AN ABSTRACT OF THE THESIS OF
Sallie Christian Beavers for the degree of Master of Science in Wildlife Science
presented on May 10. 1996. Title: The Ecology the Olive Ridley Turtle
(Lepidochelys olivacea) in the Eastern Tropical Pacific Ocean During the
Breeding/Nesting Season.
Redacted for privacy
Abstract approved:
Bruce R. Mate
The ecology of the olive ridley sea turtle (Lepidochelys olivacea) in the eastern
tropical Pacific (ETP) was studied with satellite telemetry and line transect survey
methods. A satellite-monitored olive ridley turtle was tracked from 26 November
1990 to 18 March 1991. The turtle travelled at least 2,691 km in the open ocean,
averaging 1.28 km/h. The turtle's track suggests that he did not float passively with
the currents. Comparisons with surface current divergence data indicated that the
turtle encountered several large convergence and divergence zones, but did not appear
to favor either. Submergence behaviors were diel. The turtle made more frequent,
shorter-duration submergences during the day, yet also spent more time at the surface.
Nighttime submergence durations were longer and more variable. Daytime
temperatures recorded during transmissions were cooler and more variable and were
correlated with longer submergences. Two types of submergences were recorded,
short duration submergences (<7.7 min) and long dives (8.2
95.5 min). These
behaviors exhibited the same diel pattern. Surfacing durations were long (1-10 min)
but showed no diel differences, possibly due to transmission constraints.
Hard-shelled sea turtles were sighted along shipboard line-transects from July
to December during 1989-1990 in the ETP and were used to estimate relative
densities of turtles for distribution and habitat association analyses. The probability of
animal detection decreases with distance from the trackline, and is further affected by
environmental conditions, observer, and time of day. Therefore, I used detectability
analysis to adjust the effective area surveyed for these variables with least squares
regression analysis and a standard estimator of effective halfwidth surveyed. Sea state
was the primary influence on sea turtle detection. Effective halfwidth was adjusted
for each sea state category, and then effective area surveyed and turtle density were
estimated.
The detectability model was applied to the 1986-1990 data to estimate density
of turtles in daily survey-areas. Maps of relative density distributions showed
concentrations off Baja, Mexico, nearshore from Mexico to Ecuador and out about
700 km, along the equatorial convergence zone, and to a lesser extent along the north
equatorial divergence and the Peru Current boundary. Species distributions were
consistent with reported ranges. El Nifio events appear to influence distribution
patterns. Only identified olive ridleys and unidentified turtles (most likely ridleys)
were used in habitat analyses. Presence of sea turtles was significantly correlated
with surface water density, distance to land, and thermocline depth. Densities of
turtles, where present, were minimally correlated with surface water density, distance
to land, thermocline depth, and chlorophyll concentration (R2 = 0.22).
The Ecology of the Olive Ridley Turtle (Lepidochelys olivacea) in the
Eastern Tropical Pacific Ocean During the Breeding/Nesting Season
by
Sallie Christian Beavers
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Completed May 10, 1996
Commencement June 1996
Master of Science thesis of Sallie Christian Beavers presented on May 10, 1996
APPROVED:
Redacted for privacy
essor, representing Wildlife Science
Redacted for privacy
Chair of Department of Fisheries and Wildlife
Redacted for privacy
I understand that my thesis will become part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my thesis to any
reader upon request.
Redacted for privacy
Sallie Christian Beavers, Author
Acknowledgments
I sincerely thank my major Professor, Dr. Bruce R. Mate, for the opportunity
to work with him, for his confidence in my abilities, and for his valuable insights on
my research. I thank the members of my committee, Drs. William G. Pearcy, W.
Daniel Edge, Stephen B. Reilly, and Erik Fritzell, for their helpful discussions and
comments regarding this thesis. In addition to my committee, there are several
individuals who contributed to my academic experience. I am grateful to Dr. Fred L.
Ramsey for the opportunity to work with him and to learn from him. I thank Dr.
Richard A. By les for encouraging me to undertake this project, and for his generosity
and friendship. Dr. Ken Norris taught me to be a field biologist and always had great
faith in my abilities; to him I am especially grateful. This project would not have
been possible without the cooperation of many fine people at the National Marine
Fisheries Service. I am especially indebted to Dr. Stephen B. Reilly, Dr. Paul
Fiedler, and Dr. Tim Gerrodette for generously sharing their environmental data with
me and answering my many questions; to Robert L. Pitman for sharing his sea turtle
data; and to Dr. Doug De Master for allowing me to initiate this project during the
Monitoring of Porpoise Stocks cruises. Robert Holland, Al Jackson, and Valerie
Philbrick cheerfully helped in locating data and providing graphics. Field work could
not have been completed without the support of the officers and crew of the NOAA
research vessels David Starr Jordan and McArthur, and the many oceanographers and
observers who collected data over the years. These dedicated people made the fourmonth cruises fun and wonderful experiences. I am grateful to all, and my thanks
especially to Wes Armstrong, Scott Benson, Peter Boveng, Jim Caretta, Edward
Cassano, Jim Cotton, Darlene Everhart, Gary Friedrichsen, Jim Gilpatrick, Bill
Irwin, Sue Kruse, Carrie LeDuc, Rick Le Duc, Scott Leopold, Lisa Lierheimer, Mark
Lowry, Valerie Philbrick, Robert Pitman, Joe Raffetto, Keith Rittmaster, Richard
Rowlette, Scott Sinclair, Brian Smith, Victoria Thayer, and Robin Westlake. Many
people generously provided materials, data, and technical support for this project.
Special thanks are due Don Pacropis and Den-Mat Corp., and Tony Campegna and
3M Corp. for providing the satellite-tag adhesive materials; to Dr. Masa Kamachi of
Florida State University for providing the gridded current data; and to Sea World
San Diego for allowing me to test tag-attachments on their turtles. I am indebted to
Toby Martin and Martha Winsor for their computer programming expertise, advice,
and countless stimulating conversations about telemetry and statistics. I thank Amy
Cutting for her assistance with data entry. Various chapters of this thesis benefited
from reviews by Dr. Paul Crone, Dr. Scott Eckert, Sharon Nieukirk, and Dr. Robert
Steidl. I am grateful to my fellow students Robert Weber, Dr. Robert Steidl, Dr.
Paul Crone, Sharon Nieukirk, Nobuya Suzuki, Mike Lantana, Robert Neely, Lisa
Lierheimer, and Barbara Lagerquist for their support, and for many long discussions
that helped to clarify my thinking and often raised new questions to explore. I extend
my heartfelt gratitude to Lisa Jade Lierheimer for her help as an oceanographer in the
field and especially for her unfailing love and friendship. Finally, special thanks to
Michael Buchal for making the completion of this thesis (nearly) stress-free. This
research was partially funded by National Marine Fisheries Service contract
#40JGNF210366 and was conducted under ESA permit no. 691.
This thesis is dedicated to the memory of my mother, Ode lle Milling Christian,
who showed me how to rise to the occasion and to roll with the punches.
Contribution Of Authors
Edward R. Cassano was involved in the planning, testing, and fieldwork of
chapter one. Dr. Fred L. Ramsey developed detectability analysis (Ramsey et a1.
1987), and all mathematical derivations are his. Dr. Ramsey was involved in the
analysis and interpretation of data in chapter two, and in the writing of the procedure
section of that chapter.
Table of Contents
Page
Introduction
1
Movements and Dive Behavior of a Male Sea Turtle (Lepidochelys olivacea) in
6
the Eastern Tropical Pacific.
INTRODUCTION
7
METHODS
7
RESULTS
14
DISCUSSION
20
Detectability Analysis In Transect Surveys
26
INTRODUCTION
27
METHODS
28
RESULTS
37
DISCUSSION
46
Distribution and Habitat Associations of Sea Turtles in the Eastern Tropical
50
Pacific Ocean During the Breeding/Nesting Season
INTRODUCTION
51
METHODS
52
RESULTS
61
DISCUSSION
76
Table of Contents (Continued)
Page
Summary
85
Bibliography
89
100
Appendices
APPENDIX 1
101
APPENDIX 2
102
List of Figures
Page
Figure
1.1.
1.2.
1.3.
1.4.
2.1.
2.2.
2.3.
2.4.
3.1.
Surface currents and movements of a satellite-monitored male olive
ridley sea turtle (Lepidochelys olivacea) in the eastern tropical Pacific.
.
9
Data collection and transmission schedule for a satellite-monitored male
olive ridley sea turtle in the eastern tropical Pacific, 1990-91.
11
Sampled submergence durations (10 s-95.5 min) of a satellite-monitored
male olive ridley sea turtle in the eastern tropical Pacific
18
Temperatures recorded at the time of transmission from a satellitemonitored male olive ridley sea turtle in the eastern tropical Pacific.
19
.
Study area and tracklines surveyed by the David Starr Jordan and the
McArthur in the eastern tropical Pacific Ocean, 1989 -90.
33
Covariates air temperature (ATMP), sea surface temperature (ssT), and
Beaufort state (BERT) plotted against ungrouped perpendicular sighting
distances (meters) of sea turtles in the eastern tropical
Pacific, 1989-90.
38
Effective halfwidth (meters) and 95% confidence intervals surveyed at
Beaufort sea state levels 0-5 in the eastern tropical Pacific, 1989-90. .
.
44
Density distribution of hard-shelled (cheloniid) sea turtles in the eastern
tropical Pacific, 1989-90.
45
Surface water masses and primary surface water circulation of the
eastern tropical Pacific Ocean (From Reilly and Fiedler 1994, with
permission).
53
3.2.
Combined survey tracklines for the David Starr Jordan and the McArthur
in the eastern tropical Pacific during August-November, 1986-90
56
(From Reilly and Fiedler 1994, with permission).
3.3.
Distribution of identified cheloniid sea turtle species in the eastern
tropical Pacific, 1986-90.
62
Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1986-90.
64
3.4.
List of Figures (Continued)
1e
Figure
3.5.
3.6.
3.7.
3.8.
3.9.
3.10.
Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1986.
65
Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1987.
66
Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1988.
67
Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1989.
68
Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1990.
69
Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific from line transect sighting data 1989-90.
70
List of Tables
Page
Table
1.1.
2.1.
2.2.
2.3.
3.1.
3.2.
3.3.
Mean summary and sampled submergence data for a male olive ridley
in the eastern tropical Pacific per day/night period,
29 Nov 1990 18 March 1991.
17
Forward stepwise selection of variables collected with sea turtle sightings
in the eastern tropical Pacific, 1989-90.
39
Analysis of variance results for regression model of the log of
perpendicular distance to sea turtles sighted in the ETP, 1989-90,
on Beaufort sea state.
41
Models and parameter number (p) compared by the Mean Square Error
(MSE), Cp (Neter et al. 1989), AIC (Akaike 1974), and BIC
(Schwarz 1978, Kass and Raftery 1995) selection criteria.
42
Average ()) ± standard deviation (SD) and associated coefficient of
variation (CV), and mode of habitat variables used with 1989-90 sea
turtle sighting data in the eastern tropical Pacific (ETP).
71
Logistic regression model parameter estimates for presence of sea turtles
in the eastern tropical Pacific, 1989-90.
73
Multiple linear regression parameter estimates and analysis of variance
for association of sea turtle density with habitat variables in the eastern
tropical Pacific, 1989-90.
75
Preface
Chapter one of this thesis was accepted for publication before Dr. Pamela T.
Plotkin's dissertation, Migratory and reproductive behavior of the olive ridley turtle,
Lepidochelys olivacea (Eschscholtz, 1829), in the eastern Pacific Ocean, became
available. I recommend that any reader interested in the movements and behavior of
olive ridley turtles in the eastern tropical Pacific read her excellent work.
The Ecology of the Olive Ridley Turtle (Lepidochelys olivacea) in the
Eastern Tropical Pacific Ocean During the Breeding/Nesting Season
Introduction
Currently all marine turtle species, except the indigenous Australian flatback
turtle (Natator depressus), are considered threatened or endangered under the
Endangered Species Act of 1973. Despite management programs to protect marine
turtles (primarily in nesting areas), most populations continue to decline (Limpus
1995). It is becoming apparent that intensive protection of female turtles and eggs on
nesting beaches does not necessarily ensure population growth (Crouse et al. 1987).
Frazer (1992:179) eloquently argued for intervention at the sources that threaten sea
turtle populations, rather than attempting "...to release more turtles into a degraded
environment in which their parents have already demonstrated they cannot flourish."
Sea turtles are at increasing risk in the marine environment, where they are threatened
by exposure to fisheries activities, plastic marine debris, and pollutants (Hutchinson
and Simmonds 1992).
Marine turtle management and recovery plans consistently specify studies of
turtle distribution, and quantitative characterization of marine turtle habitats as
research priorities (e.g., Hopkins and Richardson 1984, Thompson et al. 1990).
Correlations of some turtle species with sea surface temperature, water depth, and the
Gulf Stream boundary have been reported off the eastern coast of the United States
(Hoffman and Fritts 1982, Shoop and Kenny 1992, Keinath 1993, Coles et al. 1994,
Epperly et al. 1995a), and the potential influences of ocean currents on the dispersal
2
of young sea turtles has been considered (Collard and Ogren 1990, Witham 1991,
Hall et al. 1992). Aside from these studies, little is known about the biological and
physical influences on turtle distribution and abundance in open water (Carr 1986),
and hypotheses about how pelagic turtles forage, migrate and avoid predators in this
environment are controversial. Hughes and Richard (1974) proposed that sea turtles
migrate by passively riding with ocean currents, but this is not supported by recent
tracking studies (Plotkin 1994). Carr et al. (1978) and Carr (1986, 1987) also
suggested that pelagic turtles use surface currents as transportation aids, and frontal
convergence zones for forage and protection. Witham (1991) argued that
aggregations of organisms at fronts heighten the risk of predation and that food
resources are widely available away from fronts (e.g., Collard 1992).
The lack of information on the pelagic ecology of marine turtles is due to the
difficulties and prohibitive costs of long-term monitoring and extensive dedicated
surveys. The advent of remote tracking-technologies (e.g., Stoneburner 1982, Eckert
et al. 1989, Plotkin et al. 1995), and data collected from "platforms of opportunity"
(e.g., Shoop and Kenny 1992) are beginning to provide new information on turtles in
open water. The 1986-1990 National Marine Fisheries Service (NMFS) Monitoring
of Porpoise Stocks (MOPS) project in the eastern tropical Pacific, ETP (Wade and
Gerrodette 1992) provided an opportunity to apply satellite-telemetry methods to track
an olive ridley turtle (Lepidochelys olivacea) at sea, and to collect data on the
distribution and relative surface density of cheloniid (hard-shelled) turtles, particularly
the olive ridley, in relation to surface features and oceanographic indices (Fiedler et
al. 1990, Reilly and Fiedler 1994).
