

perature changes with a linear response that is identical in slope to

contemporary responses from which the equations were derived.

This entails that future hatchlings would show limited thermal

plasticity or evolution in development and performance. Secondly,

environmental temperature predicted throughout the time course

would not exceed the maximal performance temperature, after

which performance would be expected to asymptote and then

decrease due to thermal impacts on physiology (Huey & Kingsolv-

er 1989). Thirdly, an absence of data on temperature responses in

green turtle hatchling development or swimming performance data

for the local nesting beach necessitated the use of phenotype and

performance records from Heron Island green sea turtles (Booth

& Astill 2001; Booth & Evans 2011). Using data from a southern

population is likely to dismiss potential local adaptation in devel-

opment and hatchling responses. This could cause a positive bias

and overestimate thermal responses in this population. Consider-

ing evolutionary effects is a key requirement of future modelling

exercises to ensure that, where applicable, evolutionary processes

can be considered to better understand species responses to

climate effects (Williams et al. 2008).

STAT IST ICAL ANALYSES

A generalized linear model (GLM) was used to assess a three-way

interaction among the effects of year, scenario (conservative and

extreme) and critical thermal thresholds (set at 32 33, 34, 35 °C)on the probability of nest viability. Generalized additive models

(GAMs) were then used to assess the independent and synergistic

effects of year and scenario (conservative and extreme) on pheno-

typic and developmental parameters (yolk-free hatchling mass,

residual energy, maximum swimming thrust, maximum swimming

stroke rate, maximum dispersal potential and derived swimming

speed). These models were fitted with a binomial distribution and

a logit link or a Gaussian distribution and an identity link pending

their respective error distributions. A smoothing function was

applied to year, to estimate mean results for the years between

modelled scenarios. All GAMs were fitted using the mgcv package

(Wood 2010) within the R statistical framework (R 3.0.0) in

R-studio Version 0.97. 551 (R Core Team 2013).

Results

EFFECT OF CL IMATE WARMING ON SAND

TEMPERATURES AND OFFSPRING V IAB IL ITY

Average yearly sand temperatures at Cape van Diemen in

the Tiwi Islands are forecast to rise by between 2.17 and

3.34 °C over the next 60 years (Fig. 2a: GAM, F = 89.9,

P < 0.01). As predicted sand temperatures increased under

the two emission scenarios, nest incubation temperatures

increasingly approached, and at times exceeded, lethal and

sublethal thermal thresholds (Fig. 2). Predicted increases

to nest incubation temperatures resulted in a highly signifi-

cant interaction among the effects of year, scenario (con-

servative and extreme) and CTMax on probability of

viable nest incubation (GLM, v2 = 298.20, P < 0.001).

Here, nesting viability decreased over time, with lower

CTMax under extreme climate scenarios causing the high-

est levels of developmental failure (Fig. 3b).

EFFECT OF CL IMATE WARMING ON HATCHL ING

PHENOTYPES AND SWIMMING PERFORMANCE

Analyses of mean annual predicted phenotypes over

twelve scenarios indicated that increasing environmental

temperatures affected all modelled traits (Fig. 4). Warm-

ing of nests induced changes in all phenotypic traits. The

yolk-free mass of hatchlings (Fig. 4a) is forecast to

decrease steadily (6–12%) over the next 60 years (GAM,

F = 483.8, P < 0.01), but more rapidly under extreme

emissions (GAM, t = 11.02, P < 0.01), producing increas-

ingly smaller hatchlings. As expected, the trend for resid-

ual energy stores (Fig. 4b) being the reciprocal of yolk-

free mass will increase steadily (7–13%) over the next

60 years (GAM, F = 224.5, P < 0.01), but more rapidly

under extreme emissions (GAM, t = �8.9, P < 0.01).

Warming in both the developmental and dispersal envi-

ronments induced changes to hatchling locomotive perfor-

mance. Power-stroke thrust (Fig. 4c) is forecast to

decrease (15–40%), in response to both scenario (GAM,

t = 7.5, P < 0.01) and year (GAM, F = 207.9, P < 0.01).

In contrast, maximum swimming stroke rate (Fig. 4d) is

predicted to increase modestly (4–7%) in response to both

scenario (GAM, t = �6.2, P < 0.01) and year (GAM,

F = 77.9, P < 0.01).

