UNIVERSITY OF CALIFORNIASANTA CRUZ
ON THE FORMATION OF A CONSERVATION HOTSPOTFOR JUVENILE NORTH PACIFIC LOGGERHEAD SEA TURTLES(CARETTA CARETTA)
A dissertation submitted in partial satisfactionof the requirements for the degree of
DOCTOR OF PHILOSOPHY
in
OCEAN SCIENCES
byDana K. Wingfield
June 2009
The Dissertation of Dana K. Wingfield isapproved by:Professor Gary B. Griggs, Chair

Associate Professor Raphael M. Kudela

Assistant Professor Donald A. Croll

Steven J. Bograd, Ph.D.
Lisa C. Sloan
Vice Provost and Dean of Graduate Studies

UMI Number: 3367768
Copyright 2009 by
Wingfield, Dana K.

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Copyright © byDana K. Wingfield
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TABLE OF CONTENTS
LIST OF TABLES

vi

LIST OF FIGURES

vii

LIST OF APPENDICES

ix

ABSTRACTx

ACKNOWLEDGEMENTS

xii

INTRODUCTION

1

References

5

CHAPTER 1: ARE WE MISSING IMPORTANT AREAS IN MARINECONSERVATION? REDEFINING CONSERVATION HOTSPOTSIN THE OCEAN
7
Abstract'

Introduction

7
8

Terrestrial and Marine Processes

10

Biodiversity and Primary Production

10

Human-induced Threats

12

Ecosystem Functions and Services

13

Methods and Analyses

14

Results.......

15

Terrestrial and Marine Hotspots Literature Review
Discussion

15
17

Are We Incorporating Productivity into Marine Conservation
Strategies?

17

Re-Thinking Marine Conservation Portfolios.

19

iii

Conclusions

21

Acknowledgements

22

References

23

CHAPTER 2: FROM TAU TO TURTLES: THE MAKING OF A TOP
PREDATOR HOTSPOT
Abstract:

39
39

Introduction

40

Materials and Methods

42

Results

44

Discussion

45

Conclusions

49

Acknowledgements

50

References.

52

CHAPTER 3: PREDICTIVE HABITAT USE OF JUVENILE LOGGERHEADSEA TURTLES (Caretta caretta) OFF BAJA CALIFORNIA,MEXICO
61
Abstract

61

Introduction

63

Materials and Methods

68

Loggerhead Tracks

68

Simulated Tracks

69

Prey Distribution and Abundance

70

Environmental Data.

71

IVData Analysis

72

Results,

74

Discussion

76

Predictive Habitat

76

Potential Habitat: Vizcaino Bay and Ulloa Bay

78

Conclusions

80

References

82v

LIST OF TABLESTABLE 1.1

Ecosystem functions, services, their threats in each systemand the types of hotspots
30

TABLE 3.1

Output summary for the final Generalized Additive
Model (GAM)

VI

88

LIST OF FIGURESFIGURE 1.1
FIGURE 1.2

Number of conservation hotspot publications
from 1988 - 2007

32

Number of conservation hotspot publications focused
primarily on biodiversity vs. productivity

34

FIGURE 1.3

Primary conservation objectives of 27 marine conservationplanning cases at the local, regional, national, and global
levels.
36

FIGURE 1.4

Marine overlap between Conservation International's
Biodiversity Hotspots and areas of greatest Net Primary
Production ("Productivity Hotspots")
38

FIGURE 2.1(a) Juvenile loggerhead turtle tracks from 1997 - 2007; (b)Mean red crab presences and corresponding meanabundances (log-transformed for scale) at each
IMECOCAL cruise station (2000-2008)
58

FIGURE 2.2

All-time means for a) Surface Winds (m/s); b) Vertical
Ekman Transport (m/s); c) Sea-surface Temperature (°C);d) Chlorophyll-a (log mg/m3); e) Frontal Probability Index;f) Dynamic Height: 200/500dbar, (dyn-cm); g) Logged Red
Crab Distribution; h) Loggerhead Sea Turtle Kernel Density
Distribution
60

FIGURE 3.1

Utilization distribution (UD) of thirty resident j uvenile
loggerhead sea turtles at the 25%, 50%, and 75% contour
intervals off Baja California, Mexico (1997-2007)
90

FIGURE 3.2

Cumulative spatial distribution of all observed loggerhead
locations (red) versus correlated random walksimulations (blue)92

FIGURE 3.3

Density histograms of the environment sampled underneathof observed tracks ('presence') versus simulated tracks('absence') for a) sea-surface temperature (°C); b) logtransformed chlorophyll-a concentrations (mg m~3); and c)
depth (meters)
94

FIGURE 3.4

Generalized Additive Model functions of sea turtle presence
in relation to a) sea-surface temperature; b) chlorophyll-aconcentrations; and c) depth
...96
vu

FIGURE 3.5

Binary predictive habitat map showing suitable and unsuitable
habitat as defined by the response of loggerhead presence
to the environmental predictor variables of SST, Chl-a,and depth98

FIGURE 3.6

Predictive habitat map showing the probability of loggerhead
occurrence (0 -1), in relation to the environmental predictor
variables of SST, Chl-a, and depth
100

FIGURE 3.7

All-time average of red crab abundance from IMECOCAL
hydrographic surveys from 2000-2008 (log biomass) 102

viii

LIST OF APPENDICESAPPENDIX 3.1 Temporal averages of environmental variables for the entire
study period (09/1997 - 02/2007) for a) AVHRR Pathfinder seasurface temperature (°C): monthly, 5km spatial resolution; b)
SeaWiFS chlorophyll-a surface concentrations (log mg m-3): 8day, 0.1 degree spatial resolution; c) ETOP02v2 2 minutegridded bathymetry (~4km) in meters below sea
level
104

IX

ABSTRACTON THE FORMATION OF A CONSERVATION HOTSPOTFOR NORTH PACIFIC LOGGERHEAD SEA TURTLES(CARETTA CARETTA)DANA K. WINGFIELD

This research examined the incorporation of highly productive regions
within the marine system. I combined historical conservation literature, remotelysensed oceanography, ship based surveys, satellite tagged animals, and statisticalmodels to explore an integrated approach to the identification of key oceanic
regions that require incorporation into current marine conservation strategies.

In my first chapter, I undertook a literature review of the term "hotspot",
one of the most common ways by which scientists ascribe conservation
prioritization in the marine and terrestrial systems. My results showed that marine
literature has identified important areas of biodiversity and productivity (i.e. highprimary production that results in trophic linkages and species aggregations) are inneed of protection from human threats. However, current non-governmentalorganizations focus primarily on biodiversity, thus missing important areas ofproductivity for marine conservation.

In my second chapter, I demonstrated how remotely sensed oceanography,
ship-based surveys, and satellite tagged animals can help to identify the formation

of such a "productivity hotspot". Specifically, I examined the connection
between physical forcing (surface winds and vertical Ekman upwelling), seasurface temperature, primary production (chlorophyll-a concentrations), retentive
features of fronts and dynamic height, and prey abundance (red crabs) in thespatial and temporal concentration of the critically endangered North Pacificjuvenile loggerhead sea turtle (Caretta caretta) within its foraging habitat off the
Pacific coast of Baja California.

Finally, in my third chapter, I identified habitat selection of loggerheads tobetter understand the species preference within suitable habitat. I sampled several
environmental variables (depth, sea-surface temperature, and chlorophyll-a)
within 'preferred' versus 'avoided' turtle habitat. Results from a generalized
additive model showed the statistical importance of all three variables in theprediction of loggerhead presence within suitable habitat off of Baja California. I
then incorporated prey distribution to fully explore the connection between ahighly migratory species and its environment. These results show how knowledgeof threatened and endangered species habitat use within a productivity hotspot canhelp to more efficiently identify and prioritize critical areas for conservation.

ACKNOWLEDGEMENTS
There are many people I would like to thank - for without whom, thisdissertation would not have been possible. For each chapter, I have provided an
acknowledgement section for all persons that participated in the research.
However, I would like to take this opportunity to express my utmost appreciationfor everyone's assistance and dedication throughout this entire process.

I would like to thank my thesis committee: Gary Griggs, Don Croll, Raphe
Kudela, and Steven Bograd, for the support and guidance. I truly appreciate eachone of you for believing in me and for contributing advice and assistance along
the way. I thank Raphe for always being available and helpful whenever I needed
it. I thank Steven for such essential contributions and assistance in my research
and analyses. I thank Don Croll for welcoming me into his lab, for laying the
groundwork for this research, for his exceptional input and direction in this work,and for opening many doors of opportunity for me. And lastly, I thank myprimary advisor, Gary, for his unwavering encouragement and invaluable support
every step of the way. I thank Gary for always being available, for encouraging
me to chase my interests, and for genuinely being there whenever I needed help. I
am truly grateful for everything you have done for me.

I would also like to thank my collaborators who have provided integral
contributions to the scientific merit of this work. I thank Daniel Palacios, DaveFoley, Steven Bograd, and Helen Bailey of the Environmental Research Divisionxii

(ERD) in Pacific Grove. It has been an absolute pleasure to work with all of you.
I thank you for making me feel like a priority, for spending countless hours on
short notice to discuss my research, and for a truly enjoyable collaboration. I
thank my collaborators at the Marine Geospatial Ecology Lab at Duke University.
I would especially like to thank Ben Best and Pat Halpin, for welcoming theopportunity to collaborate. I also thank my collaborators at CICESE in Ensenada,
who have greatly enhanced this research. I thank Dan Costa and the TOPP
program for providing resources for this research, and I thank each of theorganizations listed at the end of each chapter for their contributions to mygraduate support and funding.

I would like to say a special thank you to Hoyt Peckham and W.J. Nichols,for providing the inspiration and motivation for this work, and for welcoming me
into the incredible community that you both have worked so hard to establish in
Mexico. Never would I have thought I'd have the opportunity to be in the field
and have the first-hand experiences of working directly with sea turtles, thecommunity, and fishermen. I thank you both for such an amazing component tothis research.

I would like to thank the Griggs Coastal Geology and Croll-Tershy labs
for all of their warm support. From the Griggs lab, I thank Nicole Kinsman,
Lynne Harden, Kate Dallas, Ian Miller, and Jeff Hansen. From the Croll-Tershy
lab, I thank Kelly Newton, Bill Henry, Erin McCreeless, Susy Honig, AngelaxiiiQuiros, and Abe Borker. I thank the Croll-Tershy lab and Bernie Tershy foralways being interested and available to help me with my research, drafts, andpresentations. I would like to thank Barry Nickel for providing the resources andassistance with this research.

I would like to give a special thanks to everyone who has been an amazing
support system for me. I thank all of my friends near and far for providing
countless laughs, good times, and for being there through all the ups and downs. I
especially thank Kelly Newton, my partner in crime for making classes, Wind to
Whales cruises, and birthdays great times. I thank Drew Briscoe for taking such
good care of me, and for all of the love and support through this - you are truly
amazing. And lastly, I thank three people who have been an invaluable team of
support: Kiki Jenkins, Larry Crowder, and Sara Maxwell - thank you. I would
like to say a special thank you to Sara Maxwell - 1 honestly don't know what I
would have down without you.

