Mechanistic modeling of the effects of climate change … modeling of the effects of climate change...

Mechanistic modeling of the effects of climate change

on sea turtle migration to nesting beaches

A Thesis

Submitted to the Faculty

of

Drexel University

by

Noga Neeman

in partial fulfillment of the

requirements for the degree

of

Doctor of Philosophy

December 2014

ii

Dedication

“The highest function of ecology is understanding consequences”

-Frank Herbert (Dune)

iii

Acknowledgements

First off, I want to express my immense gratitude to my dissertation committee.

While it’s obviously true that I wouldn’t have been able to do this without each of your

contributions, I also feel like you have all played a huge role in my development both as a

scientist and as a person.

I first met Dr. James Spotila in Costa Rica, where he kindly agreed to talk to me

while I was still in the application process at Drexel. I believe it was this first meeting

that made my entire experience at Drexel possible. For that honor, as well as for how

surreal it’s been to work with him since then, I will always be thankful to Jim.

Once at Drexel, I introduced myself to Dr. Michael O’Connor, my advisor, who

has become a very important influence for me. It’s hard to imagine what my graduate

experience would have been like with a different advisor, and I don’t care to. Mike set a

very high standard for the type of researcher and mentor I’d like to be one day. He has

always looked out for my well-being as well as for my intellectual development, and

under his care I’ve become much more capable and confident. I can only hope to pay this

forward if I get the chance to mentor students of my own.

Mike and Jim introduced me to two more of my committee members: Dr. Gail

Hearn and Dr. Frank Paladino. Gail has always kept me on track looking for conservation

implications in my work and has been a huge support for me over the years. Frank has

always pushed me to understand and properly explain why my work is novel and how it

could be useful, which has made my research stronger.

iv

Finally, Dr. Shaya Honarvar and I first met at a departmental barbecue. The

warm, friendly, and welcoming tone she set in that first meeting has been maintained

throughout our relationship and she has been both a great mentor and a great friend. I

look forward to talking about turtles and life for years to come.

Of course I couldn’t have done this work without any data. Thank you to the Sea

Turtle Conservancy, the Leatherback Trust, and the US Fish and Wildlife Service for

allowing me to use their data for my work. And thank you to the volunteers, research

assistants, field coordinators, and scientific directors who have spent countless hours in

the field monitoring and protecting these sea turtles.

The faculty and staff at Drexel have enriched the graduate experience for me in

ways I can’t express. Thanks for great discussions during grad seminar and thanks for

always being there when I had questions. Special thanks to Susan Cole (BEES) and Taz

Kwok (Office of Graduate Studies) for making countless problems disappear for me over

the years and for always being so calming whenever I came to you.

To the Bio/BEES graduate students past and present (Gabi, Lucio, Annette,

Eugenia, Elena, Victoria, Annie, Sara, Karen, Steve, Maggie, Carlos Mario, Lori, Laurie,

Claire, Emily, Samir, Aliki, Jack, Yuxiang, Abby, Jules, Kevin, Dane, LeeAnn, Ryan,

Marilyn, Pat(rick), Jake, Alexis, Drew C, Deme, Pat(ricia), Paul S, Shauna, Anna J, Anna

V, Kaitlin, Nina, Alina, Yi, Narayan, Drew S, Karl, Tom, Mitch, Suruchi, Bo, Jasmine,

Eva, Siddhita, Paul U, and anyone I may have forgotten because I’m a little tired of

typing), thank you for making my experience at Drexel complete. Thank you for guiding

me, commiserating with me, celebrating with me, and generally showing me how to have

a good time while trying to do good science.

v

To my family (Itay, Barak, Keren, Or, Noam, Aviv, Nina, Monica) and

particularly to my parents (Bruria and Avraham), thank you for setting high standards for

me all my life and thank you even more for being patient and supportive as I try to live

up to them. I couldn’t have done any of this without our weekly talks, my recharging

visits to Costa Rica, and the constant knowledge that you’re there for me whenever I need

you.

And last, but absolutely not least, thanks to Tibi. It’s been an amazing five years!

I couldn’t have gotten through them without you holding my hand and I look forward to

many more. Thanks for your love and support, especially these past couple of months.

And thanks for agreeing to adopt Schmoopsie (the hedgehog) and Honey (the cat) -

p0vbg;[;-0

ftggggggggkkkkkkkkkkkkkkl – and I believe that’s her telling me to wrap up.

vi

Table of Contents

List of Tables ...................................................................................................................... x

List of Figures .................................................................................................................... xi

Abstract ............................................................................................................................ xiv

CHAPTER 1: General Introduction .................................................................................... 1

Phenological shifts in response to climate change ...................................................... 5

Change in remigration intervals in response to climate change .................................. 6

CHAPTER 2: Phenology shifts in leatherback turtles (Dermochelys coriacea) due to

changes in sea surface temperature ..................................................................................... 9

Abstract ........................................................................................................................... 9

Introduction ................................................................................................................... 10

Methods ......................................................................................................................... 14

Study sites .................................................................................................................. 14

Nesting data ............................................................................................................... 15

Temperatures ............................................................................................................. 16

Net Primary Production ............................................................................................. 17

Statistical analysis...................................................................................................... 17

vii

Results ........................................................................................................................... 19

Discussion ..................................................................................................................... 20

Tables and Figures ........................................................................................................ 26

CHAPTER 3: A simple, physiologically-based model of sea turtle remigration intervals

and nesting population dynamics: effects of temperature................................................. 35

Abstract ......................................................................................................................... 35

Introduction ................................................................................................................... 36

Methods ......................................................................................................................... 38

Forcing functions ....................................................................................................... 38

Algorithm................................................................................................................... 40

Parameterization ........................................................................................................ 40

Output ........................................................................................................................ 41

Simulation scenarios .................................................................................................. 41

Alternate model structure .......................................................................................... 42

Results ........................................................................................................................... 43

Parameterization ........................................................................................................ 43

Standard model .......................................................................................................... 43

Cohort diffusion ......................................................................................................... 43

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Pulse perturbations .................................................................................................... 44

Sinusoidal cycles ....................................................................................................... 45

Alternate model ......................................................................................................... 45

Discussion ..................................................................................................................... 46

Cohort dynamics ........................................................................................................ 46

Comparison to published results................................................................................ 49

Possible extensions .................................................................................................... 50

Conservation implications ......................................................................................... 51

Summary .................................................................................................................... 52

Figures ........................................................................................................................... 53

CHAPTER 4: Using a simple, physiologically-based model to predict leatherback turtle

(Dermochelys coriacea) nesting numbers and remigration intervals at two rookeries .... 62

Abstract ......................................................................................................................... 62

Introduction ................................................................................................................... 63

Methods ......................................................................................................................... 65

Standard model .......................................................................................................... 65

Foraging temperature data ......................................................................................... 66

Nesting data ............................................................................................................... 68

ix

Fitting the model to specific populations .................................................................. 69

Analysis ..................................................................................................................... 70

Results ........................................................................................................................... 70

Discussion ..................................................................................................................... 72

CHAPTER 5: General Conclusions .................................................................................. 84

List of References ............................................................................................................. 89

Appendix A: Pseudocode for main and alternate models ................................................. 99

Main model ................................................................................................................... 99

Alternate model ........................................................................................................... 100

Vita .................................................................................................................................. 101

x

List of Tables

Table 2.1. Correlations tested between leatherback turtle nesting dates and potential

temperature cues at nesting beaches and foraging grounds ....................................... 26

xi

List of Figures

Figure 2.1. Boundaries of foraging grounds for compilation of sea surface temperature

and net primary production data for leatherback turtles nesting at Playa Grande,

Costa Rica. ................................................................................................................. 27


and net primary production data for leatherback turtles nesting at Tortuguero, Costa

Rica. ........................................................................................................................... 28


and net primary production data for leatherback turtles nesting at Sandy Point, St.

Croix, US Virgin Islands ........................................................................................... 29

Figure 2.4. July temperature at the Lower South Equatorial Gyre in relation to the date by

which 5% of nests have been laid at Playa Grande, Costa Rica. ............................... 30

Figure 2.5A. Yearly minimum temperature at the Western North Atlantic in relation to

the date by which 10% of nests have been laid at Tortuguero, Costa Rica. .............. 31

Figure 2.5B. January temperature at the Gulf of Mexico in relation to the date by which

10% of nests have been laid at Tortuguero, Costa Rica. ........................................... 32

Figure 2.6A. Annual minimum temperature at the Bay of Biscay in relation to the date by

which 5% of nests have been laid at St Croix, US Virgin Islands. ........................... 33

Figure 2.6B. Annual maximum temperature at the Flemish Cap in relation to the date by

which 5% of nests have been laid at St Croix, US Virgin Islands. ........................... 34

Figure 3.1. Comparison between model remigration intervals and those published by

Wallace and Saba (2009) for leatherback turtles with different values of annual net

primary production. ................................................................................................... 53

Figure 3.2. Mean number of nesting turtles, from a total population of 500, predicted

under three modeled relationships between resource availability and remigration

probability. ................................................................................................................. 54

xii

Figure 3.3. Heat map of yearly remigration intervals (A) and mean (+/- standard

deviation) nesting numbers (B) for an initial sea turtle nesting cohort under a

constant temperature of 13.6°C. ................................................................................ 55


deviation) nesting numbers (B) for an initial sea turtle nesting cohort under a

constant temperature of 14.5°C. ................................................................................ 56

Figure 3.5. Predicted yearly nesting numbers of sea turtles after a pulse perturbation in

temperature. ............................................................................................................... 57


deviation) nesting numbers (B) of sea turtles following a decrease in temperature of

0.45°C. ....................................................................................................................... 58


deviation) nesting numbers (B) of sea turtles following an increase in temperature of

0.45°C. ....................................................................................................................... 59

Figure 3.8. Modeled nesting numbers of sea turtles under sinusoidal temperature

oscillations of varying cycle duration. ....................................................................... 60

Figure 3.9. Lag between minimum temperature and minimum nesting numbers of nesting

turtles under sinusoidal temperature oscillations of varying cycle duration. ............ 61

Figure 4.1. Observed nesting numbers for the leatherback populations at Playa Grande

(Costa Rica) and St. Croix (US Virgin Islands) and those predicted by the standard

model, using global average temperature and all three different metabolic functions.

................................................................................................................................... 76

Figure 4.2. Observed remigration intervals for the leatherback populations nesting at

Playa Grande (Costa Rica) and St. Croix (US Virgin Islands) and those predicted by

the standard model, using global average temperature and all three different

metabolic functions.................................................................................................... 77

Figure 4.3. Correlations between the mean monthly temperatures for three putative

foraging grounds for the population of leatherbacks nesting at Playa Grande, Costa

Rica. ........................................................................................................................... 78

xiii

Figure 4.4. Correlations between the mean monthly temperatures for four putative

foraging grounds for the population of leatherbacks nesting at St. Croix, US Virgin

Islands. ....................................................................................................................... 79

Figure 4.5. Observed nesting numbers for the leatherback population at Playa Grande

(Costa Rica) and those predicted by the theoretical model, using temperature signals

from the Equator/Upper South Equatorial Gyre (EQS1) and Lower South Equatorial

Gyre (S2) foraging grounds and the exponential metabolic function. ...................... 80

Figure 4.6. Observed remigration intervals for the leatherback population nesting at Playa

Grande (Costa Rica) and those predicted by the theoretical model, using temperature

signals from the Equator/Upper South Equatorial Gyre (EQS1) and Lower South

Equatorial Gyre (S2) foraging grounds and the exponential metabolic function. ..... 81

Figure 4.7. Observed nesting numbers for the leatherback population at St. Croix (US

Virgin Islands) and those predicted by the theoretical model, using temperature

signals from the Bay of Biscay/Mauritania (BBMA) and Flemish Cap/North Atlantic

Subtropical Gyre (FCNA) foraging grounds and the Monod metabolic function. .... 82

Figure 4.8. Observed remigration intervals for the leatherback population nesting at St.

Croix (US Virgin Islands) and those predicted by the theoretical model, using

temperature signals from the Bay of Biscay/Mauritania (BBMA) and Flemish

Cap/North Atlantic Subtropical Gyre (FCNA) foraging grounds and the Monod

metabolic function. .................................................................................................... 83

xiv

Abstract

Mechanistic modeling of the effects of climate change on sea turtle migration to nesting

beaches

Noga Neeman

Supervisor: Michael P. O’Connor

The purpose of this dissertation was to study how sea turtles currently respond to

changes in temperature, in order to predict how they may respond to climate change in

the future. I looked at two of the most commonly reported responses to changes in

temperature: phenology shifts and changes in the duration of remigration intervals. I

studied how the timing of leatherback nesting at three nesting beaches responds to

temperature changes at both the nesting and the foraging grounds. There was no effect for

local temperatures, but there was an overall trend for delayed nesting with increased

temperatures at the foraging grounds. Deviations from this trend as well as different

trends found in other studies suggest that the phenological response is complex and

variable. To look at remigration intervals, I developed a theoretical, physiologically-

based model that links temperature to resource availability and its accumulation by sea

turtles, remigration intervals, and nesting numbers. The model shows that apparent

nesting cohorts are formed not by life history traits but rather by a population-level

response to environmental temperatures and that these cohorts are unstable over time.

Using the model to explore different temperature history scenarios showed that short

pulses of altered temperatures can have a large effect on nesting numbers. Cold pulses

tend to synchronize nesting in the following year, owing to decreased remigration

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intervals, while warm pulses tend to delay nesting in a less synchronized way. Cyclical

temperature variation increases remigration intervals in general and leads to a cyclical

response in both remigration intervals and nesting numbers, with a lag and amplitude that

vary with cycle duration. Adapting this model to specific populations of leatherback

turtles reveals that it is able to capture both year-to-year and decade-to-decade trends in

remigration intervals for both populations. Due to the difficulties in isolating the effect of

strong population trends on nesting numbers and oscillations, it is unable to predict

nesting numbers. Future model iterations should include inherent population trends to

allow for better comparison and forecasting as well as using the model to help plan

conservation efforts and properly interpret changes in nesting numbers.