3
The olive ridley is probably the most abundant and widespread sea turtle in the
ETP (Clifton et al. 1982, Pitman 1990), ranging from Mexico to Peru with primary
breeding populations occurring in Mexico and Costa Rica (Cornelius and Robinson
1986, Marquez 1990). It was the only cheloniid turtle identified in the open ocean in
this study. The olive ridley is listed as threatened, with the exception of endangered
Mexican breeding populations. Populations have declined since the mid-1960's as a
result of intensive take in Mexican and Ecuadoran turtle fisheries, exploitation of
eggs, and incidental take in coastal and offshore fisheries (Clifton et al. 1982,
Cornelius and Robinson 1986). Although commercial harvests have ceased (Frazier
and Salas 1982, Aridjis 1990), olive ridley populations are still at risk from fishery
interactions and poaching.
Three other cheloniid sea turtles inhabit the ETP (described by Marquez 1990)
and are included in a portion of the studies herein. Eastern Pacific green turtles
(Chelonia mydas) range in coastal waters from Mexico to Peru. Nesting populations
are on mainland Mexico (endangered), Costa Rica, Ecuador, and the Galapagos and
Revillagigedos Islands. Loggerhead turtles (Caretta caretta) are concentrated off Baja
California, Mexico, and do not nest in the east Pacific but are likely juveniles from
threatened Japanese breeding stocks (Bowen et al. 1995). The endangered hawksbill
turtle (Eretmochelys imbricata) nests from Mexico to Ecuador and primarily resides in
the coastal littoral zone of the mainland and Galapagos Islands (Green and Ortiz-
Crespo 1982). These three species inhabit primarily continental shelf or island waters
and are infrequently seen at sea (Pitman 1990, 1993, pers. obs.). Although many
nesting beaches are protected and most commercial harvests of adults and eggs have
4
halted, populations are still affected by poaching and incidental take in fisheries
(Marquez 1990).
Steele (1974) noted that in the study of ecosystems, answers to the questions:
where are species distributed, how many species occur there, why do they occur
there, and how do they behave, are unlikely to be gained from a single method and
that any chosen method will emphasize a different pattern or spatial/temporal scale.
The studies reported in this thesis approach the question of the relationship of sea
turtles to the ocean environment of the ETP in two ways: (1) an active, fine-scale
approach of following a single animal over the course of time through its
environment, and (2) a static, large-scale approach of marine turtle distribution in
relation to several environmental variables. Each reveals aspects of the ecology of
sea turtles in open water that would not have been obtainable from the other.
The objectives of the studies reported herein were:
1.
To examine the movements and submergence behavior of an olive ridley sea
turtle in relation to geographic surface features and surface currents with
regard to the hypotheses of current-assisted transport and "preference" for
frontal zones.
2.
To evaluate and refine data-collection protocols for future satellite-telemetry
studies of olive ridley turtles.
3.
To develop a detectability model for cheloniid sea turtles (olive ridley,
loggerhead, and "unidentified" turtles) that adjusts effective area surveyed for
conditions that may affect turtle detection during shipboard surveys, and to use
the model to calculate relative surface-densities by daily area surveyed.
5
4.
To compare the combined relative surface-density distribution of cheloniid
species in the ETP for the 1986-1990 breeding/nesting seasons with surface
features and determine if density distribution is concentrated in convergence
zones.
5.
To quantitatively correlate olive ridley turtle distribution with habitat variables
and determine whether variables previously used to describe ETP dolphin
habitats (Au and Perryman 1985, Reilly 1990, Reilly and Fiedler 1994) can be
similarly used as descriptors of sea turtle presence or density during the peak
nesting season.
The information gained from these studies provides a foundation for future
research on olive ridley turtles in the open ocean and is important for the conservation
of these turtles in their marine habitat.
6
Chapter 1
Movements and Dive Behavior of a Male Sea Turtle (Lepidochelys olivacea)
in the Eastern Tropical Pacific.
Sallie C. Beavers and Edward R. Cassano
Journal of Herpetology, 1996 Vol 3:97-104
Reprinted with Permission
7
INTRODUCTION
Most information on sea turtle biology is based on adult females, their eggs,
and hatchlings at nesting beaches. Little is known of the ecology of sea turtles after
they leave the beach, particularly of the adult males, which rarely emerge from the
ocean (Whittow and Balazs 1982). Until recently, logistics and expense limited
researchers to studying the near-shore movements and long-distance, point-to-point
migrations of sea turtles with methods such as aerial survey, conventional radio
telemetry, sonic telemetry, marking, and flipper tagging.
In 1979, the use of satellite-monitored radio transmitters enabled researchers to
follow the movements of turtles into open water (Stoneburner 1982, Timko and Kolz,
1982). Subsequently, the use of satellite telemetry has grown, primarily as a means
to investigate the inter- and postnesting movements of female turtles (Stoneburner
1982, Timko and Kolz 1982, Duron-Dufrenne 1987, Nakamura and Soma 1988, Hays
et al. 1991), juvenile turtles in near-shore areas (By les 1988, Keinath 1993), and
submergence behavior (Nakamura and Soma 1988, Keinath and Musick 1993, Renaud
and Carpenter 1994). We conducted this pilot study to examine the movements and
submergence patterns of a male olive ridley turtle (Lepidochelys olivacea) and to
refine data-collection protocols for future remote-sensing studies of sea turtles in the
open ocean.
METHODS
The eastern tropical Pacific Ocean (ETP) includes the region bounded by
25°N-20°S and the American continents to 140°W. The ETP contains several
8
principal surface currents and fronts (Wyrtki 1965, 1966; Fig. 1.1), and a permanent
shallow thermocline (20-100 m) that is sometimes approximated by the depth of the
20 °C isotherm (Donguy and Meyers 1987, Fiedler 1992). Biologically, the area is
productive and supports a diverse fauna (Wyrtki 1966, Reilly and Fiedler 1994).
We used the Service Argos (Landover, MD) satellite data collection and
location system to monitor the sea turtle (Fancy et al. 1988). In our low-latitude
study area, 7°-18°N, satellites were "visible" to the transmitter eight or nine times
per day for an average of 10 min per pass. We attached a Telonics (Mesa, AZ)
Model ST-3 platform transmitter terminal (PTT; 10 cm x 15 cm x 3 cm; 822 g) to a
large male olive ridley (straight carapace length 67.0 cm, width 59.7 cm, weight 39
kg, tail length: carapace-tip of tail 18.0 cm).
The turtle was captured in a breakaway
net taped to a 1.8 m x 0.9 m metal frame that was lowered on a long pole from the
ship's bow.
Before deployment, the PTT was wrapped in Scotch Cast (3M, Saint Paul,
MN), a fiberglass casting material, and painted with anti-fouling paint. The casting
material provided a bonding surface between the PTT and the adhesive, Geri-Stor
(Den-Mat Corp., Santa Maria, CA), a light-cured dental compound. Geri-Stor is
lightweight, generates little heat, and cures in 8 min. Tabs of casting material
extended from the PTT base to increase the attachment surface area. The PT!' was
attached to the carapace over the second neural scute. Flipper tags J0503 and X329
were attached to the front flippers. We released the animal within an hour at
17°08'N, 110°59'W.
9
MEXICO
400
Kilometers
0
800
Islas Revillagigedo
20 N
1 Dec
I
RELEASE
26 NOVEMBER 1990
3 Jan
FINAL TRANSMISSION
18 MARCH 1991
41m NEC am
Clipperton
-10N
1 Mar
120W
NECC im
2 Feb
110W
Figure 1.1. Surface currents and movements of a satellite-monitored male olive
ridley sea turtle (Lepidochelys olivacea) in the eastern tropical Pacific. The North
Equatorial Current (NEC) flows westward between 10° and 20°N, and the North
Equatorial Countercurrent (NECC) flows eastward between 4° and 10°N. A
divergence (upwelling) is between the currents at about 8°30' 10°N. The westerly
South Equatorial Current (SEC) is between 4°N and 10°S, and a strong convergence
(downwelling) is between the NECC and the SEC. The California Current (CC)
penetrates the ETP December April at 20°-10°N (Wyrtki 1965, 1966). Large open
circles correspond to the first transmission of each month from release 26 November
1990 to final transmission 18 March 1991. Closed circles represent satellite-acquired
locations
1 (N = 66).
10
The PTT broadcast a 1-watt signal at 401.650 MHz. Transmission uplinks
were made at 61 s intervals and lasted 0.44 s. A saltwater switch, formed by two
external conductivity electrodes on the PTT, allowed transmissions only when the
electrodes were out of the water and counted surfacing events. Because the turtle
passed through two time zones, +7 h (97°30'W-112°30'W) and +8 h (112°30'W127°30'W), we report transmission times in Greenwich Mean Time (GMT).
Each transmission consisted of an identification code and 64 bits of encoded
summary and sampled sensor data. Summary data were the number of submergences,
and the average duration of those submergences. For each tracking-day, these data
were summarized by the transmitter into fixed 12-h periods that closely corresponded
to night (0018-1218 GMT) and day (1218-0018 GMT; Fig. 1.2). Percentage of time
at surface was calculated from these data. Sampled data were the duration of the
most recent submergence preceding transmission, and the internal temperature of the
PTT at the time of transmission. To conserve battery power, the PTT was
programmed to transmit the previous period's summary data and the current sampled
data only during the last 6 h of each 12-h period (Fig. 1.2; Night, 0618-1218 GMT;
Day, 1818-0018 GMT). This regimen, or "duty cycle", limited the number of
satellite passes that could receive data to six per day. Satellite coverage during the
study was equal in both periods.
We defined a submergence as lasting at least 10 s. The PTT repeated the
previously transmitted submergence-duration until a new submergence occurred.
Thus, even if the turtle had been on the surface and the PTT transmitting for minutes,
or hours, before the satellite entered reception range, the duration of the most recent
11
COLLECT
NIGHT SUMMARY DATA
Sampled Data &
TRANSMIT
OFF
I
GMT o
+7
+8
DAY SUMMARY DATA
OFF
Previous Day
Summary Data
I
Sampled Data &
Previous Night
Summary Data
I
2
3 4 5
6
7
17 18 19 20 21 22 23 0
16 17 18 19 20 21 22 23
1
.
Sunset
Sunset
,
8
9
1
2
0
1
27 Nov 15' N 111'
10 11
3 4
2 3
W
17 Mar 10'N 121' W
12
13
11
5
4
6
7
6
5
15 16 17 18 19 20 21 22 23 0
8 9 10 11 12 13 14 15 16 17
7 8
9 10 11 12 13 14 15 16
1
18
17
Sunrise
Sunrise
Figure 1.2. Data collection and transmission schedule for a satellite-monitored male
olive ridley sea turtle in the eastern tropical Pacific, 1990-91. Summary data from
the preceding period were transmitted during (0618-1218, and 1818-0018 GMT).
Sampled data were collected and transmitted during (0618-1218, and 1818-0018
GMT). Local time zone descriptions are represented by +7 (<112° 30' N) and +8
(>112° 30' N). Sunrise and sunset times are given for representative locations and
dates for the beginning and end of the study.
12
sampled submergence was still available. We assumed that submergences of equal
duration were unlikely to occur sequentially, and defined transmissions of an identical
submergence-duration repeated at 61 s intervals as "Dry Transmissions." A single
Dry Transmission indicates the turtle was at the surface for 61 s with the PTT
exposed. We used these transmissions to investigate surface bouts. However, by not
clearing this sensor field, some surface information was lost and surface-duration was
likely underestimated.
Herein, we use the general term submergence and reserve "dive" for long
submergences. We used log-survivorship analysis (e.g. Slater 1974, Machlis 1977,
Fagen and Young 1978, Slater and Lester 1982) to examine the sampled
submergence-durations and assign a duration criteria to differentiate between
submergences and "dives". Because the distributions of the submergence data did not
fit the typical log-survivorship pattern (Appendix 1), we modified Machlis' (1977)
method for minimizing misclassification of behavior type by modeling the data as
overlapping negative exponential and normal distributions. We chose the point at
which a submergence had an equal probability of being in either distribution as the
criterion for minimizing the misclassification of a submergence in a behavior type.
Service Argos locates the PIT by Doppler shift of the PTT frequency as the
satellite passes overhead. At the time of our study, Service Argos classified location
reliability (+1 SD) as the following location classes (LC): LC 3 (± 150 m), LC 2 (±
350 m), LC 1 (± 1,000 m), and LC 0 (accuracy unknown). However, accuracy was
poor; Argos (1989) estimated that at least 66% of the computed locations attained the
indicated accuracies. We used only LC 1 or better in analysis. Distance data
13
(calculated with the Great Circle Equation; Bowditch 1977:1258) and transmission
times were used to calculate minimum rates of movement between locations.
We used modeled-current magnitude and direction vectors supplied by the
Mesoscale Air-Sea Interaction Group, Florida State University (FSU), to visually
compare the movements and locations of the turtle with local surface currents and
surface current divergence (au/ax + av/ay, 10' s') to describe the turtle's
movements associated with these features. Current vectors were calculated at 1/4°
grid resolution (Kamachi and O'Brien 1995) forced by ship-reported wind data
(Stricherz et al. 1992). The FSU data were available in six day intervals (N = 22).
Surface currents in 1° grids and surface current divergence in smoothed 2° grids were
centered on each turtle location of the same date or + 1 day. We conservatively used
divergence values
-0.1 x 10-6 s' as criteria for determining a location was at a
convergence, and values __0.1 x 10-6 s' as criteria for a location at a divergence.
All data transmissions were screened by a logical-consistency error checking
program (Oregon State University) and rechecked by hand. Summary submergence
data were tested for serial correlation by using partial autocorrelation analysis up to
lag 100. No serial correlation was found and the data were assumed independent.
Submergence frequency data were log-transformed to meet assumptions of parametric
statistical tests, however arithmetic means and standard errors are reported for
convenience. Equality of variances were tested with a variance ratio test (two-tailed).
Student's t-test (two-tailed) was used to test submergence day/night differences, and
Welch's approximate t-test was used when variances were unequal (Zar 1984). Chisquare goodness of fit analyses with Yates correction for continuity was used to test
14
diel differences in number of satellite passes and transmissions (Zar 1984). The
Mann-Whitney U-test (two-tailed) was used to compare day/night Dry Transmissions
and the temperature data. We excluded temperatures transmitted with Dry
Transmissions from analysis because they represent known warming at the surface
rather than the most recent temperature recorded after a submergence.
RESULTS
We monitored the turtle from 27 November 1990 to 18 March 1991. During
113 days we received 779 transmissions from the PTT (g = 6.9 transmissions/day,
SE = 0.50, range = 0-30 transmissions/day), which provided 121 locations; 66 were
± 1,000 m). More transmissions (61%) were received during the day.