I thank my family for their encouragement and love. To my mom and dad
who taught me that I could do and be anything - your support has allowed me tofollow my dreams to the fullest. Thank you.

Finally, I would like to thank Professor Robert Dolan at the University ofVirginia, who provided the early mentorship to an aspiring undergraduate
scientist, and the inspiration that has ultimately led me to where I am today.
xivINTRODUCTION
There is a widespread consensus that we are facing a global conservation
crisis (Pimm et al 1995: Myers et al. 2000, Millenium Ecosystem Assessment
2005; Jackson 2008). Threats to species, species interactions, and the services
provided by those interactions have been significantly elevated by human actions,and accelerating the wide scale loss of ecosystem functions and ecosystemservices (Ceballos et al. 2005; Hooper et al. 2006). For this reason, conservation
scientists have sought to restore and protect invaluable resources throughconservation prioritization schemes.

One of the earliest ways by which to prioritize conservation was by Norman
Myers. In 1988, Myers coined the term "biodiversity hotspots" to identify
geographical regions of "exceptional concentrations of endemic species
undergoing exceptional loss of habitat" (Myers 1988). The goal of Myers andcolleagues was to pinpoint geographical areas - hotspots of biodiversity - thatwould protect the largest number of endangered species within a relatively small
area yielding the highest return per dollar invested (Myers 1988, Myers et al2000). Over the past twenty years, the term has evolved to symbolize howscientists identify and prioritize conservation efforts in the marine and terrestrial
systems.

However, the fundamental differences in the processes that shape eachecosystem result in different threats and thus, conservation targets. The terrestrial
1system is dominated by habitat destruction and the risk of extinction; the marine
system is most affected by over-exploitation of marine productivity, resulting in
population declines. Important marine megafauna such as marine mammals,
fishes, and sea turtles aggregate within areas of high primary production ocean(e.g. upwellirig centers, fronts, and eddies), that serve to spatially and temporally
concentrate forage species. Recent studies have documented the loss of
ecologically important species within these hotspots of marine productivity(Schipper et al. 2008, Worm et al. 2003). Compared to coral reef systems, theseareas are lower in biodiversity, but have greater fishing pressure. In fact, thesehighly productive areas hold immense economic importance, as they account for
more than half the world's fisheries production (Valavanis et al. 2004).

The use of "biodiversity hotspots" as a strategy to counter species loss in theterrestrial system has worked well, as the goal closely matches its target to protect
species from extinction. However, if the main focus in the marine system is theprotection of over-exploited species, we must ask: how well do biodiversity
hotspots match the target of halting population declines within the most heavily
exploited regions of the ocean? Specifically, are we missing important areas in theocean based on the current focus conservation prioritization, and if so, how do we
define these areas in such a highly dynamic system?

For my dissertation, I examine these questions at three integral levels. In
the first chapter, I present the results of a detailed meta-analysis on the use of the2

term "hotspot" in conservation literature since the inception of the term (Myers
1988). I then compare these findings to the conservation objectives of some of themost influential non-governmental organizations (NGOs) to show how important
areas of productivity are currently being left out of marine conservation schemes.
For example, Marine Protected Areas (MPAs) can be very effective in protecting
coastal marine biodiversity; however the huge ranges of many pelagic species
make the model used for coastal MPAs an unrealistic management tool. This isunfortunate, because pelagic species that make up 70 million tons/year of marine
fisheries yields (Pauly et al. 2002), have been even more poorly managed andhave experienced more severe declines than many coastal fisheries.

Areas of high productivity are expansive and ephemeral, and thecomprehensive conservation of all productivity hotspots in the ocean isunrealistic. In response to the difficulty of forming expansive pelagic protectedareas, some scientists, fishery managers, and conservation organizations haveadvocated Ecosystem Based Management (EBM) techniques, which offer a more
holistic approach through the protection of trophic interactions (Pikitch et al.
2004; Crowder et al. 2008). These approaches must rely on an understanding of
the physical and biological processes that concentrate pelagic species during key
life history stages (i.e.breeding, juvenile feeding, etc.).

In chapter two, I seek to identify the trophic linkages in the distribution of
one such pelagic species during its most population-sensitive life history stage
3

(Crouse et al. 1987) — the North Pacific juvenile loggerhead sea turtle (Caretta
caretta). Using remotely sensed satellite oceanography and ship based surveys, I
identify the bottom-up processes that create complete relationship between
physical forcing, primary production, prey species, and loggerhead movement, off
the Pacific coast of Baja California, Mexico.

Finally, in chapter three I demonstrate an approach that combines two of themost common techniques in marine species habitat use (remotely sensed
oceanography and satellite tagging capabilities), to statistically predict loggerheadpresence based on key environmental parameters. In this way, it is possible toidentify the most important areas within productive waters for a more efficientconservation management within marine productivity hotspots.

4

REFERENCES
Ceballos, G., P. R. Ehrlich, J. Soberon, I. Salazar, and Fay, J.P. 2005. Global
Mammal Conservation: What Must We Manage? Science 309:603-607
Crouse, D.T., Crowder, L.B., Caswell, H., 1987. A stage-based population model
for loggerhead sea turtles and implications for conservation. Ecology
68(5): 1412-1423.
Crowder, L. B., N. E.L Hazen, R. Avissar, K. Bjorkland, M. Latanich, and B.
Ogburn. 2008. The impacts of fisheries on marine ecosystems and thetransition to ecosystem-based management. Annual Review of Ecology,
Evolution, and Systematics 39:259-278.
Hooper, D. U., F. S. Chapin, III, J. J. Ewel, A. Hector, P. Inchausti, S. Lavorel, J.
H. Lawton, D. M. Lodge, M. Loreau, S. Naeeem, B. Schmid, H. S. La, A.
J. Symstad, J. Vandermeer, and D. A. Wardle. 2005. Effects of
biodiversity on ecosystem functioning: a consensus of current knowledge.
Ecological Monographs 75:3-35.
Jackson, J. 2008. Ecological extinction and evolution in the brave new ocean.
Proceedings of the National Academy of Sciences 105:11458-11465.
Millenium Ecosystem Assessment: synthesis report. 2005. Millenium Ecosystem
Assessment. Island Press, Washington D.C.
Myers, N., R. Mittermeier, C. Mittermeier, G. Da Fonseca, and J. Kent. 2000.
Biodiversity hotspots for conservation priorities. Science 403:853-857.
Myers, N. 1988. Threatened Biotas: "Hot Spots" in Tropical Forests. The
Environmentalist 8:187-208.
Pauly, D., Christensen, V., Guenette, Sylvie, Pitcher, T.J., Sumaila, U.R., Walters,
C.J., Watson, R., Zeller, D., 2002. Towards sustainability in world
fisheries. Nature 418(8): 689-695.
Pikitch, E., C. Santora, E. Babcock, A. Bakun, R. Bonfil, D. Conover, P. Dayton,
P. Doukakis, D. Fluharty, B. Heneman, E. Houde, J. Link, P. Livingston,
M. Mangel, M. McAllister, J. Pope, and K. Sainsbury. 2004. Ecosystembased fishery management. Science 305:346-347.
Pimm, S. L., G. J. Russell, J. L. Gittleman, and T. M. Brooks. 1995. The Future of
Biodiversity. Science 313:351-354.
Schipper, J., J. Chanson, F. Chiozza, N. Cox, M. Hoffmann, V. Katariya, J. F.
Lamoreux, A. Rodrigues, S. Stuart, H. Temple, J. Baillie, L. Boitani, T.
Lacher, R. Mittermeier, A. Smith, D. Absolon, J. Aguiar, G. Amori, N.
Bakkour, R. Baldi, R. Berridge, J. Bielby, P. Black, J. Blanc, T. Brooks, J.
5Burton, T. Butynski, G. Catullo, R. Chapman, Z. Cokeliss, B. Collen, J.
Conroy, J. Cooke, G. Da Fonseca, A. Derocher, H. Dublin, J. Duckworth,
L. Emmons, R. Emslie, M. Festa-Bianchet, M. Foster, S. Foster, D.
Garshelis, C. Gates, M. Gimenez-Dixon, S. Gonzalez, J. Gonzalez-Maya,
T. Good, G. Hammerson, P. Hammond, D. Happold, M. Happold, J. Hare,R. Harris, C. Hawkins, M. Haywood, L. Heaney, S. Hedges, K. Helgen, C.
Hilton-Taylor, S. Hussain, N. Ishii, T. Jefferson, R. Jenkins, C. Johnston,
M. Keith, J. Kingdon, D. Knox, K. Kovacs, P. Langhammer, K. Leus, R.
L. Lewison, G. Lichtenstein, L. Lowry, Z. Macavoy, G. Mace, D. Mallon,
M. Masi, M. Mcknight, R. Medellin, P. Medici, G. Mills, P. Moehlman, S.
Molur, A. Mora, K. Nowell, J. Oates, W. Olech, W. Oliver, M. Oprea, B.
Patterson, W. Perrin, B. Polidoro, C. Pollock, A. Powel, Y. Protas, P.
Racey, J. Ragle, P. Ramani, G. Rathbun, R. Reeves, S. Reilly, J. Reynolds,
C. Rondinini, R. Rosell-Ambal, M. Rulli, A. Rylands, S. Savini, C.
Schank, W. Sechrest, C. Self-Sullivan, A. Shoemaker, C. Sillero-Zubiri, N.
DeSilva, D. Smith, C. Srinivasulu, P. Stephenson, N. Van Strien, B.
Talukdar, B. Taylor, R. Timmins, D. Tirira, M. Tognelli, K. Tsytsulina, L.
Veiga, J. Vie, E. Williamson, S. Wyatt, Y. Xie, and B. Young. 2008. The
Status of the World's Land and Marine Mammals: Diversity, Threat, andKnowledge. Science 322:225-230.
Valavanis, V. D., A. Kapantagakis, I. Katara, and A. Palialexis. 2004. Criticalregions: A GIS-based model of marine productivity hotspots. Aquat Sci.
66:139-148.
Worm, B., Lotze, H. & Myers, R. 2003 Predator diversity hotspots in the blue
ocean. Proceedings of the National Academy of Sciences 100, 9884-9888.

6

CHAPTER 1: ARE WE MISSING IMPORTANT AREAS IN
MARINECONSERVATION?
REDEFINING CONSERVATION HOTSPOTSIN THE OCEAN

ABSTRACTFor over 20 years, scientists have used 'hotspots' to call attention to areas of highbiological diversity threatened by human activity. The concept of biodiversity
hotspots has helped guide development of conservation priorities in the terrestrial
environment where the focus is often on extinction risk. But in marine systems,
differences in processes, extinction, and threats require alternative approaches tosetting priorities. Zones of high primary production in the ocean concentrateecologically and economically important species; yet these areas often are notcongruent with hotspots of marine biodiversity. Nonetheless, 'productivity
hotspots' provide important ecosystem functions and services, and are the mostheavily exploited regions of the ocean. We conducted a comprehensive review of
the conservation hotspot literature and compared that to areas identified inconservation objectives of non-governmental organizations. Marine scientists
recognize the importance of biodiversity and productivity for conservation, but
few NGOs implement strategies based on productivity hotspots. We argue that
identifying and protecting areas of high primary productivity, coupled withbiodiversity, is necessary to efficiently allocate limited marine conservation
resources.

