AN ABSTRACT OF THE THESIS OF
Abigail L McCarthy for the degree of Master of Science in Fisheries Science
presented on November 20th, 2006.
Title: Defining Habitat Preferences of Pelagic Loggerhead Sea Turtles (Caretta
caretta) in the North Atlantic through Analysis of Behavior and Bycatch
Abstract approved:
_____________________________________________________________________
Selina S. Heppell
For many species of marine turtle the characteristics that define pelagic habitat
have yet to be fully identified. A better understanding of these habitat characteristics is
critical to reduce high seas fisheries interactions with turtles, especially as the status of
many turtle populations has placed them on the threatened or endangered species list.
The combination of high-resolution satellite-tracking data with remotely sensed
oceanographic data makes it possible to identify habitat for loggerhead turtles by
analyzing the behavior of individual animals. Bycatch of loggerhead turtles in longline
fisheries can also be examined using the same high-resolution oceanographic data to
determine if there are identifiable habitat differences in high- and low- bycatch areas.
I analyzed the tracks of ten loggerhead turtles tagged in the spring and fall of
1998 near Madeira, Portugal in relation to the marine environment they occupied. To
determine the relationship between an individual turtle and its environment, some
measure of behavior was necessary. I calculated the straightness index (SI), the ratio
of the displacement of the animal to the total distance traveled, for individual weekly
segments of the ten tracks as a measure of individual behavior. I then extracted
information about the chlorophyll, sea-surface temperature (SST), bathymetry, and
geostrophic current of the ocean in a 20km buffer surrounding the tracks, and
examined the relationship between the straightness index and those characteristics
using logistic regression. Chlorophyll a value, bathymetry, and movement of the turtle
with geostrophic currents were consistently related to the straightness index of the
tracks of all ten animals (two-sided p-value from Wald’s test: 0.005, 0.0017, and
0.0018, respectively). Tracks were less straight in high chlorophyll regions and in
shallower ocean areas, and animals were more likely to be moving with prevailing
geostrophic currents during straighter track segments. These results confirm
comparable analyses of loggerhead tracks in the Pacific, and indicate that sea turtles
alter their behavior (likely representing a shift from traveling to foraging) when they
encounter high-chlorophyll regions.
Turtles with highly sinuous tracks spend more time in a given area or habitat
than those who pass straight through, and therefore may be more susceptible to
incidental capture by fisheries operating in those habitats. To address the fisheries
bycatch/ habitat interactions I analyzed longline bycatch data to determine whether the
marine environmental variables identified in the first part of my study were related to
the probability of catching a turtle on a given longline set. I performed a logistic
regression analysis using bycatch of turtles as the response variable, and bathymetry,
SST, SST gradient (indicative of frontal activity), chlorophyll a, and chlorophyll a
gradient as the independent variables. I also included the location and the date of the
longline sets as potential predictor variables. I found that the most important variables
predicting the odds that a turtle would be caught on a given set were chlorophyll a
value in the area of the haul ( Wald’s test, p=0.009) and the latitude at the beginning
of the haul (Wald’s test, p=0.0005). Turtles were more likely to be caught on sets in
lower chlorophyll regions and in higher latitude regions of the data set, and there was
no indication of important effects of bathymetry. These results disagree with my
predictions from the tracking analysis, either because the fisheries-dependent bycatch
data set did not provide enough contrast of habitat types, or because bycatch
probability is not related to turtle behavior.
My results indicate a difference between the critical variables selected as
predictors of turtle habitat using the bycatch data and those selected using the behavior
of individual tracked animals. While bycatch information is important, the distribution
of fisheries data is highly biased towards frontal zones and regions of historic high
catch. Judgments about turtle behavior based on only fisheries interactions could lead
to incorrect conclusions about where animals spend the majority of their time.
Assuming that animals are more likely to have an increased probability of interaction
with longlines in areas where they spend more time foraging, fishing pressure should
be reduced in those areas of high-use for pelagic loggerheads. It is crucial to base
fisheries time-area closures and the design of marine protected areas on the behavior
of tracked animals, and not just on fisheries bycatch data.
©Copyright by Abigail L McCarthy
November 20, 2006
All Rights Reserved
Defining Habitat Preferences of Pelagic Loggerhead Sea Turtles (Caretta caretta) in
the North Atlantic through Analysis of Behavior and Bycatch
by
Abigail L McCarthy
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Presented November 20, 2006
Commencement June 2007
Master of Science thesis of Abigail L McCarthy presented on November 20, 2006.
APPROVED:
_____________________________________________________________________
Major Professor, representing Fisheries Science
_____________________________________________________________________
Head of the Department of Fisheries and Wildlife
_____________________________________________________________________
Dean of the Graduate School
I understand that my thesis will become part of the permanent collection……..
_____________________________________________________________________
Abigail L McCarthy, Author
ACKNOWLEDGEMENTS
I would like to express my sincere appreciation to the Pelagic Fisheries
Research Program for graduate funding throughout the majority of my graduate
career. I would like to thank the Marine Technology Society for a Charles H.
Bussmann grant, and the Hatfield Marine Science Center for a Reynolds grant. I
would also like to thank the OSU Department of Fisheries and Wildlife for an Oregon
Laurels scholarship and a Scott scholarship. Dr. Molly Lutcavage also provided some
financial support for this work, as well as the data for the CNA bluefin tuna cruise.
I have had a great deal of technical assistance with this project. I want to thank
Dr. Francois Royer for allowing me to use his in-progress Kalman filter and for
valuable insight on satellite tag data and remote sensing data. Dr. Martin Saraceno
helped me to manipulate the AVISO geostrophic current data. Rob Suryan has been
very helpful both in discussing issues of data analysis and in sharing his Matlab code
and skills with me despite his busy schedule.
On a more personal level, I would like to thank my mom and dad for all their
support of my scientific aspirations. I’m glad I study things that move now too. My
graduate advisor, Dr. Selina Heppell, has been both inspiring and awfully patient, and
I am glad I did this Master’s work with her. She has given me a great deal of excellent
advice over the last three years. I also have a fantastic group of friends, both at the
HMSC and all over the world, and their support has been extremely important to me.
Many thanks to KO, Jen, Andrea, Molly, Al, Hezzer, Paul, Leslie, Josie, Jesse, Emu,
Cri-Cri, and the rest of the crew for your advice, surf advisories, and snacks.
TABLE OF CONTENTS
Page
Chapter 1 –Habitat Preferences of Pelagic Loggerhead Turtles in the North Atlantic:
High Sinuousity of Tracks in Shallow, High Chlorophyll Regions. ................. 1
Abstract...............................................................................................................1
Introduction.........................................................................................................2
Methods ..............................................................................................................5
Data Collection- tracks ............................................................................... 5
Data analysis- track filter ............................................................................ 6
Track straightness index ............................................................................. 7
Oceanographic data. ................................................................................... 9
Oceanographic data extraction. ..................................................................13
Regression of straightness index on marine habitat variables......................14
Results: .............................................................................................................16
Loggerhead movements .............................................................................16
Weekly movement and characteristics of tracks .........................................18
Discussion.........................................................................................................31
Chapter 2 - Pelagic Fisheries Interactions with Caretta caretta: Chlorophyll a as an
Important Predictor of By-catch.....................................................................35
Introduction.......................................................................................................35
Methods: ...........................................................................................................38
Data Collection- Fisheries..........................................................................38
Oceanographic data ...................................................................................40
Data analysis .....................................................................................................41
Data sampling: buffer techniques. ..............................................................41
TABLE OF CONTENTS (Continued)
Page
Statistical comparisons...............................................................................43
Results: .............................................................................................................44
Discussion:........................................................................................................52
Bibliography .............................................................................................................56
Appendices ...............................................................................................................61
Appendix A: Analysis of animal movement relative to current direction............61
Appendix B: Regression analyses of straightness index of satellite tracks on
oceanographic variables. ...................................................................................65
LIST OF FIGURES
Figure
Page
Figure 1. Schematic drawing of straightness index ..................................................... 8
Figure 2. Partial track for tracked animal “Delia” in NE Atlantic................................ 9
Figure 3. AVHRR monthly average sea surface temperature at 4km resolution for July
1998 in the region SE of Newfoundland....................................................................11
Figure 4. Buffer of 20km around weekly track for “Delia” in central North Atlantic. 13
Figure 5. Tracks of all ten tagged loggerhead turtles in the N. Atlantic. ....................17
Figure 6. Distribution of straightness index values calculated for weekly track
segments for ten satellite-tracked loggerhead turtles..................................................18
Figure 7. Tracks for all ten loggerhead turtles plotted over five-minute resolution
bathymetry data for NE Atlantic................................................................................24
Figure 8. Region 1 from Figure 7: .............................................................................25
Figure 9: Region 2 from Figure 7. Tracks of ten loggerhead turtles over shelf- break
off NW Africa...........................................................................................................26
Figure 10. Region 3 from Figure 7. ...........................................................................27
Figure 11. Weekly track of “Isabel” on weekly, 9km, SeaWiFS chlorophyll a data
(mg/ m3)....................................................................................................................29
Figure 12. Weekly track of “Lidia” on weekly, 9km, SeaWiFS chlorophyll a data (mg/
m3)............................................................................................................................30
Figure 13: Distribution of longline sets .....................................................................39
Figure 14. Buffer-zones around longline hauls for fisheries data ...............................42
Figure 15. Buffer for longline haul overlaid on 9km resolution SeaWifs chlorophyll a
data...........................................................................................................................43
Figure 16: Mapped longline hauls from commercial fisheries (SEFSC observer data)
and scientific longline cruise (CNA data) from 1999-2003 ........................................45
Figure 17: Latitude of longline hauls from the North Atlantic plotted vs. logtransformed Chlorophyll a values..............................................................................46
Figure 18: Longline hauls for the July 2000 and the monthly average chlorophyll for
the same time. ...........................................................................................................48
Figure 19. Longline Hauls and buffers for July 1999 and the monthly average 9km
SeaWifs chlorophyll(mg/ m3)....................................................................................49
Figure 20: weekly segment of “Delia’s” track with corresponding weekly AVISO
geostrophic velocity vectors......................................................................................63
LIST OF TABLES
Table
Page
Table 1. Oceanographic data sources and resolution..................................................12
Table 2. Summary of distances traveled and time tracked for ten tracked turtles........16
Table 3. Summary statistics for track data and marine habitat variables.....................19
Table 4. Logistic regression for all tracking data. ......................................................21
Table 5. Regression for subset data. ..........................................................................21
Table 6: Oceanographic Data for fisheries analysis ...................................................40
Table 7: Summary statistics for the location and marine habitat values for the two
groups of sets............................................................................................................47
Table 8. Logistic regression of turtle bycatch ............................................................51
CHAPTER 1 –HABITAT PREFERENCES OF PELAGIC LOGGERHEAD
TURTLES IN THE NORTH ATLANTIC: HIGH SINUOUSITY OF TRACKS
IN SHALLOW, HIGH CHLOROPHYLL REGIONS.