EFFECT OF CL IMATE WARMING ON DISPERSAL AB IL ITY

Analyses of mean annual dispersal phenotypes (maximum

dispersal potential and derived swimming speed) over

twelve modelled scenarios indicated that these were

affected by increasing temperatures in both the develop-

mental and dispersal environments (Fig. 5). Derived swim-

ming speed (Fig. 5a) is forecast to decrease (15–38%) over

the next 60 years (GAM, F = 207.1, P < 0.01), with a sig-

nificant effect of scenario (GAM, t = 7.1, P < 0.01). By

2070 under extreme emissions, mean annual derived swim-

ming speed is predicted to be almost half that of current

hatchlings. Even under conservative emissions, derived

swimming speed is predicted to decline by ~15%. Maxi-

mum dispersal potential (Fig. 5b) is forecast to increase

(8–13%) (GAM, F = 75.4, P < 0.01), but more rapidly

under an extreme emissions scenario (GAM, t = �5.2,

P < 0.01).

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

Climate effects on ectotherm offspring 5

Discussion

Understanding and predicting species responses to climate

change are an extremely important but complex challenge

to reconcile (Mokany & Ferrier 2010). Through a stepwise

modelling approach, we predicted the potential for climate

warming to affect offspring viability and phenotypic per-

formance in sea turtle hatchlings. Temperature, both in the

terrestrial (developmental) and marine (dispersal) environ-

ments, was predicted to have complex and significant

effects on development, phenotype, performance, dispersal

capability and viability. Since natal dispersal is a critical

life-history stage for sea turtles, as for other marine organ-

isms (O’Connor et al. 2007), these effects have important

implications for recruitment to breeding populations.

REDUCED OFFSPR ING V IAB IL ITY

Under forecasted scenarios, sand temperatures frequently

exceeded the thermal tolerance of sea turtle embryos, with

offspring viability losses of up to 40% for a CTMax of

33 °C and up to 5% for CTmax 35 °C. The average sand

temperature rose from around 28.5 °C in 1992 to above

30 °C and 31 °C by 2070 under conservative and extreme

emissions scenarios. This places average sand temperatures

on the cusp of breaching the thermal tolerance range for

sea turtles. Depending on the thermal tolerance of this

population, green turtles on the Tiwi Islands may or may

not be significantly threatened by climate-induced losses to

offspring viability alone. However, modelled nests that

incubated below each thermal threshold may still have

experienced several days above critical temperatures, due

to the nature of CTE calculation. A study of olive ridley

turtles found that nests which encountered temperatures of

34 °C and above for three consecutive days had decreased

emergence success and locomotor ability (Maulany, Booth

& Baxter 2012). In addition, sustained incubation at suble-

thal temperatures can cause mortality in hatchlings which

are unable to escape the nest (Segura & Cajade 2010).

With 60% of nests encountering incubations of 33 °Cunder high emissions by 2070, it is likely that even those

which do not perish directly as a result of lethal tempera-

tures will be exposed to sublethal temperatures that render

them phenotypically morbid. Shallow nesting species, such

as olive ridley turtles (Lepidochelys olivacea), may be more

greatly affected, due to a nest depth difference of approxi-

mately 30 cm and reduced clutch mass exposing them to

higher and more variable incubation temperatures (Limpus

2009b).

Predicting the demographic consequences of reductions

in offspring viability is difficult in long-lived species, where

overlapping generations buffer interannual losses in

recruitment (Heppell 1998; Heppell, Caswell & Crowder

2000). However, individual-based models of sea turtle pop-

ulations predict that reduced survival of eggs and hatch-

lings increases population extinction probability (Mazaris,

Fiksen & Matsinos 2005; Mazaris, Broder & Matsinos

2006). Large-scale mortality of sea turtle hatchlings due to

beach temperature is not uncommon in sustainable

populations (Matsuzawa et al. 2002; Valverde, Wingard &

G�omez 2010), however, the directional response of

Year1990 2010 2030 2050 2070

Pro

babi

lity

of n

est v

iabi

lity

0·0

0·2

0·4

0·6

0·8

1·0

32oC33oC34oC35oC

Year1990 2010 2030 2050 2070

Pro

babi

lity

of n

est v

iabi

lity

0·0

0·2

0·4

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1·0

Extreme scenario

(a)