7

INTRODUCTION
Threats to global biodiversity have been significantly elevated by humanactions, and extinctions are accelerating (Pimm et al 1995: Millenium Ecosystem
Assessment 2005; Underwood et al. 2008). Extinctions, range contractions, andpopulation declines have all led to wide scale loss of ecosystem functions andecosystem services (Ceballos et al. 2005; Hooper et al. 2006). Thus, for aesthetic,
moral and, economic reasons extensive efforts are underway to protect
biodiversity (Myers et al. 2000; Millenium Ecosystem Assessment 2005; Brooks
et al. 2006; and others).

Because conservation dollars are scarce, biologists have sought to identify
conservation efforts that maximize return on investment. One of the earliest andmost influential efforts was the 'hotspots' approach (Myers 1988), identifying
geographic regions of "exceptional concentrations of endemic species undergoing
exceptional loss of habitat" (Myers 1988). Specifically, in order to be designatedas a 'hotspot', criteria required that an area must contain > 0.5% of the world's
total vascular plant species as endemics, and have lost at least 70% of its original
habitat (Myers 1988; Myers et al 2000). The primary goal of the biodiversity
hotspots approach was to identify the most at risk areas in the terrestrial system,
where the greatest number of endemic species could be protected per conservation
dollar invested (Myers 1988; Myers et al 2000; Myers 2003).

Since its inception twenty years ago, the original hotspot definition has
8evolved as researchers have expanded upon and revised the metrics. In practice,
hotspots now describe a geographical area (terrestrial or marine) ranking highly in
one or more of the following biological criteria: species richness, species
endemism (range-rarity), number of rare or threatened species, complementarity,
taxonomic distinctiveness, and degree of habitat loss (Reid 1998; Brufnmitt 2002;
Possingham & Wilson 2005). Criticism of the hotspots approach has pointed to
the lack of congruence between hotspots based on different biological criteria, andthe difficulties in setting conservation priorities when different types of diversity
hotspots do not overlap (Harcourt et al. 2000; Kareiva & Marvier 2003; Orme etal. 2005; Possingham & Wilson 2005). In its most general sense, conservation
biologists use 'hotspots' as a value-laden term to call attention to important areasof biodiversity under imminent threat (Myers 1988; Prendegast 1993; Mittermeier
et al. 1998; Reid 1998; Myers et al. 2000, 2003; Roberts et al. 2002; Karieva &Marvier 2003). The term has become so prevalent within academia, that nearly
1,000 articles have been published in conservation literature since Myers firstcoined the term in 1988 (Figure 1). Thus, after 20 years of use, 'hotspots' havebecome a fixture within conservation biology to guide global conservation efforts.

The focus on biodiversity in conservation planning has been appropriate
for terrestrial systems, where reducing extinction risk has been the primaryconservation strategy (Baillie et al. 2004; Bode et al. 2008). But anthropogenic
extinction is less prevalent in marine systems (e.g. Dulvey et al. 2003) where
humans are more likely to drive changes in food web structure and function
9

(Steele 1985, 1991; Cohen 1994; Can et al. 2003). For this reason, it is logicalthat prioritization strategies developed for terrestrial systems may not beappropriate for marine systems. Nonetheless, 'hotspot' prioritization criteria are
increasingly being applied to marine ecosystems. Because currently the use of
biodiversity hotspots is one of the most influential prioritization frameworks, we
examine whether this approach adequately encompasses potentially importantregions for marine conservation. Specifically we: 1) review the inherent
differences in biophysical processes between the two systems that should beconsidered in conservation prioritization, 2) review differences in how humanactivities alter biodiversity and ecosystem processes, functions and services inmarine vs. terrestrial systems, 3) explore how academics have used the term interrestrial and marine conservation publications to prioritize important areas forconservation, and 4) refer to the objectives of some of the most influential
conservation organizations to examine whether productivity 'hotspots' should also
be included as a marine conservation priority.

TERRESTRIAL AND MARINE PROCESSES
Biodiversity and Primary Production
There are fundamental differences in the spatial scale, variability, anddegree of threat found in terrestrial and marine systems that effect theprioritization of areas for conservation (Steele 1985,1991; Beddington et al. 1994;
Cohen 1994; Levin 1994; Allison et al. 1998; Carr et al. 200; Shuring et al. 2006).
Dynamic coupling between physical and biological processes in marine systems
10spread interactions over spatial and temporal scales much larger than those foundin terrestrial systems (Hyrenbach et al. 2000). On land, high biodiversity is moregeographically confined because the dominant primary producers on land are
multicellular organisms that have developed specialized vascular systems andlong life cycles (Tang 2008). These terrestrial plants turnover slowly causing
species to aggregate within stable areas of high primary production. This spatial
discreteness with limited dispersal in terrestrial systems leads to regions of both
high productivity and diversity, (Carr et al. 2003; Hooker & Gerber 2004),
including diversity in upper trophic levels (Hutchinson 1959; Rosenzweig 1995;
Zhao et al. 2006). As a result, Richmond et al. (2007) and Gaston (2000) foundthat the global distribution of terrestrial biodiversity generally increases with net
primary production. This coupling is advantageous; in protecting hotspots ofspecies biodiversity, regions of high primary production are also included,
protecting the ecosystem functions and services provided by each.

In contrast, the majority of marine primary production comes from nonvascular, unicellular organisms with short life spans and rapid turnover.
Biological responses are more closely coupled to physical processes (Steele &Henderson 1994). While there are geographically predictable locations of highproductivity and high diversity in the ocean (e.g. seamounts, reefs, upwelling
regions, and intertidal zones), the lateral transport of energy, resources, andorganisms leads to a patchier environment, complex migratory behaviors, and lessclearly biogeographically defined habitats (Levin 1994; Carr et al. 2003; Lourie &11Vincent 2004). As a result, marine biodiversity occurs on much larger spatial,
functional, and genetic scales (Palumbi et al. 2009). While oceanic areas of highproductivity may have high biodiversity, the two are not necessarily congruent.
Some of the most productive marine regions (e.g. the North Atlantic, Polar Seas)
are relatively depauperate in species diversity (Leslie 2005; Schipper et al. 2008).
Many highly productive/low diversity oceanographic features (upwelling sites,
frontal gradients, eddies) form critical ecosystem linkages between trophic levels.
Defined as "productivity hotspots" (Valavanis et al. 2004), these areas serve tospatially and temporally concentrate forage species (e.g. krill, pollock, andmenhaden), and high abundances of economically important target species (e.g.
tuna and billfishes), as well as threatened marine species (e.g. sea turtles, seabirds,
sharks, and marine mammals), which creates the potential for bycatch in thesehighly valuable fisheries. Although productive areas such as upwelling regions
account for only 0.1 percent of the ocean surface (Ryther 1969), they support up
to 50 percent of the world's fisheries production (Valavanis et al. 2004). Whilethey may lack the level of endemism,richness,or stability of marine biodiversity
hotspots (see Roberts et al. 2002), these "productivity hotspots" aregeographically distinct from important areas of high conservation value andintense exploitation by humans.

Human-induced Threats
Threats resulting from the interaction between human activities andecosystem functions have greatly accelerated population declines in both systems
12

(Brooks et al. 2006; Worm et al. 2006). Habitat loss through deforestation andagricultural development has dominated the terrestrial system, producing rates ofextinction 1000 times greater than pre-human levels (Pimm et al. 1995; IUCN
2007). In contrast, while several studies have shown the mass depletion throughoverharvest of pelagic sharks, billfish, and tunas (e.g. Myers & Worm 2003;
Worm et al. 2005), and coastal species have been depleted by as much as 90% of
historical values (e.g. Lotze et al. 2006; Jackson 2008), there have only been 1016 global marine extinctions specifically attributed to anthropogenic impacts(Dulvy et al. 2003; Del Monte-Luna et al. 2007). To adequately address marine
conservation concerns, regions of high productivity must also be included inconservation priorities.

Ecosystem Functions and Services
In both marine and terrestrial systems, the loss of these diverse andproductive biota threatens a wide variety of irreplaceable ecosystem goods andservices with an estimated worth of $33 trillion per year (IUCN 2007). Because
anthropogenic threats to these resources will only intensify with growing
population demand, it is critical to invest in their maintenance (Pimm et al. 1995;
Myers et al. 2000; Carr et al. 2003; Lotze et al. 2006; Worm et al. 2006; Halpern
et al. 2008; Jackson 2008; Palumbi et al. 2009). But human activities threaten
marine vs. terrestrial ecosystems differently and thus there are differences in theefficacy of biodiversity vs. productivity hotspots to ameliorate these impacts. In
marine systems direct exploitation by humans is the primary threat, while habitat
13

destruction is the primary driver of extinction. Large scale fishing not only
depletes target species, but can damage critical benthic habitats and reduce
populations of non-target species taken as bycatch. Because of the overlap in
biodiversity and primary production in the terrestrial system, the designation of
biodiversity hotspots for important areas on land generally offers an umbrella ofprotection to the wide range of ecosystem functions and services. But because
important areas of productivity are often decoupled from regions of highbiodiversity, effective marine conservation must also include areas of highprimary productivity as well as areas of high biological diversity (Table 1) (datafrom de Groot et al. 2002 and MEA 2005).

METHODS AND ANALYSES
We reviewed all academic peer-reviewed publications from 1988-2007
that use the term for the purposes of conservation (JBiosis Previews and Web of
Science; keywords: "hotspot" and/or "hot spot" and "conservation"]). For
consistency, book chapters, proceedings, and non-English journal articles were
removed from our database. We acknowledge that this is not an all-inclusive
review of all hotspot references, however we felt it was a comprehensive
collection of publications through two highly used and widely accessible searchengines.

We then compared the percentage of marine defined hotspots of
biodiversity versus productivity with a published synthesis of global marine
14

conservation planning cases by Leslie (2005), to determine how biodiversity andproductivity have been incorporated into marine conservation organizations.

Lastly, we generated a map of the spatially summed mean global net
primary productivity (NPP) from 1998-2007 using the SeaWiFS ocean color
sensor (8-day, 9km resolution) and AVHRR Pathfinder sea surface temperature
data (same resolution) as inputs to the Vertically Generalized Productivity Model(VGPM; Behrenfeld & Falkowski, 1997). We defined the upper 10% and 25%
areas of NPP globally (referred to as "productivity hotspots"). These productivity
hotspots are where economically and ecologically important species concentrate
to form ecosystem linkages and provide valuable goods and services to humanwell-being. We then overlaid these results with the 2005 Conservation
International boundaries for marine hot spots. We used standard GIS methods todetermine the overlap between "productivity hotspots" and "biodiversity
hotspots".