Abstract
A better understanding of pelagic behavior and pelagic habitat characteristics
is critical to reduce high seas fisheries interactions with turtles, especially because the
status of many turtle populations has placed them on the threatened or endangered
species list. To address the question of how the immediate marine environment
through which an animal moves correlates with its behavior, I examined the tracks of
ten loggerhead turtles tagged in the Spring and Fall of 1998 near Madeira, Portugal in
relation to the characteristics of the water-masses they occupied. I calculated the
straightness index (SI) of the ten tracks for individual weekly segments and classified
tracks as sinuous or straight. The straightness index of track segments is used as a
measure of the area-restricted search during that segment; low SI indicates that an
animal is likely to be foraging in that area, high SI indicates that the animal is
probably traveling through the area. I extracted information about the chlorophyll, seasurface temperature, bathymetry, and geostrophic current of the ocean surrounding the
tracks over the appropriate time period, and examined the correlation between the
straightness index and those characteristics. I found a significant negative correlation
between straightness index and chlorophyll, a positive relationship between
straightness index and ocean depth, and a positive relationship between an animal’s
movement with the prevailing geostrophic current and straightness index. This
suggests that they are more likely to move with geostrophic currents when they are
2
traveling. It also indicates that animals are spending more time and searching more
thoroughly in areas with high chlorophyll and in areas that are shallower. The risk of
interactions with fisheries increases substantially in areas where animals are spending
the most time. Based on these relationships, I propose that conservation planning to
reduce overlap of turtles with fishing operations should take into account the locations
of bathymetric features such as seamounts as well as upwelling locations where
chlorophyll concentrations are high. In addition, this work indicates that more
consideration of the current field through which animals pass may be needed to fully
understand how behavioral patterns and currents are linked.
Introduction
Loggerhead turtles (Caretta caretta) have threatened status on the US
endangered species list and are listed as endangered worldwide by the International
Union for the Conservation of Nature (USFWS 1978; IUCN 2006). While Loggerhead
turtles have been extensively studied on their nesting beaches on the East Coast of the
United States and in both the Mediterranean and Japan, much less is known about their
behavior in the ocean where most of their lives occur (Meylan 1995; Bjorndal et al.
2003). Because of their status both as a wide-ranging marine species and as a
threatened species, there is a critical need for improved understanding of the ecology
of these organisms and of the threats they face.
The question of how juvenile loggerheads use oceanographic habitat during the
pelagic phase of their life-history has intrigued the sea turtle world since investigation
of the pelagic phase began (Carr 1987). Only recently, however, have scientists been
3
able to study the behavior of these animals in the pelagic using satellite telemetry
(Papi et al. 1997; Sakamoto et al. 1997; Polovina et al. 2003). These studies have
enabled us to begin to understand both how individuals interact with the ocean
environment and how populations of marine turtles are inter-connected (Nichols et al.
2000; Godley et al. 2003; Polovina et al. 2004; Polovina et al. 2006). We know that
loggerhead turtles in particular can be found in conjunction with persistent frontal
zones, identified as steep gradients in surface chlorophyll, in the Pacific near the
highly dynamic Kuroshio Extension Bifurcation Region. Juvenile loggerheads have
also been found to be resident in the dynamic upwelling regions in the waters around
the Azores (Bolten 2003).
Studies of the marine habitat associations of large pelagic vertebrates generally
fall into two groups; those which label the areas where diverse groups of animals
congregate as “hot spots” (Hyrenbach et al. 2000; Worm et al. 2003; Yen et al. 2004;
Tynan et al. 2005; Palacios et al. 2006), and those which focus on areas of high use for
tracked animals of one or two species (Polovina et al. 2004; James et al. 2005a; Eckert
2006; Hawkes et al. 2006; McMahon & Hays 2006; Polovina et al. 2006). Studies
from both groups show that animals aggregate at shelf breaks, upwelling regions, and
oceanic fronts, presumably due to increased forage availability in these highly
dynamic areas (Hyrenbach et al. 2000; Yen et al. 2004; Eckert 2006; Hawkes et al.
2006; Polovina et al. 2006). However, few studies directly link the observed behavior
of tracked animals to oceanographic characteristics in a quantitative manner. Studies
of marine turtles in particular have generally classified regions of the ocean as high-
4
use habitat based on qualitative classification of “residence time” of their tracked
animals, and in some cases on diving behavior (Polovina et al. 2004; James et al.
2005b; Eckert 2006; Hawkes et al. 2006; James et al. 2006). In this study I seek to fill
a gap in our understanding of loggerhead turtle ecology by linking the behavior, in
particular the track sinuosity, of animals to their immediate marine environment.
Behavioral ecology studies from the terrestrial world, when coupled with
oceanographic remote sensing data, give us a useful tool for understanding how
marine turtle behavioral patterns relate to the characteristics of the ocean through
which they travel. We know that in both marine and terrestrial systems increasing turn
angles and increasing sinuosity of tracks often accompany foraging (Bovet &
Benhamou 1988; Maerell et al. 2002; Newlands et al. 2004) . The ratio of an
individual’s displacement to the total distance traveled, called the straightness index, is
an effective measurement of local search efforts as it indicates the relative sinuosity of
an animal’s path (Hull et al. 1997; Benhamou 2004). When we look at individual
segments of a turtle’s track on a daily or weekly basis, we can examine the
straightness index of those tracks as a way of determining how carefully an individual
has searched a given area. This measure of search effort can then be related to the
surrounding marine habitat to improve understanding of what constitutes a foraging
area for a given animal or group of animals. It can also pinpoint areas where animals
spend the most time, and when combined with fisheries effort data can be used to
determine the overlap of high-use areas for fisheries and high-use habitat for
loggerhead turtles.
5
In this study, I investigated how the behavior of individual turtles, as
measured by weekly straightness index of movement, is related to marine habitat in
the Eastern North Atlantic. I used tracks from ten loggerhead turtles captured in dipnets off the Island of Madeira and released in the Fall and Spring of 1998. I examined
the relationship between straightness index as a measure of the behavior of ten
individual turtles relative to bathymetry, geostrophic currents, chlorophyll a,
chlorophyll a gradient, sea surface temperature, and sea-surface temperature gradient.
I also investigated the difference between the spring-release group of turtles (n=5) and
fall-release turtles (n=5) to determine whether the behavioral patterns were different
for those two groups. This analysis differs from other studies of pelagic marine turtles
in that it compares a suite of marine habitat variables to a quantitative measure of
animal behavior. By breaking tracks down into weekly segments, and relating the
sinuosity of those segments to the oceanographic characteristics of the water-mass that
the animal was occupying at that time, we can tell how the changing environment is
linked with variation in the animal’s behavior.
Methods
Data Collection- tracks
Dr. Thomas Dellinger placed ARGOS satellite tags on a total of ten juvenile
loggerhead sea turtles in the spring and fall of 1998. These two groups of turtles were
captured using dip nets in the sea around Madeira, tagged, and released in two groups
from boats near the island of Madeira (Dellinger & Freitas 1999). Tags were attached
6
using a standard attachment method , and data were recorded for the entire life of the
tag, or of the tagged animal (Dellinger & Freitas 1999).
Data analysis- track filter
Raw ARGOS data consist of latitude and longitude values, time, date, and
location classes for turtle locations from the Argos satellite system. To effectively
analyze Argos data, some method of filtering out locations which are not
representative of where a transmitter actually is must be used. One of the most
common methods is a speed filter, in which the calculated speed of the animal over the
ground is used to remove locations which are improbable due to unrealistic speed
between points, based on an estimated top maximum speed of the animal (Akesson et
al. 2001; Gaspar et al. 2006). Another method often used is to discard points with low
location classes, or some combination of the two techniques (Austin et al. 2004;
Suryan et al. 2006). Both of these methods have drawbacks; they either use arbitrary
thresholds for elimination of points or they do not incorporate all the available data
into the final track. In a field where each data point is often expensive, it is
economically sensible as well as scientifically advantageous to use all of the satellite
location estimates if possible.
The Kalman filter is an alternative method that allows a more objective
filtering of all available track data. In this context we have used it to estimate a
smoothed, time-regulated track from the Argos locations. The Kalman filter differs
from other methods in that it incorporates both the uncertainty in each Argos location
estimate and information about the animal’s previous and future movements into the
7
final “most-probable track”. This produces a smoothed track with outliers removed
and regularized time-steps, without eliminating a large portion of the data points.