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CHAPTER 1: General Introduction

According to the latest meteorological data (IPCC 2013), it is certain that global

mean temperature has increased since the late 19th century. The past three decades have

all been warmer than any of the previous decades in the historical instrumental record,

and they have each been warmer than the last (IPCC 2013). Over the past 100 years, the

global average temperature has increased by about 0.85°C and this warming trend is

expected to increase at accelerated rates, in conjunction with the continued emission of

CO2 and other greenhouse gases (Hughes 2000, McMahon & Hays 2006, Hawkes et al.

2009, IPCC 2013).

The physical features of the earth’s surface, such as sea ice and glaciers, are

responding to climate change in a predictable way (Hughes 2000). In addition, the

anomalous climate of the 20th century is already affecting many taxonomic groups in

ways that are consistent with theoretical predictions (Hughes 2000, Parmesan 2006).

Meta-analyses of long term data sets indicate widespread, globally coherent, predictable

changes in response to climate change, in biological systems ranging from polar

terrestrial to tropical marine (Hughes 2000, Parmesan & Yohe 2003, Root et al. 2003,

Parmesan 2006, Hawkes et al. 2009).

The predicted effects of climate change on species and ecological communities

can be divided into four categories: effects on physiology (such as metabolic and

developmental rates and processes like respiration and growth), effects on species

distributions (such as density and range shifts), effects on phenology (the timing of life

cycle events triggered by environmental cues), and effects on genetic frequency (this type

2

of adaptation is most likely to occur in species with short generation times and large

population growth rates) (Hughes 2000, Root et al. 2003, Parmesan 2006). These changes

in physiology, distribution, and phenology can alter interactions between species and thus

local abundances and community composition (Hughes 2000, Parmesan 2006).

Extant sea turtles species arose during the middle-late Jurassic period and have

undoubtedly survived climate shifts in their evolutionary past, probably by shifting their

migratory routes, changing their foraging and nesting grounds, and adjusting

physiological parameters (Poloczanska et al. 2009). However, it is unclear whether or not

they will be able to adapt to anthropogenic climate change at its unprecedented rate

(Davenport 1997, Hawkes et al. 2007a, Poloczanska et al. 2009, Witt et al. 2010, Fuentes

et al. 2012).

Sea turtles inhabit a wide range of habitats throughout their life history, including

temperate and tropical sandy beaches where they nest, tropical and subtropical waters,

oceanic frontal systems and gyres, coastal mangrove forests, reefs, seagrass beds and

other shallow foraging areas (Musick & Limpus 1997, Spotila 2004, Poloczanska et al.

2009). During their development they may cross and interact with major oceanic currents

(Shillinger et al. 2008, Poloczanska et al. 2009).

All marine turtle species are considered vulnerable to climate change due to their

temperature-dependent sex determination (Davenport 1997, Hawkes et al. 2009,

Poloczanska et al. 2009), their long age to maturity (Scott et al. 2012, Poloczanska et al.

2009), their fidelity to both foraging and nesting grounds (Limpus et al. 1992, Davenport

1997, Broderick et al. 2007), and their current conservation status which is already

threatened due to anthropogenic pressures (Hawkes et al. 2009, Poloczanska et al. 2009).

3

Sea turtles will likely be impacted by climate change throughout the habitats they use and

throughout their life history stages (Hawkes et al. 2009, Poloczanska et al. 2009).

As developing embryos, sea turtles will be faced with altered incubation

conditions and duration (Chaloupka et al. 2008, Hawkes et al. 2009, Poloczanska et al.

2009), which may lead to feminization of embryos due to temperature-dependent sex

determination (Hawkes et al. 2007a, Fuentes et al. 2010) as well as increased egg and

hatchling mortality (Santidrian-Tomillo et al. 2012). Females could alter aspects of their

behavior to select for cooler nest sites (e.g. shaded areas, altering phenology to nest

during rainy seasons, altering migratory routes to nest at at higher latitudes), although

their capacity for this sort of adaptation is questionable (Hawkes et al. 2009).

As hatchlings and juveniles, changes in pelagic temperatures will mean changes

in the currents upon which sea turtles depend (Hawkes et al. 2009). Since hatchlings

disperse mostly due to passive drift on oceanic currents, this will lead to altered spatial

fate (Blanco 2010, Gaspar et al. 2012, Shillinger et al. 2012) as well as changes in the

abundance and composition of their predators and their prey (Hawkes et al. 2009). This

will affect mortality, growth rates and maturation. The full extent of the effects of these

changes remain unknown and are difficult to predict (Hawkes et al. 2009).

As adults, since sea turtles forage over large oceanic areas, it is possible that

widespread distribution will mitigate the effects of temperature on their prey distribution

and, therefore, their resource acquisition (Hawkes et al. 2009). However, since sea

surface temperature (SST) is an important determinant of their distribution and currents

have an unknown influence on their migrations, the effects of climate change are difficult

to predict (McMahon & Hays 2006, Hawkes et al. 2009). Observed effects on nesting

4

females include: decreased nesting abundance (Chaloupka et al. 2008), increased (Solow

et al. 2002) or decreased remigration probability (Saba et al. 2007), and decreased clutch

size (Mazaris et al. 2008).

Leatherback turtles, Dermochelys coriacea, feed on gelatinous zooplankton, for

which they forage in cold waters (Davenport 1997, James et al. 2005). From their

foraging grounds, they migrate to nest on tropical and subtropical beaches every 2-7

years (Reina et al. 2002, Bell et al. 2003, Santidrian-Tomillo et al. 2007). Leatherback

turtles are classified as critically endangered, owing mostly to population sizes and trends

inferred from nesting abundance studies (Spotila et al. 1996, Spotila et al. 2000,

Santidrian-Tomillo et al. 2007).

The Pacific subpopulation of leatherbacks is declining dramatically (Spotila et al.

1996, Santidrian-Tomillo et al. 2007) while different Caribbean subpopulations are either

stable to slowly declining (Troëng et al. 2004, 2007) or increasing (Robinson et al. 2014).

The main threats to these populations include incidental capture by fisheries (Spotila et

al. 1996, Santidrian-Tomillo et al. 2007), killing of nesting females on nesting beaches

(Troëng et al. 2004) , low overall hatching success (Bell et al. 2003, Ralph et al. 2005),

and illegal egg collection (Troëng et al. 2004, Santidrian-Tomillo et al. 2007).

With this dissertation, I set out to determine the potential effects of climate

change on leatherback turtle nesting migrations and population dynamics. I have focused

on the effects of rising temperature on their nesting phenology and remigration intervals

and studied three nesting populations, in collaboration with the organizations in charge of

their long-term monitoring and conservation: Playa Grande (Costa Rica), Tortuguero

(Costa Rica), and St. Croix (US Virgin Islands).

5

Phenological shifts in response to climate change

By far, the majority of observations of climate change response have involved

alterations in species’ phenology (Parmesan 2006). Phenological shifts have been

reported across taxonomic groups and across habitats (Parmesan & Yohe 2003, Root et

al. 2003). These include: plant flowering and budding; insect migrations, larval

development and emergence; and fish, amphibian, and bird reproduction and migrations

(Parmesan & Yohe 2003, Root et al. 2003, Parmesan 2006, Miller-Rushing and Primack

2008).

Several sea turtle species have been reported to shift their nesting seasons in

response to increasing temperatures: Green turtles (Chelonia mydas) nest earlier

(Weishampel et al. 2010) or do not shift their nesting seasons (Pike 2009) in Florida and

delay their nesting in the South-West Indian Ocean (Dalleau et al. 2012), while

loggerhead turtles nest earlier in Florida (Weishampel et al. 2004, Pike et al. 2006, Pike

2009, Weishampel et al. 2010), North Carolina (Hawkes et al. 2007b), and in the

Mediterranean (Mazaris et al.2008, Mazaris et al. 2009). Leatherback turtles have been

shifting their nesting seasons both in St. Croix and in Playa Grande, though this is

thought not to correspond to major climatic indices (Robinson et al. 2014).

A complication in studying phenological response to changes in sea surface

temperatures is that sea turtles, due their extensive nesting migrations, might be

responding to very different temperature cues at their foraging and nesting grounds.

Some authors suggest that changes in temperature cue turtles to leave their foraging

6

grounds at particular times which determine their arrival at nesting beaches and the start

of their nesting as soon as they arrive (Mazaris et al. 2009), while others suggest that

turtles arrive early at the nesting beaches and wait for optimal environmental conditions

in order to begin nesting (Eckert & Eckert 1988, Pike 2009).

In chapter 2, I investigate potential thermal cues for leatherback migration and

nesting for three populations (Playa Grande, Costa Rica; Tortuguero, Costa Rica; and

Sandy Point, US Virgin Islands), both at their foraging grounds and their nesting beaches.

I also examine the relationship between SST and net primary production (NPP, used as

an indicator of resource availability, Solow et al. 2002, Saba et al. 2007), as a possible

underlying mechanism for any observed phenological responses to changes in

temperature.

Change in remigration intervals in response to climate change

Sea turtles spend at least one year at their foraging grounds, accumulating

sufficient resources and body fat deposits to undertake their migration to distant nesting

beaches (Kwan 1994). Temperature changes at these foraging grounds can affect the

availability of resources, either primary or secondary production (Broderick et al. 2001,

Lynam et al. 2004, Richardson & Schoeman 2004, Hays et al. 2005).

Resource availability at the foraging grounds determines energy accumulation

rates and, therefore, the migratory schedules of sea turtles. Poor foraging conditions can

lead to delayed migration schedules and longer remigration intervals while good foraging

conditions can lead to shorter remigration intervals (Carr & Carr 1970, Chaloupka 2001,

7

Solow et al. 2002, Wallace et al. 2006, Saba et al. 2007, Troëng & Chaloupka 2007,

Hatase & Tsukamoto 2008, Suryan et al. 2009).

Individuals from a nesting population tend to experience similar foraging

conditions, so that nesting behavior becomes synchronized, leading to large oscillations

in yearly nesting numbers (Limpus & Nichols 1988, Hays 2000, Broderick et al. 2001,

Chaloupka 2001, Solow et al. 2002, Price et al. 2006, Chaloupka et al. 2008, Reina et al.

2002, Reina et al. 2009). These oscillations can obscure population trends and hinder the

assessment of general population trends (Broderick et al. 2001, Chaloupka 2001).

Due to these complications in assessing nesting trends and due to the added

urgency of their assessment in order to estimate and quantify the effects of climate

change on sea turtle population sizes and abundance, several authors have called for a

theoretical model than can unify the effects of climate change on resource acquisition by

individual turtles and the resulting effects on nesting numbers population dynamics (Price

et al. 2006, Chaloupka et al. 2008, Wallace & Jones 2008, Wallace & Saba 2009). With

climate change and its resulting temperatures changes at the foraging grounds, further

resource limitation is expected for sea turtles (Richardson & Schoeman 2004, Saba et al.

2007) as well as inevitable effects on population dynamics.

In Chapter 3, I present a simple, physiologically-based model that links

environmental temperatures to resource availability at the foraging grounds, which in turn

affect the energy stores and remigration probabilities of individual sea turtles within a

stable, theoretical population. This ultimately alters observed nesting numbers and may

allow for more realistic population projections under various climate change scenarios, as

8

the model depends on global average sea surface temperatures as its environmental

signal.

With such a model, it becomes possible to isolate the effects of climate change on

nesting populations in order properly identify other factors which may play an effect on

population sizes and trends, such as anthropogenic impacts (Saba et. al 2007). This can

then lead to improved forecasting at the nesting beaches, more effective management

strategies, and a better understanding of how climate change may already be affecting

nesting populations and what measures should be taken to mitigate these effects (Saba et

al. 2007, Chaloupka et al. 2008, Wallace & Jones 2008, Wallace & Saba 2009).

In order to determine whether the proposed model could indeed be used for these

purposes, in Chapter 4 I compare the model output (remigration intervals and nesting

numbers) with observed data from two nesting populations: Playa Grande, Costa Rica

and St. Croix, US Virgin Islands. These two populations show opposite overall trends:

decreasing for Playa Grande (Spotila et al. 1996, Santidrian-Tomillo et al. 2007) and

increasing for St. Croix (Robinson et al. 2014), so that testing the model under these

different circumstances makes it possible to determine whether or not the model can

capture the climate fingerprint despite the opposing population trends. It can also help

determine whether these opposite trends may be driven by climate or if there are other

factors influencing the populations.

9

CHAPTER 2: Phenology shifts in leatherback turtles (Dermochelys coriacea) due to

changes in sea surface temperature

Abstract

Sea turtles have responded to climate change in the past, but it is unclear whether

they will be able to respond to the unprecedented rate of anthropogenic climate change.

One way to respond would be altering the timing of their nesting to align with changes in

temperature, which may lead to altered incubation conditions, hatching success, sex

ratios, and hatchling dispersal. Here, I investigate whether the timing of the nesting

season for three populations of leatherback turtles (Playa Grande, Costa Rica;

Tortuguero, Costa Rica; and St. Croix, US Virgin Islands) varies with (and putatively in

response to) sea surface temperatures at either their nesting or foraging grounds, as a

proxy for how they would respond to warming trends. At the foraging grounds I

examined several candidate temperatures: annual maximum and minimum of the year

prior to nesting and month in which turtles were estimated to leave their foraging

grounds. At the nesting grounds I considered: temperatures at the start of nesting and

over the whole season as well as a measure of seasonality at the foraging grounds.