LC 1
The mean number of transmissions received per pass was equal in day (g = 4.3) and
night (g = 4.2). Eighty-eight transmissions (11.3%) contained errors and were
discarded. Data from summary periods in which only one transmission was received
(N = 14) were also discarded. The PTT recorded 11,012 error-free submergences.
Movements
Following the 27 November 1990 release (Fig. 1.1) the turtle moved 118.4
km northeast to 18°00'N, 110°15'W on 1 December, and then southeast (-140°)
with the California Current (CC). After 4 December, the turtle turned south. He
approached the westerly North Equatorial Current (NEC) at 13°7'N, entering an area
of convergence between the CC and NEC on 16 December (see Appendix 2).
Continuing south, the turtle passed through a convergence on 6 and 7 January, and on
15
16 January 1991 was 64 km (± 350 m) west of Clipperton Island. He then turned
southwest across the NEC and crossed the divergence between the NEC and the
easterly North Equatorial Counter Current (NECC) at 9°30'N on 20 January. The
turtle crossed a convergence on 22 January and maintained a southwesterly heading
across the NECC. The turtle was in a strong convergence on 2 February, and
reached 7°N on 6 February. He moved northwest for the remainder of the month,
and crossed the divergence (8°30'N) back into the NEC between 18
22 February.
During the first two weeks of March, the turtle headed west-northwest with the NEC.
On 15 March, the turtle turned north at 120°W in a divergence area. The final
transmission was on 18 March 1991 in a convergence at 11°05'N, 120°20'W.
The turtle traveled at least 2,691 km during 113 days (Fig. 1.1). Calculated
speeds based on minimum distances between locations ranged from 0.19 km/h to
3.88 km/h (R = 1.28 km/h, SE = 0.10, N = 65). Visual comparison of the turtle's
track with bottom topography showed no observable association with particular depth
contours or sea mounts. Throughout the study, the turtle was in tropical surface
waters historically characterized by high, stable temperatures (__25 °C), low salinity
(
34.00), and a strong, fairly shallow (60-80 m at study area) thermocline (Wyrtki
1966, Fiedler 1992).
Submergence Behavior
All summary and sampled data variables differed between day and night
(P < 0.0001). The turtle made 1.6 times as many submergences during the daytime
summary period as in the night, yet also spent more time at the surface, resulting in
16
shorter average submergence times (Table 1.1). Nighttime submergence durations
were more variable than during the day (F58,51 = 2.7, P = 0.0005).
The distribution of sampled submergence-durations (Table 1.1; range =
10 s-95.5 min, N = 338) was bimodal for both periods (Fig. 1.3), suggesting that
two distinct types of submergence behavior exist. We called these surface-active
submergences and dives. Sampled submergences greater than 7.7 min (the point at
which misclassifying a behavior is minimized, a 0.17% chance of being in either
distribution) were classified as "dives" based on our model of overlapping
distributions. As in the summary data, mean duration of the dives (Table 1) sampled
at night was greater (range = 8.2-95.5 min) than during the day (range = 11.1-47.6
min), and nighttime dive durations were more variable (F44,50 = 4.1; P < 0.0001).
Surface Behavior
We found no diel difference in the percentage of transmissions that were Dry
Transmissions (Night: k = 48.8%, SE = 4.0, N = 52; Day: R = 45.3%, SE = 4.0,
N = 59, P = 0.60). In passes where Dry Transmissions occurred (73.5%), mean
surface-duration calculated from sequential Dry Transmissions did not differ between
periods (Table 1.1, Night: range = 1-7 min; Day: range 1-10 min, P = 0.25).
Temperature
The first temperature transmitted in each pass was consistently lower than, or
equal to, the subsequent temperatures in the pass. We report results from first
transmissions only (N = 149). Daytime temperatures were variable, ranging between
Table 1.1. Mean summary and sampled submergence data for a male olive ridley in the eastern tropical Pacific per day/night
period, 29 Nov. 1990 18 March 1991. Summary Data were summarized during 12-h night (0018-1218 GMT) and day (12180018 GMT) periods. Sampled submergence and temperature data were collected and transmitted during the 6 h night (06181218 MT) and day (1818-0018 GMT) duty cycle. Sequential Dry Transmissions (surface-duration) did not differ between
periods (P = 0.25)a. All other data are significantly different between periods (P < 0.0001).
Sampled Data
Summary Data
No. Dives
Submergence
Duration (min)
Dive Duration
% Time at
Surface
(min)
Sequential Dry
Transmissions
Temperature (°C)
(min)a
Period
N
x
SE
x
SE
x
SE
N
Night
59
73
6.2
12.3
0.91
11.7
0.65
45
Day
52
129
15.3
7.15
0.59
17.3
0.65
51
SE
N
x
SE
N
x
SE
49.8
2.07
59
2.4
0.19
62
31.6
0.19
35.5
0.96
67
2.9
0.25
87
26.9
0.43
.a
18
80 -/E
Night /
20
15
Dives
>s10
1
0
Cg
a)
Day
10
15
I
Dives
20
151
152
m
iII,I.IiiiiiI,I,IiI,I,ILI
0
8 16 24 32 40 48 56 64 72 80 88 96
Dive Duration (min)
Figure 1.3. Sampled submergence durations (10 s-95.5 min) of a satellite-monitored
male olive ridley sea turtle in the eastern tropical Pacific. The means of day/night
dives are significantly different (P < 0.0001). We retained the 95.5 min dive in
analysis because we have no reason to suspect that it is an inaccurate data point, and
its removal did not affect the significance level of the t-test.
36
18
1
J(l'A
NA
ell
cp
\Co
53
cp
\<0
(t)
0A
p(k
Date 1990 - 1991
Figure 1.4. Temperatures recorded at the time of transmission from a satellite-monitored male olive ridley sea turtle in the
eastern tropical Pacific, 1990-91. Closed circles represent nighttime temperatures, shaded triangles represent daytime
temperatures.
20
19.9-33.6 °C (Table 1, Fig. 1.4), and cool temperatures were correlated with longer
dive durations (Spearman Rank Correlation, r =
0.76, P < 0.0001). Temperatures
recorded at transmission were warmer at night (Table 1.1). Nighttime temperatures
ranged 24.5-33.1 °C and 93.6% of those were over 30.0 °C. There was no
correlation between temperature and dive durations at night (r = -0.02, P = 0.85).
Patterns of transmitted temperature and sampled submergences changed after 2
February. The transmitted temperatures dropped between 27 January and 2 February,
the subsequent daytime temperatures remained cool (Fig. 1.4), and fewer surfaceactive submergences were sampled after 2 February (Before: NB = 208, After: NA
=27; X2 = 89.2, P < 0 .0001). These submergences averaged 31 s (SE = 7.5),
and 74% were _.30 s, suggesting wave action across the saltwater switch. The
frequency of dives increased (NB = 39, NA = 51; X2 = 7.1, P = 0.008), but their
mean duration did not change (R, = 41.3 min, SE = 1.3; t = 0.82, 55 df, P =
0.41), and they were followed by more Dry Transmissions (NB = 150, NA = 153; X2
= 8.8, P = 0.003). Rate of travel did not change (RA = 1.20 km/h, SE = 0.15;
t= 0.23, df = 57, P = 0.81).
DISCUSSION
Movements
Our results (Fig. 1.1), and the observations of others (Cornelius and Robinson
1986, Au 1991, Pitman 1993) support the hypothesis that Pacific olive ridleys spend
much of their time in pelagic waters (Hendrickson 1980). How olive ridleys make
21
use of the pelagic surface currents and surface features during their life cycle is not
known. Carr et al. (1978) and Carr (1986, 1987) suggested that pelagic olive ridleys
utilize surface currents as an aid to transport, and frontal convergence zones for
forage and protection. Witham (1991) countered that food resources are also
available away from convergences and risk of predation is increased at fronts.
The turtle's movement across the prevailing currents suggests some active
swimming rather than continuous passive floating with the currents. The sharp
change in reported temperatures between 27 January and 2 February (Fig. 1.4) and
the cusp-shape of the turtle's track at that time (Fig. 1.1) suggest that the turtle
encountered a temperature front, possibly associated with a mesoscale eddy (Hansen
and Paul 1984), which may represent a site of food concentration as indicated by the
convergence at the 2 February location. Although the turtle in our study encountered
large-scale fronts, we found no evidence to support or refute the hypothesis that sea
turtles specifically concentrate their time in convergences (Appendix 2). However,
fronts occur on many temporal and spatial scales from meters and hours to kilometers
and days (Fedorov 1983), and a turtle may cue on a finer scale than we examined.
Information on the diets of Pacific olive ridleys in pelagic waters would provide
insight into this question, however prey items have only been reported from turtles in
nearshore areas and at oceanic islands (Fritts 1981, Montenegro and Bernal 1982,
Mortimer 1982, Barra* et al. 1992). It is likely that olive ridleys in the open ocean
are opportunistic foragers, feeding at fronts, on epipelagic organisms, or on vertical
migrators when and where food is available rather than consistently targeting a
particular food source.
22
Submergence Behavior
The turtle performed two distinguishable behaviors, surface-active
submergences and dives, which were diel in pattern (Fig. 1.3). The high frequency
of daytime surface-active submergences may reflect either short submergences while
at the surface or wave action across the saltwater switch if the turtle sometimes
floated in a "low basking posture" (Sapsford and van der Reit 1979). Daytime dives
were more often associated with lower temperatures, suggesting that the turtle made
occasional dives to deeper, cooler waters. Nighttime dives were longer and less
frequent, with less time spent in surface-active behavior. Consistently warm
nighttime temperatures suggest that the turtle either made shallow, long dives in the
surface layer or deep dives, and rose to the surface slowly, which allowed the PTT to
warm before surfacing. Renaud and Carpenter (1994) also reported warmer night
temperatures from two loggerhead turtles (Caretta caretta). Although the stratified
thermal structure of the mixed layer in our study area is stable (Wrytki 1966, Fiedler
1992), without concurrent dive profiles it is impossible to be certain if temperatures
reflect dive depths.
Diel dive patterns have been reported for leatherback (Dermochelys coriacea)
(Eckert et al. 1986, 1989, Keinath and Musick 1993) and loggerhead turtles
(Sakamoto et al. 1990, Renaud and Carpenter 1994). Other satellite telemetry studies
describe summary period (Renaud and Carpenter 1994) and sampled data (Keinath
and Musick 1993) similar to ours. Like our turtle, leatherback turtles spent more of
their time at the surface during the day (Eckert et al. 1986, 1989). Further, daytime
dive depths of the leatherbacks were variable, whereas nighttime dives were typically
23
shallow. The temperature data reported from our turtle, more variable during the day
and warmer at night, is consistent with the leatherbacks' dive patterns. Diel
differences in behavior could reflect resting and thermoregulation patterns, foraging
patterns (Eckert et al. 1986, 1989), traveling, avoiding predators, or a combination of
these and other unknown factors.
Surface Behavior
There was no diel difference in the turtle's surface behavior. In the ETP,
olive ridley turtles have been observed in the daytime floating with their carapaces
well exposed, often long enough for seabirds to use them for roosting (Pitman 1993).
We were curious to know whether this behavior indicated longer diurnal surfacings.
Although our turtle spent more time at the surface during the day, it appears to be
due to frequent surfacings rather than a difference in the transmitted behavior while at
the surface. The mean surface durations were similar between day and night, as were
the percentages of Dry Transmissions per period. However, we can not rule out the
possibility that these data were biased by our collection methods. The observation
period of the satellite ( 10 min/pass) may have been too short to measure the length
of surface bouts (e.g. Slater 1974, Fagen and Young 1978), and surface bouts were
underestimated because the submergence sensor did not clear after transmission.
Also, daytime surfacings were frequently transmitted with very short submergences,
which may be a result of wave action rather than deliberate submersion (see Keinath
and Musick 1993).
24
PTT Performance
Our transmission time of 113 days was very good given that the PTT had a
45-day battery life at maximum transmission. The turtle's surfacing behavior
permitted many transmissions per day, indicating that detailed dive data for specific
times of day can be archived and transmitted if the data collection format is planned
appropriately. Summary periods and the duty cycle should be shorter, but not so
short that there is a consistent loss of certain periods from orbit and transmission
constraints. Our long duty cycle limited battery life. The number of bits should be
increased to the maximum 256 bits, to enable transmission of sequences of archived
submergence data and prevent bias against long dives. Long dives are less likely to
be reported during the sampling period unless the turtle surfaces just as a pass is
made. Because our PTT software did not clear the submergence sensor field until a
new submergence was made, this bias was reduced, but not eliminated. Surfaceduration data should also be collected as a summary variable to avoid short sampling
periods. A depth sensor should be included. Because errors occur during
transmission, the data stream should contain either an error detection or correction
code. Sampling rate (Boyd 1993), sample size (Machlis et al. 1985), and the analysis
of time-series data are essential criteria in designing the sensor software, as are
physical tests of the tag housing, batteries, and sensors before deployment.
Satellite telemetry is an effective method for tracking sea turtles; these data
would have been difficult to obtain with conventional tracking methods. Daily
submergence behavior, dive depth, diet, and internal body temperatures from a larger
25
sample of olive ridleys at sea are needed for comparisons among cheloniid sea turtles,
and with leatherback sea turtles.
26
Chapter 2
Detectability Analysis In Transect Surveys
Sallie C. Beavers and Fred L. Ramsey
Department of Fisheries and Wildlife and Department of Statistics
Oregon State University, Corvallis, OR
Submitted to Journal of Wildlife Management
27
INTRODUCTION
Line transect sampling relies on the assumption that the probability of
detection of an animal typically decreases with increasing distance from the transect.
Several factors, such as environmental conditions, observer, and time of day, can
further influence the detection of animals (e.g. Scott and Gilbert 1982, Verner 1985,
Holt and Cologne 1987, By les 1988, Marsh and Saalfeld 1989, Buck land et al. 1992)
thus affecting the estimates of effective area surveyed and population density (Ramsey
et al. 1987). Most commonly, these factors are recognized but not included in
analysis (e.g., Scott and Gilbert 1982, By les 1988), mitigated by restricting survey
time to optimum conditions (e.g., Epperly et al. 1995b), or the data set is stratified
relative to the factors (e.g., Holt 1987, Buck land et al. 1992, 1993). However,
restricted survey time and stratification can cause sample size problems, and
conditions can vary within these restrictions. An alternative approach is to include
the influencing factors as covariates in the analysis and adjust the estimates of
effective area surveyed (Ramsey et al. 1987).