The version of the Kalman filter that I used was modified by Dr. Francois
Royer (University of New Hampshire) from methods described by Roweis and
Ghahramani (1999) and is similar to that used by Sibert et al. (2003). It consists of a
series of equations. The first produces an initial estimate of the track, the second
detects outliers through expectation-maximization, and the third does the final
smoothing of the track and regularizes the time-steps (Royer & Lutcavage In
preparation). The first step is called the Kalman smoother, and it gives a rough
estimate of the track location based on all of the points provided in the original data
file as well as the uncertainty of each of these estimated locations. Then, the variance
of each of the locations is incorporated into the expectation-maximization algorithm,
which is run on each individual point. In effect, if the point being considered is far
from the rough estimate of the track from the first step, the probability of that point
being incorporated into the final track is lowered. Then, all the points with a high
probability of being included in the final track are run through the Kalman smoother
again, which produces the most probable track with regular time-steps and no outliers.
Track straightness index
My primary response variable for the analysis of turtle behavior was an index
of sinuosity called a straightness index (SI, (Benhamou 2004). I calculated the SI of
each weekly segment of turtle track by dividing the straight-line distance, or the
displacement of the animal, by the total distance traveled by the animal during that
8
week. The straightness index of a particular segment of the track is representative of
the amount of twisting and turning that the turtle did when traveling during that week.
If the animal moves from point A to point B via the solid path, then the straightness
index is the ratio of the dashed line to the solid black line; thus, the value ranges from
near zero, if the animal moved an infinite distance between start and end-points, to 1,
if the animal moved in a perfectly straight line (Figure 1). For the purpose of this
study, point A and point B represent the start and end locations for a full week of track
data.
Figure 1. Schematic drawing of straightness index (SI)
B
A
To assure that all weekly straightness index values represented actual
movements by the animal over a period of seven days, it was necessary to remove
track segments with three or fewer original Argos locations. Missing satellite location
values led to a straight line interpolation between points, so there were several
segments in tracks that did not represent actual behavior. This portion of the animal
Delia’s track (Figure 2) shows a segment of track with interpolated points surrounded
by the red buffer. The removal of weeks with three or fewer original locations
9
reduced the sample size of weekly track segments included in the analysis from 345
total weekly segments to 305.
Figure 2. Partial track for tracked animal “Delia” in NE Atlantic. Track is in blue,with
artificially straight week surrounded in red buffer
Oceanographic data.
I obtained the sea surface temperature data from the National Ocean Data
Center (NODC) ftp server. The data are the pathfinder Version 5.0 (4km resolution),
data originated from the Advanced Very-High Resolution Radiometer (AVHRR)
sensor. Pixels with quality-flags of levels 3-7 were included in the analysis.
The SST gradient data were calculated using the Matlab ™ gradient function
on the gridded, monthly SST data (Figure 3). This function calculates the slope
10
between pixels in a data grid. In this case, the slope is defined as the change in SST
per unit distance (approximately 4km depending on the geolocation of the pixel) along
the path of steepest ascent or descent from a grid cell to one of its eight immediate
neighbors. To properly calculate the gradient, the data being examined must be good
quality with relatively few missing pixels. The weekly data were not sufficiently
coherent to use in the gradient calculations, so I chose to use monthly average data
instead. These data give a good picture of the more permanent oceanographic features
which are more relevant to higher trophic-level predators like loggerhead turtles as
well as having better coverage (Palacios et al. 2006).
I obtained the chlorophyll a data from the NASA Goddard Space Flight Center
(GSFC) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) level 3 ftp site, at 9km, 8day resolution as well as average monthly values. For chlorophyll values used in the
analysis of track data, I used the 8-day, 9km data (Table 1). Gradient values were
calculated from log-transformed monthly average chlorophyll data using the Matlab
gradient function as with the SST data. For the bathymetric data I used the GEBCO
(General Bathymetric Chart of the Oceans) 5” gridded bathymetry data, available from
the British Oceanographic Data Centre (www.bodc.ac.uk).
Figure 3. AVHRR monthly average sea surface temperature at 4km resolution for July 1998 in the region SE of Newfoundland (left);
corresponding SST slope values (right).
11
12
I calculated the geostrophic currents using data from the AVISO live access
server (www.las.aviso.oceanobs.com/las/servlets/dataset). Because geostrophic current
data are not available online in readily accessible format for 1998-1999, several steps
were taken to obtain the u and v components of geostrophic current velocity vectors.
The first step was to calculate the mean dynamic topography (MDT) to be used as a
basis for further calculations. I did this by subtracting the sea level anomalies (SLA)
for a given day from the absolute dynamic topography (MADT) available for the same
day; this produced the mean dynamic topography (MDT). I performed this procedure
using data from after 2001, when MADT data were available on the LAS. The next
step in the calculation of geostrophic currents for 1998-1999 was to add the SLA for
specific dates of interest to the MDT calculated above, and get the MADT for the
appropriate dates. Finally, I applied the geostrophic equation to the MADT to get the u
and v velocities of the geostrophic current in m/s2 at 1/3 degree resolution (Bearman
2001).
Table 1. Oceanographic data sources and resolution
type
SST
Formal name
4 km AVHRR
Pathfinder Version
5.0 SST Project
(Pathfinder V5)
Chlorophyll a
SeaWiFS level 3 8day mapped 9km
chlorophyll
Bathymetry
GEBCO
Geostrophic current AVISO sea-level
anomaly data
origin
NODC, satellite
oceanography
group
Processing
Quality levels 3-7
selected
resolution
4km 8-day and
4km monthly
Nasa GSFC
SeaWiFS project
Level 3 processed
9km 8-day and 9km monthly
BODC
AVISO Live
Access Server
5 minute grid
SSALTO/DUACS
1/3 degree, 7-day
delayed-time, merged weighted
SLA
13
Oceanographic data extraction.
To obtain the oceanographic data for analysis, I created elliptical buffers
around the section of track for which I wanted to extract oceanographic variables
(Figure 4). Buffers were 20km from either side of the track; 40 km wide. I used this
procedure to extract all oceanographic data for the weekly track sections, I
summarized data from within the buffers by calculating both the mean and the
variance of the pixels included in the buffer, excluding invalid, or “Not a Number”
(NaN) pixels.
Figure 4. Buffer of 20km around a weekly track segment for “Delia” in central North
Atlantic. Geostrophic current vectors and chlorophyll a (mg/ m3 ) for
corresponding week are shown.
14
The analysis of the weekly geostrophic current data was three-fold. First, I
calculated the mean angle of travel for the individual animal for the week of interest,
and the angular dispersion of that mean angle using basic circular statistics (Batschelet
1981). The angular dispersion measure (S) is analogous to the standard deviation of
linear data. Then, I calculated the mean angular direction and angular deviation of the
current using the same statistical method. Finally, I compared these two mean
directions to determine whether or not animals were moving in the same direction as
geostrophic currents. The variable U is a binomial indicator of whether or not the
animal is swimming with the prevailing geostrophic current; a value of 1 indicates that
the animal is swimming, or drifting, with the current, while a value of 0 indicates
movement in a direction other than the current direction (see Appendix A for details).
Regression of straightness index on marine habitat variables
I first fit a logistic regression to the entire set of weekly track data for
investigative purposes. Although autocorrelation exists within these data, this exercise
is useful in pointing out trends in the whole data set. In logistic regression, while the
relationship between the explanatory variables and the mean of the response is still
linear, the link function relates the measured response to the response variable, which
is actually the log of the odds of a specific scenario occurring, in this case, the log
odds that the track segment was straight or sinuous. The full model for the entire data
set included variables coding for the individual turtle and the spring or fall release, as
well as log-transformed chlorophyll and chlorophyll slope, bathymetry, sea surface
temperature (SST), and SST slope. I also included two current-related variables as
15
potential explanatory variables. The first was the S-value, or the angular dispersion
for the current vectors of that given week, and the second was a factor variable which
indicated whether or not the animal had been swimming with the geostrophic current
(U). Finally, I included an interaction term for log-transformed chlorophyll with
bathymetry.
The second regression analysis was a linear regression fit on a randomly
selected subset of all of the tracking information. This analysis differs from the
logistic regression because the random selection of track segments minimizes the
effects of autocorrelation. To perform this regression I randomly selected a segment of
each type of track (high and low straightness index) from each animal’s track for the
regression analysis. This resulted in a subset of ten weekly track segments with
straightness index >.7, and ten weekly track segments with straightness index of <.7,
for a total of 20 track segments and their associated marine habitat variables. I then ran
stepwise linear regressions on three sets of subset data with straightness index as the
dependent variable and marine environmental variables as the independent variables.
These included: log-transformed chlorophyll a and chlorophyll a slope, bathymetry,
SST, and SST slope. I also included the two current-related variables as potential
explanatory variables in the analysis of the subset data, and I included the turtle
identifier variable. The criteria for stepping the regression was the “AIC”, or Aikake
information criteria, which is dependent on the Cp statistic, a measure of the benefit of
adding extra model terms vs. the over-parameterization of the model (Harrell 2001).
16
Results:
Loggerhead movements
The ten tracked animals were released from Madeira in April, May, and
September of 1998. Half of the animals moved to the central or NW Atlantic, and the
other half moved southeast towards the coastal area off North Africa (Figure 5). The
animals were tracked for 58 to 349 days and traveled 919 - 7,394 km during that time
(Table 2). Four of the five fall release animals move to the SE of the release point, and
four of the five spring animals move NW of the release point.
Table 2. Summary of distances traveled and time tracked for ten tracked turtles.
Turtle I.D.