Seasonality at the foraging grounds and temperatures at the nesting beaches did not affect

nesting phenology, while temperatures at some foraging grounds did. Different

temperature signals appeared related to nesting at different foraging grounds as was the

direction in which these increased temperatures shifted nesting, suggesting that there

might be a mediating factor explaining the temperature effect. I therefore looked at the

relationship between temperature and primary production at the foraging grounds to

explain these differences but found no consistent relation between temperature and

10

production. The overall pattern is that increased temperatures at the foraging grounds

tend to delay nesting, which is different from previous studies for other species of sea

turtles that show earlier nesting with increased temperatures either at nesting or foraging

grounds. Further study is needed at the nesting beaches to determine how environmental

conditions change within the season and how these changes affect nesting success, so that

it’s possible to predict what temperature, humidity, and currents will look like in the new,

shifted nesting seasons and how that will affect hatching success, sex ratios, and

hatchling dispersal; i.e., will delayed nesting seasons help mitigate climate change effects

on these populations or exacerbate them?

Introduction

The ecological effects of climate change are already apparent in an increasing

number of species (Hughes 2000). Meta-analyses have discerned widespread, predictable

changes in species distribution and phenology in response to climate change (Parmesan &

Yohe 2003, Root et al. 2003). There are several possible mechanisms by which species

can adapt to climate change, including density and range shifts, morphological changes,

shifts in genetic frequency (Root et al.2003), and physiological adaptations (Hughes

2000). The most commonly studied responses to climate change are shifts in phenology,

or changes in the timing of seasonal events (Parmesan 2006). Phenological changes have

been observed across diverse groups including shifts in plant flowering, tree budburst,

arrival of migrant butterflies, bird nesting, amphibian spawning, insect larval

development, spring greening, and fish spawning (Parmesan & Yohe 2003, Root et al.

2003, Parmesan 2006, Miller-Rushing and Primack 2008).

11

Sea turtles have survived climate shifts in their evolutionary past but it is unclear

how and whether they will be able to adapt to the unprecedented rate at which climate

change is currently occurring (Davenport 1997, Hawkes et al. 2007b, Poloczanska et al.

2009, Witt et al. 2010, Fuentes et al. 2012). Some effects of increased temperatures have

already been observed in sea turtles, such as decreased nesting abundance (Chaloupka et

al. 2008), feminization of embryos due to temperature-dependent sex determination

(Hawkes et al. 2007b, Fuentes et al. 2010), increased (Solow et al. 2002) or decreased

remigration probability (Saba et al. 2007), decreased clutch size (Mazaris et al. 2008),

and increased egg and hatchling mortality (Santidrian Tomillo et al. 2012).

Changes in phenology may mitigate the effects of climate warming, e.g. by

nesting when temperature is lower (Saba et al. 2012). These changes play an important

role in the capacity of sea turtles to survive climate change because their fidelity to both

foraging and nesting grounds (Limpus et al. 1992, Davenport 1997, Broderick et al.

2007) limits their ability to adapt to climate change through spatial changes in nesting

distribution. It also means that they might have to respond to two sets of cues at two

distinct, geographically separated locations: their foraging and nesting grounds. Sea

turtles are also limited in their response to climate change by several life history features:

their long generation times (Scott et al. 2012), their physiological dependence on

favorable temperatures which makes physiological adaptation more difficult (Davenport

1997, Hawkes et al. 2009), and by the fact that they are already endangered due to

anthropogenic factors (Hawkes et al. 2009, Poloczanska et al. 2009).

Phenological shifts might also mean that developing nests now face a potentially

different set of conditions such as temperature and rainfall, which will determine their sex

12

ratios (Ackerman 1997, Hays et al. 2010, Katselidis et al. 2012) and hatching success

(Miller 1997, Saba et al. 2012). In addition, since hatchlings disperse after emergence

mostly due to passive drift on oceanic currents, which change at different times of the

year, phenological shifts may alter their spatial fate and possible survival (Blanco 2010,

Gaspar et al. 2012, Shillinger et al. 2012).

Sea turtles have been demonstrated to shift their nesting seasons in response to

increased temperature, but in different ways. Green turtles (Chelonia mydas) nest earlier

(Weishampel et al. 2010) or do not shift nesting dates (Pike 2009) in Florida and nest

later in the South-West Indian Ocean (Dalleau et al. 2012), while loggerhead turtles nest

earlier in Florida (Weishampel et al. 2004, Pike et al. 2006, Pike 2009, Weishampel et al.

2010), North Carolina (Hawkes et al. 2007b), and in the Mediterranean (Mazaris et

al.2008, Mazaris et al. 2009). Differences between sites might be explained by stronger

temperature responses at higher latitudes (Mazaris et al. 2013). Previous studies on

leatherback populations in Costa Rica and the US Virgin Islands show that leatherback

turtles are shifting their seasons, but that the shift does not correspond to major climatic

indices (Robinson et al. 2014). Thus, the signals that sea turtles respond to are unclear

and the driving forces that determine their behavior may also vary between species and

populations.

Leatherback turtles forage for gelatinous zooplankton in cold waters (Davenport

1997, James et al. 2005) and migrate to nest in tropical and subtropical beaches every 2-7

years (Reina et al. 2002, Bell et al. 2003, Santidrian-Tomillo et al. 2007). If temperature

provides the cue for reproductive migration, the mechanism by which this happens is

unclear, whether temperature influences turtles directly or is mediated by effects on food

13

availability. It is also unclear which temperature cue is more important. Some authors

suggest that turtles start their migration due to a temperature cue and then nest soon after

arriving at their nesting grounds (Mazaris et al. 2009), while others suggest that turtles

arrive early at the nesting beaches (Eckert & Eckert 1988, Pike 2009) in order to mate

with males and complete the development of their first clutch of eggs (Schofield et al.

2013). The cues would then be at the nesting grounds, where they would wait to nest

when the environmental conditions are optimal. (Eckert & Eckert 1988, Pike 2009,

Katselidis et al. 2012). This is confounded further due to findings that some leatherback

populations engage in foraging excursions during their internesting interval (Georges et

al. 2007, Byrnes et al. 2009), potentially encountering temperature or resource cues not

considered before.

Here I examine thermal cues for leatherback turtles to nest, by looking at

phenological shifts in turtles nesting at three beaches (Playa Grande, Costa Rica;

Tortuguero, Costa Rica; and Sandy Point, US Virgin Islands) and how those shifts relate

to sea surface temperature (SST) at both foraging and nesting sites. I also examine the

relationship between SST and net primary production (NPP, used as an indicator of

resource availability, Solow et al. 2002, Saba et al. 2007), as a possible underlying

mechanism for phenological responses to temperature changes. Since sufficiently long

data sets for the studied beaches are not available to look at climate change directly, I use

interannual variability to determine if leatherback turtles change their nesting phenology

in response to changes in oceanic temperature associated with climate change. From this

information, it becomes possible to make predictions about how leatherback turtles will

be able to adapt to conditions of climate change.

14

Methods

Study sites

Playa Grande (Figure 2.1) is a 3.5km long, low-medium energy beach located in

Pacific Northwest Costa Rica (10°20’N, 85°51’W). For this study, I considered it as one

nesting unit together with adjacent Playa Ventanas (Steyermark et al. 1996). I

circumscribed the following putative foraging grounds for leatherback turtles nesting at

Playa Grande (Saba et al. 2007,Shillinger et al. 2008, Shillinger et al. 2011): Equator

(5.5°S-5.5°N, 84.5-110.5°W), Upper South Equatorial Gyre (4.5-20.5°S, 79.5-120.5°W),

and Lower South Equatorial Gyre (24.5-35.5°S, 84.5-105.5°W). I split the South

Equatorial Gyre into two different areas to better represent its triangular shape and

possibly different conditions due to its extensive N-S range.

Tortuguero (Figure 2.2) is a 35.4km long, highly dynamic beach located on the

Caribbean coast of Costa Rica, between the Tortuguero River mouth (10°35’N, 83°31’W)

and the Parismina River mouth (10°19’N, 83°21’W) (Fowler 1979). I delineated the

following putative foraging sites for leatherback turtles nesting at Tortuguero, by looking

at tracking data available on the Sea Turtle Conservancy website

(http://www.conserveturtles.org/seaturtletracking.php): Gulf of Mexico (24.5-29.5°N,

83.5-95.5°W), Western North Atlantic (36.5-43.5°N, 54.5-68.5°W), and Eastern North

Atlantic (35.5-43.5°N, 10.5-20.5°W).

Sandy Point (Figure 2.3) is a 3km long, dynamic beach on St. Croix, US Virgin

Islands (17°40’N, 64°52’W) (Boulon et al. 1996). I chose putative foraging sites for

15

leatherback turtles nesting at Sandy Point, to match those studies for the Trinidad

population (Eckert 2006) due to a lack of tracking data for Sandy Point. They were as

follows: Bay of Biscay (34.5-45.5°N, 9.5-15.5°W), Flemish Cap (44.5-50.5°N, 34.5-

45.5°W), Mauritania (14.5-30.5°N, 14.5-25.5°W), and North Atlantic subtropical front

(34.5-40.5°N, 34.5-45.5°W).

Nesting data

At each beach, nesting by leatherbacks is documented each year (October to

March at Playa Grande, March-June at Tortuguero, and March-August at Sandy Point)

and nesting has been monitored by conservation organizations for at least 9 seasons. The

data used in this study are for the following nesting seasons (which occur at each beach

during the months listed above) for each of the sites (Playa Grande:1993-2012,

Tortuguero: 2002-2010, Sandy Point:1983-2010).

Turtle tracks are recorded during nightly patrols and daily morning surveys at

Playa Grande and Sandy Point, while they are recorded every three days at Tortuguero.

For this study, I counted all recorded tracks, without separating nests from tracks that did

not lead to a nesting event (false crawls) due to the difficulty in distinguishing real nests

and false crawls (both by volunteers in the field and while curating resulting data). A few

very early nests may be missed at the beginning of the season, so that no exact date can

be given for the start of nesting. Therefore, I approximated the start of nesting by looking

at the dates by which a small percentage of total nests for the season were laid, the 5th and

10thpercentiles of all nests during a single season. I interpolated these percentiles by

considering cumulative nests as a function of Julian date. Since nesting season extends

from one calendar year to the next in Playa Grande, I merged Julian dates between pairs

16

of years that corresponded to one nesting season (January first being 366, etc). Early

percentiles were comparable between the three nesting beaches and among different

years, with about 1% of the season’s nests laid each day, so it was not necessary to

standardize the percentiles between different nesting years.

For three out of the nine seasons in Tortuguero, more than 5% of nests were

missed at the beginning of the season (known because the first nesting survey counts all

visible nests on the beach and leatherback nests can stay visible for up to two weeks). It

was not possible to accurately parameterize the nest distributions for these seasons, so the

earliest date used was the 10th percentile. When looking at the nest distributions for the

seasons in which the 5th percentile was missed, they appear to be truncated at an early

stage in the season in which the number of nests per day is still low.

Temperatures

I used monthly average sea surface temperature (SST) from the NOAA NCEP

EMC CMB GLOBAL Reynolds and Smith v2 data set (Reynolds et al. 2002), available

on the International Research Institute for Climate and Society website

(http://iridl.ldeo.columbia.edu/docfind/databrief/cat-airsea.html). This dataset combines

ship, buoy and satellite-corrected temperature data at a resolution of 1x1°. For each

foraging site, I averaged out temperatures over the entire area, for each month. I

considered the following candidate SST cues at each foraging site: yearly maximum and

minimum for the year prior to nesting, and the temperature for the month at which I

estimated (based on available tracking data) that turtles would be starting their migration

to the nesting beach. Annual minimum and maximum temperatures were a proxy for long

term temperature shifts that might affect foraging conditions and resource acquisition,

17

while the temperature for the month at which migration was thought to start was a

possible cue for migration. For each nesting site, I looked at a 1x1 degree area centered

on the nesting beach and evaluated the average seasonal temperature (starting two months

prior to the start of nesting and including all nesting months) and the temperature for the

early part of the nesting season (two months prior to nesting and first month of nesting). I

include two months prior to the recorded start of the nesting season to account for sea

turtles arriving early to mate and complete first clutch development (Schofield et al.

2013).

Net Primary Production

I took average monthly Net Primary Production (NPP) estimates from the Ocean

Productivity website

(http://orca.science.oregonstate.edu/1080.by.2160.monthly.hdf.vgpm.s.chl.a.sst.php).

They were calculated using the Vertically Generalized Production Model (Behrenfeld &

Falkowski 1997) that estimated production based on surface chlorophyll concentrations

(from SeaWiFS for one data set and from MODIS for another), sea surface temperature

and photosynthetically active radiation. I averaged NPP estimates for all the cells in each

foraging area, for each month. In order to obtain a longer time series, MODIS NPP

estimates were regressed to the SeaWiFS estimates for which their dates overlapped and

(because they were very highly correlated) converted to SeaWiFS estimates and merged

with them.

Statistical analysis

18

For each nesting beach, I correlated the 5th and 10th percentile dates (or dates by

which 5 or 10 percent of all the nests for the season were laid) with each of the candidate

temperatures for each of the population’s foraging sites (maximum, minimum, and

migration month) as well as with the first month and seasonal average for local

temperature at the nesting site.

In order to determine whether there was an effect of seasonality (i.e., early or late

annual shifts in warming or cooling) at the foraging grounds on nesting dates, I used the

de-trended temperature data from 1982 to 2010 at each of the foraging grounds and

calculated the temporal deviation between when temperatures are reached in an average

year and when they are reached each recorded year. For each foraging ground, I

correlated the average deviation for the months prior to the estimated departure date with

the 5th and 10th percentile dates.

For the relationship between NPP and temperature, NPP estimates are only

available from 1997 to 2009, which omits some of the years for which nesting data are

available. Therefore, it was not possible to correlate nesting data with NPP without losing

a large volume of nesting data. Instead, to see if any of the relationships actually reflected

an underlying relationship with NPP, I correlated temperature with NPP for each of the

foraging sites. Since the magnitude and direction of the relationship between temperature

and NPP changes throughout the year, these analyses were completed separately for each

month as well as between the relevant temperature (for each foraging ground) and

maximum, minimum, and average yearly NPP.