(b)

a

b

32oC33oC34oC35oC

28

29

30

31

1990 2010 2030 2050 2070Year

Dai

ly s

and

tem

pera

ture

(°C

)

Conservative scenario

Fig. 3. Predicted temporal effects of climate warming on (a) mean

annual sand temperatures at green turtle nest depth and (b) off-

spring viability for a range of sea turtle egg critical thermal max-

ima (CTMax 32–35 °C) from 1992 to 2070, under conservative

(upper panel) and extreme (lower panel) warming emission scenar-

ios. For (a), solid lines are the predicted fit derived from the

GAM regression model, and associated shading is the 95% confi-

dence intervals. For (b), different critical thermal maxima are

shown as different symbols, as per the associated key.

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

6 C. Cavallo et al.

mortality to temperature suggests this may be a significant

determinant of adult population size for sea turtles in the

future. The potential for thermal maxima to adapt in sea

turtles remains unknown, although a recent study has

shown that local adaptation in thermal reaction norms can

be incredibly fine scale (Weber et al. 2012). The question

to be asked is whether sea turtle thermal maxima adapt

and whether adaptation can keep pace with rapid climate

change.

POTENT IAL CONSEQUENCES OF CL IMATE- INDUCED

PHENOTYPES

Larval dispersal is an important trait for many marine

organisms, contributing significantly to population recruit-

ment and dynamics (Shima & Swearer 2010). For sea

turtles, successful hatchling dispersal and recruitment rely

in part on minimizing time spent in high-predation coastal

zones (Whelan & Wyneken 2007) and having enough

energy to fuel dispersal into pelagic habitats (Kraemer &

Bennett 1981). Our model results predict that rising incu-

bation temperatures have opposing effects on these traits

(speed and dispersal distance), causing significant reduc-

tions in speed, while modestly increasing energy stores. It

is difficult to predict the consequences of these complex

effects. However, the interaction between phenotype and

environment should influence the effects on dispersal: first,

because the water temperature hatchlings encounter will

either hinder or enhance their locomotor ability (Booth &

Evans 2011), and secondly, because of the potential for a

match or mismatch between phenotype and environmental

context. For example, oceanographic features at the

21

22

23

24

1990 2010 2030 2050 2070Year

Mea

n yo

lk−f

ree

mas

s (g

)

34

36

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40

1990 2010 2030 2050 2070Year

Res

idua

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rgy

stor

es (k

J)

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1990 2010 2030 2050 2070Year

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imum

sw

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st (m

N)

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1990 2010 2030 2050 2070Year

Max

imum

sw

imm

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stro

ke−r

ate

(Hz)

(a) (b)

(c) (d)Fig. 4. Predicted temporal effects of climate

warming on green turtle hatchling pheno-

type responses to (a, b) increasing incuba-

tion temperature and (c–d) the interaction

of incubation temperature and sea surface

temperature from 1992 to 2070, under con-

servative (blue) and extreme (red) warming.

Panels (a–d): (a) wet yolk-free hatchling

mass (g); (b) residual energy stores (kJ); (c)

maximum swimming thrust (mN); and (d)

maximum swimming stroke rate per second

(Hz). Solid lines are the predicted fit

derived from the GAM regression model,

and associated shading is the 95% confi-

dence intervals.

0·4

0·6

0·8

1·0

1990 2010 2030 2050 2070Year

Der

ived

sw

imm

ing

spee

d (k

m/h

)

42·5

45·0

47·5

50·0

1990 2010 2030 2050 2070Year

Max

imum

dis

pers

al p

oten

tial (

km)(a) (b)

Fig. 5. Predicted temporal effects of climate

warming on mean annual (a) derived swim-

ming speed (km/h), and (b) maximum dis-

persal potential (km) of green turtle

hatchlings in response to increasing incuba-

tion temperature and sea surface tempera-

ture from 1992 to 2070, under conservative

(blue) and extreme (red) warming. Solid

lines are the predicted fit derived from the

GAM regression model, and associated

shading is the 95% confidence intervals.

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

Climate effects on ectotherm offspring 7

coastal–oceanic interface can present obstacles to dispers-

ers, trapping them in coastal waters (Hamann et al. 2011),

and beaches fringed by reefs and rocky substrate experi-

ence very high levels of predation by fishes (Gyuris 2000).