RESULTSTERRESTRIAL VS. MARINE HOTSPOTS LITERATURE REVIEWOver the past twenty years, the hotspots concept has been the subject ofhundreds of academic publications, and has influenced conservation policy andon-the-ground conservation actions. In the process, "hotspots" has become asymbolic term to call attention to important areas under threat, and, althoughoriginally defined to prioritize biodiversity loss on land, it is increasingly used to15

identify important marine conservation areas (Roberts et al. 2002; Myers 2003).
However, because a focus on marine biodiversity alone may not protect
the most important areas of the ocean, we compared how scientists ascribe
biological importance (i.e. biodiversity vs. productivity) to the term 'hotspot' ineach system. We evaluated all academic peer-reviewed publications from 19882007 that define hotspots for conservation and compare how the term is used toprioritize important areas within each system. ([Biosis Previews and Web of
Science; keywords: 'hotspot'and/or 'hot spot'and 'conservation']).

The use of the term 'hotspot' has increased steadily, at least through 2006,
with over 85% of the 943 studies applied to the terrestrial systems (Fig. 1). Over
87% of these terrestrial articles used 'hotspot' to identify biodiversity under
threat, with 63.1% using the original Myers definition (Myers 1988; Myers et al.
2003). In contrast, the fewer articles published on marine hotspots in the past 20
years defined important marine 'hotspots' in terms of both biodiversity andproductivity (defined as species abundance and primary production). Fifty-fourpercent of articles defined hotspots of marine biodiversity, while 38.1% used theterm to define areas of high productivity (Figure 2). The presence of productivity
hotspots within marine literature shows that academics appear to be modifying theoriginal definition of hotspots to match the different conservation needs within
marine systems.

16

DISCUSSION
ARE WE INCORPORATING PRODUCTIVITY INTO MARINECONSERVATION STRATEGIES?
Because academic analyses have often guided international andgovernment conservation strategies, we determined whether the primaryobjectives of conservation organizations aligned with those identified by thescientific literature. Traditionally, terrestrial organizations have closely matchedacademic targets, as biodiversity loss has been the foremost priority in terrestrial
conservation strategies (e.g. Conservation International's Biodiversity Hotspots,
WWF-US's Global 2000, IUCN/WWF-International's Centers of Plant Diversity,
TNC's Conservation Initiatives, and Birdlife's Endemic Bird Areas) (Myers et al.
2000; Mittermeier et al. 1998; Olsen and Dinerstein 1998; Davis et al. 1994; TNC
2008; Birdlife International 2008, respectively). Although terrestrial in origin,many of these well-known organizations have now expanded to incorporate
marine programs into their conservation portfolios and prioritization schemes. A
synthesis of marine conservation planning approaches by Leslie (2005) found that
few organizations are prioritizing important marine areas by objectives other thanbiodiversity (Figure 3). While some organizations are incorporating additional
criteria in their priority-setting process (e.g. environmental and ecological factors,
stock recovery, and endangered species) a primary focus on biodiversity
conservation may exclude important marine regions of high productivity. Thus,
important regions of high productivity are likely missing from prominent nongovernmental marine conservation schemes (James et al. 1999; Murdoch et al.
17

2007).
We examined one of the most well-funded and influential priority-setting
conservation NGOs worldwide, Conservation International (CI). Using the
Biodiversity Hotspot approach (Myers 1988; Mittermeier et al. 1999; Myers et al.
2000) as its foremost strategy in conservation planning, CI has generated over$750 million of funding for terrestrial and marine conservation (Myers 2003). We
chose the CI Hotspots approach as an example because it is the leader inconservation prioritization schemes globally, and has been used extensively byprominent conservation foundations and international conservation efforts (e.g.
Global Environment Facility, World Bank), and many threatened ecosystem
functions and services are protected under terrestrial biodiversity hotspot regions(Table 2). Specifically, we questioned: how well do marine biodiversity hotspot
priorities protect the areas of the ocean that are most threatened by human action?

We mapped Conservation International's marine hotspots of biodiversity
with the areas of greatest Net Primary Production (NPP) to identify exactly how
well important areas of biodiversity and productivity overlap in each system, andthus, how effective a terrestrial-developed conservation strategy works in themarine system. CI's marine biodiversity hotspots cover 26.4% of the world's
oceans, and include 20.5% of the upper 25% of annual oceanic NPP (Figure 4).
But our results show that there is a mismatch between marine biodiversity andproductivity, as there are large areas of primary productivity that do not overlap
with CI's biodiversity hotspots. Notably, the productivity hotspots of the North
18

Atlantic sea, as well as the South American, and southern African coasts areabsent from the biodiversity hotspot regions, yet these regions are some of themost highly exploited areas of the world's oceans (Halpern et al. 2008).

By

failing to include these areas, potentially important regions of high productivity
and likely intense human exploitation may be excluded from conservation priority
portfolios.

RE-THINKING MARINE CONSERVATION PORTFOLIOSResults from this review show that the majority of marine conservation byNGOs and conservation foundations remains focused on the "coral triangle",
where biodiversity is high but fishing pressure is low, compared to other locations
where productivity is high (e.g. upwelling regions, fronts, eddies) and importantmarine megafauna (e.g. marine mammals, seabirds, sea turtles) aggregate. Whilebiodiversity must be included as a factor in conservation planning, some of themain drivers of marine species decline (over-exploitation and pollution) are mostintense in areas of high primary productivity, and are often overlooked inconservation prioritization schemes. Coastal and upwelling zones represent only asmall percentage of the global ocean (Ryther 1969) and do not have the levels of
speciesrichnessdocuments in terrestrial systems, but these productivity hotspots
account for at least half of the world's fisheries production, as many highly
migratory and economically important species use these areas (Valavanis et al.
2004). In comparison to the terrestrial system, inherent differences within a fluid
environment and the highly transient nature of these species make their
19movements less identifiable within the marine realm. For this reason, inclusion ofhighly productive regions of the ocean, coupled with biodiversity, may serve as anumbrella in the protection of marine species and ecosystem services.

Emerging strategies in ecosystem-based management may be one type ofopportunity to include important areas of biodiversity and productivity within themarine environment (Pikitch et al 2004; Crowder et al. 2008; EBM 2008). Large
Marine Ecosystems (LMEs) are characterized by distinct bathymetry,hydrography, productivity, and collectively account for 95% of the world's annual
fisheries yields. They contain the highest levels of global marine primary
production, are where coastal destruction is most severe, and are also where
eutrophication is increasing (Sherman & Duda 1999; Sherman et al. 2005). Thus,
they provide an important target for marine conservation prioritization - whereanthropogenic threats are driving species declines, and where the impending
effects of climate change will most heavily affect ecosystem functions andservices on which humans depend. Areas of biodiversity and productivity requirea separate set of strategies for effective protection. While we do not provide asilver bullet approach to marine conservation, we have shown here the importanceof maintaining highly productive areas of the ocean, and our current emphasis on
biodiversity protection may fail to protect important regions with high levels ofanthropogenic impact. In order to effectively prioritize and efficiently allocate
limited resources within the marine system, conservationists must also focus on
important areas of primary productivity within their conservation portfolios.
20

CONCLUSIONS
Since the inception of the term twenty years ago, hotspots have been used tocall attention to the most important areas under threat that require immediate
conservation action. Biodiversity hotspots have worked well in the terrestrial
environment, where conservation goals are driven by biodiversity loss andextinction risk. But marine systems are driven by different processes, so threats
and conservation goals that require different prioritization strategies. In terrestrial
systems regions of high biodiversity and regions of high productivity often
overlap. But intrinsic differences in marine processes lead to mismatches between
high biodiversity, high productivity, and human impact. Inclusion of productivity
hotspots in conservation priority portfolios would provide the opportunity toinclude important regions of high value for ecological function and services.
Furthermore, such areas are often the focus of intense human exploitation. As wemove forward in realizing the costs, benefits, and successes in conservation
planning, we urge marine scientists and organizations to integrate regions of highproductivity as an ecosystem-based approach for conservation prioritization.

21

ACKNOWLEDGEMENTS
We thank B.Tershy, L. Crowder, and S. Maxwell for insightful comments
and suggestions on this work. Financial support for D.K.W. for this research wasprovided by the ARCS Foundation (Achievement Awards for College Scientists).

22

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28

TABLE 1. Ecosystem functions, services, their threats in each system and thetypes of hotspots that could conserve them.
{Data from de Groot et al. 2002)

29

Pollution

Habitat Loss,
Pollution
Habitat Loss,invasives

Maintenance ofsuitable climate
Buffering fromnatural hazards

Climate Regulation

Disturbance Prevention

Disease resistancethrough genetic
evolution
Consumption of
Carbon Fixation
plant and animal
biomass;fuelMaintenance ofsuitable habitat for
Refuge.'Reprodyction
wild plants andanimals
Maintenance of
Pollinatlon/Dispersal
diversity
Maintenance of
Nutrient Cycling
arable soils andcrops
Maintenance ofWater Regulation/Supply
hydraulic cycle
Pharmaceutical andMedicinal Resources
biochemical
substances

Biological/Population
Control

Biodiversity
Biodiversity

Habitat Loss

Pollution
Pollution
Pollution
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Habitat Loss
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FIGURE 1.1 Number of conservation hotspot publications from 1988 - 2007
(n = 966).

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FIGURE 1.2 Number of conservation hotspot publications focused primarily on
biodiversity vs. productivity

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FIGURE 1.3 Primary conservation objectives of 27 marine conservationplanning cases at the local, regional, national, and global levels.
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CHAPTER 2: FROM TAU TO TURTLES
THE MAKING OF A
TOP PREDATOR HOTSPOTABSTRACT
Biologically-rich regions of the ocean provide favorable conditions for theconcentration of many large marine predators. These top predator hotspots areformed by the complex physical forcing mechanisms that are often several trophic
levels removed from pelagic predators. Understanding how hotspots persist
requires knowledge of the spatio-temporal linkages between physical forcing,biological responses, and predator utilization. Here we integrate remotely sensed
oceanography, ship-based prey measurements, and satellite telemetry to show theformation of an upwelling shadow that promotes regional circulation patterns,
enhanced primary productivity year-round, and the aggregation of prey that forma hotspot for endangered loggerhead sea turtles (Caretta caretta) off Baja
California, Mexico. These results enhance our understanding of how migratory
species utilize productive regions of the ocean and will contribute to thedevelopment of spatially explicit management strategies for marine conservation.

39

INTRODUCTION
Highly migratory species are known to associate with dynamic andproductive areas of the ocean (coastal upwelling centers, fronts, eddies) (e.g.,
Worm et al. 2003; Polovina et al. 2006; Gremillet et al. 2008). Many species
spend a portion of their life history utilizing these productivity 'hotspots' as they
move between foraging and breeding grounds (Sydeman et al. 2006).
Specifically, within eastern boundary currents, enhanced primary production at
localized upwelling centers sustains dense concentrations of forage species that, inturn, provide optimal foraging opportunities for top predators (Croll et al. 2005).
While several studies have established relationships between predator distribution
and physical or biological variables like bathymetry, sea-surface temperature orchlorophyll (e.g., Tynan 1998; Ainley et al. 2005; Etnoyer et al. 2006; Polovina et
al. 2006), pelagic predators are several trophic levels removed from the abiotic
factors or the primary producers, such that these relationships may be indirect orUnpredictable. Few studies have simultaneously measured the linkages between
physical forcing, primary and secondary producers, and the pelagic predators that
exploit them (Croll et al. 2005; Baumgartner & Mate 2005; Gremillet et al. 2008).
Thus while dynamic physical oceanic processes may indirectly attract predators,
understanding the formation of these productivity hotspots requires knowledge ofthe spatio-temporal linkages that serve to concentrate their prey within a patchy
environment.