We illustrate this method with line transect data, including potential covariates,
collected on sea turtle sightings during 1989-90 of the National Marine Fisheries
Service (NMFS) Monitoring of Porpoise Stocks (MOPS) project in the eastern
tropical Pacific (ETP; Wade and Gerrodette 1992). In this paper we identify sea state
from several covariates as the primary influence on the detection of sea turtles,
incorporate this factor in a model to estimate effective area surveyed, and estimate the
surface density of sea turtles in the ETP.
28
METHODS
Procedure
The theory for transect surveys is presented by Seber (1982) and by Burnham
and Anderson (1976). Buck land et al. (1993) provide a comprehensive treatment.
In a survey, let N be the number of animals present in the target region, R.
Let n be the number of animals detected by the transect survey. The perpendicular
distance from the transect to the ith detected animal is the detection distance, y,. A
detectability function, g(y), equals the conditional probability that an animal is
detected, given that its distance from the transect is y.
The basic results of transect theory are
1.
E {n} = Da, where D = N/Area(R) is the population density, and where a is
the effective area surveyed.
2.
The effective area surveyed in a line transect of length L is a = 2*L*w, where
w is the effective halfwidth,
=
3.
g(y) dy
Conditional on n, the set of perpendicular detection distances, fyl,
,y,il are
independent with common probability density function f(y) = g(y)/w.
The following model of varying detectability comes from Ramsey (1979) and
Ramsey et al. (1987). Associated with the ith detected animal is a set of covariates,
xi = (x11,
,x0) describing detectability conditions. Assume that the covariates alter
29
the effective halfwidth multiplicatively so that the effective halfwidth surveyed under
conditions x, is 0.) where
log (6);) = Po
EoJ
1.1
:1=1
If it is also assumed that the effective halfwidth is a scale parameter in the
distribution f(y) , then the ordinary least squares regression of {log(yi); i = 1,
on the covariates provides an unbiased estimate of the parameters {01,
least squares estimates of {01,
,
,
,
n}
(3p). The
(3p} are not as efficient as maximum likelihood
estimates (MLE), but obtaining MLEs of WI,
,
Op} and 00 relies on knowing the
shape of the detectability function. Multiple linear regression will not, however, yield
a sensible estimate for the constant, (30. A maximum likelihood approach is possible,
but also relies on the shape of the detectability function. Likelihood theory indicates
that the estimate of the constant term is approximately independent of the estimates of
the regression parameters if the covariates are centered at their averages (Ramsey et
al. 1987).
This theory suggests the following non-parametric strategy:
1.
Estimate the parameters {fib
,
fip} by least squares regression with log(y)
as the response.
2.
Determine average detectability conditions for the covariates, X.
3.
Adjust all detection distances to the average conditions according to
= y. x exp(
.i= 1
30
4.
Select a non-parametric estimator of the effective halfwidth, (.-40, at average
conditions using the adjusted detection distances. Programs DISTANCE
(Laake 1994) or CUMD (Wildman and Ramsey 1985, Scott et al. 1986)
provide an estimate of effective halfwidth.
5.
Estimate the constant term with
po = log (60)
This strategy enables the researcher to estimate the effective halfwidth under
the prevailing detectability conditions x(t) = (x, (t),
, x, (t)) at any point, t, along a
transect:
6(0 =
cbo x
exp(ESi(xj(t)
i=1
In the multiplicative model, estimation is performed on the logarithmic scale, where
the variance of this estimate is
\far llog [6) (t)]1
Var(60)
P
+ PE E
kit)-
fri
[xt(t)
c'iwoppti.
In this expression, the variance of i.soo comes from the estimate provided by
DISTANCE or CUMD, and the estimated covariances for the regression coefficients
come from least squares analysis.
31
Then, the total effective area surveyed is estimated by
AT = 2 f
(t) dt
where T represents the totality of transect segments in the region. Dividing the total
number of detections, nr, by the total effective area surveyed yields a final estimate of
density (DT) .
Factors affecting animal populations usually do so multiplicatively, therefore
statistical inferences concerning densities are also constructed on a logarithmic scale.
The following variances are approximate. First calculate the quantities
2 f (xj(t)-7vi)6(t)dt
B
for j = 1,...,p.
T
A
T
Then, the variance for the log density is
Var {log(A)}
Var(nr)
nT
2
Var(60)
2
6o
P
P
E E BiBt coy rilj,f3j.
j=1 k =1
The first term can be approximated as in Burnham et al. (1980:54) by setting
\far (n 7.) = 2(n 7.). The sum of the last two terms is the variance of the log(AT). An
approximate 95% confidence interval for the log density is the log estimate +1.96
times the square root of this variance. The exponentiated endpoints provide the
density interval.
Densities may also be compared in two subregions or from the same region in
different years by estimating their ratio. In doing so, the baseline effective halfwidth
32
estimate, (Jo, cancels. The comparison is therefore unaffected by the choice of
detectability model in DISTANCE, and is equivalent to a comparison of raw counts
adjusted for differing detectability conditions, as recommended by Verner (1985).
For testing and confidence intervals on the ratio of densities in two regions, the
variance of the log-ratio is approximately
ar
b2)
\far (ni)
Var (112)
n2
n22
1
P
P
+ E E (B11 B2j)(Bli.
B21) czn, oppki
j = 1 k =1
In this equation, the B-coefficients are calculated separately in the two surveys or
subregions. Inference is then drawn to multiplicative factors affecting density, which
agrees with log-linear analysis in Poisson regression (McCullagh and Nelder 1983).
Survey Methods
The ETP extends from a narrow continental shelf along the west coast of the
Americas out to about 140°W between 25°N and 20°S, and encompasses more than
19 million km2 (Fig. 2.1; Holt and Sexton 1990). The area is biologically productive
(Fiedler et al. 1991) and supports several species of cheloniid (hard-shelled) sea
turtles (Marquez 1990): the olive ridley (Lepidochelys olivacea), the eastern Pacific
green (Chelonia mydas), the hawksbill (Eretmochelys imbricata), and the loggerhead
(Caretta caretta).
Sea turtle sighting data were collected from 23 August to 5 December 1989
and 3 August to 5 December 1990 aboard the National Oceanographic and
Atmospheric Administration (NOAA) research vessel David Starr Jordan, and from 6
30
20
\
10
\\
\\
\\
3
/
--,,,,
.-.
zi
a
..,
i
V\
\I
<
''''
----- / -?
--......
l
'----.
(\
-7"---
\
-
3.'"
/'
I \ \/(/
/7 /
/ ,.
t I fr..1.
/1
,(,
1.4f.
.4.4
:
....
x/ ----
---- 1-->---fL-
---
-)
/.---
Y
-"I\
.-/-
2 /
10
20
160
150
140
130
120
110
100
90
80
Longitude
Figure 2.1. Study area and tracklines surveyed by the David Starr Jordan and the McArthur in the eastern tropical Pacific
Ocean, 1989-90. Broken lines represent daily search effort.
34
October to 6 December 1989 and 30 July to 5 December 1990 aboard the NOAA R/V
McArthur (Fig. 2.1). Survey dates coincided with the peak nesting season for some
sea turtle populations in the ETP (Richard and Hughes 1972, Marquez 1990). The
ships surveyed in different areas of the ETP, maintained speeds of approximately
18.5 km/h, and remained underway at night to allow greater spatial distribution of the
tracklines.
Each vessel carried two teams of three experienced observers. Port and
starboard-side observers used 25x binoculars mounted on the upper-most deck,
approximately 10.7 m above the sea surface, to search from the trackline out to 90°
on their respective sides. The third observer recorded data and searched the trackline
with the naked eye and with hand-held 7x50 binoculars. Teams rotated equally
through the three duty-stations in 2-h shifts. The bearing and radial distances of
turtles sighted with the 25x binoculars were obtained from 360°-graduated washers
mounted at the binocular base and from distance-calibrated reticles etched in the right
ocular. Perpendicular distance (y) was calculated as radial distance multiplied by the
sine of the sighting angle. When turtles were sighted with 7x binoculars or by naked
eye, observers estimated the perpendicular distance from the transect.
Records of environmental conditions were kept throughout the day's effort.
The sighting time (TIME), the observer team on duty (TEAM, 1-8), the Beaufort-scale
sea state (BFRT; where Beaufort 0 = flat, calm seas, and Beaufort 5 = rough, whitecapped seas with some spray; Bowditch 1977:1267), the absence of sun (NosuN), and
the vertical (vsuN) and horizontal (HsuN) position of the sun (sun glare) in relation to
the trackline were recorded at each sighting. We used a clock-face schematic, with
35
12 o'clock at the trackline, to classify HSUN. The VSUN was classified as a 1/4
clock-face with 3 o'clock at the horizon and 12 o'clock directly overhead. Sea surface
temperature (ssT), collected by thermosalinograph, and dry-bulb air temperature
(ATMP) were recorded hourly. Temperatures used for analysis were within one
half-hour of a sighting.
The number of turtles, their relative size, activity, associations with debris or
other species, and species (if known) were also recorded. Identification of turtle
species at sea is difficult, therefore all hard-shelled (Cheloniidae) species were
combined for these analyses. When possible, species were identified by trained
observers or identified later from photographs.
Daily search-effort continued from sunrise to sunset in BFRT conditions
5.
Transect lengths were unequal and effort was occasionally interrupted when the ship
left the trackline to identify cetacean species.
Analysis
Three assumptions must be met for line transect methods to be reliable: (1)
turtles exactly on the transect are always detected, (2) prior to detection, turtles do
not move in response to the ship, and (3) measurements of turtle position are accurate
with no rounding errors. Some turtles on the transect are likely to be missed because
they are submerged. An adjustment for submerged animals can be applied to
estimates of density (e.g., By les 1988, Hiby and Hammond 1989), but it requires
adequate information on diurnal submergence behavior of each turtle species,
therefore we estimate surface density only. We have not seen evidence of turtles
36
avoiding the ship. Sightings were typically of sea turtles floating motionless at the
surface. Turtles on or near the trackline remained at the surface as the ship passed
by, or dived as the ship's bow wave touched them. Turtles floating off the trackline
did not appear to respond to the ship. Given the speed of the vessels relative to the
motion of the turtles, it is unlikely that a turtle was counted more than once per
transect. Observers have a tendency to round measurements, especially when
estimating distance perpendicular to the transect. Rounding problems were alleviated
prior to adjusting detection distances by grouping data at 20-m intervals.
The influence of environmental and other factors on the detectability of sea
turtles was initially examined with scatter plots of ungrouped perpendicular distance
against each factor. Horizontal sun data were transformed with the equation
Cos(27r(HsuN/12
sighting angle/360)) to examine glare in relation to each sighting.
Vertical sun data were transformed with Cos(27(vsuN/12)). We built a log-linear
multiple regression model of the detection distances on the influencing factors by
stepwise variable selection (F-to-enter = 4), and tested it against several models
containing quadratic and interaction terms. Final model selection was made using the
Bayes Information Criterion (BIC; Schwarz 1978, Kass and Raftery 1995) assisted by
the C, statistic (Neter et al. 1989) and Akaike's Information Criterion (AIC; Akaike
1974). Detection distances were adjusted to average detectability conditions with
parameters from the final model. The adjusted distances, truncated at 1,200 m, were
submitted to programs DISTANCE and CUMD, which provided estimates of the
effective halfwidth surveyed under average conditions. Effective halfwidths under
non-average detectability conditions were then estimated by applying model
37
adjustments to the estimate under average conditions. Adjusted area surveyed was
calculated from distance traveled per sighting condition, and density was estimated
from the number of sightings in the adjusted area.
RESULTS
Detectability Analysis
During the 1989-90 surveys, 346 transects, totaling 50,024 km, were searched
and 579 sea turtles were detected under a variety of conditions. Most (90%) of
survey effort was in BFRT
3, and the average BFRT during sightings was 1.87 (SD=
1.54). Sea surface temperature at sightings ranged 18.3-31.2 °C (average = 28.2
°C, SD = 2.3, n = 577), and ATMP ranged 19.0-30.5 °C (average = 27.7 °C, SD
= 1.8, n = 572). Equipment failure caused the loss of nine temperature values.
Average sighting time was 12:57 (range 05:57-18:44). Sightings were made in all
sun conditions and 55% of the sightings were made while some glare was present in
the search area.
Scatterplots of the variables indicated that BFRT, SST, and ATMP may influence
detection (Fig. 2.2). However, the relationship of SST and ATMP to detection
distances could be an artifact of too few observations at low temperatures and the
influence of wind (BFRT) on temperature; higher winds result in cooling by convection
of SST, and cooler ATMP.
Forward stepwise selection of the possible covariates (Table 2.1) led to a
regression model with BFRT as the only significant covariate (slope: -0.444, SE =
8000
E
a)
0
C
co
U)
6
4000
i
U_
.. .
..
.
C
a)
e-
a)
0_
"
0
1
2
1
1
3
4
Beaufort Sea State
I
5 18
20
22
24
26
28
30
Air Temperature (°C)
I
I
- .,
-.-
':'-',..
....;:..
...:3'4:
: .-,:;;;..-., 4..,..
- .:. ........:.;,,..t.....
11. wii- .1
:
r '.1
16 18 20 22 24 26 28 30
Sea Surface Temperature (°C)
Figure 2.2. Covariates air temperature (ATMP), sea surface temperature (SST), and Beaufort state (BFRT) plotted against
ungrouped perpendicular sighting distances (meters) of sea turtles in the eastern tropical Pacific, 1989-90.
39
Table 2.1. Forward stepwise selection of variables collected with sea turtle sightings
in the eastern tropical Pacific, 1989-90. F-to-enter set as 4.0. BFRT = Beaufort sea
state, SST = sea surface temperature, ATMP = air temperature, TIME = time of
sighting, TEAM = observer team, NOSUN = the absence of sun, VSUN = vertical
position of the sun, and HSUN = horizontal position of the sun.
F to Enter
Variable
df
df
Step 1
BFRT
578
129.14
SST
569
44.71
568
0.23
ATMP
569
36.68
568
0.02
TIME
578
12.21
577
1.97
TEAM
572
6.47
570
1.25
NOSUN
578
5.22
577
0.12
HSUN
578
3.64
577
0.13
VSUN
578
2.31
577
0.11
HSUN x VSUN
578
0.37
577
0.89
Step 2
40
0.039, t = -11.17, P < 0.0001; Table 2.2). We compared the Beaufort model and
several candidate models (Table 2.3). Two more complex models were also
suggested by the data analysis. One, the sea state temperature model, (Table 2.3)
contained an SST x ATMP interaction term that was strong in higher Beaufort
conditions and possibly reflects some decreased detectability in poor weather
associated with either factor. The relationship between environmental temperatures
and sea turtle presence at the surface could be related to thermoregulation behavior
(Heath and McGinnis 1980) and may affect the number of turtles observed at the
surface, but not necessarily the distribution of detection distances. A second model of
the sun variables (Table 2.3) contained the interaction VSUN x HSUN suggesting
decreased detectability for sea turtles in the line of the sun when it is low to the
horizon. However, because vessel speed is fairly slow and the turtles are floating, the
entire field of view is eventually surveyed without sun-glare (Holt 1987) and the
distribution of the perpendicular detection distances should not be affected.