Turtle Name
Release Date
Total days
tracked
Total distance
traveled (km)
Fall/ Spring
Release
1
2
3
4
5
6
7
8
9
10
Delia
Lidia
Carla
Magda
Helena
Isabel
Maria
Samina
Sofia
Tamia
5/18/1998
4/1/1998
5/27/1998
5/18/1998
9/10/1998
9/10/1998
5/27/1998
9/10/1998
9/10/1998
9/10/1998
278
274
160
109
337
312
349
275
123
58
7394
4539
3371
2434
6133
5276
5263
4267
1963
919
S
S
S
S
F
F
S
F
F
F
Figure 5. Tracks of all ten tagged loggerhead turtles in the N. Atlantic. Spring-release turtles (April and May, 1998) are indicated in
red, fall-release turtles (September, 1998) in blue. Animals were released from waters near Madeira.
17
18
Weekly movement and characteristics of tracks
Straightness index (SI) frequency for 305 weekly track segments showed a
continuous distribution but had some indication of subdivisions within the data
(Figure 6). To summarize the data efficiently and provide categorization for regression
analyses, I assigned track segments as curvy or straight according to one subdivision
of the data set at .7 (Figure 6).
Figure 6. Distribution of straightness index values calculated for weekly track
segments for ten satellite-tracked loggerhead turtles. Animals were tracked in
1998 and 1999 in the North Atlantic Ocean.
50
Straight >.7
Curvy <=.7
45
Frequency
40
35
30
25
20
15
10
5
0
0.1
0.2
0.3
0.4
0.5
0.6
Straightness Index
0.7
0.8
0.9
Segments with straightness index greater than .7 were designated as straight,
those with straightness index less than .7 were designated as curvy (Figure 6). This
division was necessary so that the tracks could be randomly sub-sampled for further
1
19
analysis to avoid confounding by autocorrelation. It was also helpful to divide
tracks into two distinct groups for the purpose of summarizing the data
Table 3. Summary statistics for track data and marine habitat variables. Data are
subset by straightness index into two groups: low- and high-sinuosity. Notable
difference in mean values for the two groups are highlighted in yellow.
Track segments with high sinuosity (curvy)
(Straightness index <= .7)
N=137
1st Qu.:
Mean:
3rd Qu.:
Std Dev.:
SST (°C)
SST slope
Chlorophyll a Chlorophyll
(mg/ m3)
slope
16.97
18.10
19.18
2.03
0.00124
0.00205
0.00247
0.00107
0.106
0.362
0.295
0.610
0.00020
0.00034
0.00041
0.00021
Bathymetry
(m)
S: current
angular
dispersion
-3979
-3013
-2164
1270
19.14
41.87
58.04
27.74
Bathymetry
(m)
S: current
angular
dispersion
-4443
-3609
-3009
1083
29.18
50.50
68.15
26.37
Tracks with low sinuosity (straight)
(Straightness index high > .7) N=168
1st Qu.:
Mean:
3rd Qu.:
Std Dev.:
SST (°C)
SST slope
Chlorophyll a Chlorophyll
(mg/ m3)
slope
16.86
17.99
19.12
2.10
0.00136
0.00223
0.00287
0.00123
0.084
0.197
0.223
0.250
0.00020
0.00030
0.00034
0.00018
The values for all investigated marine habitat variables are summarized in
Table 3, grouped by the high and low straightness index values. Columns are the SST,
SST slope, Chlorophyll a, Chlorophyll a slope, Bathymetry, and s, the angular
dispersion of weekly current. The values shown are the mean and 1st and 3rd quartile
values for the two groups of track-types; straightness index > .7, N=168, straightness
index <=.7, N=137.
Differences between the marine habitat variables for the straight tracks and the
curvy tracks are evident. Chlorophyll a values are higher for the more sinuous tracks,
20
and the mean bathymetry indicates that the more sinuous tracks are predominantly
in shallower areas (Table 3). In addition to these differences, it is apparent that the
tracks are straighter in areas where the angular deviation of the current is higher. All
tracked animals spend the majority of their time in waters with surface temperature
between 17 and 19 degrees Celsius. There are no obvious differences between the two
groups in terms of the SST slope, or the Chlorophyll a slope.
The differences between the straight and sinuous tracks outlined above are also
demonstrated both by a logistic regression of the entire data set and by visual
examination of the tracking data. The logistic regression was run with high or low
straightness index as a response (0= low straightness index, 1= high straightness
index). The final, best fit, generalized linear model with high/ low straightness index
as a response included log-transformed chlorophyll, bathymetry, and whether or not
the animal was swimming with the current (U: 1= with current, 0=not with current) as
predictor variables: logit(π)= β0 + β1 (Bathymetry) + β2 (U) + β3 Log(chlorophyll a)
(Table 4). Other variables which were considered but were not significant in the final
model included a variable coding for the individual turtle, one for the spring or fall
release, log-transformed chlorophyll slope, sea surface temperature (SST), SST slope,
and the S-value, or the angular dispersion for the current vectors of a given week.
This logistic regression indicates that while none of the variables tested explain
the data entirely, the high-or low-straightness index of the tracks is related to logtransformed chlorophyll, bathymetry, and whether or not the animal in question is
moving with or against geostrophic currents (Table 4). The straighter track group is
21
negatively associated with bathymetry, indicating that straighter tracks occur over
deeper areas, and more sinuous tracks occur over shallower areas. A negative
relationship also exists for log-transformed chlorophyll; straighter tracks are in lowerchlorophyll areas, and more sinuous tracks are in higher chlorophyll areas. Finally,
during the straighter track segments the animal was more likely to be swimming with
the prevailing geostrophic current. The Wald’s test provides a p-value for the
significance of the logistic regression coefficients (Table 4). Specifically, for the
hypothesis that the log odds of having a high straightness index are different for values
of all three coefficients, a p-value of <.05 allows us to reject that hypothesis. We can
reject that null hypothesis for all three of the coefficients as all terms in the final
model are highly significant (Table 4).
Table 4. Logistic regression for all tracking data. N= 305. Highly significant p-values
for the regression are marked with two *. Regression equation: logit(π)= β0 +
β1 (Bathymetry) + β2 (U) + β3 Log(chlorophyll a). “U” indicates movement
with or against geostrophic currents.
Coefficients
(Intercept)
Bathymetry (β1)
U (β2)
Log(Chlorophyll)
(β3)
Value
-1.34823
-0.00035
0.57041
Std. Error
0.42779
0.00011
0.18218
z statistic
-3.15
-3.11
3.13
P-value
.0018 **
.0017 **
-0.46387
0.16658
-2.79
.0055 **
Table 5. Linear regression for subset data.: “S” is the current angular dispersion and
“U” indicates movement with or against geostrophic currents. Highly
significant p-values for the regression are marked with two * and suggestive
values are marked with one *.
Repeated
regressions
Final model:
Straightness index as
Value of
coefficient(s)
Significance of R2
coefficient(s) at
P-value for
regression
22
response, all models
include intercept (n=20
for each model)
Regression 1 Bathymetry
Log(Chlorophyll a)
Log(SST slope)
Regression 2 SST
U
Log(Chlorophyll a)
Log(Chlorophyll slope)
Regression 3 Bathymetry
S
Log(Chlorophyll a)
Log(Chlorophyll slope)
P> .05
-0.0001
-.1952
-.4032
-0.0237
0.2283
-0.1497
0.2103
-0.0001
-0.0027
-0.2184
-0.5091
0.0433*
0.0026**
0.0026**
0.1562
0.0205**
0.0714*
0.1217
0.0068**
0.0943*
0.0072**
0.0014**
0.5637
0.0034**
0.4986
0.0267**
0.6592
0.0013**
The results of the stepwise linear regression on the random subsets of the data
support the results of the logistic regression on the whole data set (Table 4, Table 5).
The subset data consist of one weekly segment from the sinuous group, and one from
the “straight” group for each individual animal, for a total of 20 samples (Figure 6). I
have included three separate regression analyses for the subset data. Each linear
regression is run on a data set of 20 weekly track segments.
The coefficient that appears the greatest number of times in all three of these
regressions on the subset data is Chlorophyll a (Table 5), which is not surprising,
considering that the results of the logistic regression on the entire data set show that
higher chlorophyll a is an important predictor of more sinuous tracks (Table 4). Two
of the linear regressions also include bathymetry as a predictor variable in the final
model, again supporting results of the logistic regression on the whole data set, which
shows that straighter tracks are more likely to occur in the deeper places where the
animals swam. In addition to these two variables, the linear regressions on the subset
data show that the slope of the surface chlorophyll a may also play an important part
in predicting the straightness of a track (Table 5). In the regression where chlorophyll
23
a slope is significant, the track straightness index decreases as chlorophyll a
gradient increases (Table 5). The SST gradient variable is also included as a
component of one regression, where straightness index decreases as SST gradient
increases. Finally, S, the angular deviation of the current in the region of the weekly
track segment, is included as a significant variable in one regression, suggesting that
straighter tracks are more likely to occur in regions where the current is less
unidirectional. The regression plots for all three of the regressions of the subset data
are included in Appendix B.
