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Age and growth in Kemp’s ridley sea turtles: evidence from mark recapture and
skeletochronology
Melissa L. Snover1, 2, 3, Aleta A. Hohn4, Larry B. Crowder3, and Selina S. Heppell5
1NOAA Fisheries, Southwest Fisheries Science Center, Pacific Fisheries Environmental
Laboratory, 1352 Lighthouse Avenue, Pacific Grove, CA 93950 USA
2Joint Institute for Marine and Atmospheric Research, University of Hawaii, 1000 Pope
Road, Honolulu, HI, USA 96882
3Duke University Marine Lab, 135 Duke Marine Lab Road, Beaufort, NC 28516 USA
4 NOAA Fisheries, Southeast Fisheries Science Center, Center for Coastal Habitat and
Fisheries Research, Beaufort, NC 28516 USA
5 Oregon State University, Department of Fisheries and Wildlife, 104 Nash Hall,
Corvallis, Oregon 97331 USA
Address all correspondence to:
Melissa L. Snover
Pacific Fisheries Environmental Lab
1352 Lighthouse Ave.
Pacific Grove, CA 93950 USA
phone: 831-648-5339
fax: 831-648-8440
email: melissa.snover@noaa.gov
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1.0) Introduction
2.0) Estimating age and growth with mark-recapture data
2.1) Background and methodology
2.2) Estimates of growth rates
2.3) Estimates of age
3.0) Estimating age and growth with skeletochronology
3.1) Background and methodology
3.2) Validation
3.3) Application to Kemp’s ridleys
3.3.1) Methods
3.3.2) Results and Discussion
3.3.2.1) Size at age
3.3.2.2) Growth rates
3.3.2.3) Individual growth patterns
4.0) Conclusions and future research needs
5.0) Acknowledgements
6.0) Literature Cited
1.0) INTRODUCTION
Ridleys are the smallest sea turtles, reaching just 60-70 cm straight carapace
length (SCL) as adults (Marquez, 1994; Van Buskirk and Crowder, 1994). Captive
animals show rapid growth in their first year (Caillouet et al., 1995; 1997), and limited
recaptures of wild Kemp’s ridleys tagged as hatchlings corroborate this (unpublished
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data, Benjamin Higgins, National Marine Fisheries Service, Southeast Fisheries Science
Center, Galveston Lab). In general, ridleys are on the “fast” end of the growth rate
continuum for sea turtles, with the earliest estimated age at maturation for any of the
hardshell species (Heppell et al. 2003).
In poikilothermic animals such as sea turtles, growth rates are inexorably linked
to environmental conditions that include temperature and resource availability (Bjorndal,
2003). This phenomenon has the potential to create large differences in individual growth
rates and size at age among individuals in a population, especially for animals that can
take a decade or more to reach reproductive maturity. Most of what we know about
growth rates in sea turtles has come from mark-recapture studies (Chaloupka and Musick,
1997), however, with such high variability in sea turtle growth rates, large sample sizes
are needed from mark-recapture studies to understand how regional and age-specific
variability contribute to the mean growth rates for the species (Heppell et al., 2003).
While mark-recapture has been the most common method of collecting growth
information on sea turtles, there are other methods used to gain information on age and
growth, including skeletochronology and studies of captive or recovered known-age
individuals. All of these methods have been applied to Kemp’s ridley (Lepidochelys
kempii) sea turtles (Caillouet et al., 1995; Zug et al., 1997; Schmid and Witzell, 1997;
Snover and Hohn, 2004). In particular, new developments in applying skeletochronology
to sea turtles (Snover and Hohn, 2004; Snover et al., 2004) open up the possibility of
rapidly accumulating size-at-age and growth rate data as well as individual growth
trajectories.
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Chaloupka and Musick (1997) present a thorough review of the methodologies
involved in estimating age and growth in sea turtles; in this chapter, we’ll review how
mark-recapture and skeletochronology studies of both known- and unknown-age
individuals have been applied to Kemp’s ridley sea turtles. We review the literature on
mark-recapture and skeletochronology studies and present the findings of a new
skeletochronology study of Kemp’s ridley sea turtles, focusing on both inter- and intra-
individual variation in growth.
2.0) ESTIMATING AGE AND GROWTH WITH MARK-RECAPTURE DATA
2.1) BACKGROUND AND METHODOLOGY
Tagged Kemp’s ridleys have come from many sources. There are numerous
coastal in-water tagging projects that have captured and tagged wild Kemp’s ridleys as
well as other sea turtle species (e.g. Epperly et al., 1995; Schmid, 1995; Schmid, 1998).