19

Results

I tested a total of 72 correlations between nesting dates and candidate

temperatures, of which 10 were significant or suggestive for both the earliest and second

percentiles tested. I chose these relationships as ecologically relevant and explored them

further by looking at the relationships between SST and NPP. I also tested 20 correlations

between seasonal offsets in the timing of temperatures and nesting dates, of which one

was significant at the earliest percentile tested but not at the second percentile tested. I

did not consider this relationship ecologically relevant and, therefore, did not study it

further.

There was no correlation between nesting dates and local beach SST for any of

the nesting beaches, either for the early nesting period or for the average seasonal

temperature. Each nesting beach had at least one foraging site at which temperature was

correlated to nesting date (Table 1).

For Playa Grande, nesting date was correlated with the July temperature at the

lower South Equatorial Gyre (r = 0.501, p = 0.024, n = 20, Figure 2.4).

For Tortuguero, nesting date was correlated with annual minimum temperature at

the Western North Atlantic (r = 0.824, p = 0.006, n = 9, Figure 2.5A) and with January

temperatures in the Gulf of Mexico (r = -0.760, p = 0.017, n = 9, Figure 2.5B).

For Sandy Point, nesting date was correlated with the annual minimum at the Bay

of Biscay (r = 0.386, p = 0.043, n = 28, Figure 2.6A) and with the annual maximum at the

Flemish Cap (r = 0.398, p = 0.035, n = 28, Figure 2.6B).

20

There were no consistent correlations between the relevant temperatures for each

foraging ground and yearly minimum, average, or maximum NPP, nor between

temperature and NPP for the relevant months (data not shown). There was also no

correlation between the measure of seasonality used and nesting dates (data not shown).

Discussion

I attempted to distinguish whether it was temperature at the foraging grounds or at

the nesting grounds that served as a cue for leatherback turtles to travel to their nesting

sites. There was no shift in nesting phenology at any site due to local temperatures near

the nesting beach. This is more consistent with the hypothesis (Mazaris et al. 2009) that

these turtles start their migration due to temperature cues at the foraging grounds than

with the hypothesis that they arrive at the nesting beach early and wait for an optimal

local temperature cue (Eckert & Eckert 1988, Pike 2009). It is worth noting that these

individuals might travel to foraging grounds during their nesting period (see Georges et

al. 2007, Byrnes et al. 2009), providing new foraging areas that have not been studied

and cues not explored here.

At the foraging grounds, there was no correlation between nesting phenology and

the seasonal offsets in the timing of temperatures, i.e. early vs late dates of achieving

mean seasonal temperatures. There were some significant correlations of nesting dates

with candidate foraging ground temperatures. None of these correlations are strongly

significant and there were many correlations, so it is possible that these relationships are

false positives. Given the multiplicity of foraging sites per nesting beach and the many

21

factors that may affect how turtles decide when to migrate (Parmesan 2006) it remains

surprising that each of the beaches in this study had at least one foraging site for which

temperature correlated with observed nesting dates.

The data suggest that migratory cues are complex and might differ between

nesting beaches and among foraging grounds for the same nesting beach. There was no

indication that sea turtles respond to changes in seasonality at the foraging grounds, but

there were diverse responses to temperature: some foraging populations appear to

respond to minimum annual temperatures (Western North Atlantic and Bay of Biscay),

some to maximum temperatures (Flemish cap) and some to temperature in the month in

which their migration starts (Lower South Equatorial Gyre and Gulf of Mexico). It is not

apparent that any common underlying factor drives all of these correlations.

The most common trend is for increased temperatures at the foraging grounds to

lead to later nesting for leatherback turtles. This was observed for all except one of the

foraging grounds. This is different from results of other studies on nesting beaches

(Weishampel et al. 2004, Hawkes et al. 2007b, Mazaris et al.2008, Mazaris et al. 2009,

Pike 2009, Weishampel et al. 2010) in which increased temperatures lead to earlier

nesting or did not affect nesting dates. This difference might be explained by mean sea

surface temperature, with warmer waters showing different effects than colder waters as

observed in the different foraging grounds for the Tortuguero nesting population in this

study.

At Tortuguero, increased temperatures at different foraging grounds appear to

have opposite effects on nesting dates. This means that looking at median nesting dates

22

would no longer show the effect, because individuals would have arrived from several

foraging grounds therefore obscuring the separate effects for each site. Indeed, median

nesting dates showed no correlation to temperature in the present study (data not shown).

Another possible explanation for not finding an effect of temperature on median nesting

dates is that multiple nests are already counted for each individual turtle when looking at

median nesting dates, so they become very sensitive to any changes in nesting start date,

end date, and interesting interval. This highlights the importance of looking both at

median nesting dates and at early nesting dates. These two possibilities offer different

signals with different sensitivities as well as potentially different evolutionary

implications

The relative lack of net primary production data made it impossible to correlate

nesting dates directly with NPP without losing a large part of the nesting data, but no

patterns emerged from the relationship between temperature (correlated with nesting) and

NPP at the foraging grounds. The diversity of relationships between temperatures (that

correlated with nesting date) and simultaneous NPP estimates suggests that the effect of

temperature on nesting phenology was not driven by NPP. The NPP was used as an

indicator of resources available to the turtles. It is possible, however, that NPP is poorly

correlated with the abundance of jellyfish, the dominant food item for leatherback turtles,

or that a lag exists between temperature changes and jellyfish population response such

that temperature may actually be a better indicator of leatherback resource availability

(Sherrill-Mix et al. 2008). Another possibility is that this study did not adequately reflect

the relationship between temperature and primary production at each site, due to

insufficient data.

23

Sea turtles at one nesting beach come from different foraging grounds, which is a

confounding factor when trying to find cues at different foraging grounds for the same set

of nesting data because temperature changes at one foraging site may not correlate with

those at another site. Further, there were no data on whether turtles from certain foraging

grounds arrive at the nesting beach earlier than others. These factors would tend to

weaken or mask the correlations between nesting dates and foraging ground

temperatures. There were, however, some significant correlations. If individual females

could be associated with particular foraging areas (e.g., by identifying the turtle that

created each track and estimating foraging area via stable isotope analysis), these

confounding factor could be reduced or eliminated. Additional studies at foraging

grounds, such as Sherrill-Mix et al. (2008), would also help determine how temperature

affects date of migration departure and nesting site arrival.

Further study is needed to determine why the Gulf of Mexico has a pattern that

deviates from the majority. Richardson and Schoeman (2004) find that colder waters

show a direct relationship between temperature and production while warmer waters

show an inverse relationship. If the warmer waters in the Gulf of Mexico mean a more

productive environment, individuals may respond to this by delaying their migration in

order to accumulate more reserves before their migration. It is possible that this

relationship was not accurately represented in this study due to the limited NPP data set.

It is also possible that resource availability in the Gulf of Mexico is driven more by other

factors than by temperature. Primary production is linked to fluvial discharge during the

winter months in the Gulf of Mexico, which may affect jellyfish abundance (Graham

2001). Another possible explanation is that jellyfish abundance responds not to NPP but

24

to episodes of hypoxia in the Gulf of Mexico, which are linked to temperature (Diaz &

Solow 1999, Diaz & Rosenberg 2008). Such hypotheses could explain a resource driven

effect that resulted in no apparent correlation with NPP, but a significant correlation with

surface temperatures.

In conclusion, I am unable to offer specific management implications because

there were no consistent patterns other than an overall trend towards later nesting with

increased temperatures at the foraging grounds. Instead, it appears that migratory cues are

complex and variable between sites. This raises the possibility that there might not be an

overarching effect of temperature on nesting dates, but rather various local population

effects. There was no support for local sea surface temperature effects on nesting dates

but there was a trend for altered nesting dates due to increased temperatures at the

foraging grounds. This favors the hypothesis that foraging grounds are central to nesting

phenology. The present study also highlights the importance of looking at early nesting

percentiles as well as median nesting dates when studying nesting phenology. The trend

toward delayed nesting due to increased temperatures at the foraging grounds is not

consistently driven by temperature effects on NPP. Further study is needed to determine

why the Gulf of Mexico shows the opposite trend and what this says about which cues

sea turtles actually respond to when initiating their nesting migrations: how does

temperature cue nesting and why is this different at different sites?

Further studies at the foraging sites should determine what other factors are

related to the timing of sea turtle migration other than temperature, e.g. bathymetry and

currents, in order to present a more complete picture of the cues that turtles respond to. At

the nesting beaches, research should focus on how environmental conditions change

25

within the season and how they affect nesting success, so that it is possible to predict

what temperature, humidity, and currents will look like in the new, shifted nesting

seasons and how that will affect hatching success, sex ratios, and hatchling dispersal.

Will delayed seasons help mitigate climate change effects on these populations or

exacerbate them?

26

Tables and Figures

Table 2.1. Correlations tested between leatherback turtle nesting dates and potential

temperature cues at nesting beaches and foraging grounds

Nesting populations

Playa Grande, Costa

Rica

Tortuguero, Costa

Rica

St. Croix, US Virgin

Islands

Local temperature

Early nesting period r = 0.174, p = 0.462 r = 0.587, p = 0.097 r = -0.023, p = 0.905

Seasonal Average r = 0.249, p = 0.289 r = 0.281, p = 0.463 r = -0.043, p = 0.824

Foraging temperatures

Equator Gulf of Mexico Flemish Cap

Annual maximum r = -0.026, p = 0.912 r = -0.182, p = 0.639 r = 0.337, p = 0.079

Annual minimum r =0. 391, p = 0.088 r = 0.367, p = 0.331 r = 0.210, p = 0.283

Departure month r = 0.180, p = 0.450 r = -0.760, p = 0.018* r = 0.278, p = 0.152

Upper South

Equatorial Gyre

Eastern North.

Atlantic

North Atlantic

Subtropical Front

Annual maximum r = 0.162, p = 0.495 r = -0.084, p = 0.831 r = 0.229, p = 0.241

Annual minimum r = 0.481, p = 0.031* r = -0.335, p = 0.379 r = 0.108, p = 0.585

Departure month r = 0.211, p = 0.371 r = 0.421, p = 0.260 r = 0.281, p = 0.147

Lower South

Equatorial Gyre

Western North

Atlantic Bay of Biscay

Annual maximum r = 0.237, p = 0.315 r = 0.242, p = 0.531 r = 0.145, p = 0.463

Annual minimum r = 0.415, p = 0.068 r = 0.824, p = 0.006* r = 0.386, p = 0.043*

Departure month r = 0.501, p = 0.024* r = 0.169, p = 0.664 r = -0.052, p = 0.791

Mauritania

Annual maximum

r = 0.151, p = 0.443

Annual minimum

r = 0.351, p = 0.067

Departure month r = 0.157, p = 0.424

Note: these results correspond to the earliest percentiles tested for each nesting population. I only

considered correlations meaningful if they were significant or suggestive (p<0.1) for both the earliest and

second earliest percentile tested.

27


and net primary production data for leatherback turtles nesting at Playa Grande, Costa

Rica.

28


and net primary production data for leatherback turtles nesting at Tortuguero, Costa Rica.

29


and net primary production data for leatherback turtles nesting at Sandy Point, St. Croix,

US Virgin Islands

30

Figure 2.4. July temperature at the Lower South Equatorial Gyre in relation to the date by

which 5% of nests have been laid at Playa Grande, Costa Rica.

31

Figure 2.5A. Yearly minimum temperature at the Western North Atlantic in relation to

the date by which 10% of nests have been laid at Tortuguero, Costa Rica.

32

Figure 2.5B. January temperature at the Gulf of Mexico in relation to the date by which

10% of nests have been laid at Tortuguero, Costa Rica.

33

Figure 2.6A. Annual minimum temperature at the Bay of Biscay in relation to the date by

which 5% of nests have been laid at St Croix, US Virgin Islands.

34

Figure 2.6B. Annual maximum temperature at the Flemish Cap in relation to the date by

which 5% of nests have been laid at St Croix, US Virgin Islands.

35

CHAPTER 3: A simple, physiologically-based model of sea turtle remigration

intervals and nesting population dynamics: effects of temperature

Abstract

Variation in the yearly number of sea turtles nesting at rookeries can interfere

with population estimates and obscure real population dynamics. Previous theoretical

models suggested that this variation in nesting numbers may be driven by changes in

resources at the foraging grounds. I developed a physiologically-based model that uses

mean global temperatures to predict foraging conditions, resource accumulation,

remigration probabilities, and, ultimately, nesting numbers for a stable population of sea

turtles. I used this model to explore several scenarios of temperature variation at the

foraging grounds, including one-year perturbations and cyclical temperature oscillations.

I found that thermally driven resource variation can indeed synchronize nesting in groups

of turtles, creating cohorts, but that these cohorts tend to break down over 5-10 years

unless regenerated by environmental conditions. Cohorts were broken down faster at

lower temperatures. One-year perturbations of low temperature had a synchronizing

effect on nesting the following year, while high temperature perturbations tended to delay

nesting in a less synchronized way. Cyclical temperatures lead to cyclical responses both

in nesting numbers and remigration intervals, with the amplitude and lag of the response

depending on the duration of the cycle. Overall, model behavior is consistent with

observations at nesting beaches. Future work should focus on refining the model to fit

particular nesting populations and testing further whether or not it may be used to predict

observed nesting numbers and remigration intervals.

36

Introduction

Abiotic environmental factors, resource limitation, and physiology are major

influences on an organism’s life history. An individual’s physiological state interacts with

the biophysical environment to determine the total amount of resources available to

allocate to different processes, such as maintenance, growth, storage, and reproduction

(Dunham et al. 1989).

For sea turtles, this sort of consideration is crucial at the foraging grounds, where

adult individuals spend at least one year acquiring sufficient body fat deposits to

undertake their migration to distant rookeries to reproduce (Kwan 1994). Changes in

oceanographic conditions or climatic indices affect resource availability at the foraging

grounds: either primary production, relevant to herbivorous green turtles (Broderick et al.