A visual examination of the tracking data supports the trends seen in the
logistic regression performed on the entire data set (Table 4). When all of the tracking
data are plotted over the bathymetry, the patterns shown by the summary data and the
logistic regression are evident. Sinuosity of track increases in the more shallow
regions of the ocean (Figure 7). The same pattern can been seen in “Region 1” where
“Lidia’s” movements parallel the edges of a rift (Figure 8), and near the coastal shelf
(Region 2) where three individual tracked animals spend a significant portion of their
tracked period (Figure 9). This same pattern is especially evident in the track of
“Delia” over the Flemish Cap region, Region 3 (Figure 10)
Latitude
20
25
30
35
40
45
-45
Tamia
Sofia
Samina
Maria
Isabel
Helena
Magda
Carla
Lidia
Delia
-40
-35
Region 3
-30
Longitude
-25
-20
-15
Region 1
-10
Region 2
-8000
-7000
-6000
-5000
-4000
-3000
-2000
-1000
Figure 7. Tracks for all ten loggerhead turtles plotted over five-minute resolution bathymetry data for NE Atlantic. Regions 1, 2, and 3
highlight areas where tracks increase in sinuosity over shallower regions.
24
Latitude
37
38
39
40
41
42
43
44
45
46
-30
Delia
Lidia
Carla
Magda
Helena
Isabel
Maria
Samina
Sofia
Tamia
Region 1
-28
-26
-24
Longitude
-22
-20
-18
-8000
-7000
-6000
-5000
-4000
-3000
-2000
-1000
Figure 8. Region 1 from Figure 7: Tracks from six loggerhead turtles plotted over five-minute resolution bathymetry data in the NE
Atlantic.
25
Latitude
26
28
30
32
34
36
-20
Tamia
Sofia
Samina
Maria
Isabel
Helena
Magda
Carla
Lidia
Delia
-18
-16
Longitude
-14
-12
-10
Region 2
-8
Figure 9: Region 2 from Figure 7. Tracks of ten loggerhead turtles over shelf- break off NW Africa.
-6
-8000
-7000
-6000
-5000
-4000
-3000
-2000
-1000
26
Latitude
42
42.5
43
43.5
44
44.5
45
45.5
46
46.5
-44
Delia
Lidia
Carla
Magda
Helena
Isabel
Maria
Samina
Sofia
Tamia
-43
-42
Longitude
-41
-40
-39
-38
Region 3
-37
-8000
-7000
-6000
-5000
-4000
-3000
-2000
-1000
Figure 10. Region 3 from Figure 7. Track of “Delia” in NW Atlantic over five-minute resolution bathymetry data in the region of the
“Flemish Cap”.
27
28
The correlation between sinuosity and chlorophyll demonstrated both by the
logistic regression and the differences between the straight and sinuous tracks (Table
3) is supported by visual examination of the tracks relative to chlorophyll a data as
well as bathymetry. Figure 11 shows weekly SeaWiFS chlorophyll a values with the
same week of track for “Isabel”. The upper-left plot shows the data for the week
starting November 28, 1998, with the corresponding week of track shown in magenta,
and the previous weeks of track in white. The upper right, lower left, and lower right
plots show the chlorophyll a data from the weeks starting December 5th, December
11th, and December 18th. The track is relatively straight until the animal reaches the
high chlorophyll coastal upwelling region, at which point the track becomes extremely
sinuous. The change in track sinuosity from the upper-left plot to the lower right plot,
in conjunction with the animal’s encounter with the high chlorophyll region, is
evident. The same type of behavior occurs with “Lidia’s” movements relative to the
high-chlorophyll region in the NE Atlantic, where the animal changes direction to
move into the higher chlorophyll area, and remains in that area for several weeks
(Figure 12). The behavioral patterns shown by these two individuals are representative
of those shown by the other eight tracked animals.
342
342
342.5
342.5
343
343
23.5
24
24.5
22
340
22
340
22
340
22.5
341.5
Longitude
341.5
Longitude
22.5
341
341
23
340.5
340.5
22.5
23
23.5
24
24.5
23
23.5
24
24.5
22
340
22.5
23
23.5
24
24.5
Latitude
Latitude
Latitude
Latitude
340.5
340.5
341
341
341.5
Longitude
341.5
Longitude
342
342
342.5
342.5
343
343
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Figure 11. Weekly track of “Isabel” on weekly, 9km, SeaWiFS chlorophyll a data (mg/ m3) for November 28, 1998- December 26,
1998. Magenta track represents week corresponding with weekly chlorophyll.
29
336
336
337
337
338
338
339
339
340
340
45
46
47
48
42
330
42
330
335
Longitude
335
Longitude
42
330
334
334
43
333
333
43
332
332
44
331
331
43
44
45
46
47
48
44
45
46
47
48
42
330
43
44
45
46
47
48
Latitude
Latitude
Latitude
Latitude
331
331
332
332
333
333
334
334
335
Longitude
335
Longitude
336
336
337
337
338
338
339
339
340
340
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 12. Weekly track of “Lidia” on weekly, 9km, SeaWiFS chlorophyll a data (mg/ m3) for November 14, 1998- December 15,
1998. Magenta track represents week corresponding with weekly chlorophyll.
30
31
Discussion
All ten of the tracked animals show an alteration in their behavior in highchlorophyll areas and shallower parts of the ocean, as demonstrated both visually and
in the logistic regression on the entire data set (Figure 7, Figure 11, Table 4). The
results of the linear regressions of the randomly-selected subset data also support the
importance of chlorophyll a and bathymetry as predictors of loggerhead turtle
behavior (Table 5). I performed both types of regression, the logistic regression on the
entire data set and the linear regression on the randomly selected subset data, to assure
that the results of the logistic regression were not confounded by autocorrelation of the
tracking data.
Track sinuosity increases in shallower areas; this link is due, most likely, to the
time spent by individual animals over seamounts and over the shelf region. The
positive relationship between high chlorophyll water-masses and increased sinuosity
of tracks indicates that animals may prefer those areas to lower-chlorophyll areas for
foraging. In addition to the clear relationship between shallow areas, high chlorophyll,
and higher sinuosity, a suggestive relationship exists between the sinuosity of tracks
and both chlorophyll and SST fronts (Table 5). More specifically, there is evidence of
a link between more sinuous paths and higher SST and chlorophyll a gradient values.
While this relationship is not borne out by the summary of the entire data set, it is
suggested by the linear regression on the randomly selected subset of the data. Finally,
there is a clear relationship between the indicator variable, U, which indicates
movement with or against geostrophic currents, and the track sinuosity (Table 4). The
32
logistic regression analysis indicates that animals that swim with the prevailing
geostrophic current are more likely to have straight tracks than those who are not
swimming with the current.
Assuming that sinuosity is an indicator of foraging behavior, the negative
correlation between bathymetry and straightness index is consistent with the foraging
of many marine animals at seamounts and shelf regions (Guinet et al. 2001; Burns et
al. 2004; Suryan et al. 2006). The relationship I found between increased sinuosity and
high chlorophyll concentration is supported by the fact that many animals forage in
areas of persistently high chlorophyll, which are sometimes be related to bathymetric
anomalies (Yen et al. 2004; Palacios et al. 2006).These results also support studies of
juvenile loggerhead behavior in the Pacific which show that juvenile loggerheads
seeks out dynamic, high-chlorophyll regions for foraging (Polovina et al. 2004;
Polovina et al. 2006).Although our data do not conclusively prove that animals are
foraging in high chlorophyll or high-resolution bathymetric regions, the link between
simple movements and easily-observed marine environmental variables, such as
bathymetry and chlorophyll a, is a useful one. It is not usually possible to survey large
regions of the ocean for the prey items of pelagic megafauna, so some kind of proxy
must be used to infer the location of foraging grounds in the absence of concrete
information about prey distributions. Hypotheses about the location of foraging
grounds can be generated by examining how animals change their behavior relative to
remotely-sensed oceanographic variables, and these hypotheses can be considered
when making conservation decisions regarding critical habitat for pelagic megafauna.
33
This study is limited in that we considered only geostrophic currents in our
analysis of the tracking data. The logistic regression of the entire tracking data set
indicated that straighter track segments were more likely to occur when an animal was
moving with the geostrophic current. This suggests that current plays a part in
determining the movement of these ten animals. However, we did not include Ekman
transport in this analysis, and total surface current, including Ekman transport, is an
important component of animal behavior (Gaspar et al. 2006). It is possible that
including the measure of total surface current in the regression, or subtracting the
current and performing the same regression procedure on current-corrected tracks,
would be worthwhile (Gaspar et al. 2006).
There are several issues pertaining to the temporal and spatial scaling of the
data which could be addressed in future studies of this kind. First, it would be useful to
obtain climatologies of weekly SST and Chlorophyll a data to improve the temporal
resolution of the data used in the gradient calculations. I used monthly average data for
the gradient calculations for both SST and Chlorophyll a, but if these had been
calculated using weekly averages, the time-scale would have matched the weekly
time-scale of the data. Second, a more sophisticated edge-detection algorithm than the
gradient function we used could more effectively pinpoint the location of SST and
chlorophyll fronts in space. These two modifications to improve both the spatial and
temporal resolution of the frontal systems might clarify some of the suggestive
relationships between track sinuosity and SST and Chlorophyll a gradients from the
regression of subset data (Table 5).
34
In conclusion, this study provides valuable insight into the quantitative
relationship between the marine environment and the behavioral patterns of these ten
individuals. If extended to tracks of other loggerhead turtles this methodology could
assist in the assessment of available habitat on an ocean-wide scale and be used for
risk assessment for potential fisheries interactions.
35
CHAPTER 2 - PELAGIC FISHERIES INTERACTIONS WITH CARETTA
CARETTA: CHLOROPHYLL A AS AN IMPORTANT PREDICTOR OF BYCATCH.
Introduction
Many high trophic-level, large, pelagic species are declining at alarming rates
due either to directed harvest in fisheries or to bycatch in those fisheries (Myers &
Worm 2003; Lewison et al. 2004). Marine turtle populations are a small fraction of
what they were historically (Eckert & Sarti M 1997; Spotila et al. 2000; Jackson et al.