Although these more complex models are suggested by liberal model selection
criteria such as the C, statistic, they are eliminated by the more conservative BIC
(Table 2.3), which emphasizes the best model-fit with the least complexity in
parameters (Lutkepohl 1985, Kass and Raftery 1995).
We adjusted detection distances of sea turtles to average detectability
conditions with the model
log(yi) = po
(-0.444)(7,-B,RT BFRT).
41
Table 2.2 Analysis of variance results for regression model of the log of
perpendicular distance to sea turtles sighted in the ETP, 1989-90, on Beaufort sea
state.
Sum of
Source
Model
df
Squares
1
269.62
Residual
577
1,204.65
Total
578
1,474.27
Mean
Square
269.62
2.09
F-value
P
Adj R2
129.14
<0.0001
0.1815
42
Table 2.3. Models and parameter number (p) compared by the Mean Square Error
(MSE), Cp (Neter et al. 1989), AIC (Akaike 1974), and BIC (Schwarz 1978, Kass
and Raftery 1995) selection criteria. Lowest number in bold type indicates model
selected by that criterion. n = 570 because of seven missing values in ATMP and two
missing values in SST. BFRT = Beaufort sea state, SST = sea surface temperature,
ATMP = air temperature, TEAM = observer team, NOSUN = the absence of sun, VSUN
= vertical position of the sun, and HSUN = horizontal position of the sun.
Model
p
MSE
CP
AIC
BIC
All Main Effects
15
2.085
35.7
437.0
471.7
Full 2nd order model
41
1.981
33.4
448.9
514.0
All Interactions
35
2.001
32.6
465.4
617.5
5
2.477
136.3
527.0
548.8
2.071
22.1
425.0
446.7
NOSUN, HSUN, VSUN,
HSUN x VSUN
BFRT, ATMP, SST,
SST X ATMP
5
BFRT, TEAM
3
2.070
25.7
432.7
471.8
BFRT
2
2.084
23.0
422.6
431.3
43
The hazard-rate model (Ramsey 1979, Buck land 1985) with a 2-term cosine
adjustment of the detection function, g(y), provided the best fit to the standardized
distance data submitted to program DISTANCE (X 2 = 3.83, df = 5, P = 0.52). The
estimated effective halfwidth under average conditions was 192 m (SE = 19.9 m).
The CUMD estimate was also 192 m. Effective halfwidths searched during various
Beaufort conditions (Fig. 2.3) were calculated as
Effective halfwidth = 192 m (0.641 )(BFRT
BFRT)
Survey Results
Total effective area surveyed was 8,678 km2 (95% CI 6,879-10,947 km2).
Estimated turtle surface density was 0.067 turtles per km2 (95% CI 0.051-0.086
turtles/km2). Turtles were detected 8.9 to 2,825 km from the nearest mainland shore.
Two main concentrations of density were from the central American coast out to
about 700 km, and along the Equatorial Convergence around 4°N (Wyrtki 1966) out
to 125°W (Fig. 2.4). Other areas were along the southern boundary of the study
area, off Baja California, Mexico, and near the Reviallagigedos Is., Mexico (ca.
19°N, 111°W).
An additional 108 turtles were detected while off survey-effort. Twenty
percent of all sightings were identified to species. Of these, 90% were olive ridley,
8% were loggerhead, and 2% were green turtles. Olive ridleys were near nesting
areas and in open water. All loggerhead turtles were off Baja California, Mexico.
The green turtles were near islands. Our identifications and the known distributions
of turtles in the ETP (Marquez 1990) suggest that most sightings were olive ridley
44
600
500
E
_c 400
475
I
.,---
To 300
a)
>
.15
a)
200
i
w
100
i
0
0
1
2
3
4
5
Beaufort Sea State
Figure 2.3. Effective halfwidth (meters) and 95% confidence intervals surveyed at
Beaufort sea state levels 0-5 in the eastern tropical Pacific, 1989-90.
45
o
ZON,
°
0
0o
0 00R§)
0
0r_
00O0
O.
0
ION
0
0.
0
00
0
0
..0
9.
-
4
0
.
0°Q
Q
0
0
O.. O0 , %
O.
.
.
.°
0
ao.
o
0
P
.
0
96C6c6°8. (
.0
0
.
Po'
.
o
0
10S
0 0.01-0.05 0 0.05-0.1
0 0.5-1.0 0 >1.0
110W
100W
90W
BOW
Figure 2.4. Density distribution of hard-shelled (cheloniid) sea turtles in the eastern
tropical Pacific, 1989-90. Symbols are centered on the local noon position of each
day's trackline. Small closed points indicate no turtles detected. From smallest to
largest, the open circles represent: 0.01-0.05 turtles/km2, 0.05-0.1 turtles/km2, 0.10.5 turtles/km2, 0.5-1.0
turtles/km2, and
turtles/km2.
46
turtles. Species identified during effort and used in analyses were olive ridley and
loggerhead turtles.
We compared surface densities between 1989 and 1990. In 1989, surface
density was 0.12 turtles/km2 (95% CI 0.092-0.15 turtles/km2; nT = 396, AT = 3,316
km2, 95% CI 2,653-4,144 km2), and in 1990, surface density was 0.04 turtles/km2
(95% CI 0.029-0.054 turtles/km2; nT = 182, AT = 4,581.9 km2, 95% CI 3,600-5,831
km2). The 1990 area does not include transects west of 125°W (Fig. 2.1) as that area
was not surveyed in 1989, but does include the additional transects surveyed east of
125°W in August and September 1990. The ratio of the two density estimates was
3.0 (95 % CI 2.34-3.86 turtles/km2), indicating a significant difference between years.
Unusually high counts of turtles were recorded along five transects in
September and November 1989 (n = 261) and along only one transect in October
1990 (n = 20) 29 to 440 km off olive ridley nesting beaches in Mexico and Costa
Rica. All identifications (8.8%) were olive ridley turtles. These loose aggregations
were likely precursors to the mass nesting-assemblages characteristic of some
populations of olive ridleys (Richard and Hughes 1972, Plotkin et al. 1995).
DISCUSSION
Survey Data
Of the variables we examined, sea state was the primary factor influencing the
detection of sea turtles. Sighting distances decreased in high Beaufort conditions.
Sea state has also been cited by several authors as a primary factor in affecting sea
47
turtle detections in aerial surveys (Scott and Gilbert 1982, By les 1988, Marsh and
Saalfeld 1989). However, other unexamined variables may bias our density estimates.
Potential detection biases are individual turtle size, and behavioral and morphological
differences among species. Larger turtles are probably seen farther from the
trackline. Size can be entered as a covariate in detectability analysis; however,
although individuals of all sizes were seen in this study, size-data collection was too
inconsistent among observers for inclusion. Species morphology may influence
detectability. For example, although relatively small, olive ridley turtles have domed
carapaces that make them well suited for sighting from ships.
Estimated turtle density in the ETP is a minimum because submerged turtles
were missed. Some authors have calculated correction factors for submerged animals
(e.g., Bayliss 1986, Barlow et al. 1988, By les 1988), but note that activity (i.e.,
feeding, migrating) and species differences may cause variation in dive behavior. A
correction factor for some species might be estimated from the limited telemetry data
available on pelagic turtles in the ETP (Balazs 1994, Plotkin 1994, Beavers and
Cassano 1996), but would be grossly imprecise. Alternatively, Forney et al. (1991)
point out that if the data are to be used as indices of relative abundance and the
proportion of animals detected at the surface in an area does not change over time, a
submergence correction factor is unnecessary. Both alternatives depend on improved
species identification.
Surface density differences between years is likely due to survey location and
timing, and sea turtle reproductive behavior rather than true density differences.
Surface densities can vary widely, at least within 440 km of certain nesting areas,
48
depending on the timing of olive ridley mass nesting-aggregations during peak nesting
season. Also, between nestings, olive ridley turtles that remain in nearshore, shallow
waters spend more time submerged during the day than post-nesting turtles in the
open ocean (Plotkin 1994), indicating that Forney et al.'s (1991) approach would be
more applicable in deeper, offshore regions. The reproductive behavior of olive
ridley turtles in particular (Richard and Hughes 1972, Plotkin 1994) should be
considered in the timing of nearshore transects and analysis of future surveys.
Detectability Analysis
The use of detectability analysis to adjust distances for sighting conditions is
applicable in variable-distance surveys where sighting conditions are systematically
recorded. This analysis relies on the assumptions that effective area surveyed is a
multiplicative scaling parameter for detection area, and is a link to the influencing
covariates (Ramsey et al. 1987). Because detectability conditions vary,
survey-specific adjustments are desirable and can be modelled easily with this
procedure. For factors that vary with distance, this method provides a simple
alternative to stratifying data or restricting survey conditions. However, other factors
that directly influence the animals' movements with respect to the observer, such as
large variations in vessel speed, or height and speed of aerial survey craft, affect the
shape of the detection function and therefore cannot be modelled with this method.
Some researchers have used covariate methods with sighting rate as the
response variable to identify detection biases (Scott and Gilbert 1982), and to adjust
sighting rates accordingly (Gunnlaugsson and Sigurjonsson 1990). However
49
corrections for estimates that are calculated from the same data to be adjusted are not
independent (Scott and Gilbert 1982). Adjusting effective area surveyed rather than
adjusting density is appropriate because sighting and environmental conditions do not
directly affect true density, they alter the area the observer is able to effectively
search. Therefore, by adjusting the area surveyed, the detection bias is eliminated.
50
Chapter 3
Distribution and Habitat Associations of Sea Turtles in the Eastern Tropical
Pacific Ocean During the Breeding/Nesting Season.
Sallie Christian Beavers
Department of Fisheries and Wildlife
Oregon State University
Corvallis, Oregon
51
INTRODUCTION
Physical and biological oceanographic features have been used to describe the
distribution and habitats of large pelagic vertebrates such as tuna (e.g., Blackburn
1965, Sund et al. 1981) dolphins (e.g., Reilly 1977, 1990, Au and Perryman 1985,
Polacheck 1987, Fiedler and Reilly 1994, Reilly and Fiedler 1994) and whales (e.g.,
Volkov and Moroz 1977, Reilly and Thayer 1990) in the eastern tropical Pacific
Ocean (ETP). Recent literature records the pelagic distribution of sea turtles,
particularly of olive ridley turtles (Lepidochelys olivacea), in this region (Au 1991,
Pitman 1990, 1993, Plotkin 1994, Beavers and Cassano 1996 (Chapter 1), Chapter 2);
however, marine turtle distribution in relation to the oceanic environment has not
been studied in detail. Obtaining information on the influence of oceanographic
features on the distribution of sea turtles is an important first step towards defining
critical habitat of threatened and endangered marine turtle species (Thompson et al.
1990).
The 1986-1990 National Marine Fisheries Service (NMFS) Monitoring of
Porpoise Stocks (MOPS) project in the ETP (Wade and Gerrodette 1992) provided an
opportunity to investigate the influence of several oceanographic features on sea turtle
distribution in the eastern Pacific. In particular, frontal zones are of interest because
they have been suggested as important oceanic foraging areas for turtles (Carr 1978,
Carr et al. 1986, 1987). The objectives of this chapter were to 1) examine the
relative density distribution of cheloniid (hard-shelled) sea turtles in the ETP with
respect to large-scale oceanographic features, and 2) investigate whether the presence
and the relative density of olive ridley sea turtles in the ETP can be described by
52
simple habitat models developed from the same oceanographic habitat variables used
to describe ETP dolphin habitats (Au and Perryman 1985, Reilly 1990, Reilly and
Fiedler 1994).
METHODS
Study Area
The ETP extends from a narrow continental shelf along the west coast of the
Americas out to about 140°W between 25°N and 20°S and encompasses more than
19 million km' (Holt and Sexton 1990). The regional oceanography is defined by
several water masses and surface currents (Wyrtki 1966, 1967; Fig. 3.1), and a
permanent shallow thermocline (20-100 m) that is sometimes approximated by the
depth of the 20 °C isotherm (Donguy and Meyers 1987, Fiedler 1992). The three
surface-water masses in the ETP are categorized as; (1) tropical surface water (TSW),
centered in the ETP along 10°N and characterized by very warm temperatures and
low salinity; (2) subtropical surface water (SSW), cool, high-salinity water positioned
at the poleward ends of the ETP; and (3) equatorial surface water (ESW), cool water
of intermediate salinity, located between the TSW and the southern SSW masses
extending west along the Equator from the coast of Peru. Upwelling occurs locally in
coastal areas, along 10°N, at the Equator, and at the Costa Rica Dome (a semipermanent cyclonic eddy, ca. 9°N and 89°W; Wyrtki 1964).
The two westerly currents, the North Equatorial Current (NEC) between 10°
and 20°N, and the South Equatorial Current (SEC) between 4°N and 10°S are driven
53
30
20
SUBTROPICAL
...SURFACE WATER
MEXICO
North Equatorial Current
TROPICAL
SURFACE WATER
North Equatorial Countercurrent
:;-t; 0
.............
South Equatorial Current
c992tPri.51
EQUATORIAL
SURFACE WATER
Costa Rica Dom.
Front
......
Galapagos "
ECUADOR
10-
PERU
SUBTROPICAL
SURFACE WATER
91.))
20
160
150
140
130
120
110
100
90
80
Longitude
Figure 3.1. Surface water masses and primary surface water circulation of the
eastern tropical Pacific Ocean (From Reilly and Fiedler 1994, with permission).
70
54
by the northeast and southeast trade winds and are fed by the two eastern boundary
currents, the California Current and the Peru Current. The Intertropical Convergence
Zone (ITCZ), where the trade winds meet and weaken, lies between 5° and 10°N.
Because the winds are light there (the doldrums), the North Equatorial Countercurrent
(NECC) is able to flow opposite the SEC and NEC currents, "down" the eastward
horizontal pressure gradient between 4°N and 10°N. The seasonal movement of the
ITCZ north of 10°N in the boreal "summer" half of the year (June December), and
south to near the Equator in "winter" (January June) causes the NECC to strengthen
in September
December and to weaken in February
April east of 120°W (Fiedler
1992).
The thermocline shoals eastward near the coast (40-60 m) from > 150 m
offshore and is strongest under TSW, but weakens in September
November (Fiedler
1992). A divergence (upwelling), and thus "ridging" of the thermocline is between
the NEC and NECC at about 8°30'N to 10°N. A strong convergence (downwelling)
lies between the NECC and the SEC at the equatorial front, causing a "trough" in the
thermocline. These features are intensified during the boreal summer.