In these environments, a reduction in speed could contrib-

ute significant losses to recruitment, since slower swimmers

are less well equipped to traverse this ‘wall of mouths’ and

escape inshore retention produced by currents and wave

activity (Okuyama et al. 2009; Putman et al. 2012). Smal-

ler hatchlings may also be at an enhanced risk of predation

by gape-limited predators (Gyuris 2000). Alternatively,

under relaxed predation or oceanographic entrapment,

increased energy stores could promote hatchling survival

until pelagic foraging grounds are reached.

Phenotypic diversity produced by incubation tempera-

ture may be an important risk-spreading adaptation for

temperature-dependent organisms in fluctuating environ-

ments (Hopper 1999). For example, production over a

beach or season of a range of phenotypes may allow ani-

mals to bet-hedge and increase the likelihood that some of

their total progeny will survive the dispersal phase. Our

results show that phenotypic variation in this population

will become canalized over the course of this century, due

to the increasing proportion of ‘warm’ incubations relative

to ‘cool’, with cohorts to be predominated by small, weak

and slow hatchlings, albeit with greater stores of residual

energy. Nest temperature-induced phenotypic canalization

such as this may increase the incidence of phenotype–envi-ronment mismatch (DeWitt, Sih & Wilson 1998) such that

overall cohort survival is reduced.

INCUBAT ION ENV IRONMENT VS . D ISPERSAL

ENV IRONMENT

A critical consequence of climate warming for marine spe-

cies is rising sea surface temperature (Brierley & Kings-

ford, 2009). Based on the intrinsic link between

temperature and locomotive performance, it might be

expected that warmer seas could ameliorate negative

effects of developmental temperature on larval perfor-

mance and dispersal. In this study, however, modelled

results indicated that accelerated stroke rate related to

warm sea surface temperature is negatively offset by

greatly diminished hatchling thrust, resulting from high

incubation temperatures (See Fig. S3b). It appears that

although warm ambient temperatures facilitate locomotive

performance through increasing metabolic rate [see (Booth

& Evans 2011)], realized performance is still highly depen-

dent on hatchling phenotype. Studies of the thermal plas-

ticity of myogenesis in fish larvae suggest that temperature

influences the number and type of muscle fibres laid down

during development (Galloway, Kjørsvik & Kryvi 1998;

Johnston et al. 1998). Given the relationship between tem-

perature, development and growth in turtles, longer incu-

bation lengths associated with cooler incubation

temperatures may allow for more advanced development

of the neuromuscular system (Spencer & Janzen 2011).

Further, ‘warm’ hatchlings are smaller, with smaller flip-

pers (Mickelson & Downie 2010). Therefore, warm seas

might speed up processes and facilitate faster stroke rate,

but if the musculature is less developed or the flippers

smaller, their maximum thrust is still going to be less than

that of a more developed phenotype. This suggests that,

for sea turtles, the incubation environment is a more sig-

nificant determinant of hatchling performance and that the

influence of sea surface temperature on dispersal is second-

ary. Results across other ectothermic marine dispersers

can be expected to be complex and diverse, since many are

highly temperature sensitive, and development as well as

performance may be entirely marine (Green & Fisher

2004; Martins et al. 2010).

Interestingly, the model predicted that green turtle dis-

persal distance would increase over the next 60 years,

despite the negative effect of elevated nest surface temper-

ature on speed. This is largely due to increased residual

energy stores (See Fig. 4b). These increased energy stores

may be important to future population recruitment, given

that survival relies on hatchlings arriving at suitable for-

aging grounds before starvation, coupled with the chang-

ing nature of ocean currents and plankton distribution. If

predation pressure is low, increased endurance may help

to counterbalance the diminished speed of hatchlings,

ensuring that at least some individuals recruit to pos-

thatchling communities. In other marine dispersers, how-

ever, reduced larval duration at higher temperatures may

actually restrict dispersal distance and affect broad-scale

ecological processes such as connectivity (Munday et al.

2009).

For sea turtles, the complicated and opposing responses

of endurance and speed to nest and sea surface tempera-

ture complicate the prediction of consequences for dis-

persal success. While incubation environment appears to

define dispersal performance in sea turtle hatchlings, this

may not be the case in other species, if labile performance

responses to temperature are stronger than those that

become fixed during development.