2001). Several researchers and conservation organizations have proposed that
observed declines of loggerhead and leatherback turtles can be largely attributed to
bycatch mortality in fisheries, with recent attention focused on pelagic longline
fisheries (Crowder 2000; Lewison et al. 2004). Many pelagic megafauna, including
turtles, either compete for food resources with fisheries, are caught incidentally in
fisheries, or are themselves targeted by fisheries (Baum et al. 2003; Christensen et al.
2003; Carranza et al. 2006). Concern over the decline of pelagic species and the
development of research tools such as remote sensing data and satellite tags have led
to an increase in efforts to characterize pelagic habitat for wide-ranging, higher trophic
level species as well as to understand the driving force behind fisheries interactions in
an effort to mitigate them and potentially to slow or stop population declines (Lewison
et al. 2004; Gilman et al. 2006; D'Agrosa et al. In Press).
We know that many high trophic-level marine organisms seek out frontal
systems for feeding in the pelagic, and that these fronts are characterized by steep
gradients in sea-surface temperature and chlorophyll a (Olson & Backus 1985;
36
Polovina et al. 2000; Polovina et al. 2001; McCarthy 2006; Palacios et al. 2006).
We also know that fisheries effort is focused near SST and chlorophyll a fronts in an
effort to catch some of these high trophic-level organisms (Seki et al. 2002; Beverly
2006). In essence, longline fisheries in the North Atlantic are a limited, highly-biased
sampling method which only surveys frontal regions. By examining bycatch of sea
turtles in this fishery in the context of remotely sensed oceanographic data, we can tell
whether marine environmental variables can predict bycatch of turtles within this
specific frontal environment.
The majority of the research done on turtle interactions with fisheries has
consisted of gear-modification studies, mainly to examine the effects of hook and bait
alteration on bycatch (Swimmer et al. 2005; Watson et al. 2005; Gilman et al. 2006).
However, some studies of turtle bycatch have focused on the relationship between
bycatch and ocean conditions, and have utilized observer data from longline fisheries
in the North Atlantic, the Hawaii- based longline fishery in the North Pacific, and the
Northeast Distant Fishery, in addition to some studies of fisheries in the South Atlantic
(Witzell 1999; Kotas et al. 2004; Watson et al. 2005; Gilman et al. 2006; D'Agrosa et
al. In Press). In the studies that did investigate oceanographic variables relative to
bycatch (Kotas et al. 2004; Watson et al. 2005; D'Agrosa et al. In Press), the resolution
of the data was often very low, and the conclusions and recommendations do not
always agree. For example, D’Agrosa et al examined the relationship of leatherback
and loggerhead turtle catch per unit effort (CPUE) to spatially explicit, low resolution,
oceanographic data including sea surface temperature and sea-surface height, using
37
bycatch data from the Hawaiian swordfish longline fishery (from 1994-2000) and
the Atlantic longline swordfish fishery (from 1992-1999). The authors found a
positive correlation between loggerhead catch and SST in the Atlantic, where turtles
were caught mainly 16 and 22 °C (D'Agrosa et al. In Press). These findings led the
authors to recommend a shift in fishing effort to warmer waters as one potential
solution for bycatch in the Atlantic. In contrast, an experimental study of turtle
interactions with different fishing gear in the Northeast Distant longline fishery (NED)
showed that the probability of loggerhead catch increased substantially as SST
increased, and recommended that fishermen target fish in cooler water as a bycatch
solution (Watson et al. 2005). The discrepancy between these two studies may be due
to the fact the first used satellite-derived sea-surface temperature at 100km resolution
for their temperature variable, and the second used in-situ SST recorded at the time the
gear was set and when gear was retrieved. If all bycatch data were analyzed relative to
high-resolution satellite data, this discrepancy might be resolved. However, as yet no
study has examined the relationship between loggerhead bycatch and high-resolution
marine environmental variables in both commercial and scientific longline fisheries.
I present an analysis of how presence or absence of turtle bycatch on longline
sets is related to the marine environment around those sets. The data for this analysis
come from two different types of longline fisheries: scientific and commercial. The
marine environmental variables assessed include high-resolution satellite-derived seasurface temperature, chlorophyll a, gradient in both SST and chlorophyll a,
bathymetry, location of set, and the date of the set. This analysis asks the question of
38
whether any of these marine environmental variables are effective as predictors of
loggerhead turtle catch.
Methods:
Data Collection- Fisheries
We obtained longline fisheries data from two sources. The first was the
commercial fisheries Pelagic Observer Program (POP) database. The data are
available for use by the general public from www.noaa.gov, and are collected by
trained fisheries observers aboard commercial fisheries vessels in the North Atlantic.
Location and size of the longline sets are recorded for individual vessels as are time of
day, commercial target, actual catch (including non-target species), weather
conditions, etc. The second data source was scientific longline cruises for bluefin tuna
in the central North Atlantic (Lutcavage 2000-2001). These cruises occurred for the
summer months of 2001 and 2002; June and July for 2001, June, July, and August for
2002. Two boats, the Hamilton Baker and the Atlantic Optimist, participated in the
research for 2001. For the 2002 cruise, the Shoyo Maru and the Eagle Eye II were the
two boats participating. The total number of longline sets for these four boats for both
years was ninety-three sets. For both data sets, the average number of hooks per set
was approximately 1,000. Boats used in the scientific longline cruise were formerly
commercial fishing boats registered in Canada, the United States, and China, and as
such were similar to fishing vessels observed for the POP data set.
39
Figure 13: Distribution of longline sets from the scientific cruise for bluefin tune
(CNA) and the commercial fisheries observer data (SEFSC).
Fisheries data from the CNA and SEFSC were subset to retain only sets from
the Central North Atlantic to maximize spatial overlap with tracking data analyzed in
Chapter 1. The majority of the data from the SEFSC observer database were from the
coastal shelf off the NE seaboard; however, I limited this study to sets located within
the region bounded by 25-55° N Latitude and 60-20° W Longitude. This sub-sampling
of sets ensured that the fisheries data analyzed would be only pelagic fisheries, and not
on the continental shelf; therefore, these data are more relevant to interactions with
pelagic-phase loggerhead turtles. The resulting number of sets from the SEFSC
observer data for the years 1998-2003 was 144, with only sets from 1999, 2000, 2001
40
and 2003 occurring within the region of interest. The total number of sets from the
CNA cruise within this region was 69, with sets from all four boats and both years
(2000, 2001) included in the analysis.
Oceanographic data
The oceanographic variables were satellite-derived data from several different
sources. Table 6 outlines the types of data, the spatial and temporal resolution of those
data, and the original source of the data.
Table 6: Oceanographic Data for fisheries analysis
type
SST
Chlorophyll a
Bathymetry
Formal name
4 km AVHRR
Pathfinder Version
5.0 SST Project
(Pathfinder V5)
SeaWiFS level 3 8day mapped 9km
chlorophyll
GEBCO
origin
Processing
resolution
NODC, satellite
Quality levels 3-7 4km monthly
oceanography group selected
Nasa GSFC SeaWiFS Level 3 processed 9km 8-day and 9project
km monthly
BODC
5 minute grid
I obtained the sea surface temperature data from the National Ocean Data
Center (NODC) ftp server. The data are the pathfinder Version 5.0 (4km resolution),
data originated from the Advanced Very-High Resolution Radiometer (AVHRR)
sensor. Pixels with quality-flags of levels 3-7 were included in the analysis.
The SST gradient data were calculated using the Matlab ™ gradient function
on the gridded, monthly SST data (Figure 3). This function calculates the slope
between pixels in a data grid. In this case, the slope is defined as the change in SST
per unit distance (approximately 4km depending on the geolocation of the pixel) along
the path of steepest ascent or descent from a grid cell to one of its eight immediate
41
neighbors. To properly calculate the gradient, the SST or Chlorophyll a data must
be good quality with relatively few missing pixels. The weekly data were not
sufficiently coherent to use in the gradient calculations, so I chose to use monthly
average data instead. These data give a good picture of the more permanent
oceanographic features which are more relevant to higher trophic-level predators like
loggerhead turtles as well as having better coverage (Palacios et al. 2006).
I obtained the chlorophyll a data from the NASA Goddard Space Flight Center
(GSFC) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) level 3 ftp site, at 9km, 8day resolution as well as average monthly values. For chlorophyll values used in the
analysis of track data, I used the 8-day, 9km data (Table 1). Gradient values were
calculated from log-transformed monthly average chlorophyll data using the Matlab
gradient function as with the SST data. For the bathymetric data I used the GEBCO
(General Bathymetric Chart of the Oceans) 5” gridded bathymetry data, available from
the British Oceanographic Data Centre (www.bodc.ac.uk).
Data analysis
Data sampling: buffer techniques.
I used haul start and end-points to represent the location of each of the longline
sets, as these were likely more representative of the environment in which the animals
were caught (Heppell 2005). To extract oceanographic data I mapped individual sets
as lines connecting these two points. I created buffer-zones around each of those lines
and then took the average and the variance of the pixels included in that buffer. Figure
42
14 shows the construction of buffers around the dark lines showing the start and
end-points of the hauls.
Figure 14. Buffer-zones around longline hauls for fisheries data from CNA and
SEFSC in NW Atlantic
The size of the buffer was 20km for the SST, SST gradient, chlorophyll a, and
chlorophyll a gradient data, and 5km for the bathymetry grid data. I used a 20km
buffer for the satellite data as this size ensured capture of more than one pixel of
oceanographic data within the buffer for both the 9km and 4km resolution data sets
(Figure 15). This meant that the ellipse of each buffer was 40km wide and an average
of 65 km long, as the majority of the sets were approximately 30km long. On average,
the buffer zones encompassed between 40 and 50 pixels of the chlorophyll a data, and
150-160 pixels of the 4km SST data (Figure 15). Due to gaps in coverage of SST and
43
Chlorophyll data, especially during winter months, it was necessary to use monthly
average data to obtain values for more than half of the haul locations.