2001, Richardson & Schoeman 2004), or the abundance of prey items for carnivorous

species (Lynam et al. 2004, Hays et al. 2005).

These environmental conditions and their associated resource availability at the

foraging grounds, may shorten or extend the remigration interval, the period between

nesting attempts for an individual turtle. Poor foraging conditions lead to longer

remigration intervals and good conditions lead to shorter remigration intervals (Carr &

Carr 1970, Chaloupka 2001, Solow et al. 2002, Wallace et al. 2006, Saba et al. 2007,

Troëng & Chaloupka 2007, Hatase & Tsukamoto 2008, Suryan et al. 2009).

Since many individuals may experience similar foraging conditions, nesting

behavior may become synchronized and lead to great variation in the number of turtles

37

observed nesting each year (Limpus & Nichols 1988, Hays 2000, Broderick et al. 2001,

Chaloupka 2001, Solow et al. 2002, Troëng & Rankin 2004, Price et al. 2006, Chaloupka

et al. 2008, Reina et al. 2009). This interannual variability in nesting numbers can

obscure long term trends and make it more difficult to assess general population size and

trends through nesting numbers (Broderick et al. 2001, Chaloupka 2001).

Several authors have called for a theoretical model that can bring together the

effects of changing climatic conditions at the foraging grounds, resource acquisition by

individual turtles, and the resulting population dynamics at nesting beaches (Price et al.

2006, Chaloupka et al. 2008, Wallace & Jones 2008, Wallace & Saba 2009). The need

for this sort of model is more urgent due to the effects of anthropogenic climate change.

With climate change, temperatures will rise at the foraging grounds, leading to further

resource limitation for sea turtles (Richardson & Schoeman 2004, Saba et al. 2007) and

inevitable effects on population dynamics.

Hays (2000) developed a model for a stable population of adult female sea turtles

in which each individual chooses to re-nest every 3 or 4 years with equal probability.

This re-nesting interval is then adapted due to the average environmental conditions

(randomly assigned to each year) since the last nesting attempt: having experienced better

than average years leads to nesting one year earlier while having experienced worse than

average years leads to delaying nesting by one year. I refined this model so that the

quality of the year (production, in this model) is a function of temperature.

I propose a simple model that links environmental temperatures to resource

availability at the foraging grounds, affecting individual energy stores and, therefore, the

probability of remigrating to the nesting beach. This ultimately links to nesting numbers

38

and could allow for more realistic population projections under various climate change

scenarios.

Methods

I used an individual-based model of turtle remigration, similar to that of Hays

(2000), in which each year a turtle migrates to the nesting beach or not depending on the

net primary production since its last nesting year. In order to introduce thermal and

resource accumulation effects into the Hays (2000) framework, I developed simple

representations of the effects of temperature on production and those of production on

remigration probabilities. Temperature is represented as the Global Land-Ocean

Temperature Index (LOTI), an estimate for mean global temperatures since 1880 as a

deviation from 13°C, available on the Goddard Institute for Space Studies website

http://data.giss.nasa.gov/gistemp/).

Forcing functions

The relationship between production and sea surface temperature is complex, with

colder waters showing a positive correlation between temperature and production while

warmer waters show a negative correlation (Chavez et al.1999, Richardson & Schoeman

2004). The transition between the two patterns occurs around 13°C (Richardson &

Schoeman 2004). Because sea surface temperature for sea turtles is virtually always

above 13°C and because the 15°C isotherm is the cold limit to sea turtle distribution

(Davenport 1997, McMahon & Hays 2006), an inverse relationship between sea surface

temperature and production is appropriate.

39

Richardson and Schoeman (2004) present a good summary of how the

relationship between temperature and production may change with mean temperature, but

they do not find significant relationships for particular temperatures or ranges of

temperatures. I therefore chose a simple, inverse relationship between temperature and

annual production, and adapted the coefficients so that the annual production values

included those available in the literature for the foraging grounds of studied populations

(Wallace and Saba 2009). The final relationship was

PP = 300/T – 125 (1)

where PP is annual production and T is the temperature deviation from 13°C in

the LOTI dataset, which ranges between 0.54 and 1.65°C.

The probability of remigration to the nesting beach in any given year is thought to

depend on resource (e.g. energy) accumulation and hence on metabolic rates and resource

acquisition rates (Wallace & Jones 2008). Since metabolic rates for sea turtles and their

relationship to nutrient availability are largely unknown (Wallace & Jones 2008, Wallace

& Saba 2009), I ran the model using three different saturating functions (logarithmic,

Monod, and exponential) to try to reasonably represent a process that must reach a certain

threshold (Kwan 1994, Madsen & Shine 1999, Hays 2000). In each case, resource

accumulation was modeled as the available net primary production since the last nesting

attempt:

PR = ln(∑(PP)/150) (2)

PR = ∑(PP)/ (∑(PP)+150) (Monod) (3)

PR = 1-e-0.25∑(PP) (4)

40

where PR is the probability of remigration for a given year and PP is the annual

primary production. Production is summed over the years from the last nesting attempt to

the proposed nesting year (not including nesting years).

Algorithm

See appendix for the pseudocode (or brief outline) for the algorithm. Briefly, the model

iterates through years and then through individual turtles to determine whether or not

they will nest given their accumulated resources since the last nesting year.

Parameterization

I ran the models (see Appendix) under constant annual production values

corresponding to those found by Wallace and Saba (2009) for the leatherback populations

nesting at Playa Grande (Costa Rica) and St. Croix (US Virgin Islands) and compared the

model remigration intervals with the published values for these populations. The

logarithmic function, representing a flatter function which saturates less quickly than the

other proposed functions, provided the best fit to the published data (Figure 3.1) so it was

used for the modeled scenarios. This model “metabolic” function can be refined as the

process is understood better in sea turtles, by adjusting the type of equation (logarithmic,

Monod, exponential) as well as its parameters.

The model can be adapted to specific populations in two ways: by altering the

function that connects temperature to production to account for populations foraging at

sites with more or less resources (e.g. warmer vs. colder sites) and by altering the

41

function that defines the remigration probability to account for populations with faster or

slower resource acquisition rates.

Output

For each simulation I measured yearly: average temperature, average net primary

production, accumulated resources since last nesting attempt for each turtle, whether or

not each turtle nested, and its remigration interval if it did. This made it possible to

calculate the number of nesting turtles and the distribution of remigration intervals for

each year, which comprise the main output variables for the model.

Simulation scenarios

I ran the model under several scenarios, to explore how changes in temperature

and production at the foraging grounds might be reflected in nesting numbers and

remigration intervals. I ran the model 100 times under each scenario and present mean

values between runs for the results. The scenarios were as follows:

Standard model: The standard model uses temperatures directly from the LOTI

database of average global temperatures, which represents an overall warming trend, with

some oscillation (GISS 2014).

Cohort diffusion: In order to examine the fate of a cohort of simultaneously

nesting turtles, I ran the model with every individual nesting in the first year (instead of

spread over the first four years as described in the appendix) and observed nesting

numbers and remigration intervals in subsequent years. I ran this scenario at two constant

temperatures to determine whether temperature affects the time it takes for cohorts to

42

break down. I used temperatures that correspond to deviations of +/-0.45°C from the

midpoint of the LOTI dataset (13.645°C and 14.545°C).

Pulse perturbations: To determine the effects of variations in sea surface

temperature on nesting I used what systems engineers call an impulse response, i.e. I kept

all the years in the model at a constant temperature (in the midpoint of the range for the

LOTI dataset) and then introduced perturbations of temperatures with varying amplitude.

I examined the number of nesting turtles and remigration intervals in succeeding years.

Sinusoidal cycles: In order to look at the effect of oscillations in environmental

conditions without an overall warming or cooling trend (e.g. El Niño Southern

oscillation, North Atlantic Oscillation), I created four temperature functions that oscillate

smoothly and regularly between the minimum and maximum values in the LOTI dataset

at different frequencies. I looked at cycles with a duration of two, four, eight, and sixteen

years to determine how changes in cycle duration affect the amplitude of the resulting

oscillations in nesting numbers as well as the lag between temperature deviations and

response in nesting numbers.

Alternate model structure

The described model encodes one possible biological structure of the system

(migration probability depending on accumulated resources). In order to determine

whether the results and patterns observed were a result of the choice of model, I also

developed an alternate model starting from a second, but also individually-based

framework. The alternate model created energy thresholds that each turtle must reach

before nesting, as opposed to responding based on remigration probabilities. These

43

thresholds are different for each turtle but maintained for the entire simulation. Turtles

also incorporate a varying amount of the total available resources each year, owing to

individual physiological states, and providing annual and individual variability in the

model. I used the same model outputs and scenarios with this alternate model.

Results

Parameterization

When comparing the different relationships between resource accumulation and

the probability of remigration (Equations 2-4), the best fit between published and

modeled remigration intervals at constant annual production values corresponded to the

logarithmic model (Figure 3.1). This model was used for the rest of the simulations.

Using the other relationships gave similar results (data not shown).

Standard model

The standard model based on the global LOTI signal (Figure 3.2) produced yearly

oscillations in nesting numbers with an overall slowly decreasing trend in nesting

numbers. The change in nesting numbers as a response to temperature was most

pronounced for the logarithmic model. In all three models, there was an apparent long-

term, three year cycle in both input temperature (from the LOTI dataset) and in number

of nesting turtles (Figure 3.1).

Cohort diffusion

44

Tracking an initial cohort of turtles nesting in the same year showed that the

cohort was broken down exponentially, dissipating almost entirely after approximately

10-15 years (Figures 3.3 and 3.4). Colder temperatures (Figure 3.3) caused the cohort to

break down faster than warmer temperatures (Figure 3.4). Colder temperatures led to

increased primary production and, therefore, shorter remigration intervals, and an

increased number of nesting turtles at steady state (Figure 3.3) while warmer

temperatures led to decreased production, longer remigration intervals, and fewer nesting

turtles (Figure 3.4).

Pulse perturbations

Single-year pulse perturbations of temperature produced altered nesting numbers

the following year, followed by a year of the opposite extreme then several years of

oscillations (with about a three year cycle) until equilibrium was reached. The magnitude

of the oscillations decayed exponentially with a time constant of about 3 years for the

lower temperatures and about 7 for higher temperatures.

Larger perturbations had a larger effect on nesting numbers, though this

relationship was not linear (Figure 3.5). In a strictly linear system, the effect of a series of

increased temperatures can be represented as a series of impulse responses that can be

summed. In a non-linear system, such as this model, this approach is not valid but the

impulse responses still reveal important information about the behavior of the system

(Lathi 1992).

Negative temperature perturbations (cooler sea surface temperature) increased

nesting immediately after the pulse and had a stronger effect than equally strong, positive

45

temperature perturbations (Figure 3.5). Cooler sea surface temperature pulses (increased

primary production) promoted remigration and caused cohorts to form (Figure 3.6).

Warmer sea surface temperature pulses (decreased primary production) delayed nesting,

forming several cohorts in the following years, as delayed turtles began to nest (Figure

3.7).

Patterns in yearly remigration intervals showed that negative temperature

perturbations led to synchronized nesting following the perturbation (Figure 3.6) while

positive temperature perturbations delayed nesting in a less synchronized way (Figure

3.7). Once nesting resumed, it was not as synchronized as with negative temperature

perturbations (Figures 3.6 and 3.7).

Sinusoidal cycles

With the sinusoidal model temperature inputs, nesting numbers appeared as

cohorts with large oscillations in nesting numbers. Increasing the duration of the

temperature cycles led to a decrease in the amplitude of the response in nesting numbers

(Figure 3.8). With longer cycles, there was also an increased lag between observed

minimum temperatures and the ensuing minimum nesting numbers (Figure 3.9).

Alternate model

With the alternate formulation (set threshold for migration and variable

acquisition rates among individuals and years), though remigration intervals and nesting

numbers were slightly different, the fundamental results remained the same as with the

standard model (data not shown). Cohorts broke down via diffusion and did so faster at

46

colder temperatures, and pulse perturbations and sinusoidal patterns in the temperature

input led to similar impulses and oscillations as in the standard model (data not shown).

Discussion

Year-to-year variation in the number of sea turtles nesting at rookeries, including

cyclic variation attributed to cohorts of nesting turtles, interferes with population

estimates and obscures the population dynamics of turtles (Broderick et al. 2001,

Chaloupka 2001). Hays’ (2000) model suggests that such nesting cohorts would occur

spontaneously given annual variation in resources. This current, more physiologically

structured model, suggested that thermally driven resource variation can indeed

synchronize nesting in groups of turtles, creating cohorts (Figure 3.1). It also suggested,

however, that such cohorts do not persist, i.e. cohorts tended to break down over 5-10

years unless regenerated by environmental conditions (Figures 3.3 and 3.4).

The predictions of the model were similar in two fundamentally different

formulations. Neither the dynamics of the cohorts nor the dependence on environmental

effects changed when the model was reformulated with strict resource thresholds for

migration rather than accumulated resources affecting the remigration probability. Thus,

model results did not depend on the specific mechanisms of the model. Rather, they

seemed to rely primarily on the dependence of nesting migrations on accumulated

resources (Carr & Carr 1970, Hays 2000, Solow et al. 2002, Wallace et al. 2006, Saba et

al. 2007, Troëng & Chaloupka 2007).

Cohort dynamics

47

In the standard model, when the temperature history supplied to the model was

the empirical data in the LOTI dataset (GISS 2014), predictions included both a long-

term oscillation in the number of nesting turtles (Limpus & Nichols 1988, Hays 2000,

Broderick et al. 2001, Solow et al. 2002, Price et al. 2006, Chaloupka et al. 2008, Reina

et al. 2009), suggesting cohorts, and an overall decrease in the number of nesting turtles

over time as temperature increased (Figure 3.2). Because the total population of nesting

turtles in the model was held constant over time, this suggested that thermally driven

changes in resources with climate change could produce artifactual changes in estimated

turtle population sizes if the estimates were based on observed nesting numbers

(Broderick et al. 2001, Chaloupka 2001).