These studies generally use combinations of internal, passive integrated transponder
(PIT) tags, and external, Inconel or Jumbo Roto plastic tags placed in the fore flippers
(e.g. Epperly et al., 1995; Schmid, 1995; Schmid, 1998). The turtles are measured and
tagged at the time of initial capture and measured again when the turtle is recaptured.
A drawback to generalizing growth rates from mark-recapture data is the
variability in the recapture interval. Growth in Kemp’s ridleys is not constant over
seasons and is reduced in the winter months (Snover and Hohn, 2004). Hence, if annual
growth rates are estimated based on growth over a portion of the summer, growth rates
will be overestimated. Similarly, growth will be underestimated if the recapture interval
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occurred over the winter or early spring. Ideally, only recapture intervals of a year or
multiples of a year should be used, but such data are rare. In an extensive mark-recapture
study of juvenile Kemp’s ridleys in Florida, USA, Schmid (1998) noted that when all
growth increments were compared with only growth increments of > 180 days, both the
mean and variance in growth rates decreased with increasing time at large.
A second problem in mark-recapture growth rate studies is the tendency for
researchers to exclude negative growth rates from analyses. It seems unlikely that turtles
actually shrink in carapace length between captures, so such measurements are obviously
due to measurement errors, and hence the temptation to exclude them. However,
measurement errors likely occur in both directions with equal probability and removing
negative growth rates will truncate the error in a dataset, biasing it towards errors that
over-estimate growth. Hence, all measurement data, including those that indicate
negative growth should be kept in any analyses of mark recapture data.
Another source of tagged Kemp’s ridleys results from intensive conservation
efforts, including the head-start and coded wire tag (CWT) projects, that have led to the
release of large numbers of tagged, known-age animals. Both projects are part of a bi-
national program operated jointly by state and federal U.S. agencies, including the
National Marine Fisheries Service (NMFS) Galveston Laboratory, and the Instituto
Nacional de la Pesca (INP) of Mexico (Klima and McVey, 1995; Caillouet et al., 1997,
Chapter 15). The head-start program has used a variety of tagging methods (see review in
Chapter 15) that have allowed the identification of individuals up to 10 years post-release
(Caillouet et al. 1995). In the CWT project, hatchlings receive internal CWTs in the fore
and rear flippers and are released at the nesting beach in Mexico. Placement of the tags
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indicates the animal’s year class (Caillouet et al., 1997; Higgins et al., 1997). These
studies have already led to the acquisition of unique datasets containing actual size-at-age
data and the potential for continued collection of these valuable data. With the head-start
program, caution must be made in applying the resulting size-at-age information to the
general population as the head-start animals were raised in captivity for up to a year or
more (Chapter 15) and, as a result, their subsequent growth rates in the wild may not be
representative of normal growth rates for their ages or sizes.
A common method used in analyzing mark-recapture growth rates is to fit the
capture and recapture lengths and time intervals to a growth interval model based on the
von Bertalanffy or logistic growth models (Fabens, 1965; Frazer, 1987, Chaloupka and
Musick 1997). Unfortunately, most sets of mark-recapture growth data do not span the
entire life cycle and hence curves created from those data should only be used to infer
average growth information over the size range of data used (Chaloupka and Musick,
1997). Such curves should not be used to estimate age at maturity if animals within that
category were not in the dataset, although the expected number of years spent in the stage
represented by the data set can be estimated.
Recently, sea turtle researchers have been trying to determine how much
variability in growth is attributable to external factors. A common approach taken is to
apply generalized additive models to growth rates from sea turtle mark-recapture data
(Chaloupka and Limpus, 1997; Limpus and Chaloupka, 1997; Seminoff et al., 2002;
Balaz and Chaloupka, 2004). The data sets typically include one or multiple growth
records from numerous individual turtles. These studies have highlighted the importance
of considering variables such as size, year, sex and recapture interval when analyzing
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growth rate information as these factors can all contribute to growth rates, though such
analyses have not yet been applied to Kemp’s ridley sea turtles.
2.2) ESTIMATES OF GROWTH RATES
One of the big questions with growth rates in Kemp’s ridleys is the difference in
growth between juveniles that stay in the Gulf of Mexico versus those that utilize feeding
areas on the Atlantic coast of the USA (Chapter 11). Schmid and Witzell (1997)
compared growth data from mark-recapture studies on the east (Atlantic Ocean) and west
(Gulf of Mexico) coasts of Florida, USA. Though somewhat inconclusive due to small
sample sizes, their data suggest slower growth rates in the Gulf of Mexico. This
observation is based on both estimated annual growth rates (5.9-8.8 cm/yr Atlantic and
3.6-5.4 cm/yr Gulf of Mexico) and growth modeled from von Bertalanffy equations
(Table 1).