The Pacific coast of the Baja California Peninsula, Mexico, has been40

identified as a hotspot for ecologically and economically important species
inhabiting the California Current System (CCS), including several species oftunas, elasmobranchs, sea turtles, seabirds, whales and other marine mammals(Etnoyer et al. 2006; Kitagawa et al. 2007; Peckham et al. 2007; Schaefer et al.
2007; Wolf et al. 2009). The pelagic red crab (Pleuroncodes planipes) is
considered the principal intermediary in the energy flow from primary producers
to a wide array of invertebrate and vertebrate predators, and probably serves as thelink that attracts a diverse assemblage of top predators to the Baja California
Peninsula (Longhurst et al. 1967; Longhurst 2004). Among these, juvenile
loggerhead sea turtles (Caretta caretta) are unique in that they occur there yearround, remaining tightly aggregated off the coast for up to two decades before
returning to their natal beaches off Japan to breed (Nichols 2000). Basic life
history strategy would predict that juvenile loggerheads, which are notgeographically constrained to centralized breeding grounds, should maximize
fitness by seeking productive areas that optimize growth during their juvenile life
history stage (Stearns 1992). Thus, the extended and localized presence ofjuvenile loggerheads off Baja California is indicative of a region of persistently
favorable foraging conditions.

Red crabs are the primary prey of juvenile loggerheads (Ramirez-Cruz etal. 1991; Peckham & Nichols 2002), and they occur throughout the entire Baja
California Peninsula (Aurioles-Gamboa et al. 1992). It has been hypothesizedthat strong ocean fronts act to concentrate red crabs, secondarily leading to41

increased abundance of foraging predators in this area (Longhurst 2004; Etnoyer
et al. 2006). This study examines the trophic linkages between physical forcing
and the distribution of juvenile loggerhead sea turtles as a model system tounderstanding the development and persistence of top predator foraging hotspots(Fig.l). Specifically, we combine a suite of remotely sensed and ship-based
oceanographic measurements with long-term prey data sets and loggerheadsatellite tracking to provide one of the first comprehensive assessments of thebottom-up creation of a top predator hotspot. In addition to understanding theecological processes leading to a persistent predator hotspot, the results from thiswork will also provide the spatial and temporal information needed for thedevelopment of spatially explicit conservation strategies.

MATERIALS AND METHODS
Satellite transmitters were deployed on 41 juvenile loggerhead turtles
captured off the coast of Baja California Sur, Mexico from 1997 to 2007. Position
estimates and location accuracy were provided by the Argos satellite Location
classes identified as 1, 2, or 3 were included in analysis. Remaining data positions
were filtered based on a location class of A, B, and O and a maximum travel rateof 5 km h"1. Location classes of Z were automatically discarded. Consecutivea

ARGOS locations were interpolated every 12 hours to reduce spatial
autocorrelation (Tremblay et al. 2006). Weighted kernel density analysis of all
filtered positions was used to derive highly utilized areas over the study period42

(Worton 1989). Utilization distributions were calculated for the 25%, 50%, 75%,
100% contour intervals.

Hydrograhic surveys were conducted by the Investigaciones Mexicanas dela Corriente de California (HV1ECOCAL) program. Surveys occurred seasonally(January-February, April, July, and September-October). Sub-surface dynamic
height (200/500 dbar) was derived from station data from 2000-2007. Red crab
samples were collected from oblique net tows (Lavaniegos et al. 2003) andseasonally recorded from 2000-2008. Abundance was log-transformed andaveraged at each station and gridded for spatial interpolation.

Gridded digital bathymetry at 30 arc-seconds was extracted from theSRTM30_PLUS global database (Becker et al. 2009). Remotely sensed
oceanographic data were obtained for the study period from January 2000 December 2007, except for frontal probability, a probability index that is
calculated by the number of times a pixel is classified as a temperature front
divided by the number of cloud free days for the given time period (Breaker et al.
2005). Frontal Probability was obtained from January 2001 - December 2007.
Long-term climatologies were computed from remotely sensed satellite products.
Satellite data used include: surface wind speed from QuikSCAT (25-km spatial
resolution) and the derived vertical Ekman velocity from wind-stress curl (Risen
and Chelton, 2008), Pathfinder sea surface temperature (SST, 4.4-km spatial
resolution), SeaWiFS chlorophyll-a concentrations (1 km resolution) and GOES
43

frontal probability index (10-day and 5-km temporal and spatial resolution,
respectively). The geometric mean was used for surface chlorophyll-a, and values
were log-normalized.

RESULTS
The predominant wind direction throughout the study area was from thenorthwest (Fig. 2a). Winds were most intense around the Point Eugenia headland,with a long-term mean of 7 m s"1. Elevated vertical Ekman transport, driven bypositive wind-stress curl, was localized at three main locations along the coast(northern Baja California, Ulloa Bay, and at the southern tip of the Baja California
Peninsula). A small area of high vertical Ekman velocities was also seen in thelee of Guadalupe Island (Fig. 2b). Mean SST showed a strong north-south
gradient, with increasing values equatorward (Fig. 2c). Long-term chlorophyll-aconcentrations greater than 1 mg m"3 were found inshore along most of the coast(Fig. 2d). On average, the highest probability of SST fronts extended as a band
along the coast, being narrowest off northern Baja California and widest south of
Point Eugenia, especially offshore of Ulloa Bay (Fig. 2e). Sub-surface dynamic
height contours showed a large cyclonic feature off the Point Eugenia at 28°N
(Fig. 2f). The greatest mean abundance of adult pelagic red crabs was foundwithin the continental shelf waters of Ulloa Bay and extending offshore from thislocation (Fig. 2g). Kernel density analysis of 41 juvenile loggerhead turtles
tracked from 1997-2007 identified Ulloa Bay as the most highly utilized area off
the entire Peninsula (Fig. 2h).
44

DISCUSSION
The prevailing oceanic conditions along the Baja California Peninsula areprimarily determined by the interaction of persistent physical forcing with coastal
topographic irregularities. Our results show that the complex geomorphology ofPoint Eugenia plays a particularly important role in coastal oceanographic patterns
and ecosystem dynamics of the region, as suggested in previous studies (Hewitt
1981; Lynn & Simpson 1987; Lavaniegos 1994; Durazo & Baumgartner 2002;
Batteen et al 2003; Espinosa-Carreon et al. 2004). Dominant winds from thenorthwest sustain upwelling-favorable conditions year-round (Bakun & Nelson
1977), and are intensified at the Point Eugenia headland (Fig. 2a). The
prominence of the headland shelters the southern portion of the Peninsula fromdirect wind exposure, most notably within the Ulloa Bay embayment just south ofthe point. This discontinuity in the wind field also generates persistent positive
curl-driven upwelling within Ulloa Bay (Fig. 2b), similar to what has been
documented in the lee of other major headlands, providing a significant source ofnutrients to coastal ecosystems (Koracin et al. 2005; Rykaczewski & Checkley2008). The year-round persistence of this positive wind-stress curl, tau (T), leads
to enhanced vertical Ekman transport, increased nutrient availability, andregionally elevated chlorophyll-a biomass year-round.

Previous studies have documented SST along the Baja California
Peninsula, as cooler SSTs are associated with the strengthening of theequatorward flow of the CCS and the most intense northwesterly winds found45

during the spring and early summer upwelling maximum (Bakun & Nelson 1977;
Lynn & Simpson 1987; Espinosa et al. 2004; Legaard & Thomas 2006). Winddriven circulation in the lee of Point Eugenia is also a mechanism for advecting
recently upwelled waters into a relatively calm, stratified area. This is evident in
the warmer mean SST values within Ulloa Bay (Fig. 2c), which may in turnprovide more suitable habitat for foraging marine species with thermoregulatory
constraints such as sea turtles.

Variability in chlorophyll-a concentrations is associated with coastal
upwelling intensity in the CCS (Espinosa-Carreon et al. 2004; Rykaczewski &Checkley 2008), and our results show a direct correspondence between localized
areas of curl-driven upwelling and high chlorophyll-a concentrations (Figs. 2b,2d). Despite the seasonal cycles, the long-term means of SST and chlorophyll-a
identify Ulloa Bay as a distinct area of relatively warm surface temperatures andsustained productivity (Figs. 2c, 2d).

The prominence of Point Eugenia's headland separates the northern andsouthern portions of the Baja California coastline (Espinosa et al, 2004). The
coastal geometry and the strong vertical shear from the poleward flow of the
California Undercurrent (CUC) are the primary mechanisms in the developmentof instabilities and meanders off the coast (Lynn & Simpson 1987; Batteen et al.
2003). Increased probability of SST fronts south of Point Eugenia (Fig.2e) may
be reflective of the widened shelf and the convergence of the cool equatorward46

flow of the CCS with the warmer undercurrent. Persistent cyclonic eddy activity
off Point Eugenia (Fig. 2f) is influenced by the CUC (Soto-Mardones et al. 2004),
and the combination of frontal convergence with eddy retention provides
favorable conditions for foraging species.

Red crabs primarily forage on phytoplankton and detrital matter (Aurioles
et al. 1992). Localized within coastal upwelling sites of increased productivity,
they exhibit a unique life history for a meroplanktonic species, as the adults havean extended pelagic stage lasting several years (Longhurst et al. 1967; Longhurst
2004; Robinson et al. 2004). They undertake a distinct inshore-offshore seasonal
migration over the continental shelf, from winter-spring to summer-fall periods,
reflecting the offshore Ekman transport and changes in food availability and watertemperature in the second half of the year (Aurioles-Gamboa et al. 1992).
However, recent research has shown that red crabs are also capable of maintaining
high densities inshore within coastal upwelling sites irrespective of the season(Robinson et al. 2004; Gomez-Gutierrez et al. 2000). Although our data do notallow us to comprehensively describe the complex life history cycle nor the fullrange of red crab distribution off the entire Baja California Peninsula, it is clear
from our study that adult pelagic red crabs are present year-round within Ulloa
Bay (Fig.2g). Here, increased frontal probability and persistent cyclonic eddy
activity create a region of localized convergence and retention for thismicronektonic species.