Cohort dynamics can be dissected by applying sudden, brief temperature and

resource disturbances (the pulse perturbation scenarios) to a population experiencing

otherwise constant environmental conditions. Such one-year long perturbations created

an effect that lasted several years. This has also been observed in previous studies which

show a large effect of one or two years with extreme temperatures (Saba et al. 2007). The

effect of extreme years on nesting numbers in the model was mainly due to the

synchronizing effect on the turtle population, which was more pronounced with cold

perturbations than with warmer ones. Following a particularly cold year, a large number

of turtles will migrate to nest because of the increased resource accumulation during the

cold pulse (Figure 3.6). This cohort effect wore off within approximately one decade

(Figures 3.6 and 3.7), which supports the hypothesis that sea turtles respond to recent

environmental conditions but do not show multi-decadal responses (Arendt et al. 2013).

48

Alternatively, a warm pulse, with attendant decreased resource accumulation,

decreased the number of nesting turtles in the following year. However, a compensatory

increase in nesting occurred the following year when some of the turtles that did not nest

the previous year now migrated to nest (Figure 3.7). As with a cold pulse, the oscillating

effects wore off over the next few years (Figure 3.7).

When I simulated complete synchronization of nesting, by having all the turtles

nest at once in a single cohort and then examining nesting in the following years, there

were similar results. The forced cohort breaks down over the next 5-10 years (Figures 3.3

and 3.4). I describe this process as ‘diffusion’ with some turtles nesting slightly ahead of

the majority of the cohort and others nesting slightly later (Figures 3.3 and 3.4). Thus,

instead of cohorts persisting stably, they diffusively spread over the next 5-10 years.

Cooler years, with higher resource accumulation, led to a faster breakdown of cohorts

than did warmer years with lower net primary production (Figures 3.3 and 3.4).

As predicted from the impulse responses (Figures 3.3 and 3.4), temperatures that

varied cyclically produced a cyclical variation in both remigration interval and predicted

number of nesting turtles. Both the amplitude of the variation (Figure 3.8) and the lag

between temperature minimums and predicted minimums in the number of nesting turtles

(Figure 3.9) varied with the duration of the sinusoidal cycle chosen.

When looking at temperatures that vary as a sinusoidal function, the lag between

temperatures and nesting numbers was remarkably short with short sinusoidal cycles.

With a cycle of 4 years, there was an observed lag of only 2 years between changes in

temperature and corresponding changes in nesting numbers, which corresponded to the

lag observed for the Playa Grande leatherback population responding to temperature

49

variations due to El Niño Southern Oscillation (Saba et al. 2007). In the model this lag

tended to increase with cycle duration, which merits further study to determine if this

effect can be observed in nesting populations. One possibility to study this is to determine

whether longer ENSO cycles have a longer observed lag when compared to shorter ones.

Cyclical temperatures in this model also tended to increase overall population

remigration intervals (data not shown). This corresponds well with observations by

Wallace et al. (2006) and Saba et al. (2008) that increasing variability at the foraging

grounds leads to poor resource acquisition rates and longer remigration intervals for

leatherback turtles in the Eastern Pacific.

Comparison to published results

Though the model uses a different mechanism to predict production and

remigration intervals, its results are consistent with observations by Wallace and Saba

(2009) who found that increasing net primary production from 3.3 to 12.5 teragrams of

Carbon (Tg C) per 12 days (values corresponding to the foraging areas of Eastern Pacific

and Western Atlantic leatherback turtles respectively) leads to a near halving of

remigration intervals. These values are similar to the remigration intervals produced by

the logarithmic model for the corresponding values of annual net primary production.

While this is due, in part, to the calibration of the production and remigration functions

(Eq. 1-4), those calibrations provided remigration behavior consistent with the published

behavior of the turtles.

A model linking SST anomaly at foraging grounds to nesting numbers at Playa

Grande, Costa Rica, was developed by Saba et al. (2007). The present model differs in

50

that it uses global average temperatures and can thus be applied to populations for which

foraging grounds are unknown. The model also explicitly takes into account resource

acquisition rates, allowing for either individual or population based metabolic response to

changes in resource availability. This made it possible to explore how different

temperature patterns may affect remigration intervals and how this is reflected by annual

nesting numbers.

As in Saba et al. (2007), overall warmer years lead to larger nesting populations

than colder years, due to warm years extending the remigration interval because of their

associated decreased resource availability. This can be seen when comparing the

remigration intervals in the cohort tracking models for all warm years with those for all

cold years (Figures 3.3 and 3.4).

Possible extensions

There are several refinements that should be considered for the current model.

Further development should include a way to account for anthropogenic impacts.

Currently the population of adult females is stable but future versions should include a

population growth rate that can be built in to account for the different adult survivorships

of different populations. Another possibility is to make survivorship depend on available

resources, with females having a probability of dying off if they encounter several

consecutive bad years.

In terms of the relationship between accumulated resources and nesting efforts,

the present model assumes that when a turtle migrates to nest, it consumes all its

accumulated resources. One extension of the model could use the amount of resources an

51

individual has accumulated prior to nesting to determine measures of reproductive effort

(e.g clutch size, number of nesting attempts) that would then vary between individuals

and affect future population dynamics. Individuals with excess resources may also have

carryover effects for their next nesting attempt, either in their growth rate (and resulting

resource accumulation rates) or in the reproductive effort for their next nesting season.

The relationship between temperature and resources should also be evaluated

differently for different species of sea turtles, as it may change depending on the diet of

the species. Special considerations can also be made for species that rely on patchy

resources, such as leatherback turtles feeding on jellyfish (Davenport 1997, Lynam et al.

2004, James et al. 2005).

In addition to these refinements, the model should be fine-tuned for particular

rookeries and compared with their nesting trends and remigration data. This would help

to determine whether the model reflects observed behavior and whether or not it could be

used to project observed nesting numbers for future years and under different climate

change scenarios.

Conservation implications

Proper adjustment of the model will make it possible to use to forecast nesting

numbers, which can help conservation and management planning. Being able to predict

patterns in nesting numbers can also prevent over interpretation of nesting oscillations.

Since the model predicts cycling between good and bad years, short-term trends should

not be seen as a reflection of long-term population dynamics. Policy should then be based

on long-term monitoring efforts rather than shorter (1-3 year) datasets.

52

Summary

Overall, both developed models are consistent with the published effects of

temperature on nesting numbers and remigration intervals. Any observed cohort

dynamics at the nesting beaches are driven by temperature and resource dynamics at the

foraging grounds. This emphasizes the importance of considering temperatures when

trying to estimate population trends from nesting data, since temperature variation may

confound nesting numbers and lead to an apparent but false population trend. Also, any

synchronizing effect of environmental temperature on nesting cohorts is short-lived rather

than multi-decadal, since nesting cohorts tend to break down over a period of 5-10 years.

However, there is a lag between temperature changes and their responses in nesting

numbers, which increases with the duration of temperature cycles. Though part of this

effect is driven by a built-in lag in the model (as turtles cannot respond to temperature in

the current year), it may also reflect real lags in observed nesting numbers following

temperature changes at the foraging grounds. Future work should focus on model

refinement to particular nesting populations in order to determine whether its behavior

matches the observed trends in nesting numbers and remigration intervals.

53

Figures

Figure 3.1. Comparison between model remigration intervals and those published by

Wallace and Saba (2009) for leatherback turtles with different values of annual net

primary production.

100 150 200 250 300 350 4002

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

Net Primary Production (Tg C per year)

Re

mig

ratio

n In

terv

al

Wallace & Saba data

Logarithmic model

Monod model

Exponential model

54

Figure 3.2. Mean number of nesting turtles, from a total population of 500, predicted

under three modeled relationships between resource availability and remigration

probability.

1920 1940 1960 1980 200050

100

150

200

250

300

Year

Ne

stin

g tu

rtle

s

Logarithmic model

Monod model

Exponential model

55


deviation) nesting numbers (B) for an initial sea turtle nesting cohort under a constant

temperature of 13.6°C.

Re

mig

ratio

n In

terv

al

5 10 15 20 251

2

3

4

5

5 10 15 20 250

200

400

600

Ne

stin

g tu

rtle

s

Year

A

B

56


deviation) nesting numbers (B) for an initial sea turtle nesting cohort under a constant

temperature of 14.5°C.

Re

mig

ratio

n In

terv

al

5 10 15 20 252

3

4

5

6

7

5 10 15 20 250

500

Ne

stin

g tu

rtle

s

Year

B

A

57

Figure 3.5. Predicted yearly nesting numbers of sea turtles after a pulse perturbation in

temperature. Note: perturbation occurs in year two.

2 4 6 8 10 12 140

50

100

150

200

250

300

350N

estin

g tu

rtle

s

Year

+0.45°C

+0.30°C

+0.15°C

-0.15°C

-0.30°C

-0.45°C

58


deviation) nesting numbers (B) of sea turtles following a decrease in temperature of

0.45°C. Note: temperature included for reference (C).

Re

mig

ratio

n In

terv

al

2 4 6 8 10 12 142

3

4

5

6

7

2 4 6 8 10 12 140

200

400

Ne

stin

g tu

rtle

s

2 4 6 8 10 12 140.5

1

1.5

Te

mp

era

ture

(°C

-13

)

Year

B

A

C

59


deviation) nesting numbers (B) of sea turtles following an increase in temperature of

0.45°C. Note: temperature included for reference (C).

Re

mig

ratio

n In

terv

al

2 4 6 8 10 12 142

3

4

5

6

7

2 4 6 8 10 12 140

100

200

300

Ne

stin

g tu

rtle

s

2 4 6 8 10 12 141

1.5

2

Te

mp

era

ture

(°C

-13

)

Year

A

C

B

60

Figure 3.8. Modeled nesting numbers of sea turtles under sinusoidal temperature

oscillations of varying cycle duration.

2 4 6 8 10 12 14 16 18 200

50

100

150

200

250

300

350

400

450

500N

estin

g tu

rtle

s

Year

2-year cycle

4-year cycle

8-year cycle

16-year cycle

61

Figure 3.9. Lag between minimum temperature and minimum nesting numbers of nesting

turtles under sinusoidal temperature oscillations of varying cycle duration.

4 6 8 10 12 14 162

3

4

5

6

7

8L

ag

(ye

ars

)

Cycle duration (years)

62

CHAPTER 4: Using a simple, physiologically-based model to predict leatherback

turtle (Dermochelys coriacea) nesting numbers and remigration intervals at two

rookeries

Abstract

Year-to year variation in the number of nesting sea turtles and their remigration

intervals is often influenced by temperature and resource availability at the foraging

grounds. Since nesting censuses are heavily used to estimate population sizes and trends,

this means that any signal in population trends may be obscured by these year-to-year

oscillations. I compared predictions from a simple, physiologically-based model with

observed nesting numbers and remigration intervals (obtained in collaboration with

monitoring and conservation organizations) for two leatherback nesting beaches with

opposite population trends (Playa Grande, Costa Rica, and St. Croix, US Virgin Islands)

in order to determine whether it was possible to isolate the temperature signal from the

overall population trends. When running the model with global mean temperatures, any

correlations between model and observed nesting numbers were dominated by the overall

population trends, while the remigration intervals in both the model and observed data

was driven by a long-term temperature decrease and its consequential remigration

interval increase. However, when fine-tuning the model to the foraging temperature

signals for each population, the model captured the basic behavior of the remigration

intervals both on a year-to-year and decade-to-decade trend. The model was not able to

predict nesting numbers, as they were dominated by the overall trend for each population.

Future work should focus on building growth rates into the model so it may be used to

forecast both nesting numbers and remigration intervals for different populations. This

63

would allow the model to be used for conservation and management planning as well in

properly interpreting oscillations in nesting numbers.

Introduction

Population assessments for sea turtles, as well as for other seasonal migrants, are

based on census numbers for nesting females, due to the relative ease of monitoring these

sites and estimating abundance there (Mazaris et al. 2005, Saba et. al 2007, Calvert et al.

2009). However, nesting can be affected by events throughout the migratory cycle such

as changes in temperature and resource availability, which can alter nesting frequency

and numbers (Seminoff & Shanker 2008, Calvert et al. 2009).

Changes in temperature have been shown to affect nesting numbers of sea turtles

(Limpus & Nicholls 1988, Solow et al. 2002, Saba et al. 2007, Chaloupka et al. 2008,

Reina et al. 2009). This relationship is mediated by the effects of temperature on resource

availability at the foraging grounds: increased temperatures lead to poorer conditions at

the foraging grounds, which tend to extend remigration intervals, while lower

temperatures lead to increased resource availability and shorter remigration intervals

(Solow et al. 2002, Wallace et al. 2006, Saba et al. 2007).

Because individuals in the same nesting population tend to experience similar

foraging conditions, nesting may become synchronized (Limpus & Nicholls 1988, Hays

2000, Broderick et al. 2001, Solow et al. 2002, Reina et al. 2002, Reina et al. 2009). This

is then seen as large oscillations at the nesting grounds that do not necessarily reflect

64

changes in the overall population and may even obscure population trends (Hays 2000,

Broderick et al. 2001).

Being able to separate the effects of temperature on nesting population trends is

key to isolating other factors which may affect sea turtle populations, such as

anthropogenic impacts which can define population trends (Saba et. al 2007). This

understanding can lead not only to improved forecasting at nesting beaches (and thus,

more efficient management and conservation) but also to a better understanding of how

climate change could affect sea turtles and what measures should be taken to protect

populations from this added threat (Saba et al. 2007, Chaloupka et al. 2008, Wallace &

Jones 2008, Wallace & Saba 2009).