Figure 15. Buffer for longline haul overlaid on 9km resolution SeaWifs chlorophyll a
data. This buffer encompasses 42 data points.
1
41.8
0.9
41.7
0.8
41.6
0.7
41.5
0.6
Latitude
41.4
41.3
0.5
41.2
0.4
41.1
0.3
41
0.2
40.9
0.1
40.8
-51.4
-51.2
-51
-50.8
-50.6
-50.4
-50.2
-50
-49.8
0
Longitude
Statistical comparisons
I used logistic regression to investigate the relationship between turtle bycatch
and the extracted oceanographic data from all fisheries hauls; where “0” is a set
without turtle bycatch and “1” is a set with turtle bycatch. In logistic regression, while
the relationship between the explanatory variables and the mean of the response is still
linear, the link function relates the measured response to the response variable, which
is actually the log of the odds of a specific scenario occurring. In this case, the log
44
odds that a turtle was caught on a given set. Unlike linear regression, where f(µ)= β0
+ β1 + β2……+ βt , and the link function, or f, is the identity function, the link function
in logistic regression is the logit function. The population mean in a logistic regression
is the proportion of events with one response, or the probability that something will
occur; in this case, the proportion of sets with one turtle or more caught as bycatch.
Also, unlike simple linear regression, logistic regression uses maximum likelihood
estimates to estimate the most-likely values for the coefficients of the model terms
given the observed outcome instead of the least sum-squares method.
To test the hypothesis that the proportion of longline sets where turtles were
caught as bycatch (π) was related to either the location or date of the set or to
bathymetry, chlorophyll, or sea-surface temperature, I ran a generalized linear model,
family: binomial, link: logit, with the original form: Logit (π)~ β0 + β1 date + β2 beginhaul latitude + β3 begin-haul longitude + β4 SST+ β5 SST gradient+ β6 chlorophyll a
gradient+ β7 bathymetry + β8 chlorophyll a + β9 CNA/ SEFSC data. The variable
CNA/SEFSC is an indicator variable for the scientific longline data vs. the commercial
data, and allows us to address the question of whether the proportion of sets with turtle
bycatch was different for the commercial fishery observer data (SEFSC) or the
scientific longline data (CNA). From this extensive model, I stepped the model and
removed the predictor variable with the least-significant t-value (to test that coefficient
is equal to zero) first for each step (Zar 1999). I continued this elimination procedure
until I had a model where all of the explanatory values were significant.
Results:
45
The locations of the longline sets used in this analysis are shown in Figure
16. The hauls from both the CNA and SEFSC data sets are pictured, sets without turtle
catch are shown in blue, and sets with turtle catch are shown in red. There are no
immediate spatial patterns evident in the distribution of the sets with and without
bycatch (Figure 16). The tracking data from the ten tracked turtles from Chapter One
are also pictured, some overlap in the fishing areas and the areas used by turtles
occurs, but the tracked animals and the fishing operations were not in the same place
at the same time.
Figure 16: Mapped longline hauls from commercial fisheries (SEFSC observer data)
and scientific longline cruise (CNA data) from 1999-2003, with loggerhead
tracks from ten tracked turtles from 1998-1999
The differences between longline sets with and without turtle bycatch are not
obvious upon initial examination of their oceanographic characteristics (Table 7). Sets
46
with turtle bycatch appear to have slightly higher average temperature than those
without bycatch, as well as slightly lower chlorophyll, and the start and end locations
are also slightly different; sets with turtle bycatch are located somewhat farther North
and East of those without bycatch. A visual representation of the data helps to clarify
this relationship somewhat. A plot of chlorophyll vs. begin-haul latitude coded by
turtle catch shows that catch of turtles occurs more frequently on higher latitude sets
and lower chlorophyll sets (Figure 17).
Figure 17: Latitude of longline hauls from the North Atlantic plotted vs. logtransformed Chlorophyll a values for the water-masses around longline hauls.
Data points are coded by turtle bycatch. N=207.
0
-0.5
Log(Chlorophyll a)
-1
-1.5
-2
No
Bycatch
-2.5
-3
Turtle
Bycatch
-3.5
-4
25
30
35
40
Begin-Haul Latitude
45
50
55
38.559
43.100
5.677
Mean:
3rd Qu.:
Std Dev.:
35.205
40.326
45.008
5.241
1st Qu.:
Mean:
3rd Qu.:
Std Dev.:
Begin-Haul
Latitude
Sets with turtle bycatch, N=66
32.967
1st Qu.:
Begin-Haul
Latitude
Sets with no bycatch, N=147
6.740
-42.891
-48.916
-53.181
Begin-Haul
Longitude
6.066
-49.124
-52.174
-57.058
Begin-Haul
Longitude
4.633
23.385
19.656
16.935
SST
5.811
22.018
17.850
14.159
SST
0.005
0.008
0.007
0.003
SST slope
0.004
0.007
0.006
0.004
SST slope
1503.914
-3485.400
-4029.673
-5132.900
0.0001
0.0004
0.0003
0.0002
1177.384
-3620.900
-4162.404
-4943.350
Chlorophyll slope Bathymetry
0.0002
0.0005
0.0004
0.0003
Chlorophyll slope Bathymetry
0.082
0.263
0.185
0.109
Chlorophyll a
0.173
0.262
0.217
0.115
Chlorophyll a
Table 7: Summary statistics for the location and marine habitat values for the two groups of sets; those with turtle catch and those
without turtle catch.
47
42
43
44
45
46
47
48
49
-54
-52
-50
-48
-46
Longline sets with turtle bycatch
Longline sets without turtle bycatch
-44
-42
-40
0
0.5
1
1.5
Figure 18: Longline hauls and buffers for July 2000 and the monthly average 9km SeaWifs chlorophyll(mg/ m3) for the month of
July.
48
Latitude
-52
42
42.5
43
43.5
44
44.5
45
45.5
46
46.5
47
-50
-48
-46
Longitude
-44
-42
Longline sets with turtle bycatch
Longline sets without turtle bycatch
0
0.5
1
1.5
Figure 19. Longline Hauls and buffers for July 1999 and the monthly average 9km SeaWifs chlorophyll(mg/ m3) for July, 1999.
49
50
When we examine a month of fisheries data and the corresponding month of
chlorophyll a data, we find that most of the sets are located along frontal features, as
expected (Figure 18). While sets with bycatch appear to be located in the lower
chlorophyll regions, and sets without bycatch are often in higher chlorophyll regions,
it is difficult to see a clear difference between the two (Figure 18). There is a
possibility that some of the months with a high proportion of turtle bycatch are, in
essence, swamping the data set (Figure 19). The logistic regression analysis presented
in Table 8 helps to clarify these differences.
The initial model for the logistic regression of turtle catch on oceanographic
variables and fisheries variables was: Logit (π)~ β0 + β1 date + β2 begin-haul latitude +
β3 begin-haul longitude + β4 sst+ β5 sst slope+ β6 chlorophyll a slope+ β7 bathymetry +
β8 chlorophyll a + β9 CNA/ SEFSC, where π is the proportion of sets with turtle
bycatch. The best-fit model for this regression was: Logit (π)~ β0 + β1 begin-haul
latitude + β2 Log(Chlorophyll a) (Table 8). The inclusion of begin-haul latitude and
chlorophyll a concentration in this final model indicate that within the areas fished,
turtles are caught more often on sets in low-chlorophyll regions and on higher latitude
sets. Specifically, for the null hypothesis that either of the coefficients is zero, the zvalue for each coefficient shows that we can reject this hypothesis, indicating that the
final model is the best-fit model (Table 8). The Wald’s test provides a p-value for this
hypothesis, both terms in the final model are highly significant (Table 8).
51
Table 8. Logistic regression of turtle bycatch (1= turtle bycatch, 0=no bycatch) on
Chlorophyll and latitude at the beginning of longline hauls. Regression
equation: Logit (π)~ β0 + β1 begin-haul latitude + β2 Log(Chlorophyll a). N=
207. Highly significant p-values for the regression are marked with two * and
suggestive values are marked with one *
coefficient
Value
(intercept)
-12.199
Begin-haul latitude .219
Log(Chlorophyll a) -1.894
Std. error
z-statistic
2.68
.0495
.468
-4.627
4.437
-4.047
2-sided p-value at
.05
0.000012**
0.00005**
A likelihood ratio test to compare the effectiveness of the final model (with
both chlorophyll and latitude) to that of a reduced model with only latitude as an
explanatory variable shows that without chlorophyll, the model does not properly
describe the variation in the data. The two-sided p-value for the hypothesis that the
coefficient of log-transformed Chlorophyll a is zero, based on comparison of the
likelihood ratio statistic with the chi-squared distribution, is 3.301 x 10-6, indicating
that the model with log-transformed chlorophyll as an explanatory variable is more
effective in describing the probability of turtle catch than the reduced model with only
latitude included. We also tested several interaction terms within the context of the full
model, specifically latitude*SST, latitude*chlorophyll and chlorophyll*SST. None of
these were significant. Additionally, the indicator variable for the CNA/SEFSC data
was not significant in the model, indicating that performing analyses on the two
different types of fisheries data together is acceptable.
52
Discussion:
There is a strong negative relationship between the concentration of
chlorophyll a in the water surrounding a given longline set and the proportion of sets
with turtle bycatch. This is true within the context of pelagic longline sets from both
the Central North Atlantic experimental fishery longline cruises of 2001 and 2002 and
those from the SEFSC POP data set. An equally strong positive relationship exists
between the latitude of the longline sets and the proportion of sets with turtle catch.