Biologically, the area is productive (Fiedler et al. 1991) and supports a diverse
fauna (e.g., Wyrtki 1966, Reilly and Fiedler 1994), including several species of
cheloniid sea turtles (Marquez 1990). The olive ridley is the most abundant sea turtle
in the ETP (Clifton et al. 1982, Marquez 1990) and widely distributed in open water
(Au 1991, Pitman 1990, Pitman 1993, Plotkin 1994, Chapter 2). The eastern Pacific
green turtles (Chelonia mydas) are primarily distributed around islands and coastal
areas (Marquez 1990). Sightings of hawksbill turtles (Eretmochelys imbricata) are
55
rare in the eastern Pacific, particularly offshore (Marquez 1990, Pitman 1993), and
loggerhead turtles (Caretta caretta) are common in the cooler waters off Baja
California, Mexico (Marquez 1990).
Data Collection
Sea turtle sightings and oceanographic data were collected yearly between
1986 and 1990, from 28 July to 6 December aboard the National Oceanographic and
Atmospheric Administration (NOAA) research vessels David Starr Jordan and
McArthur during the MOPS project (Fig. 3.2). Detailed line transect data on sea
turtles were collected only during the final two years of the project (see Chapter 2).
Survey dates coincided with the peak nesting season for some sea turtle populations in
the ETP (Richard and Hughes 1972, Marquez 1990).
The two ships simultaneously surveyed in different areas of the ETP,
maintained speeds of approximately 18.5 km/h during sunrise to sunset survey effort,
and remained underway at night to allow greater spatial distribution of the tracklines.
Each vessel carried two teams of three experienced observers. Port and starboardside observers used 25x binoculars to search from the trackline out to 90° on their
respective sides. The third observer recorded sightings, and searched the trackline
with the naked eye and with hand-held 7x50 binoculars. Teams rotated equally
through the three duty-stations in 2-h shifts.
Identification of turtle species at sea is difficult; when possible, species were
identified by trained observers or identified later from photographs. Of the 1,799
turtles counted between 1986-90, 313 (17.4%) were identified. Most (83%) were
56
1986
1987
1988
1989
1990
T
120
110
100
T
I
90
80
70
Longitude
Figure 3.2. Combined survey tracklines for the David Starr Jordan and the McArthur
in the eastern tropical Pacific during August-November, 1986-90 (From Reilly and
Fiedler 1994, with permission).
57
olive ridley turtles. Density distributions of species consistently followed described
distribution patterns (Marquez 1990). All identified olive ridley turtles were at sea or
near coastal nesting areas, and other species were in nearshore or island waters.
More than one species was never identified per transect. Therefore, I followed Au
(1991), Pitman (1990), and Pitman (1993), and assumed that oceanic sightings of
unidentified turtles were olive ridley, and excluded all identified sightings of other
species from the habitat analyses. However, the common term "sea turtle" is used in
the discussion.
Habitat Variables
The oceanographic data used in these analyses were originally collected and
summarized by Fiedler et al. (1990) and Reilly and Fiedler (1994) for comparison
with cetacean sightings. Oceanographic data were collected throughout each day's
transect (see Fiedler et al. 1990 and Reilly and Fiedler 1994 for detailed collection
techniques). Sea surface temperature (ssT) and salinity (SAL) were collected
continuously by thermosalinograph and averaged for each transect. A sea-water
density index, a, (SIGMA -T), typically used to describe water masses, was formed by
combining SST and SAL in a standard non-linear function (Pickard and Emery
1990:17-18). Depth of thermocline in meters (z20, defined as the 20 °C isotherm;
Donguy and Meyers 1987) and strength of thermocline (zD), the difference in depth
(m) between the 20 °C and 15 °C isotherms (where a low value indicates a strong
thermocline), were estimated from expendable bathythermographs deployed four to
six times per day. Surface chlorophyll a (cHL) , a standing stock indicator of
58
productivity, was measured by fluorescence and converted to chlorophyll
concentration (mg/m3). Chlorophyll data were collected continuously and calibrated
by at least six discrete surface samples per day, then averaged for each transect. I
calculated distance to land (LAND) as the shortest distance (km) from the approximate
center (noon location) of each transect to the nearest mainland or the nearest island
(Camris® geographic information system software, Ford 1991). Chlorophyll and land
data were log-transformed (LOGC, LOGLAND) prior to analyses to meet assumptions of
parametric statistical models.
Explanatory variables ultimately used in analyses were (1) thermocline depth,
Z20; (2) thermocline strength, zD; (3) chlorophyll, LOGC; (4) distance to nearest land,
LOGLAND; and (5) surface density, SIGMA-T. Surface salinity and surface temperature
were dropped in favor of SIGMA-T as a descriptive variable of water-mass.
Data Analysis
I estimated relative density of cheloniid sea turtles from the daily number of
turtles sighted divided by the effective area searched per day. A model for effective
area searched that linked the area surveyed to prevailing Beaufort-scale sea state
conditions (where Beaufort 0 = calm seas and Beaufort 5 = rough, white-capped seas
with some spray, Bowditch 1977:1267) was constructed by covariate analysis from
the 1989-90 line transect data (Ramsey et al. 1987, Chapter 2). With this model, I
estimated effective area surveyed from distance traveled by Beaufort sea state per day
in 1986-88 (Chapter 2), and then converted daily 1986-88 counts of sea turtles to
density estimates based on the area surveyed. Density estimates represent minimum
59
turtle surface densities, because no correction was made for submerged turtles
(Chapter 2). Relative surface density distribution patterns of all cheloniid species in
the ETP were examined from the combined 1986-90 data.
All transects selected for habitat analyses were surveyed for
conditions
h in Beaufort
Areas surveyed ranged from 3 km2/d to 98.3 km2/d (average = 25.8
km2/d). Turtle sighting data were analyzed with habitat variables collected during the
same transect. Habitat association analyses were conducted only on the 1989-90
survey data because during those years the collection of sighting data was most
rigorous and there was no detected El Nino event (Fiedler et al. 1992); thus,
interannual variability in the habitat variables was probably low. The relationship of
presence of sea turtles to habitat variables was explored with logistic regression
(Neter et al. 1989), and the relationship of turtle density (turtles/km2), where turtles
were present, to habitat variables was further examined with multiple linear
regression. Full models included all first order, interaction, and quadratic terms.
Logistic regression is an effective method for analyzing wildlife-habitat data
with a binary response function (e.g., y = 1,0; sea turtles present, or sea turtles not
present in an area) and one or more continuous factors (e.g., Ramsey et al. 1994).
The response represents the probability, 7, that y = presence for given levels of the
explanatory variables. The fitted multiple logistic response function is estimated by
exp (bo+E bixi)
1 + exp(bo+E bix)
i=1
60
where the response variable, 1, is the estimated probability that a sea turtle is present
in an area, bo is the estimated intercept, and b1 is the estimated regression coefficient
for j = (1,
,
p) explanatory habitat variables. The practical interpretation of the
response variable, 1, is as an estimated proportion or percent. The odds of turtle
presence, given a set of habitat variables measured within an area, is the estimated
odds ratio, 1/(1-1).
The logistic response function is linearized by the logit
transformation of the odds ratio and is referred to as the estimated logit response
function,
loge(1/1-1) = b0 +> bixi.
The estimated logistic response function yields a fitted regression line that is both
sigmoidal and monotonic, is approximately linear where ir is between 0.2 and 0.8,
and is curvilinear at the ends of the range of xi near the asymptotes at 0 and 1 (Neter
et al. 1989). The method of maximum likelihood was used to estimate the parameters
of the logistic response function. The regression coefficients, exp(b), can be
interpreted as the increase in the odds of the response variable (i.e., sea turtle
presence) with a unit increase in a specific habitat variable (xj), keeping other x3
variables constant. The measure of the contribution of the independent variables
towards explaining variation in the response variable in logistic regression is the chi-
square statistic, and can be interpreted analogously to the F-statistic in multiple
regression (Neter et al. 1989).
A model was selected by fitting a logistic model to habitat variables separately
to determine whether a regression relation existed, and then fitting a full model and
performing a likelihood ratio test, or "drop in deviance" test between a full model
61
and successively reduced models (Neter et al. 1989). Variables that do not
significantly reduce the deviance can be dropped from the model. The deviance test
statistic (X2) is distributed approximately as a chi-square statistic (X2dfr_dff) and is
estimated as
X2 = -2 [loge
loge Lp],
where Loo is the value of the likelihood function for the reduced model, LIF-) is the
value of the likelihood function for the full model, and dfr and dff are the degrees of
freedom associated with the respective reduced and full models.
The aptness of the logistic regression model was examined graphically and by
informal goodness of fit procedures (Pregibon 1981, Neter et al. 1989). Model
diagnostic procedures based on the R2 statistic were not used in these analyses
because of the limitations of this statistic when applied to binary response variables
(Agresti 1990:112).
Multiple linear regression analyses were based on standard regression
methods (e.g., Neter et al. 1989). The response variable, estimated sea turtle density,
was log-transformed prior to analysis, to meet model assumptions. Final model
selection was made with the Bayes Information Criterion, BIC, (Schwarz 1978).
RESULTS
From 1986 to 1990, 1,799 cheloniid turtles were counted and 313 (17.4%)
were identified to species (Fig 3.3). All loggerhead identifications (12.8%, n = 40
turtles) were off Baja California, Mexico. Green turtles were identified near the
Galapagos Islands, Ecuador (4%, n = 13), and one juvenile hawksbill was sighted
62
o°
20N
0
,9
0o
O
o0
lON
o.
00
0
:
o
.04
.
Oo
0
0 a
(i)
.
0.0 0
0
0°
0
.00
0. 0 0
44.40
07 X
° a 0 o-
Q.
.
0
po..
0 o.
%
o
v...-
0
Turtles per Square Km
10S
0
0 0.1-0.5
150W
° 0.01-0.05 0 0.05-0.1
0 0.5-1.0 0 >1.0
140W
130W
120W
110W
100W
90W
80W
Figure 3.3. Distribution of identified cheloniid sea turtle species in the eastern
tropical Pacific, 1986-90.
63
with a piece of wood at 14°28'N, 100°7'W. All identified olive ridleys (83%, n =
259) were in oceanic waters or near coastal nesting areas.
The 1986-90 density distribution of all cheloniid species (Fig. 3.4) shows four
primary areas of concentration: (1) off southern Baja California, Mexico; (2) a
nearshore band along the Mexican mainland to Ecuador out to about 700 km; (3)
along the Equatorial Convergence Zone, around 4°N, from 90° to 130°W; and (4)
scattered along 10°N, attenuating at about 110°W where there is a depression in the
thermocline ridge (Fiedler 1992), and increasing from 122°W to 142°W. The
boundary of the Peru current in the southern-most part of the study area was also an
area of light distribution.
When separated by year (Figs. 3.5-3.9), strong interannual variation was
evident in the relative density distribution patterns; suggesting that El Nirio events
(Fiedler et al. 1992) influenced turtle distribution. In "near normal" years, 1986,
1989, and 1990, turtles were distributed along 4°N and near the Central American
coast (Figs. 3.5, 3.8, 3.9). Also in 1989, some turtles were west of 120°W along
10°N (Fig. 3.8). Offshore distribution patterns in 1987, an El Nino year, were more
southerly than in other years (Fig. 3.6). In 1988, an anomalously cool year (La
Nina), turtles were mostly concentrated along the thermocline ridge at 10°N out to
135°W (Fig 3.7).
The 1989 and 1990 line transect data (Fig. 3.10) provided 333 suitable
transects, totaling 49,684 km in length and 8,603 km2 in area for habitat analyses
(Table 3.1). During survey effort, 579 turtles were sighted, and an additional 108
turtles were seen while off survey-effort. Of all turtles counted, 19.6% (n = 135)
64
a
o
0
0 ,too
(bc,
20N
e
.
.
.00 o
,.0
.
10N
0.. .Q
0
. '.
0
0:.
.
0
...
?.
0
-
,`.3
0
..
.
d8 09,:,
4 .0- 0 99
'
2- ccb-o.. .- a0 4c,co.
.
0,
: 00..
'
% 0.1, go 6.
'a
.0
-''..... :-... ..... caP...°°0.%
0
..toGIN9
.t;
O.°
0
'
o
..
Q
O
4
0 0.
.
.
Turtles per Square Km, 1986-90
IOS
0
0
0.01-0.05 0 0.05-0.1 0 0.1-0.5
0 >2.0
0 >1.0
0
0 0.5-1.0
150W
.07
tr-
.
o. Q. @ s.. -.0
*.'0 ...- .0'...:
o
0
140W
130W
120W
110W
100W
90W
BOW
0 0
b
()N
0
.
0"C:0 °40
61117.:
.
o
ION
9.
o°
o
?
.
0
c.° Q
o
o 00
.0
0
0
0°
Bo
0° °
6) C/, g)
000 °°oOD13 0;9
GS.
go
0
boa
00
0d
.
0
0
.
.0 g
0
O
e
0
°
0
0
0
0
°
o 9 ,S9
0
Turtles per Square Km, 1986-90
10s
0.01-0.05 0 0.05-0.1 0 0.1-0.5
0 -1.0
0 >2.0
150W
140W
130W
0
0.
*,5
0
c)c. 0 0
a
0 0®b o 0
oo
0
cog0
°
Cto.
ata
0000
0
°
0
00
0
0°
0 0
0
0
0 0.5-1.0
120W
110W
100W
90W
BOW
Figure 3.4. Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1986-90. All symbols are centered on the local noon position of
each transect. a) Turtle density distribution, including tracklines surveyed where no
turtles were seen (small points). b) Turtle density distribution only where turtles
were seen.
65
a
0
0
0
20N
0
o
o
0
°
°o
ION
000
of
0
o 000
o
.
0
.
O
0o
0
o
0
0
0*
0
a 9
6 v,
0
0
.
Turtles per Square Km, 1986
0
0 0.5-1.0
150W
0.01-0.05 0 0.05-0.1
0 >2.0
0 0.1-0.5
0 >1.0
140W
120W
130W
b
0
90W
100W
110W
80W
0
0
0
20N
o
0
0
°
°0
000
03
0
0 000
ION
bo
0
0
0
0
0
o9
0.01-0.05 0 0.05-0.1
0 >2.0
8
0
0
0 0.1-0.5
0 1.0
150W
140W
130W
120W
110W
100W
90W
BOW
Figure 3.5. Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1986. All symbols are centered on the local noon position of each
transect. a) Turtle density distribution, including tracklines surveyed where no
turtles were seen (small points). b) Turtle density distribution only where turtles
were seen.