47

Located within the lee of Point Eugenia's headland, Ulloa Bay represents a
unique assemblage of physical and biological features that make this area an
ecologically important hotspot for loggerhead sea turtles (Fig. 2h). As
ectotherms, sea turtles have specific physiological constraints that must bebalanced with foraging needs. For them, higher water temperatures are reported
to be metabolically favorable as long as they do not approach upper thermal limits(Bjorndal 1980). Thermoregulatory costs conflict with enhanced prey detection,
prey capture, ingestion, digestion, and absorption of food (Congdon 1989;
Dunham et al 1989). We suggest that foraging juvenile loggerheads are tightly
aggregated within Ulloa Bay because it represents an "upwelling shadow"(Graham et al. 1992), providing relatively warmer waters within the embayment.
The irregular shape of the coastline shelters Ulloa Bay from direct exposure towinds, while the wide continental shelf provide neritic habitat adequate for thejuvenile life history stage (Musick & Limpus 1997). Persistent wind-curl-driven
upwelling promotes enhanced primary production adjacent to Ulloa Bay, while
the frontal structures formed by the poleward flow of the CUC and the local
cyclonic circulation of surface waters helps to congregate food through surface
convergences, and thus crabs as secondary producers throughout the entire year.
The combination of these conditions would maintain high red crab abundances
year-round, thus satisfying both foraging and thermal requirements for juvenile
loggerheads.

48

CONCLUSIONS
The identification and understanding of the mechanisms driving marine
predator hotspots is of increasing importance, as many of these species are
threatened by commercial fisheries and unintentional capture, and also faceclimate-driven shifts in suitable habitat (Myers et al. 2003, Peckham et al. 2007;
Jackson 2008). Marine Protected Areas (MPAs) that focus on vulnerable life
history stages (i.e., breeding, juvenile foraging, etc.) have the promise to yield
conservation benefits (Hooker & Gerber 2004); however there is little theoreticalframework for the selection, design, and efficacy of MPAs for pelagic predators(Hooker & Gerber 2004), beyond "drawing lines around the features" Norse et al.
(2005). To develop life-history based MPA models that can be realistically usedby managers, advances in satellite oceanography that provide considerable spatial
and temporal detail about when and where physical forcing leads to increased
primary productivity, must be combined with advances in satellite telemetry thatprovide detailed information on the long-term movement patterns of pelagic
predators at various life history stages over large spatial scales (Block et al 2001;
Etnoyer et al. 2006). This study shows the emerging capabilities of couplingthese satellite-based data sets to characterize the unique combinations of bottomup processes in the formation of a top predator hotspot. Knowledge of how theseprocesses vary in space and time will allow for development of more effective
marine conservation strategies.

49

ACKNOWLEDGEMENTS
We thank R. Kudela, G. Griggs, and H. Bailey for insightful comments ofthis manuscript. We thank X. Qui, Y. Hua, Y. Tremblay, A. Walli, and the
Tagging of Pacific Pelagics (TOPP) for providing integral support and assistancein this project. We thank the fishers of Puerto Lopez Mateos, Santa Rosa andother Pacific Baja California Sur communities for their trust and partnership. We
are very grateful to V. de la Toba, D. Maldonado, J. Lucero, R. Rangel, J. Solis,
A. and B. Resendiz, and the ProCaguama, Grupo Tortuguero and ProPeninsula
teams for field assistance. We thank P. Dutton, G. Balazs, J. Polovina of NMFS
plus D. Costa and B. Block of TOPP for their support of the satellite telemetry.
SRMT30_PLUS bathymetry was obtained from the Scripps Institution of
Oceanography, University of California San Diego. Pathfinder SST and Quikscat
wind data were provided courtesy of the PODAAC at the NASA Jet Propulsion
Laboratory (California Institute of Technology). Quickscat wind stress curl wasprovided by C. Risien of Oregon State University. SeaWiFS data were provided
courtesy of NASA Goddard Space Flight Center, and NOAA CoastWatch
program. Research was authorized by the Mexican government through
SEMARNAP and SEMARNAT permits 150496-213-03, 280597-213-03, 190698213-03, 280499-213-03, 280700-213-03, SGPA/DGVS/002 4661,
SGPA/DGVS/10358, and SGPA/DGVS/03501/06. Animal handling was in fullcompliance with CARC/IACUC protocol at UC Santa Cruz. Satellite telemetry
was supported by the David and Lucile Packard Foundation, NMFS-SWFSC,
ProPeninsula, PADI Foundation, T&E Foundation, Wallace Research Foundation,50

Earthwatch Institute, TOPP and ONR/National Oceanographic Partnership
Program. DKW was supported by a UCMEXUS-CONACYT collaborative grant,
the Center for the Institute of Marine Technologies, and the Center for Remote
Sensing, UCSC. SHP was supported with CILS-NSF, Project Global and
UCMEXUS graduate fellowships and awards.

51

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for the seabird Cassin's Auklet. Ecology, 90, 742-753
55

Worm B., Lotze H. & Myers R. (2003) Predator diversity hotspots in the blue
ocean. Proceedings of the National Academy of Sciences, 100, 9884-9888
Worton, B. (1989) Kernel methods for estimating the utilization distribution inhome-range studies. Ecology, 70, 164-168

56

FIGURE 2.1 a) Juvenile loggerhead turtle tracks from 1997 - 2007 (n = 41),(b) mean red crab presence and corresponding mean abundance(log num/m3) at each IMECOCAL cruise station (2000-2008).

57

58

FIGURE 2.2 All-time means for a) surface winds (m/s); b) vertical Ekman
transport (decimeters day"1); c) sea-surface temperature (°C); d)chlorophyll-a (mg/m3); e) Frontal Probability Index; f) dynamic
height: 200/500dbar, (dyn-cm); g) gridded red crab distribution(log num/m3); h) loggerhead sea turtle kernel density distribution59

10ffl%ner ••

tir

60

CHAPTER 3: PREDICTIVE HABITAT USE OF JUVENILE
LOGGERHEADSEA TURTLES(Caretta caretta)
OFF BAJA CALIFORNIA, MEXICO

ABSTRACT
Understanding of how species respond to and utilize their environment has been
inherently difficult in the marine system. However, knowledge of theenvironmental conditions where they went, versus where they chose not to go iscritical in the effective conservation and management of a species. Predictive
habitat models have emerged as a means to identify the species-environment
relationship where information on a population-level may be limited. In thisstudy, we construct a spatially explicit habitat model to identify habitat selectionof the critically endangered loggerhead sea turtle (Caretta caretta) off the Pacificcoast of Baja California, Mexico. Specifically, we compare the home-range
conditions of depth, sea-surface temperature, and chlorophyll-a concentrations of
30 satellite-tagged loggerheads over 10 years, against those conditions foundoutside of the habitat, as defined by the generation of simulated random-walkers.
A generalized additive model (GAM) shows the significance of all threeenvironmental variables to turtle response, and is used to spatially map theprobability of loggerhead presence against the mean environmental conditions
over the study period. We then overlaid a map of pelagic red crabs (Pleuroncodes
planipes), the main prey source for Baja loggerheads, to fully describe optimal
habitat. Our model identifies two explicit regions off the Baja California
61

Peninsula, Vizcaino Bay and Ulloa Bay as highly suitable for loggerhead habitat,
however only Ulloa Bay combines these favorable environmental conditions andhigh prey abundance. The use of a statistical framework to identify available
versus preferred habitat use may provide a more informative approach towards
conservation strategies of pelagic species.

62

INTRODUCTION
A fundamental challenge in the physiological ecology of animals is
optimizing conditions for physiological function within the context of of
ecological constraints (Emlen 1966, MacAurthur & Pianka 1966). Often optimal
locations for physiological processes such as metabolism and growth are not
optimal within ecological constraints such as prey availability or predation;
resulting in trade offs between physiological and ecological constraints. Tradeoffs may be particularly important for ectotherms, where thermo-regulatory
requirements can significantly constrain movement and distribution. Several
terrestrial studies have shown that reptiles deal with this problem by selecting
habitats that may be a compromise between physiological and ecological optima(e.g. Huey 1991, Law & Dickman 1998, Regosen et al. 2003). The study of these
tradeoffs has been more difficult in the marine environment due to the technicaldifficulties of monitoring animal movement, physiological condition, and thespatio-temporal scales of physical and biological processes in marine
systems(Levin 1994, Steele & Henderson 1994).

The combination of recent advancements in electronic tagging of marinevertebrates with synoptic spatio-temporal measurements of marine habitats via
satellite oceanography has provided new opportunities to examine
physiological/ecological tradeoffs in marine vertebrates. Specifically, thecombination of these data allow the characterization of environmental conditions
in utilized vs. unutilized habitats, facilitating the development of simple predictive
63

models of habitat preference resulting from physiological/ecological tradeoffs.
Predictive habitat models have become a resourceful tool in understanding thespecies-environment relationship where ecological information may be limited(Guisan & Zimmerman 2000). Traditionally, predictive habitat models have beenused in the terrestrial system to assess the effects of vegetation change or climatic
variability on the distribution of organisms (Lischke et al. 1998, Kienast etal.1995, 1996, 1998, Guisan & Theurillat 2000, Guisan an&d Zimmerman 2000,
Mladenoff et al. 1995, Mace et al. 1999, Shao & Halpin 1995, Corsi et al. 1999).
Spatially explicit predictive models have recently been used in marine systems todefine the environmental conditions that establish spatial habitat use. For
example, Ferguson et al (2006) used a quantitative method to predict whale
populations from oceanographic variables in the eastern tropical Pacific Ocean.

Characterizing habitat use of threatened species within highly utilized areas is
critical to effective conservation actions over spatial and temporal scales. Highly
productive regions of the ocean - "productivity hotspots" (Wingfield et al.,submitted) serve to spatially and temporally concentrate foraging marine species(i.e. sea turtles, seabirds, and marine mammals). Likewise fisheries activities andincidental, bycatch can be high in these productivity hotspots, leading toprecipitous declines of non-target species (Spotila et al. 2000, Baum et al. 2003,
Lewison and Crowder 2003, Read et al. 2006). As a result, marine scientists,fisheries managers, and policy makers have begun to advocate conservation
practices that focus on regions where pelagic predators concentrate at vulnerable
64

life history stages (Norse et al. 2005, Pikitich et al. 2004, Hobday & Hartmann2006). Thus, the combination of remotely sensed oceanography, satellite tags,
and statistical models may not only characterize the habitats of where animals go,
but also the ability to delineate potential habitats for conservation focus.

The waters off Southern Baja California are characterized as a transitionalzone between the subarctic-influenced waters of the California Current System(CCS) and subtropical/equatorial water masses. Significant coastal upwelling
occurs year-round, and these conditions collectively lead to persistent mesoscale
physical oceanographic features (Hewitt 1981, Batteen et al. 2003). Thesephysical oceanographic characteristics lead to consistently high primary
productivity; these waters are the most productive of the entire CCS (Longhurst
2004, Legaard & Thomas et al. 2006). Several studies have shown that adiversity of pelagic vertebrates (e.g. elasmobranchs, tuna, sea turtles, seabirds, andwhales, tuna, and sea turtles) consistently aggregate in this region (Etnoyer et al.
2006, Kitagawa et al. 2007, Peckham et al. 2007, Schaefer et al. 2007, Croll et al.
in prep).