While other variables, such as sea-surface temperature, gradient in both sea-surface
temperature and in chlorophyll a, and bathymetry, were considered in this analysis,
they were not significant in explaining the proportional distribution of the catch of
turtles.
In comparison to previous studies of bycatch, these data show that turtles are
caught within the same range of temperatures as the study by D’Agrosa et al, but in
somewhat warmer water than shown by other studies (Watson et al. 2005; Gilman et
al. 2006). This work also demonstrates a new negative correlation between bycatch of
turtles and concentration of chlorophyll a in the waters around longline sets. This
study is unique in the examination of bycatch relative to high-resolution remotelysensed marine environmental variables, so there are few extant studies of bycatch in
this context for comparison.
Future studies of this type could be improved by the use of higher temporal
resolution oceanographic data as it becomes available. The data used in the bycatch
analysis for both sea-surface temperature and chlorophyll a concentration were
monthly average data. However, longline fisheries sets are located in such close
53
proximity to the edges of chlorophyll and SST fronts that a shift of only a few
hundred kilometers in the location of a frontal edge of over the period of a month
could change the results of this analysis substantially. In addition to this temporal
averaging of oceanographic data, the habitat variables used in the regression analyses
were means of the habit along the 30km length of an average longline set. For
example, a single set could overlap both high- and low- chlorophyll areas, yet the
mean chlorophyll along the length of the set would not reflect this dichotomy. This
combination of the limitations of the satellite data and the methodology may have
limited my ability to discern differences between the sets with and without bycatch.
One potential solution for this problem is the use of weekly climatologies for SST and
chlorophyll a to “fill in the blanks” and provide higher temporal resolution data for the
sets which are missing that information.
This research indicates a difference between the critical variables selected as
predictors of turtle habitat using the behavior of individual tracked animals, and those
selected using the bycatch data. The first method showed that the ten tracked
individuals spent more time in high-chlorophyll areas (Chapter 1, this document),
while the bycatch analysis suggested that animals were more likely to be caught on
longlines set in lower-chlorophyll areas. While one might expect that animals are more
likely to be caught in areas where they spend more time foraging, due to increased
probability of interaction with longlines in those areas, these data do not appear to
support this theory. The main reason for this discrepancy is that longline sets are
distributed within a much narrower window of conditions than are turtle tracks. This is
54
demonstrated by the much larger variance in the chlorophyll values of the habitat
covered by the traveling turtles (Table 3) than in the longline sets (Table 7). The
fishery data do not effectively sample the entire habitat used by turtles, so the
distribution of turtles in low-chlorophyll areas suggested by the fishery data may be
due to the limitations of the satellite data and analysis, rather than to an actual increase
in the risk of interaction between turtles and longlines in low-chlorophyll regions. If
the fishery data were randomly distributed, we would expect to see increased
encounter rates between turtles and longlines in the areas in which turtles are foraging
through simple probability.
While it is tempting to use fisheries bycatch data to make management
decisions, this study shows that use of these data alone give an incorrect picture of
which areas management should prioritize. Because fisheries data are so biased in
their distribution, they give an erroneous picture of the distribution of bycatch species
and of the habitat those species occupy. In this particular instance, the bycatch analysis
alone suggests that turtles are more likely to be caught where the tracking analysis
says they spend less time: in the more oligotrophic regions of the ocean. Without the
tracking analysis to complement the bycatch study, we might conclude that the lowerchlorophyll areas should be conserved and the higher-chlorophyll areas fished more
frequently. Assuming that our hypothesis about encounter rates increasing in high-use
areas is correct, this management plan would actually increase the encounter rate
between loggerheads and the longline fisheries. This would accomplish the opposite
goal to that intended. It is critical, then, that when planning conservation efforts such
55
as fisheries time or area closures, the locations and preferred habitat of satellitetagged animals should be taken into consideration.
56
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APPENDICES
Appendix A: Analysis of animal movement relative to current direction
I analyzed animal movement relative to geostrophic current for each weekly
segment of track to determine 1) the mean direction and angular dispersion of the
animal, 2) the mean direction and angular dispersion of the current, and 3) whether or
not the animal was swimming against the geostrophic current. To do this I used the
modified Rayleigh’s z-test. This test was developed to examine the homing direction
of an animal. Its application to the relationship between animal movement direction
and current direction is new (White & Garrott 1990; Mansfield 2006)
For 1, I calculated the bearing of the animal (a) from point x1, y1 to point x2,
y2, and so on, for each individual track segment. I then calculated the mean angle of
movement among those bearings to find the mean travel direction for a week of
tracking locations. This quantity was calculated using equations 1 and 2 (Batschelet
1981; White & Garrott 1990).
Equations 1 & 2:
n
X = 1 ! cos ai
ni= 1
Y = 1 ! sin ai
ni= 1
n
Where ai i, n= 13 track segments per week, Y is the mean relative distance
traveled in the Y-direction, and X is the mean relative distance traveled in the X
direction. From these quantities, r, a measure of the concentration of angles, is
calculated using equation 3, and s, the angular deviation (analogous to std. deviation
for linear systems) is calculated using Equation 4.
Equation 3:
r=
2
X +Y
2
Equation 4:
S = 180
r
(- 2 ln r)
We have then calculated three variables: the mean angle (a), the measure of
angular concentration (r) and the measure of angular deviation, (s).
I repeated this procedure on the current vectors included in the buffer around
each weekly track segment. Figure 20 shows the included vectors from one weekly
segment of “Delia’s” track as an example of the vectors included in the analysis.
Figure 20: weekly segment of “Delia’s” track with corresponding weekly AVISO
geostrophic velocity vectors
Delia track 23-Sep-1998
41.6
41.4
41.2
Latitude
41
40.8
40.6
40.4
40.2
40
39.8
324.5
325
325.5
326
Longitude
326.5
327
Angular direction and magnitude of individual vectors was calculated from the
u and v components of the current vectors, that measure was converted to
oceanographic convention, and equations 1,2,3, and 4 were applied to calculate the
mean angular direction of the current (a), the angular concentration (r), and the angular
dispersion (s). After calculating these quantities for both current direction and animal
direction, I calculated Rayleigh’s Z-statistic: z=nr2 , to determine whether or not the
distribution of the angles differs significantly from a uniform circular distribution
(White & Garrott 1990). If the z-statistic was significant, and the null hypothesis that
movement was random could be rejected, I proceeded with testing the modified
Rayleigh’s z-test for uniformity vs. a specified mean angle (White & Garrott 1990;
Zar 1999). I applied this test to determine whether the animal in question was
swimming with the prevailing geostrophic current for the week in question. The null
hypothesis was that the animal was not moving in the direction specified; a U-statistic
larger than the critical value was grounds to reject the null hypothesis. If the null
hypothesis that the animal was swimming in the same direction as the predicted value
(current direction) could be rejected, the U-value was assigned as one. If the null could
not be rejected, the U-value was assigned as 0. Thus a U-value of one indicates that
the animal is swimming in the same direction as the mean geostrophic current (Zar
1999).
Appendix B: Regression analyses of straightness index of satellite tracks on
oceanographic variables.
This Appendix includes details of the stepwise linear regression analyses of
straightness index on marine habitat variables. The data sets on which the regression
was performed were randomly selected from the data set of ten satellite tracks of
loggerhead turtles released near the Island of Madeira in the Spring and Fall of 1998.
Subset data sets contain information on 20 weekly track segments, including the
straightness index, or the ratio of the displacement to the actual path traveled, of each
segment and associated marine habitat variables. The three regressions for these subset
data were all stepped down from
Table 5. Linear regression for subset data. Highly significant p-values for the
regression are marked with two * and suggestive values are marked with one *.
Repeated
regressions
Final model:
Straightness index as
response, all models
include intercept (n=20
for each model)
Regression 1 Bathymetry
Log(Chlorophyll a)
Log(SST slope)
Regression 2 SST
U
Log(Chlorophyll a)
Log(Chlorophyll slope)
Regression 3 Bathymetry
S
Log(Chlorophyll a)
Log(Chlorophyll slope)
Value of
coefficient(s)
Significance of
coefficient(s) at
P> .05
-0.0001
-.1952
-.4032
-0.0237
0.2283
-0.1497
0.2103
-0.0001
-0.0027
-0.2184
-0.5091
0.0433*
0.0026**
0.0026*
0.1562
0.0205**
0.0714*
0.1217
0.0068
0.0943
0.0072
0.0014
R2
P-value for
regression
0.5637
0.0034**
0.4986
0.0267**
0.6592
0.0013**
Regression 1 for subset tracking data. Fit of linear regression of Straightness Index on
SST, U, Log (Chlorophyll a) and Log (Chlorophyll slope).
1.0
Straightness Index
0.8
0.6
0.4
0.2
0.5
0.6
0.7
0.8
0.9
1.0
Fitted : SST, U, Log (Chlorophyll a) and Log (Chlorophyll slope).
Regression 2 for subset tracking data. Fit of linear regression of Straightness Index on
Bathymetry, Log (Chlorophyll), and Log(SST gradient).
Straightness index
0.8
0.6
0.4
0.2
0.4
0.6
0.8
Fitted:Bathymetry, Log (Chlorophyll), and Log(SST gradient)
Regression 3 for subset tracking data. Fit of linear regression of Straightness Index on
Bathymetry, Log (Chlorophyll), Log(Chlorophyll gradient), and s.
1.0
Straightness index
0.8
0.6
0.4
0.2
0.2
0.4
0.6
0.8
Fitted: Bathymetry, Log (Chlorophyll), Log(Chlorophyll
gradient), and s