66
a
0
20N
O
0
o
ION
.
o
0
0:0 aoa
0
0
0
O
'Os
0
Turtles per Square Km, 1987
0
o 0.1-0.5
150W
°
0
0
0.01-0.05 0 0.05-0.1
0 0.5-1.0
0
140W
130W
120W
110W
100W
90W
80W
0.01-0.05 0 0.05-0.1 0 0.1-0.5
0 0.5-1.0
Figure 3.6. Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1987. All symbols are centered on the local noon position of each
transect. a) Turtle density distribution, including tracklines surveyed where no
turtles were seen (small points). b) Turtle density distribution only where turtles
were seen.
67
a
0
20N
0
o
ION
o
.0 0 o
0
0
0
°
0.
0
0
0
O
.
0 0
150W
000
b
O
Turtles per Square Km, 1988
0 0.1-0.5
o
.!"f
0
0
0
Q.
0
O
°
-
0
b
o OD
°000
.0
:
8
J
0
0
o
0
0 0.01-0.05 0 0.05-0.1
0 0.5-1.0
140W
130W
120W
110W
100W
80W
90W
0
20N
0
0
c
O
10N
0
0
0
0
0
0
0
0
0
8
O
0
O
0
0
0o
o
0 CO
° °000
0
4
0
0 0
000
0
1.
0
o
O
Turtles per Square Km, 1988
0.01-0.05 0 0.05-0.1
0
. 0
0
10S
J
0
0
0
0 0.1-0.5
o 0.5-1.0
150W
140W
130W
120W
110W
100W
90W
80W
Figure 3.7. Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1988. All symbols are centered on the local noon position of each
transect. a) Turtle density distribution, including tracklines surveyed where no
turtles were seen (small points). b) Turtle density distribution only where turtles
were seen.
68
0
20N
0 o
0
.
0
o
0 o0 0ono o0 0 o
-
0
°
o
0
o
ION
o
00
0
0
.
o
0
o
0.
0
°
0
0
0
08
°
o
0
:El
0
0
0
0
o
0 CO°
0
0
Q
0
o
0
o
Turtles per Square Km, 1989
10s
0
0 0.1-0.5
150W
0
0.01-0.05 0 0.05-0.1
0 0.5-1.0
140W
0
'.1.0
120W
130W
6
0
03
o
0
o
0
° 00
0
0
0
0 0 Go
0
e o 00 00
0 00 0 0
0
O
o
0u
00
0
0
0
0
0
0
0
0
0 CO0
o
O
80W
90W
0
0
ION
100W
110W
°
0
0
0
08
0
tsd
O
0
0
0
O
0
0
'jo
0
Turtles per Square Km, 1989
I0S
0
0.01-0.05 0 0.05-0.1 0 0.1-0.5
0 0.5-1.0 0 -1.0
150W
140W
130W
120W
110W
100W
90W
80W
Figure 3.8. Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1989. All symbols are centered on the local noon position of each
transect. a) Turtle density distribution, including tracklines surveyed where no
turtles were seen (small points). b) Turtle density distribution only where turtles
were seen.
69
a
20N.
0
0
0 0
0
ION
0
0
0
O
9
Co
00 0
o
o
0
.0
oo
.0 0
O
0
(?
°
0
0
O
0°a
0.
0.
0o
.
oo
o
o
Turtles per Square Km, 1990
0
0 0.1-0.5
150W
D
0.01-0.05 0 0.05-0.1
0 0.5-1.0 0 -LO
140W
130W
120W
110W
80W
90W
100W
20N
0
0
ION
0
0 O
0
0
.0
°
o
00
0
0
0
0
0
0.01-0.05 0 0.05-0.1
O 0.5-1.0
0 -.1.0
150W
140W
00 0
0
00
°
0
0
oo
0
0
0
0
Turtles per Square Km, 1990
10S
9
co
0
0 0.1-0.5
130W
120W
110W
100W
90W
80W
Figure 3.9. Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific, 1990. All symbols are centered on the local noon position of each
transect. a) Turtle density distribution, including tracklines surveyed where no
turtles were seen (small points). b) Turtle density distribution only where turtles
were seen.
70
0- 0,
0 00
0oo
0
0
0
.
Line Transect Sighting Data
Turtles per Square Km, 1989
0
0 0.1-0.5
0.01-0.05
0
0 0.5-1.0
140W
0
0.05-0.1
0
>1.0
130W
120W
100W
110W
20N.
0o
o
0
0
0
ION
0
0
0
8
co 00
0
o
0
0
0
.0
o°
00 0
0
(6
0
10s
Line Transect Sighting Data
Turtles per Square Km, 1990
0
0 0.1 -0.5
150W
0
0
o
0
0.01-0.05 0 0.05-0.1
0 >1.0
0 0.5-1.0
140W
130W
120W
110W
100W
90W
BOW
Figure 3.10 Relative density distribution of cheloniid sea turtles in the eastern
tropical Pacific from line transect sighting data 1989-90. All symbols are centered
on the local noon position of each transect, including tracklines surveyed where no
turtles were seen (small points). a) 1989. b) 1990.
± standard deviation (SD) and associated coefficient of variation (CV), and mode of habitat variables
used with 1989-90 sea turtle sighting data in the eastern tropical Pacific (ETP). Range in parentheses. SIGMA-T (kg /m3) =
surface sea-water density, Z20 (m) = depth of thermocline, LOGC (mg/m3) = chlorophyll concentration, LOGLAND (km) =
distance to nearest land, ZD (m) = strength of thermocline, SST (°C) = sea surface temperature, and SAL (psu) = salinity of
surface water. Arithmetic summary statistics are presented for LOGLAND and LOGC for convenience.
Table 3.1 Average
Variable
SIGMA-T
(R)
All data
Present
Not present
n = 333
n = 117
n = 216
R
+ SD
22.09 ± 1.3
CV
6.1
57.4 ± 26.3
45.8
0.249 ± 0.15
61.1
815.5 ± 751.0
92.0
30.0 ± 15.7
52.3
25.9 ± 2.8
10.8
33.77 ± 0.87
(28.95-35.32)
53.99 ± 22.18
0.25 ± 0.13
600.4 ± 468.4
28.6 ± 13.8
26.9 + 2.6
2.6
33.58 ± 0.86
(30.64-35.32)
R
SD
22.33 ± 1.3
CV
Mode
5.9
20.81
47.4
0.0
65.5
0.01
90.6
624.8
54.0
23.4
10.9
26.1
2.5
33.90
(18.79-24.88)
41.1
51.6
59.3 + 28.1
(0.0-151.9)
52.6
0.2
0.247 ± 0.16
(0.05-1.28)
78.0
474.6
932.0 ± 844.9
(6.8-4,044.6)
48.3
22.5
30.7 ± 16.6
(11.3-115.5)
9.6
27.8
25.4 + 2.8
(17.8-30.6)
(19.3-30.4)
(17.8-30.6)
SAL
21.04
(10.8-91.6)
(10.8-115.5)
SST
6.0
(9.2-2,019)
(6.8-4,044.6)
ZD
Mode
(0.06-1.07)
(0.053-1.28)
LOGLAND
21.66 ± 1.29
CV
(0.0-119.8)
(0.0-151.9)
LOGC
SD
(19.23-24.89)
(18.79-24.89)
Z20
R A-
2.6
32.76
33.86 ± 0.86
(28.95-35.25)
72
were identified and 90% (n = 122) of these were olive ridley. Turtles (n = 573)
were present on 127 transects designated suitable for analyses. Ten transects
exclusively contained species other than the olive ridley (n = 14) and were omitted.
Turtle densities ranged from 0.015 to 1.27 turtles /km2 (average = 0.12 turtles/km2,
SD = 0.16).
In the individual logistic regression analyses, SIGMA-T (P = 0.0001) and
LOGLAND (P = 0.0039) were negatively correlated with turtle presence. Depth of
thermocline, Z20, showed a negative trend with increasing probability of turtle
presence (P = 0.08). During the 1989-90 nesting/breeding seasons, the probability of
sea turtle presence was best described by a shallow thermocline, lower density of
surface water, decreasing distance from nearest point of land, and an interaction of
the latter two variables (-2 loge L = 388.9, X2 = 42.8, df = 4, P = 0.0001;
Table 3.2). The odds of sea turtle presence increased by exp(-0.0128) = 0.987, or
98.7%, with a 1-m decrease in thermocline depth, holding the other variables constant
in the model. Because of the interaction term, exp(b3) for SIGMA-T and LOGLAND
cannot be interpreted as simply the odds ratio of a unit increase in SIGMA-T or in
LOGLAND. To understand the relationship between SIGMA-T and LOGLAND, it is useful
to find the coefficient for SIGMA-T in the estimated response function (Table 3.2):
log-odds of turtle presence = b0 0.128*Z20 -9.419*log(LAND)
[3.097
0.4292*log(LAND)]*SIGMA-T
.
Thus a unit increase in SIGMA-T (kg/m') is a change of exp -[3.097
in the odds. An increase in SIGMA-T near land reduces the odds
of presence, but the amount of this reduction decreases farther from land. At
73
Table 3.2. Logistic regression model parameter estimates for presence of sea turtles
in the eastern tropical Pacific, 1989-90. SIGMA-T (kg/m3) = surface sea-water
density, Z20 (m) = depth of thermocline, LOGLAND (km) = distance to nearest land.
The logistic response function is:
= [1 + exp( -bo
b1z20 - b2siGmA-T - b3 LOGLAND - b4 SIGMA-T*LOGLAND)]-1.
Variable
Coeff.
SE
Wald's
Chi Square
Constant
68.09
16.68
16.67
0.0001
Z20
-0.0128
0.006
4.91
0.0270
SIGMA-T
-3.0968
0.77
14.34
0.0001
LOGLAND
-9.4394
2.55
24.57
0.0002
SIGMA-T X
LOGLAND
0.4292
0.12
13.29
0.0003
P-value
74
distances greater than exp(3.097/0.4292) = 1,360 km (obtained by setting the
coefficient, -[3.097
0.4292*log(LAND)], equal to 0 and solving for LAND), the sign
of the coefficient becomes positive and thus increases in SIGMA-T far from land equal
increases in the odds of turtle presence. For example, the odds ratio for increasing
SIGMA-T at various distances are:
Distance from LAND
Odds ratio of 1-unit increase in SIGMA-T
10 km
0.12
100 km
0.33
1,000 km
0.88
4,000 km
1.59
The 1989-90 sea turtle density data were not well explained by the available
habitat variables. The best model, which contained distance to land, surface water
density, chlorophyll, and thermocline depth, accounted for only 22.3% of the
variation in sea turtle density in the ETP (F4,112 = 9.3, P < 0.0001; BIC = -15.6;
Table 3.3). The interaction of SIGMA-T and LOGLAND was a possible candidate for
inclusion, but was marginally significant (P = 0.04, BIC = -14.4) and its addition to
the model increased the adjusted R2 to only 24%. The effect of this interaction is
similar to the one above.
75
Table 3.3. Multiple linear regression parameter estimates and analysis of variance for
association of sea turtle density with habitat variables in the eastern tropical Pacific,
1989-90. SIGMA-T (kg/m3) = surface sea-water density, Z20 (M) = depth of
thermocline, Locc (mg/m3) = chlorophyll concentration, LOGLAND (km) = distance
to nearest land.
t-value
P-value
1.24
1.97
0.05
-0.196
0.084
-2.34
0.02
-0.171
0.065
-2.63
0.009
LOGC
0.514
0.187
2.75
0.007
Z20
0.012
0.004
3.31
0.001
Variable
Coeff.
SE
Constant
2.45
LOGLAND
SIGMA-T
Sum of
Squares
Mean
Square
F-value
P-value
Adj. R2
9.32
<0.0001
0.223
Source
df
Model
4
22.48
5.62
Residual
112
67.53
0.603
Total
116
90.01
76
DISCUSSION
Geographic distribution
The spatial shifts in yearly relative density distributions of turtles were likely
related to the periodic El Nino/Southern Oscillation (ENSO) events that drive
interannual variability in the ETP. During an El Nino in the eastern Pacific, there is
a warming of surface waters and a reduction in productivity because the trade winds
are weaker and the thermocline deepens, and so upwelled water in the equatorial and
coastal upwelling regions is nutrient-poor (Mann and Lazier 1991). Fiedler et al.
(1992) described the following anomalies in sea surface temperature, SST, thermocline
depth, and chlorophyll concentration data collected during the MOPS study in AugustNovember 1986-89. In 1986, most of the study area was within normal ranges.
However, around the northern Galapagos and along the equator west of 100°W, the
thermocline was 20-30 m deeper, SST was
1 °C above normal, and chlorophyll was
high south of 5°N; sea turtles were present near these areas. In 1987, warm nutrientpoor water covered the equator and sea turtles were distributed to the south off Peru,
where chlorophyll was slightly higher than in tropical surface water, TSW. Turtle
densities were lower in the central TSW where chlorophyll was reduced and the
thermocline ridge and trough were flattened. Surface temperatures in 1988 were
colder than normal over most of the study area, but the central band of TSW, where
turtles were mostly distributed, was only 1 °C below normal and the thermocline
ridge at 10°N was 20-40 m shallower than usual. In 1989 and 1990, conditions were
within normal ranges and turtle distribution was similar to that of 1986 with most of
77
the offshore distribution concentrated around the equatorial convergence (2°N-5°N),
with the addition of light distribution at 10°N in 1989. Turtle distribution patterns
show a flexible response to the dynamic oceanographic conditions dominating the
habitat, shifting to areas of higher productivity as conditions change. Shifts in olive
ridley distributions during El Nitio events have also been noted by others (Cornelius
and Robinson 1986, Plotkin 1994).
In addition to the expected nearshore density distribution during the peak
nesting season, major concentrations of sea turtles in 1986-90 coincided with two
primary features of the oceanic eastern tropical Pacific, the equatorial convergence
zone at about 2°- 5°N, and to a lesser extent, the area around the divergence zone at
10°N in 1988 and 1989. These two zones may provide pelagic foraging grounds for
sea turtles. The equatorial convergence is formed by the meeting and sinking of cool,
saline and nitrate-rich waters of equatorial upwelling and Peru Current origin, and the
warmer, less saline TSW (Pak and Zaneveld 1974). As nutrient-rich water moves
away from the area of upwelling towards the front, a community of macroplankton
and nekton forms and is aggregated at the convergence, attracting large predators
(Sette 1955, Vinogradov 1981). This process is strengthened in the summer season.
Likewise, the divergence zone at 10°N is an area of increased biological productivity
(Fiedler et al. 1991) and is underlain by a shallow, strong thermocline, which may
confine potential prey to surface waters (Green 1967, Reilly 1990). Communities of
potential prey may also form some distance laterally away from the divergence zone
by horizontal advection as described above, which accounts for the scattered turtle
distribution to either side of the 10° latitude line (Figs. 3.7, 3.8).