The biological productivity that sustains foraging pelagic species in thisregion has also resulted in significant overlap with indusrial- and small-scale
fisheries, causing precipitous declines in non-target species (Peckham et al. 2007,
Schaeffer et al. 2007). For example, juvenile loggerhead sea turtles aggregate atunusually high densities off the Baja California Sur coastline, exposing them to65high bycatch risk in long-line and gillnet fisheries (Peckham et al 2006; Seminoff
2006). Peckham et al. (2007) estimated a minimum of 1,000 juvenile loggerhead
turtles are incidentally taken in Southern Baja California artisanal fisheries
bycatch each year. This is one of the highest known sources of mortality for theentire North Pacific loggerhead population, and has likely contributed to a 5090% decline of loggerheads at their nesting beaches in Japan and their IUCN
listing as the most endangered large marine vertebrates (Kamezaki et al. 2003).
The persistence of juvenile loggerheads within a productivity hotspot off Baja
California presents a unique opportunity to examine habitat selection of a pelagic
predator. Figure 1 demonstrates this long-term residence of thirty satellite tagged
loggerheads off Baja California from 1997-2007.

In general, it is believed that loggerheads undergo an ontogenetic shift
from foraging in oceanic to neritic waters some time during their juvenile life
history stage (Bolten et al. 2003). Some time after hatching from nesting beaches
in Japan, North Pacific Loggerheads undertake a trans-oceanic migration to theEastern Pacific and subsequently remain in neritic waters off Baja California yearround for up to two decades before returning again to their Western Pacific natalbeaches (Peckham et al. 2007). Peckham et al. (2008) proposed that the neritic
region off Southern Baja California provides energetically favorable thermal
conditions and productive foraging opportunities. Because juvenile loggerheads
are free of constraints to return to a central location (such as breeding or haulout

66

areas), their extended and localized presence in these waters suggests conditions
that likely serve to maximize survival and growth (Stearns 1992).

Aarts et al (2008) defined habitat as a collection of conditions within anenvironmental space that may be preferred or avoided. We used this definition to
examine habitat selection in juvenile loggerhead sea turtles off Southern Baja
California. We used spatially-explicit habitat models to examine the importanceof a suite of environmental variables to the distribution of juvenile loggerheads.
Specifically, we examined: 1) which environmental variables significantly
correlate to loggerhead habitat; 2) what properties of these variables define thehabitat that juvenile loggerheads prefer to occupy; and 3) how do these conditions
differ with adjacent regions that they could realistically reach, but choose not toinhabit? We test the hypothesis that resident (non-migratory) juvenile
loggerheads are randomly distributed off of Baja California against the alternative
hypothesis that loggerheads preferentially select habitat that includes shallow(neritic) depths, thermally optimal conditions, and high abundance of food.
To test this hypothesis we compared the movement of satellite-tagged
juvenile loggerheads to a theoretical population of individuals moving in acorrelated random walk (CRW). The CRW approach generates animal movement
data based upon a statistically generated distribution of step-lengths and turning
angles (Kareiva and Shigesada 1983). This analytical approach has been applied
across a range of species to relate differences in CRW individuals with individuals

67moving in response to environmental and landscape features (Kareiva andShigesada 1983, Bergman et al. 2000, Morales et al. 2004, Aarts et al. 2008).

We defined preferred habitat through the utilization distribution of 10
years of observed turtle locations, and compared the underlying environment withthe spatial extent of a simulated population of correlated random walkers to
represent all possible locations a Baja California loggerhead could 'choose' to go
without regards to their environment. We then determined the relationshipbetween loggerhead presence and environmental conditions through a generalized
additive model (GAM). Finally, we used this information to develop predictive
maps of suitable loggerhead habitat to understand how this species exploits thewaters off Baja California Mexico. Knowledge of how these juveniles utilize theirhabitat is also important for effective conservation management of the entire
North Pacific loggerhead population.

MATERIALS AND METHODS
Loggerhead Tracks
Thirty juvenile loggerhead sea turtles were tracked from September 1997
to February 2007, with satellite platform transmitting terminals (PTT) (Wildlife
Computers, Redmond, Washington, USA). Turtle carapaces were outfitted withsatellite tags using a polyester resin and fiberglass cloth (Peckham 2008; Balazs etal. 1996). Movements were monitored through the Argos satellite system. Turtle
68positions were determined with the Argos classification system. Location classes
identified as 1, 2, or 3 were included in analysis. All other raw data positions
were filtered based on a location class of A, B, and O and a maximum travel rateof 5km h-1. Location classes of Z were automatically removed. Consecutive
ARGOS location hits were interpolated every 12 hours to reduce spatial
autocorrelation (Tremblay et al 2005, Peckham et al. 2007).

Utilization distributions (UD) represent the probability of animal
occurrence within a defined home range. Utilization distributions were
determined using a Gaussian kernel density analysis of all interpolated positions(Peckham 2008). An index of turtle residence probability per unit area bygridding the number of turtle positions multiplied by the number of individuals
found within a 5km2 cell. Contours representing the percent area of habitat
utilized were then generated from 1-99%. For display purposes, the UD contours
at 25%, 50%, 75% and 95% were displayed (Figure 1).

Simulated Tracks
Correlated random walks (CRW) were generated to account for 'pseudoabsences' - places available in space and time that a turtle could choose to go, but
did not. Therefore, random walkers show no preference to habitat (Aarts et al.
2008). Random walk trajectories were simulated using the 'adehabitat' package
in R software program (The R Project for Statistical Computing, 2009). A
minimum of 500 CRW simulations were generated per observed track and were69

allowed to move unconstrained except for on land, in which a new location along
the track length was sampled with replacement. Each simulation started at theobserved track latitude/longitude location, and was built iteratively so that thesimulated movement was wrapped from a normal distribution. Each simulation
along a track maintained the same distance, turning angle, and duration in time
between successive relocations (Calenge et al. 2009). One drawback to methods
such as this is the inability to account for hidden processes variables (e.g. regime
shifts in movement and behavior), as noted in Royer et al. (2005), however for the
purposes of our study, CRWs provide a statistical way to delineate turtle
movement without regards to biological preference of habitat.

Prey Distribution and AbundanceHydrograhic surveys were conducted by Investigaciones Mexicanas de laCorriente de California (JJV1ECOCAL). Detailed JJVIECOCAL survey design andsampling methodology is provided in Lavaniegos (2006). Briefly, surveys
occurred seasonally (January-February, April, July, September-October) andzooplankton samples were collected with bongo net. Adult pelagic red crab
abundance was measured and recorded seasonally from 2000-2008 (Lavaniegos etal. 2006). Abundance was log-transformed arid averaged at each station andgridded for spatial interpolation using MATLAB 2007a (Mathworks 2007).
Boogaart (2003) noted that a central problem of spatial interpolation (e.g. kriging)is the uncertainty of having enough data to infer the true structure of a population.

70However, this method is still known to be one of the best ways to linearly predict
individual measurements using known observations (van den Boogaart 2003).

Environmental Data
Satellite data for sea-surface temperature and chlorophyll-a was extracted
along the track of each observed and simulated turtle, using a 0.01° x 0.01° spatial
grid under each track location. Products were sampled from the OceanWatch
Thematic Real-time Environmental Distributed Data System (THREDDS) (PFEL
2009, Seminoff et al. 2008). Sea-surface temperature (SST) was obtained from
NOAA's Advanced Very-High Resolution Radiometer (AVHRR) Pathfinder v5,
Using a 5-day, 4km spatial resolution; Chlorophyll-a concentrations (chl-a) fromthe Sea-Wide Field of Viewing System (SeaWiFS), using an 8-day, 1km spatial
resolution. For SeaWiFS-derived surface chl-a, values were log-normalized for
subsequent analyses. Two-minute (~4km), gridded digital bathymetry was
extracted from the ETOP02v2 (2006) database, provided by National Oceanicand Atmospheric Administration's National Geophysical Data Center.

The long-term temporal averages for SST and chl-a were computed over
the tagging study period (September 1997 - February 2007) in order to generate asingle environmental layer for each dynamic response variable to be used for
habitat prediction. Because loggerheads have rarely been recorded in the Gulf ofCalifornia and this region represents a much different oceanic regime, strongly
influenced by tidal mixing and complex geomorphology, we excluded the region
71east of 110°W that encompassed the Gulf of California from our analysis. We alsochose to exclude the area north of 31°N latitude, as our PTT data indicated thatthe region north of this latitude is not utilized by loggerhead sea turtles. Likewise,
the extent of the simulated tracks did not reach north of this latitude.

Data Analysis
In order to identify the separation in environment between the observed
turtles and simulated tracks, we first defined 'preferred' (high-use) habitat as thearea within the 75% kernel density contour interval. All datasets were exploredfor normality, and chlorophyll-a was log-transformed prior to subsequent
analyses. Density histograms of the environmental conditions sampled underobserved tracks were compared to the conditions synchronously sampled in time
under the simulated tracks (Figure 3).

Generalized Addiditve Models (GAMs) were used to relate turtle presence
to environmental variables. A GAM is a nonparametric extension of ageneralized linear model, in which the predictor variables may be transformed to
identify non-linear relationships between an animal and its environment (Hastie &Tibshirani 1990) GAMs are often used when the response variable is binary, suchas in the case of presence/absence data, and where the linear function of thepredictor variables can be replaced with a smoothing function, allowing for a72

more flexible non-parametric fit between the response and predictor variables(Redfern et al. 2006, Redfern et al. 2008).

In preparation for fitting the GAM, all observed points were assigned aspatial reference value of 1 (presence), and simulated points were given a value of
0 (absence). Aarts et al. (2008) recommend a random sampling of at least twice
as many absence points from the CRW to presence points. Thus, from an initialarray of 500 "pseudo-absence" CRW points, we randomly sampled a subset of 5
CRW tracks per turtle.

Because models cannot be tested as "true" or "false, but rather assessed by
the accuracy of their predictive power, cross-validation techniques must be
applied to evaluate their overall predictive performance (Guisan and Zimmerman
2000, Elith et al. 2006). Cross-validation of our subsampled dataset was done byrandomly selecting 2/3 of our data points for model calibration, and reserved theremaining 1/3 of the dataset for model evaluation (Guisan and Zimmerman 2000).

The GAM was fitted using the 'mgcv' package in the R statistics program
and the MGET Toolbox within ArcGIS (Wood 2009, Roberts et al. in review),
with a quasi-binomial distribution and the logit link function. The final model
was included with spline smoothers to SST, log-transformed chlorophyll, anddepth.

73

A Receiver Operator Curve (ROC), a diagnostic to test the performance of themodel, was run on the remaining portion of the cross-validation dataset (1/3) set
aside for evaluation (Aarts et al. 2008). The area under the curve represents theaccuracy of the diagnostic test, and the closer the value is to 1.0, the moreaccurate the test is deemed to be (Tape, 2001).

The all-time temporal means of each environmental variable were layeredon top of each other, and predictive habitat maps were generated by systematicallyincorporating the output of the statistical model into each cell of the layers to
calculate a probability of turtle presence. The largest spatial resolution cell size
(0.83° x 0.83°) of the environmental was used for consistency in generating
habitat maps. We excluded distribution from our model, because of the spatial
mismatch in data extent. Ship transects from IMECOCAL do not encompass theentire Pacific coast of the Baja California Peninsula; therefore we could not makean adequate statistical inference as to turtle presence based on red crab survey
extent. We generated two types of predictive maps: binary (0 or 1 habitat
suitability), and percent probability, were values ranged from 0 up to 1.