A THREE-STEP MODEL TO PREDICT ECTOTHERM

OFFSPR ING VIAB IL ITY , PHENOTYPE AND

PERFORMANCE

Recently, there have being major improvements in the

capacity to predict ectotherm responses to climate change

using mechanistic, or process driven, approaches including

biophysical (Kearney & Porter, 2004; Buckley 2008) and

dynamic energy budget models (Kearney 2012). However,

such models, as yet, have not accommodated additional

life-history processes such as natal dispersal that can con-

tribute substantially to how species, in the absence of

adaptive responses, will be affected by climate change. Our

flexible, integrative framework attempts to remove this

obstacle and make best use of the wealth of recorded

physiological data that exist for sea turtles. In three steps,

it incorporates a mechanistic microclimate model to

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

8 C. Cavallo et al.

predict sand temperatures, mechanistic development mod-

els to predict incubation temperature and duration and

empirically derived functions to estimate the effect of those

incubation conditions on final hatchling viability, pheno-

type and dispersal. The use of empirically derived func-

tions to predict phenotypic effects in this iteration is simple

and flexible, yet is limited by the availability of recorded

responses over a range of temperatures. Improvements to

this framework could be reached by the development of a

mechanistic, first principles model to replace correlations,

which would preclude reliance on available physiological

data sets. However, given the diversity of responses to tem-

perature by different ectotherms, such a model would be

less flexible in its applications across species. Importantly,

even as currently used, the model produces estimates that

appear biologically plausible. For example, predicted

swimming speeds of hatchling green turtles to global

warming approximate the range of hatchling swimming

speeds currently observed across sea turtles where there is

considerable phenotypic variation in body size that directly

influences swimming performance (O’Hara 1980).

Importantly, we acknowledge that there are two aspects

that could further improve our modelling approach to ulti-

mately ensure that the predicted estimates are of high qual-

ity and utility. First, consistent with all predictive

modelling approaches, we expect that there are several

potential sources of error that propagate uncertainty

around our derived estimates (Regan, Colyvan & Burgman

2002; Conlisk et al. 2013). Sources of uncertainty include

any assumptions that were made within and among mod-

els, and in part necessary to reconcile the complex dynam-

ics underpinning interplay among development,

environment and performance of organisms. We do not

discount the importance of recognizing uncertainty in our

model predictions but feel that quantifying it is beyond the

scope of the present paper whose main objective is to sim-

ply undertake exploratory analysis into potential responses

of organisms to climate change using a novel integrated

modelling approach. However, with further refinement of

our modelling techniques, we anticipate that directly quan-

tifying uncertainty would be a necessary prerequisite. Sec-

ondly, we see our model outputs are iterative estimates,

which should be updated as new information such as

updated climate forecasts or improvement in mechanistic

modelling approaches comes to hand. Adhering to these

important practices would serve to ensure predicted esti-

mates maintain the best utility for especially understanding

the impacts of climate change on sea turtle populations of

high conservation concern. Otherwise poor estimates of

high uncertainty could impede recovery effort and

waste valuable conservation resources (Regan, Colyvan &

Burgman 2002).

Conclusions

Our study suggests that climate warming has the potential

to significantly affect traits in individuals that are impor-

tant for recruitment and population stability, an area that

has received relatively poor attention in the context of cli-

mate change effects on ectotherms. Although here we have

focussed on a sea turtle application, this modelling

approach could be applied to many temperature-sensitive

species and will be particularly valuable for forecasting

population scale responses such as effects on cohort

survival and recruitment.

Acknowledgements

Funding was provided by a Mazda Foundation Grant to TSJ, TD, EK and

CC, and Holsworth Grants to EK and CC. We thank the Tiwi Land Coun-

cil, who gave permission for research to be conducted on their land and

assisted with fieldwork and remote site logistics.

Data accessibility

Data for this paper are deposited in the Dryad Digital Repository.

doi:10.5061/dryad.1t1n7 (Cavallo et al. 2014).

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Supporting Information

Additional Supporting information may be found in the online

version of this article:

Table S1. IPCC emissions scenarios and predictions.

Fig. S1. Seasonal variation in temperature and phenotypes for

2012.

Appendix S1. Supplementary methods.

Fig. S2. Validation of modelled temperatures against datalogger

records.

Fig. S3. Modeled thermal performance curves for measured traits.

© 2014 The Authors. Functional Ecology © 2014 British Ecological Society, Functional Ecology

Climate effects on ectotherm offspring 11