78
The distribution of turtles along the convergence zone in the summer is
complementary to seasonal spatial patterns reported for some delphinid species (Reilly
1990). In the summer, spotted and spinner dolphin (Stenella attenuata and S.
longirostris), and to a lesser extent striped dolphin (S. coeruleoalba), distributions
apparently shift away from their winter centers at 4°N and 6°S, to along 10°N (Reilly
1990). Whereas in this study, sea turtles were more prevalent along 4°N than at
10°N especially in the 1986-87, and 1989-90 summers. Complementary offshore
distributions of turtles and dolphins have been noted in other areas. Primary
concentrations of marine mammals and sea turtles sighted in the waters off the NE
United States rarely overlap, except along the continental shelf edge (Shoop and
Kenny 1992). Sea turtle distribution and the distributions of several dolphin species
in the ETP overlap in the nearshore waters from southern Mexico to northern Peru
(Au and Perryman 1985, Reilly 1990, Reilly and Fiedler 1994), but apparently less so
offshore during the summer months. It is possible that there is seasonal component in
sea turtle distribution as well. Plotkin (1994) tracked post-breeding male and female
olive ridleys in the ETP and they ranged predominantly north of 5°N.
Because survey sampling stopped at the boundary of the Peru Current, it is
unknown if the southern distribution of sea turtles also discontinues south of the
boundary. Certainly turtle densities were lower in the area ( 0.1 turtles/km2) and
were more concentrated there during the 1987 El Niiio. It is likely that the southern
extent of olive ridley distribution is limited by the colder water of the Peru Current.
79
Habitat association
The presence of olive ridley sea turtles in the ETP during August
December
1989-90 was significantly associated with nearness to land, low surface-water density
especially when close to land, and moderately shallow depths of the 20 °C isotherm.
These conditions are components of tropical surface water, TSW (Fig. 3.1; Wyrtki
1967), and are consistent with "coastal tropical habitat," as described by Reilly and
Fiedler (1994) for mixed schools of eastern spinner dolphins and spotted dolphins.
Coastal tropical habitat is characterized by a well-stratified surface layer, occasional
and localized upwelling, a relatively shallow thermocline, lower SIGMA-T (higher
surface temperatures, lower salinity), and high chlorophyll concentrations (Reilly and
Fiedler 1994).
The effect of distance to land in describing the presence of sea turtles is
expected, particularly during the peak nesting season when turtles are more likely to
be near certain beaches. Furthermore, the distribution near land is generally
consistent with the trend towards shallower thermocline depths nearshore and lower
surface water density in TSW, where precipitation exceeds evaporation. However
other unmeasured components are included in this variable that may also be
meaningful. For instance, topographically induced fronts, and fronts associated with
wind-driven coastal upwelling (Mann and Lazier 1991, Franks 1992) attract predators
(e.g., Laurs et al. 1977) and may be strong attractors near land for turtles. Fixed
geographic effects were also significant in habitat models of dolphins (Polacheck
1987, Reilly and Fiedler 1994). The increase in odds of turtle presence offshore with
increasing SIGMA-T possibly reflects the distribution of turtles along the equatorial
80
front, where surface water density is higher from input of equatorial surface water,
ESW at the boundary of water-masses.
The inshore (within about 700 km, Fig 3.4) distribution of sea turtles visually
overlaps two types of inshore habitats described by others for Stenella spp. ("coastal
tropical habitat") and common dolphins (Delphinus delphis; "upwelling-modified
habitat") (Au and Perryman 1985, Reilly 1990, Reilly and Fiedler 1994). However,
the overall summary statistics (Table 3.1) of surface water density and thermocline
depth in areas of sea turtle presence are most comparable to those collected in
summer for striped dolphins
(xSIGMA-T
21.6, SD = 1.35, n = 341; XZ20 = 57.2 m,
SD = 23.2, n = 341; Reilly 1990) and overlapped those of spinner and spotted
dolphins (isiGmA_T = 21.2, SD = 1.42, n = 401; x Z20 = 67.7 m, SD = 21.3, n =
401; Reilly 1990). These similarities further suggest that sea turtle distribution in the
summer is primarily coastal tropical habitat. Plotkin (1994) characterized distribution
of satellite-acquired locations of olive ridleys as "upwelling-modified." However, the
majority of tracking-days of turtles in that study were during winter months when
coastal upwelling systems intensify due to strong winds from the Gulf of Mexico
funneling through mountain passes into the Gulfs of Tehuantepec, Papagayo, and
Panama (McCreary et al. 1989). Therefore many nearshore areas occupied by sea
turtles in the winter may be more upwelling-modified.
The multiple linear regression model for sea turtle density was similar to the
model for turtle presence, with the addition of chlorophyll concentration as an
explanatory variable. However, the habitat variables were poor descriptors of sea
turtle density in the ETP during 1989-90. Only 22% of the variation in sea turtle
81
surface density was accounted for by SIGMA-T, Z20, LOGLAND, and LOGC, suggesting
these variables are linked indirectly to sea turtle density.
Comparable R2 values were reported for these environmental parameters and
the abundance and distribution patterns of cetaceans, similarly indicating indirect
associations with these variables. Using the same variables as this study and
including geographic locations, Reilly and Fiedler (1994) explained 20.5% of the
variation in the combined relative abundance data of several delphinid species, and
8.8
41.2% of individual species abundance. Cetacean sighting-rate and chlorophyll
concentration were strongly correlated off California (r = 0.92; Smith et al. 1986).
Polacheck (1987) found the highest correlations of several cetacean species'
abundance with sea surface temperature, thermocline depth, 02-minimum layer
thickness, and water depth (a reflection of distance from land). However these
variables also accounted for less than 38% of variation for any species.
The low explanatory ability of the environmental parameters for large
vertebrate abundance and density is not surprising. These variables represent mesoand large-scale features that are likely linked to the occurrence and density of sea
turtles through finer-scale processes and features. In particular, a lag in time and
space is expected between favorable physical and chemical configurations and the
occurrence of high-level predators. Such a lag can be on the order of months and
hundreds of kilometers (Mann and Lazier 1991). Furthermore, the sampling scale of
this study was large; daily survey areas were often extensive and turtle patchiness
within transect-areas was not examined.
82
Additionally, several other factors likely affect the amount of variation
explained in these data. Because 80% of the sea turtles were unidentified, habitat
preferences and behavior of other species inadvertently included in the sighting data
will increase variation in the results. However, olive ridley turtles are easy to detect
and therefore unidentified sightings in this study were likely biased towards members
of that species (see Chapter 2).
Also, reproductive behaviors influence distribution and density but are not
considered in the model. During the peak nesting season, July December, the
highest inshore densities of olive ridleys (>1 turtle/km2) are likely due to their unique
nesting behavior. The majority of females of this species nest synchronously in large
aggregations (hundreds to thousands of turtles) on certain Central American beaches
(e.g., Richard and Hughes 1972, Plotkin et al. 1995). Turtles loosely aggregate prior
to the mass-nesting emergences, apparently awaiting the appropriate physiological and
environmental cues to go ashore (Plotkin 1994). It is not known how long the
aggregations initiate before the emergence or how far from the beaches they form,
however, the highest density in 1989 (1.27 turtles/km2) was > 100 km offshore.
Finally, an obvious and important omission from the data presented here is the
distribution of prey species of olive ridley sea turtles and its influence on sea turtle
distribution. Unfortunately there are not many studies of the feeding habits of olive
ridleys, particularly in pelagic regions. Available information on prey items suggests
they are generalists, feeding opportunistically in benthic and epipelagic habitats on
mollusks, bryozoans, shrimp, pelagic crabs, fish eggs, cnidareans, salps and tunicates
(Fritts 1981, Montenegro and Bernal 1982, Mortimer 1982, Barragan et al. 1992). A
83
generalist foraging strategy is consistent with the evolution of a oceanic migratory
species in a dynamic habitat like the El Nino/Southern Oscillation dominated ETP.
The presence and density of olive ridley sea turtles in the ETP during 1989-90
was significantly associated with distance to land, water-mass density, depth of
thermocline and chlorophyll concentration. The reader is cautioned, however, that
these analyses are exploratory, rather than predictive. The results presented are valid
only for these data in the ETP during the study period. Even so, the results suggest
the following directions for future research:
1.
There may be a seasonal component to sea turtle density distribution
corresponding to seasonal variability in the ETP. Examination of existing data
(e.g., Pitman 1990), or data from fishing vessels in the appropriate seasons
could reveal such changes in distribution.
2.
Do sea turtles co-occur spatially and temporally with cetaceans, or are their
distributions complementary (e.g., Shoop and Kenny 1992)? Overlap in
distributions can be determined from comparisons of MOPS cetacean and turtle
daily-sighting data.
3.
Interannual variation in the ETP seems to affect turtle distribution. The
relationship of turtle presence and density to annual variation can be studied
statistically with the currently available data.
4.
Foraging behavior, prey species availability, and prey distribution are
important elements of sea turtle distribution and are necessary to further study
how marine turtles actively use their habitat.
84
Information on seasonal and interannual distributions, and habitat relationships
is important to conservation and management efforts. Data are especially needed in
areas where sea turtles overlap with, and are incidentally captured in, commercial
fishery operations. Sea turtles occur in local and international waters throughout
Pacific Central America. Management and protection plans will require cooperative
international effort and a strong data base for effective conservation decisions.
85
Summary
Until recently there was little information on the distribution range and habitat
of the olive ridley turtle during the non-nesting portion of its life history. Reports
from tuna vessels (Cornelius and Robinson 1986, Au 1991) and other vessels (Oliver
1946, Pitman 1990, 1993) have documented the presence of olive ridleys in the open
ocean. Recent tracking studies of the olive ridley (Plotkin 1994, Plotkin et al. 1995)
have vastly increased knowledge of their behavior and migrations. However, no
attempts have been made to estimate relative densities or to quantitatively correlate
distribution with oceanographic indices and surface features. The goals of this thesis
were to examine the behavior of an olive ridley with respect to primary surface
currents and features
especially convergence zones, evaluate satellite-telemetry as a
method for collecting such data, calculate relative density and compare density
distributions with convergence zones and other primary surface features, and to
quantitatively correlate presence and density of turtles with oceanographic habitat
variables during the peak breeding/nesting season.
The track of the satellite-monitored olive ridley turtle suggests some active
swimming rather than continuous passive floating with the currents, and comparisons
with surface current divergence data indicated that the turtle encountered several large
convergence and divergence zones, but did not appear to favor either. The turtle
travelled at least 2,691 km in the open ocean, averaging 1.28 km/h. Diel patterns
were evident in the turtle's submergence and diving behavior. Daytime dives were
frequent, and of short-duration, yet more time was spent at the surface. Nighttime
86
submergence durations were longer and more variable. Temperatures recorded during
daytime transmissions were cooler and variable, and were correlated with longer
submergences. Nighttime temperatures were consistently warm and there was no
correlation with submergence time. It is possible that temperatures reflect dive
depths, but that remains to be tested by transmitters with depth sensors. Two discrete
submergence behaviors were recorded, short duration submergences (<7.7 min) and
long dives (8.2 95.5 min). These behaviors exhibited the same diel pattern.
Surfacing durations were long (1-10 min) but showed no diel differences, possibly due
to transmission constraints. The surfacing patterns of this olive ridley allowed many
transmissions, indicating that greater amounts of data could be archived and
transmitted in future studies.
Detectability analysis was an effective method for testing the effect of
environmental conditions, observer, and time of day on the detection of turtles, and
for adjusting the effective area surveyed. Sea state was the primary factor influencing
sea turtle detection. Effective halfwidth surveyed was adjusted for each sea state
category. Effective area surveyed under each sea state condition was calculated for
the 1986-1990 data from effective halfwidth and transect length (km/sea state).
Detectability analysis is applicable in any variable distance survey where sighting
conditions are systematically recorded and target animals are not influenced by the
observer. This method provides a simple alternative to stratifying data or restricting
survey conditions.
Turtles were widely distributed throughout the ETP and species distributions
were consistently within reported ranges. All loggerhead turtles were identified off
87
Baja California, all green turtles were identified near the Galapagos, one hawksbill
was sighted off Mexico, and olive ridleys were identified in both nearshore and
offshore waters.
Relative density distributions showed primary concentrations of turtles off
Baja, Mexico, nearshore from Mexico to Ecuador and out about 700 km, along the
equatorial convergence zone, and to a lesser extent along the north equatorial
divergence and the Peru Current boundary. Strong interannual variation was evident
in the yearly relative density distribution patterns, suggesting that turtle distribution
was influenced by the 1987 El Niiio and 1988 La Nina events.
Presence of olive ridley turtles was significantly correlated with surface water
density, distance to land, and thermocline depth. Densities of these turtles, where
present, were minimally correlated with surface water density, distance to land,
thermocline depth and chlorophyll concentration.
Sea turtle density was noticeably concentrated along the equatorial convergence
zone, particularly in 1986, and 1989-90. However the data from these studies are
inconclusive with regard to the relationship of sea turtles to frontal zones. Turtles
were also present in areas of coastal and offshore upwelling divergence (1988, 1989).
Both convergence and divergence features are areas of high productivity (Mann and
Lazier 1991). Whether turtles were directly associated with the divergences, or fronts
related to them (e.g., coastal upwelling fronts), cannot be determined from the data in
these studies. The spatial scales of currents and surface features examined in these
studies are too large to reveal the true association between turtles and these physical
features. The available data (Plotkin 1994, these studies) indicate that olive ridley
88
turtles in the ETP are generalists in their habitat, ranging widely, continuously
moving and probably foraging opportunistically on food when and where it is
available rather than consistently targeting a particular food source.
89
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100
Appendices
101
APPENDIX 1
1000
1'1'1'1'1'1
11
T III 1
1
I
I
I
Night
n = 274
4-0
o Day
n = 403
A
co
o
Long Dives
100
cv
0
a)
0
4-
0
10
-0
1
I 1111111 II
0
8 16 24 32 40 48 56 64 72 80 88 96
Dive Duration t (min)
Log-survivorship plot of day and night sampled submergence durations. The slopes
of the curves are proportional to the probability of a submergence longer than a
particular duration (t) occurring. A change in the slope is used to discriminate
between types of submergence behavior (Fagan and Young 1978).
102
APPENDIX 2
.
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Date
Dates of satellite-acquired locations of an olive ridley turtle in the eastern tropical
Pacific plotted against modeled surface current divergence (10-6 s'). Plotted locations
were one day before, after, and on the same day as available divergence data.
Surface divergence data was available in six day intervals. The criterion for
determining a location was at a convergence (shown as dotted lines on the graph) was
values __ -0.1 x 10-6 s-1. The criterion for a location at a divergence was values
>0.1 x 10' s'.