In order to study temperature effects on nesting numbers, I used a

physiologically-based model (Chapter 3, this dissertation) that predicts remigration

intervals and nesting numbers for a theoretical sea turtle population based on yearly

global average temperatures since 1900. I compared the results from this model with the

observed numbers for two nesting populations: Playa Grande, Costa Rica and St. Croix,

US Virgin Islands. I also adapted the model inputs and forcing functions to the chosen

populations to determine if this provided a better fit than the standard model.

I chose these two populations because they have opposite overall trends: the Playa

Grande population is declining precipitously (Spotila et al. 1996, Santidrian-Tomillo et

al. 2007) while the St. Croix population is increasing (Robinson et al. 2014). Testing the

model under these different circumstances made it possible to determine whether the

climate signal was still apparent, independently of population trends and whether or not

65

the model could be fine-tuned to predict the nesting behaviors of two very different

populations which experienced potentially different temperature signals.

Methods

Standard model

I used a theoretical model (Chapter 3, this dissertation) that predicts remigration

intervals and nesting numbers for a stable population of sea turtles subject to temperature

increases (See appendix for the algorithm’s pseudocode). The model uses global average

temperatures from the Global Land-Ocean Temperature Index (LOTI, available on the

Goddard Institute for Space Studies website http://data.giss.nasa.gov/gistemp/), to

calculate a measure of production or resource availability, with the following relationship

PP = 300/T – 125 (1)

where PP is annual production and T is the temperature deviation from 13°C in

the LOTI dataset, which ranges between 0.54 and 1.65°C. This equation was fitted in

order to produce the yearly Net Primary Production (NPP) values for the populations of

leatherback turtles nesting in Playa Grande and St. Croix.

Individual turtles in the model select their first nesting year randomly from the

first four years in the model. After this initial nesting year, each year the remigration

probability for each turtle is calculated from the accumulated annual NPP since the last

nesting year. This relationship between accumulated resources and remigration

66

probability is modeled with three different “metabolic” equations each of which

approximates a metabolic process that must reach a certain threshold, as follows:

PR = ln(∑(PP)/150) (2)

PR = ∑(PP)/ (∑(PP)+150) (3)

PR = 1-e-0.25∑(PP) (4)

where PR is the probability of remigration for a given year and PP is the annual

primary production. Production is summed over the years from the last nesting attempt to

the proposed nesting year (not including nesting years).

I ran the standard model ten times using a stable population of 500 turtles and

tracked the mean yearly nesting numbers and remigration intervals in order to compare

them with those observed for each nesting population.

Foraging temperature data

In order to fine-tune the model to respond to the foraging conditions experienced

by each population rather than mean global temperatures, I looked at monthly average sea

surface temperature (SST) from the NOAA NCEP EMC CMB GLOBAL Reynolds and

Smith v2 data set (Reynolds et al. 2002), available on the International Research Institute

for Climate and Society’s website (http://iridl.ldeo.columbia.edu/docfind/databrief/cat-

airsea.html). This dataset combines ship, buoy and satellite-corrected temperature data at

a resolution of 1x1°.

67

For each nesting population, I took the average temperatures for the foraging

grounds proposed by Neeman et al. (2015) from 1982 to 2012. I then used these foraging

temperatures in order to derive a foraging temperature signal for each population.

For the Playa Grande population, I circumscribed the following putative foraging

grounds for leatherback turtles (Saba 2007,Shillinger et al. 2008, Shillinger et al. 2011,

Neeman et al. 2015): Equator (5.5°S-5.5°N, 84.5-110.5°W), Upper South Equatorial

Gyre (4.5-20.5°S, 79.5-120.5°W), and Lower South Equatorial Gyre (24.5-35.5°S, 84.5-

105.5°W). I divided the South Equatorial Gyre into two different areas to better represent

its triangular shape and possibly different conditions due to its extensive N-S range

(Neeman et al. 2015).

For St. Croix, I chose putative foraging sites for leatherback turtles to match those

studies for the Trinidad population (Eckert 2006, Neeman et al. 2015) due to a lack of

tracking data for Sandy Point. They were as follows: Bay of Biscay (34.5-45.5°N, 9.5-

15.5°W), Flemish Cap (44.5-50.5°N, 34.5-45.5°W), Mauritania (14.5-30.5°N, 14.5-

25.5°W), and North Atlantic subtropical front (34.5-40.5°N, 34.5-45.5°W).

For each population, I calculated the deviations from mean temperature for each

foraging ground. I then ran correlations between these deviations from the mean for all

the foraging grounds for a particular population in order to determine which could be

merged into a single temperature signal. This made it possible to derive two independent

signals for each population, representing those foraging grounds which were most

strongly correlated.

68

For Playa Grande, I averaged the Equator foraging ground with the Upper South

Equatorial Gyre to create the EQS1 signal and left the Lower South Equatorial Gyre

alone as the S2 signal (Figure 4.3). For St. Croix, I averaged the Bay of Biscay foraging

ground with the one for Mauritania to create the BBMA signal and the Flemish Cap

foraging ground with the North Atlantic Subtropical Gyre to create the FCNA signal

(Figure 4.4).

Once all four signals were defined, I rescaled each of them so it would have a

range similar to that of the LOTI dataset so that it could be used it with the theoretical

model. This involved the following four functions:

EQS1s = (EQS1t+1)/2 (5)

S2s = (S2t+1) (6)

BBMAs = (BBMAt+1) (7)

FCNAs = (FCNAt+2.05)/2 (8)

where in each case, the “s” subscript indicated the final signal used and the “t”

subscript indicates the average of the deviations from the mean for the foraging grounds

used. Each of these signals was applied in the model in place of the LOTI dataset for its

corresponding nesting population.

Nesting data

Playa Grande (Figure 2.1) is a 3.5km long, high energy beach located in Pacific

Northwest Costa Rica (10°20’N, 85°51’W). For the data used in this study, I considered

it as one nesting unit together with adjacent Playa Ventanas (Steyermark et al. 1996,

69

Neeman et al. 2015). Leatherbacks nest on these beaches between October and March.

When I refer to nesting numbers in a particular year, those data correspond to the nesting

season that started in October of said calendar year. For this study, I used nesting data

from 1993 to 2010.

Sandy Point (Figure 2.3) is a 3km long, dynamic beach on St. Croix, US Virgin

Islands (17°40’N, 64°52’W) (Boulon et al. 1996). Leatherbacks nest on St. Croix

between March and August. For this study, I used nesting data from 1981 to 2013.

At each nesting beach, leatherback turtles are individually marked and recorded

during nightly patrols. I obtained the data used in this study from the organizations in

charge of monitoring and conservation for each site. I used the list of tagged turtles seen

each year at each beach to calculate nesting numbers as well as the yearly remigration

intervals (or the mean remigration intervals for all turtles nesting in a particular year).

Fitting the model to specific populations

In order to fit the standard model (Chapter 3, this dissertation) to particular

nesting populations, both the equation that predicts NPP from the temperature signal

(Equation 1) and the metabolic function (Equations 2-4) need to be altered. To calculate

NPP for each of the foraging ground temperature signals, I simply rescaled Equation 1

from the standard model so the corresponding production was approximately the average

production for each nesting population in the literature (Wallace and Saba 2009). The

adapted equations for each signal were as follows:

PP = (300/EQS1 – 125)/7 (9)

PP = (300/S2 – 125)/1.9 (10)

70

PP = (300/BBMA – 125)*1.4 (11)

PP = (300/FCNA – 125)*1.3 (12)

In order to select the appropriate metabolic function for each population, I ran the

model with all three metabolic functions for each population and selected the function

that provided the best fit to observed yearly nesting numbers and remigration intervals. I

chose the best fit because there is no theoretical reason to prefer one function over

another. They can all adequately represent a metabolic function that must reach a certain

threshold for nesting to occur, but they give slightly different predictions due to their

differences in curvature. The logarithmic function is fairly flat throughout the range of

accumulated resources, while both the Monod and exponential functions are steep at

lower productions and then flatten out.

Analysis

For each nesting population, I ran correlations between the observed numbers of

nesting turtles and the numbers predicted by the model for the same range of years as

well as between the observed yearly remigration intervals and those predicted by the

model. I ran these correlations both with the predictions of the standard model and those

for the model adapted to each nesting population as described previously.

Results

For the standard model, using the global LOTI signal, there was a correlation

between model and observed nesting numbers for two of the models for Playa Grande

71

(Logarithmic model: r = 0.395, p = 0.105, n = 18; Monod model: r = 0.534, p = 0.023, n

= 18; Exponential model: r = 0.558, p = 0.016, n = 18; Figure 4.1) and a negative

correlation between model and observed nesting numbers for all models for St. Croix

(Logarithmic model: r = -0.509, p = 0.003, n = 33; Monod model: r = -0.595, p < 0.001, n

= 33; Exponential model: r = -0.630, p < 0.001, n = 33; Figure 4.1). For remigration

intervals, there was a marginally significant correlation between all models and the

observed data for Playa Grande (Logarithmic model: r = 0.461, p = 0.072, n = 18; Monod

model: r = 0.482, p = 0.058, n = 18; Exponential model: r = 0.456, p = 0.076, n = 18;

Figure 4.2), and a stronger correlation between all models and the observed data for St.

Croix (Logarithmic model: r = 0.490, p < 0.001, n = 33; Monod model: r = 0.457, p <

0.001, n = 33; Exponential model: r = -0.464, p < 0.001, n = 33; Figure 4.2).

When adapting the model to the Playa Grande population, the best fit was

provided by the exponential metabolic function (Equation 4). There was no correlation

between model and observed nesting numbers for either temperature signal (EQS1: r = -

0.022, p = 0.929, n = 16; S2: r = 0.237, p = 0.343, n = 16; Figure 4.5) but there was a

correlation between model and observed remigration intervals for the S2 temperature

signal (EQS1: r = -0.375, p = 0.153, n = 16; S2: r = 0.555, p = 0.026, n = 16; Figure 4.6).

For the St. Croix population, the best fit was provided by the Monod metabolic

function (Equation 3). There was a negative correlation between model and observed

nesting numbers for the FCNA signal (BBMA: r = -0.236, p = 0.186, n = 31; FCNA: r = -

0.486, p = 0.004, n = 31; Figure 4.7) and a correlation between model and observed

remigration intervals for the BBMA temperature signal (BBMA: r = 0.446, p = 0.012, n =

31; FCNA: r = 0.324, p = 0.075, n = 31; Figure 4.8).

72

Discussion

I tried to test whether it was possible to isolate climate effects from the effects of

overall demographic trends on nesting numbers in leatherback turtles. For this, I chose to

compare the predictions from a simple, physiologically-based model (Chapter 3, this

dissertation) with the observed nesting numbers and remigration intervals of two

leatherback populations with opposite demographic trends (Spotila et al. 1996,

Santidrian-Tomillo et al. 2007, Robinson et al. 2014) to test whether the predictions work

independently of population trends. I also fine-tuned the standard model to the foraging

temperature signals, production, and metabolic function best fitted to each population to

determine whether the model can be adapted to capture the basic nesting behaviors seen

in specific populations.

When using the standard model for both populations, there was a positive

correlation with the nesting numbers observed at Playa Grande and a negative correlation

with those for St. Croix. These correlations were dominated by the relationship between

the overall nesting population trend for the model (decreasing) and those for each beach -

decreasing for Playa Grande and increasing for St. Croix (Spotila et al. 1996, Santidrian-

Tomillo et al. 2007, Robinson et al. 2014) – and did not reflect year-to-year variation.

For the remigration intervals, which are not dominated by overall population

trends but rather by a response to foraging conditions (Solow et al. 2002, Wallace et al.

2006, Saba et al. 2007), there was a marginal or significant correlation between the

model and observed data. The correlation was stronger for the St. Croix population

73

because its foraging grounds were more closely correlated to the global LOTI

temperature dataset used in the standard model than those of the Playa Grande population

(data not shown). For both populations, this correlation probably corresponded to an

overall increase in temperatures and an ensuing decrease in resource availability and

increase in remigration intervals in all temperature signals, regardless of year-to-year

variation which may not have been adequately represented by the mean global

temperatures used in the standard model. From the relationships between observed and

predicted nesting numbers and remigration intervals, it is apparent that the standard

model did not adequately represent the underlying climate signal.

When using the fitted model to adapt to the temperatures, NPP and metabolic

functions of each population, the model was not able to predict nesting numbers. The lack

of correlation between model and observed nesting numbers was due to the inherent

growth rates of each population, which affect not only nesting numbers but their

oscillations as well. A growing population shows larger oscillations in nesting numbers

due to new, first-time nesters appearing as a variable fraction of the total nesters each

year as they reach maturity. These effects on numbers and their oscillations are difficult

to isolate in the data in order to provide a reasonable comparison to a model that uses

stable populations. There may also be non-thermal factors driving some of the larger

oscillations (e.g. climatic indices such as El Niño Southern Oscillation) which the model

does not consider.

However, the model adequately represents the behavior of the yearly remigration

intervals for one of the temperature signals for each population. This is true both for the

year-to-year trend and for the long-term, decade-to-decade, trend (Figures 4.6 and 4.8).

74

Neither of the signals will have a very strong correlation with the observed data because

the observed data correspond to a mixture between all foraging grounds and, therefore, a

mixture between both signals. Another complication is that the model does not allow for

a one-year remigration interval, though they are sometimes (but rarely) present in the

observed data and can become important in some years (e.g the first few years in the

Playa Grande data, Figure 4.2). However, the remigration intervals in the first few years

of the empirical data may also be skewed towards small remigration intervals for

technical reasons. Only individuals with very short remigration intervals can be recorded

for 1-3 years after tagging starts because they are the only marked ones which have

returned to nest, which acts as a selective bias in the available data.