RESULTS
Figure 2 shows the spatial distribution of all observed loggerhead locations
versus simulated points. The simulated locations show the wide spatial extent
potentially available to a foraging loggerhead off of Baja California. Density
histograms for depth, SST, and chl-a show a separation in the distribution of the74

environment sampled under the observed versus simulated tracks. Table 1 lists asummary of the relationship between each environmental variable and the
response variable, turtle presence. Results from our generalized additive model
showed that depth and chlorophyll-a concentrations most significantly explained
presence (p < 0.001), followed by sea-surface temperature (p < 0.01). Output
model functions show the smoothed relationship between each predictor variable
and the response (Figure 4). Dashed lines represent upper and lower limits oftwice the standard error. Figure 4a suggests that SST follows a slight bow shape,with probability of presence greatest between 20-25 °C, and decreasing withtemperatures cooler and warmer than this range. Similarly, the probability ofpresence is greater in shallower depths, and an probability decreases with increasein chlorophyll Figures 4b and 4c. This indicates that turtles are inhabiting waters
with lower chl-a concentrations.

The area under the ROC curve was found to be 0.95, suggesting a very
good performance by the model.

The binary predictive habitat map identified the majority of the continental
shelf waters as suitable habitat for juvenile loggerhead sea turtles (Figure 5). The
probability of occurrence map showed Vizcaino Bay and Ulloa Bay as the twoareas along the Pacific coast of Baja California that a turtle is most likely to befound, with predictions 0.6 and greater (Figure 6).

75

Gridded red crab distribution shows the highest abundances were foundbetween 25°N and 28°N latitude, near Point Eugenia.

DISCUSSION
Predictive Habitat
Our understanding of marine animal movement has been greatly enhancedwith advances in satellite telemetry. To date, most studies have used presence
only observations to identify a relationship between an animal and the prevailingenvironmental conditions (Aarts et al. 208). However, the increase in quantitative
analyses in spatial ecology now presents a statistical framework for ecologists to
explore the space-use and preference of species on a population level, using
predictive models to derive a relationship between environmental covariates andpopulation distributions (Guisan & Zimmerman 2000, Aarts et al. 2008).

Long-term tag deployment of 30 juvenile loggerheads over a 10 year study
period have shown a strong site fidelity to the coastal waters of Ulloa Bay, andour results indicate there is a clear separation in the environmental conditions ofdepth, SST, and chl-a between this home-range habitat, and the surrounding
available habitat. Generally, we found that loggerheads preferred shallow depths,
areas of lower chlorophyll concentrations and temperatures closest to 25 °C.

The predictive model suggests that all three predictor variables (depth,
SST, and chl-a) explain significant variability in turtle presence in the waters off76

of Baja California. These results correspond with recent studies in the North
Atlantic and the Central North Pacific that show the influence of such variables astemperature, depth, and the magnetic field on the habitat selection of loggerhead
sea turtles (Hawkes et al. 2007, Kobayashi et al. 2008).

Using our extrapolated relationship of these variables with turtle presence,
we generated two types of spatially-explicit predictive maps that identify suitableversus unsuitable habitat (Figure 5) and the probability of loggerhead occurrence(Figure 6). The binary map identifies the majority of coastal waters off Baja
California as suitable habitat. This is expected for depth and chlorophyll, as it is
known that foraging juvenile loggerheads prefer neritic waters (Bolten 2003)
where chlorophyll concentrations are generally high off Baja California (Espinosa
et al. 2004). However, it is unrealistic to assume that we can protect the entire
Pacific coast of Baja California from overlap with fisheries activity. For thisreason, we used the probability of occurrence map to go a step further in
identifying areas where it is most likely to find a turtle within the defined suitable
habitat for conservation target (Figure 6). Two areas off the Pacific coast of thepeninsula that show a higher probability of loggerhead presence are Vizcaino Bay
and Ulloa Bay. Despite the fact that both of these regions satisfy the conditions ofshallow depths and high chl-a concentrations for a foraging turtle, the strong site
fidelity of loggerheads to Ulloa Bay required further inspection of preference
between these two locations.

77

Potential Habitat: Vizcaino Bay and Ulloa Bay
We suggest two possible reasons why juvenile loggerheads would prefer
the waters of Ulloa Bay over Vizcaino Bay. Firstly, average sea-surface
temperatures within Vizcaino Bay are much cooler than those found within Ulloa
Bay (less than 18°C compared to greater than 20°C, respectively) (Legaard andThomas 2006). While this difference in temperature seems negligible, it has been
determined that higher water temperatures are energetically more favorable forsea turtles in terms of growth, digestion, and maintenance of core bodytemperature, up to a 30°C thermal maxima (Bjordnal 1980, Coles and Musick
2000, Peckham et al. 2008). Additionally, Wingfield et al. (in prep - ch2) haveidentified Ulloa Bay as an "upwelling shadow" (see Graham et al. 1992), where
the warmer, more thermally optimal SSTs and biomass are retained downcoast of
a localized upwelling center.

This leads to the second suggested reason why Ulloa Bay provides moresuitable habitat than Vizcaino Bay. Although Ulloa Bay more appropriately
satisfies the thermal constraints of loggerheads while maintaining high chl-aconcentrations, like most apex predators, juvenile loggerhead sea turtles do not
forage directly on chlorophyll biomass. In fact, studies have shown that while
inhabiting the waters off Baja California, loggerheads forage primarily on the
pelagic red crab (Pleuroncodes planipes) (Villanueva 1991, Peckham and Nichols
2002). Phytoplankton comprise a major component of red crab diet (Aurioles etal. 1992), and the distribution of this micronecktonic species has been correlated78

to areas of increased coastal productivity (Longhurst et al. 1967, Aurioles et al.
1992, Robinson et al. 2004). Red crab concentrations have been documented inboth Vizcaino Bay and Ulloa Bay (Gomez-Gutierrez et al. 2000, Aurioles et al.
1992), however results from our analysis of long-term ship based surveys of red
crab biomass off the coast of Baja California highlight the abundance of thisspecies off Ulloa Bay (Figure 7). Wingfield et al. (submitted) discusses in depth
the development and retention of red crab abundance within this upwellingshadow, and it is this large concentration of prey within the warm, neritic watersof Ulloa Bay that creates the ideal habitat for a foraging juvenile loggerhead off
the Baja California peninsula. While visual comparison in this study shows thepotential overlap between high red crab abundance and the probability of turtle
presence, a more detailed quantitative analysis must be undertaken to identify thecorrelation between these two variables.

This study explores a novel approach to the use of environmental
predictors to identify the probability of species occurrence, on a population level.
Because prey datasets are often unavailable to draw direct trophic linkages
between the physical processes that serve to concentrate marine predators, theseconnections generally rely on biological proxies (i.e. sea-surface temperature andphytoplankton standing stock) to indirectly relate species to their environment.

For the purposes of this study, we used a very basic additive model that
did not account for temporal variability or interaction terms. We used all-time79averages for SST, chl-a, and red crab distributions as a first-step analysis, due tothe long-term persistence of loggerheads within Ulloa Bay year-round. Future
steps for this work will include the exploration of a more fully developed modelwith environmental covariates, such as the interactive influence of SST and Chl-a,further exploration in auto-correlation effects, and greater consistency between
sampling of the environment along animal tracks (i.e. spatial and temporal
resolution of satellite products), and the corresponding environmental layer for
output predictive habitat maps.

CONCLUSIONS
Identification of species habitat use within areas identified as'productivity' hotspots is a critical first step in the ability to conserve them. Thisis especially important for threatened and endangered species, where theimplementation of marine protected areas and seasonal closures during vulnerable
life history stages can greatly reduce extinction risk of an entire population.
Spatially explicit habitat models such as the one used in this study offer a morerobust statistical framework to make these indirect associations where population
data is limited.

As a first-order approach to better understanding of a species habitat use,
we have identified the basic habitat preference of the critically endangered
loggerhead sea turtle throughout its long-term residence off of Baja California,
Mexico. Significant threats to this population have been identified within the80

Central North Pacific, and interaction avoidance has been a high priority of
NOAA Fisheries (NMFS 2004, Gilman et al. 2006). However, effective
conservation of this population requires an international focus on management,
and the development of strategies that include protection from detrimental overlap
within the waters off Mexico. Spatially-explicit predictive models may allow
fisheries to use environmental correlates to identify areas of high avoidance
throughout the year, and with further development, on a seasonal level as well as
potential application to projected shifts in oceanic conditions of sea-surface
temperature and productivity, with impending climate change. Further, it is hopedthat these models can be expanded to identify critical habitat where species may
choose to concentrate during vulnerable life history stages, and where theestablishment of protected areas will be most effective.

81

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86

TABLE 3.1 Output summary for the final Generalized Additive Model (GAM).

87

Environmental Variable

Effective degrees
of freedom

F

Probability

Sea-surface temperature
Chlorophyll-a
Depth

1.919
1.953
1.995

6.49
185.69776.52p<0.01
p< 0.001
p< 0.001

88

FIGURE 3.1 Utilization distribution (UD) of thirty resident (non-migratory)
juvenile loggerhead sea turtles at the 25%, 50%, and 75% contour
intervals off Baja California, Mexico (1997-2007).

89

90

FIGURE 3.2 Cumulative spatial distribution of all observed loggerhead
locations (red) versus correlated random walk simulations (blue).

91

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OCD^»$&<$*>V4?

92

FIGURE 3.3 Density histograms of the environment sampled underneath ofobserved tracks ('presence') versus simulated tracks ('absence') fora) sea-surface temperature (°C); b) log-transformed chlorophyll-aconcentrations (mg m"3); and c) depth (meters).

93

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95

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96

FIGURE 3.5 Binary predictive habitat map showing suitable and unsuitable
habitat as defined by the response of loggerhead presence to theenvironmental predictor variables of SST, Chl-a, and depth.

97

S-B-M-R

98

FIGURE 3.6 Predictive habitat map showing the probability of loggerhead
occurrence (0 -1), in relation to the environmental predictor
variables of SST, Chl-a, and depth.

99

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100

FIGURE 3.7 All-time average of red crab abundance from IMECOCAL
hydrographic surveys from 2000-2008 (log biomass).

101

TUWffl_ l

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Distribution(log biomassjs

1_ J004 <H6F~~l O K 3.25

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TXT

102

APPENDIX 3.1 Temporal averages of environmental variables for the entire
study period (09/1997 - 02/2007) for a) AVHRR Pathfinder seasurface temperature (°C): monthly, 5km spatial resolution; b)
SeaWiFS chlorophyll-a surface concentrations (log mg m-3): 8day, 0.1 degree spatial resolution; c) ETOP02v2 2 minutegridded bathymetry (~4km) in meters below sea level.

103Appendix 3.1a)

SST("C)
Hifh: 30.S

104

Appendix 3.1b)105

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