Overall, after fine-tuning to specific populations, the present model captured the

basic behavior of remigration intervals for the studied populations, both in terms of short

and long-term variation, and was unaffected by overall population trends (Figures 4.6 and

4.8). Future work should focus on implementing inherent growth rates in the model so

that it may be used to forecast nesting numbers as well as remigration intervals and serve

as an important tool to help managers plan patrolling and hatchery efforts as well as help

with caution when interpreting short-term oscillations in nesting numbers. The model

should also be adapted to different species of sea turtles and further tested on additional

populations. It is important to note that this model relies on temperature estimates

averaged out over large oceanic areas, so that high-resolution tracking data is not

necessary for this type of analysis to produce meaningful results. This analysis may

therefore be used for populations for which precise tracking data are not available. This

type of analysis can be used both for forecasting and to isolate temperature effects from

75

the effects of population trends in order to better determine whether any observed trends

are real, or may correspond to a stable population responding to anomalous climate.

76

Figures

Figure 4.1. Observed nesting numbers for the leatherback populations at Playa Grande

(Costa Rica) and St. Croix (US Virgin Islands) and those predicted by the standard

model, using global average temperature and all three different metabolic functions.

1980 1985 1990 1995 2000 2005 2010 20150

50

100

150

200

250

300

350

400

450

Year

Ne

stin

g tu

rtle

s

Logarithmic model

Monod model

Exponential model

Playa Grande

St. Croix

77

Figure 4.2. Observed remigration intervals for the leatherback populations nesting at

Playa Grande (Costa Rica) and St. Croix (US Virgin Islands) and those predicted by the

standard model, using global average temperature and all three different metabolic

functions.

1980 1985 1990 1995 2000 2005 2010 20151.5

2

2.5

3

3.5

4

4.5

5

5.5

Year

Re

mig

ratio

n In

terv

al

Logarithmic model

Monod model

Exponential model

Playa Grande

St. Croix

78

Figure 4.3. Correlations between the mean monthly temperatures for three putative

foraging grounds for the population of leatherbacks nesting at Playa Grande, Costa Rica.

19 19.5 20 20.5

Lower South Equatorial Gyre

23 23.5 24 24.5 25

Upper South Equatorial Gyre

24 25 26 27 28

19

19.5

20

20.5

Equator

Lo

we

r S

ou

th

Eq

ua

tori

al G

yre

23

23.5

24

24.5

25

Up

pe

r S

ou

th

Eq

ua

tori

al G

yre

24

25

26

27

28E

qu

ato

r

79

Figure 4.4. Correlations between the mean monthly temperatures for four putative

foraging grounds for the population of leatherbacks nesting at St. Croix, US Virgin

Islands.

20 22 24

Mauritania

18 20 22

NA Subtropical Front

10 12 14

Flemish Cap

16 17 18

20

22

24

Bay of Biscay

Ma

uri

tan

ia

18

20

22

NA

Su

btr

op

ica

lF

ron

t

10

12

14

Fle

mis

h

Ca

p

16

17

18B

ay o

f B

isca

ry

80

Figure 4.5. Observed nesting numbers for the leatherback population at Playa Grande

(Costa Rica) and those predicted by the theoretical model, using temperature signals from

the Equator/Upper South Equatorial Gyre (EQS1) and Lower South Equatorial Gyre (S2)

foraging grounds and the exponential metabolic function.

1992 1994 1996 1998 2000 2002 2004 2006 2008 20100

50

100

150

200

250

300

350

400

450

Year

Ne

stin

g tu

rtle

s

Predicted by EQS1

Predicted by S2

Observed data

81

Figure 4.6. Observed remigration intervals for the leatherback population nesting at Playa

Grande (Costa Rica) and those predicted by the theoretical model, using temperature

signals from the Equator/Upper South Equatorial Gyre (EQS1) and Lower South

Equatorial Gyre (S2) foraging grounds and the exponential metabolic function.

1990 1992 1994 1996 1998 2000 2002 2004 20061.5

2

2.5

3

3.5

4

4.5

Year

Re

mig

ratio

n In

terv

al

Predicted by EQS1

Predicted by S2

Observed data

82

Figure 4.7. Observed nesting numbers for the leatherback population at St. Croix (US

Virgin Islands) and those predicted by the theoretical model, using temperature signals

from the Bay of Biscay/Mauritania (BBMA) and Flemish Cap/North Atlantic Subtropical

Gyre (FCNA) foraging grounds and the Monod metabolic function.

1980 1985 1990 1995 2000 2005 2010 20150

50

100

150

200

250

300

Year

Ne

stin

g tu

rtle

s

Predicted by BBMA

Predicted by FCNA

Observed data

83

Figure 4.8. Observed remigration intervals for the leatherback population nesting at St.

Croix (US Virgin Islands) and those predicted by the theoretical model, using

temperature signals from the Bay of Biscay/Mauritania (BBMA) and Flemish Cap/North

Atlantic Subtropical Gyre (FCNA) foraging grounds and the Monod metabolic function.

1980 1985 1990 1995 2000 2005 2010 20152

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

Year

Re

mig

ratio

n In

terv

al

Predicted by BBMA

Predicted by FCNA

Observed data

84

CHAPTER 5: General Conclusions

With this dissertation, I aimed to mechanistically examine how sea turtles

currently respond to changes in temperature, in order to provide some insight into how

they may respond to climate change. I looked at two of the main ways in which sea

turtles have been reported to respond to altered temperatures and their corresponding

altered resource availability: shifting the timing of their nesting seasons (Weishampel et

al. 2004, Pike et al. 2006, Hawkes et al.2007b, Mazaris et al.2008, Mazaris et al. 2009,

Pike 2009, Weishampel et al. 2010, Dalleau et al. 2012) and shortening or lengthening

their remigration intervals (Carr & Carr 1970, Chaloupka 2001, Solow et al. 2002,

Wallace et al. 2006, Saba et al. 2007, Troëng & Chaloupka 2007, Hatase & Tsukamoto

2008, Suryan et al. 2009) which ultimately leads to large oscillations in annual nesting

numbers (Limpus & Nichols 1988, Hays 2000, Broderick et al. 2001, Chaloupka 2001,

Solow et al. 2002, Price et al. 2006, Chaloupka et al. 2008, Reina et al. 2009).

In order to examine possible phenological changes, I determined whether the

timing of nesting seasons for three populations of leatherback turtles (Playa Grande,

Costa Rica; Tortuguero, Costa Rica; and St. Croix, US Virgin Islands) has varied in

response to interannual temperature variation both at their nesting and foraging sites.

Since previous studies have found contrasting trends in sea turtle response to

temperatures (Weishampel et al. 2004, Pike et al. 2006, Hawkes et al.2007b, Mazaris et

al.2008, Mazaris et al. 2009, Pike 2009, Weishampel et al. 2010, Dalleau et al. 2012), I

looked at the relationship between temperature and net primary production at the foraging

85

grounds in order to determine whether or not resource availability mediated any observed

relationships between temperature and nesting dates.

I found that, though foraging temperatures (and not local temperatures) did

overall contribute to shifting nesting dates for all the populations studied, these

relationships were neither consistent nor very strong. The most common trend was for

increased temperatures to delay nesting, which differs from results on other nesting

beaches (Weishampel et al. 2004, Hawkes et al. 2007, Mazaris et al.2008, Mazaris et al.

2009, Pike 2009, Weishampel et al. 2010) where increased temperatures lead to earlier

nesting or did not affect nesting dates. However, one of the nesting grounds for the

Tortuguero nesting population, the Gulf of Mexico, showed the opposite trend with

increased temperatures leading to earlier nesting. These contrasts in observed trends were

not explained by differences in the relationship between temperature and net primary

production. Rather, the inconsistencies between studies and sites may be due to

differences in mean sea surface temperature, with warmer waters showing opposite

effects than colder waters (Richardson and Schoeman 2004). Specifically for the

contrasts in the present study, it is also possible that resource availability in the Gulf of

Mexico is driven by other factors such as hypoxia (Diaz & Solow 1999, Diaz &

Rosenberg 2008) or changes in fluvial discharge (Graham 2001) rather than strictly by

temperature.

Overall, this study suggests that migratory cues are complex and vary between

different sites. This raises the possibility that there might not be an overarching effect of

temperature on the timing of nesting seasons, but rather various local population effects.

86

It also highlights the importance of studying both local and foraging temperature signals

when looking for possible effects on phenology.

For possible changes in remigration intervals and nesting numbers, I developed a

physiologically-based mechanistic model that links global or foraging ground

temperatures to resource availability and accumulation by sea turtles, remigration

intervals, and observed annual nesting numbers. I found that thermally-driven resource

variation, rather than specific life history traits, synchronizes nesting in groups of sea

turtles and can lead to large fluctuations in yearly nesting numbers. These cohorts of

nesting turtles do not persist long-term, they diffuse out over a period of 5-10 years

unless environmental conditions regenerate them.

Using the proposed theoretical model to explore different temperature scenarios, I

found that one-year perturbations of cold temperatures shorten remigration intervals and

can have a synchronizing effect on nesting numbers. In contrast, warm temperature

pulses tend to delay nesting in a less synchronized way. I also looked at cyclical

temperature variation and found that it leads to cyclical responses in both remigration

intervals and nesting numbers, with lag and amplitude both varying with cycle duration.

Model behavior was consistent with general observations on nesting beaches, so I

wanted to test whether it was also consistent with the behavior of specific nesting

populations. I looked at two leatherback populations with opposing growth trends: Playa

Grande, Costa Rica, which is dramatically decreasing (Spotila et al. 1996, Santidrian-

Tomillo et al. 2007), and St. Croix, US Virgin Islands, which is increasing (Robinson et

al. 2014), in order to determine whether it is possible to observe and isolate the

temperature signal despite opposing population trends.

87

When I ran the model using mean global temperatures, there was a positive

correlation with nesting numbers for Playa Grande and a negative correlation with

nesting numbers for St. Croix. These relationships were dominated by the overall trend

for each population. There was a correlation between predicted and observed remigration

intervals, but this was driven by an overall warming trend both in global temperatures

and in the relevant temperatures for each nesting population and did not adequately

reflect year-to-year variation.

When I adapted the model to use foraging temperature signals for each beach, it

was able to capture both the year-to-year and decade-to-decade variation in remigration

intervals for both populations. Nesting numbers were not well predicted, due to large

oscillations in the observed data (which may be driven by non-thermal factors) as well as

the difficulty in isolating the effects of population trends both on nesting numbers and

their oscillations. Future versions of the model should incorporate population growth

rates in order to allow for better comparison.

Considering these studies together, I am able to offer some general management

recommendations as well as recommendations for future work. With increasing

temperatures, nesting seasons will shift. Studies of specific populations should focus on

determining the magnitude and direction of the shift in order to successfully plan for

management and conservation. It will also be important to use climate change scenarios

to determine whether these shifted seasons will mean altered incubations conditions for

the populations and whether any additional measures need to be taken to improve

hatching success and manage hatchling sex ratios, such as shading nests or relocating

them to a hatchery.

88

The remigration interval study suggests that, unless there are strong population

trends, with temperatures increasing globally an overall decreasing trend in nesting

numbers is expected even for stable populations. This can obscure population trends

when estimating them from nesting censuses and should be taken into consideration when

determining population sizes and their trends. With improvements to this theoretical

model, it could potentially be used to forecast nesting numbers for specific populations

leading to better planning for individual seasons, even when no precise tracking data are

available. Additional studies could include using the model to explore the magnitude of

the effect of temperature on remigration intervals and nesting numbers under different

climate change scenarios.

89

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Appendix A: Pseudocode for main and alternate models

Main model

Temperature = LOTI dataset

Production = 300/loti-125

Number of turtles = 500

While year is between 1 and 4

Each turtle selects one of first four years randomly to nest

For each subsequent year, for all turtles:

If turtle did not nest that year:

Calculate cumulative production since last nesting year

Remigration probability = function of cumulative production (Eqs. 2-4)

If random number is smaller than remigration probability

Nest and record remigration interval

Iterate through all years, count nesting turtles and calculate yearly remigration intervals

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Alternate model

Temperature = LOTI dataset

Production = 300/loti-125

Number of turtles = 500

Set resource threshold for each turtle

While year is between 1 and 4

Each turtle selects one of first four years randomly to nest

For each subsequent year, for all turtles:

If turtle did not nest that year:

Calculate cumulative production (available yearly production * yearly

resource accumulation rate) since last nesting year

If cumulative production is greater than threshold

Nest and record remigration interval

Iterate through all years, count nesting turtles and calculate yearly remigration intervals

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Vita

Noga Neeman

[email protected]

Education

Ph.D., Biological Sciences, Drexel University (Philadelphia, PA) 2014

M.Sc., Biology, University of Costa Rica (San Jose, Costa Rica) 2009

B.Sc., Biology, University of Costa Rica (San Jose, Costa Rica) 2006

Professional Experience

Teaching Assistant, Drexel University 2009-2014

Project Leader, International Student Volunteers 2008

Research Assistant, Sea Turtle Conservancy and Global Vision International 2006-2008

Research Assistant, University of Costa Rica 2004-2007

Funding and awards

Dissertation Fellowship, Office of Graduate Studies, Drexel University 2014

Travel Subsidy, Office of Graduate Studies, Drexel University 2013 and 2014

Travel Grant, BEES Department, Drexel University 2013 and 2014

Charlotte Mangum Student Support Program, Society for Integrative and Comparative

Biology 2013

Selected publications

Neeman, N., E. Harrison, I.S. Wehrtmann & F. Bolaños (Accepted). Nest site selection

by individual leatherback turtles (Dermochelys coriacea, Dermochelyidae) in

Tortuguero, Caribbean coast of Costa Rica. Journal of Tropical Biology.

Neeman, N., N.J. Robinson, F.V. Paladino, J.R. Spotila & M.P. O’Connor. 2015.

Phenology shifts in leatherback turtles (Dermochelys coriacea) due to changes in

sea surface temperature. Journal of Experimental Marine Biology and Ecology

462:113-120.

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