In a more thorough analysis of Gulf of Mexico Kemp’s ridleys, Schmid (1998)
reports size-based annual growth rates for Kemp’s at a mean of 4.6 cm/yr. for animals in
the 30-40 cm SCL size, 6.2 cm/yr. for animal 40-50 cm SCL and 4.6 cm/yr. for animals
50-60 cm SCL. For Kemp’s that were at large for > 180 days, he found growth rates
ranging from 1.2 – 5.4 cm/yr over all size categories.
We currently have no growth rate information for wild olive ridleys (Lepidochelys
olivacea). Captive records of growth indicate that they have rapid first year growth rates
similar to Kemp’s ridleys, reaching an average of 24.9 cm carapace length in one year
(Banerjee et al., 1987)
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2.3) ESTIMATES OF AGE
The primary reason to fit a von Bertalanffy growth curve to growth increment
data is to approximate mean time to sexual maturity in sea turtles. Such information is
critical to studies of population dynamics and recovery rates. For studies where an
appropriate range of values has been included in the modeling, literature estimates of age
at maturation from mark-recapture studies range from 10 to 13 years (Table 1; Caillouet
et al., 1997; Schmid and Witzell, 1997; TEWG, 2000).
3.0) ESTIMATING AGE AND GROWTH WITH SKELETOCHRONOLOGY
3.1) BACKGROUND AND METHODOLOGY
Skeletochronology is the technique of estimating age from growth marks found in
cross-sections of long bones. This technique has been widely applied to reptiles and
amphibians (Castanet, 1994), and was first applied to sea turtles by Zug et al. (1986). In
current skeletochronology studies of sea turtles, primarily the humerus bone has been
used. Zug et al. (1986) determined that this was the optimal bone for observing growth
marks in cross section. A site on the humerus just distal to the deltopectoral crest has the
highest ratio of compact bone and allows for a consistent sampling site from bone to bone
(Snover and Hohn, 2004). The humerus also has the advantage of being relatively easy to
sample from a carcass, which is an important consideration for large-scale sample
collection.
The technique of skeletochronology is based on the fact that bone growth is
cyclic, and that there is a predictable periodicity in which bone formation ceases or slows
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before new, relatively rapid bone formation resumes (Simmons, 1992; Castanet et al.,
1993; Klevezal, 1996). This interruption of bone formation is evidenced within cross-
sections of the humerus by histological features, which take two forms in decalcified and
stained thin-sections. The most common feature is a thin line that appears darker than the
surrounding tissue, termed Line of Arrested Growth (LAG) (Castanet et al., 1977). The
second, less-common feature is a broader and less distinct line that also stains darker,
referred to as an annulus (Castanet et al., 1977). Both of these features indicate a slowing
or cessation of growth. Alternating with LAG or annuli are broad zones that stain
homogeneously light, indicating areas of active bone formation. Together, a broad zone
followed by either a LAG or an annulus comprises a skeletal growth mark (Fig. 1;
Castanet et al., 1993)
Chaloupka and Musick (1997) identify three obstacles to skeletochronology that
must be overcome before it can be considered a valid method for age estimation. The first
of these is to establish that the observed growth marks are deposited on an annual, or
otherwise predictable, basis. In sea turtle bones there is considerable resorption and
remodeling of periosteal tissue resulting in the loss of the earliest growth marks. Hence,
the other two obstacles given by Chaloupka and Musick (1997) both deal with methods
for estimating the number of layers lost to resorption, the development of a robust
method for modeling the number of growth marks lost and the validation of a constant
proportionality between bone dimensions and body size (i.e. carapace length). An added
benefit to establishing a constant proportionality between bone dimension and body size
is the ability to estimate body size from early growth marks (Bjorndal et al., 2003; Snover
et al., 2004). With this methodology, individual growth trajectories over periods of years
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can be assessed. In addition, the methodology allows for the rapid accumulation of size at
age data with constant (1 year) time intervals.
3.2) VALIDATION
Snover and Hohn (2004) analyzed four head-start and 13 CWT Kemp’s ridleys
that had been recovered stranded on beaches. From these animals they were able to
validate the annual nature of the growth marks. In addition, an analysis of wild Kemp’s
ridleys demonstrated that growth marks are deposited in the spring (Snover and Hohn,
2004).
For interpretation of the growth marks, the known-age CWT Kemp’s revealed
that the first year growth mark is different from subsequent marks (Snover and Hohn,
2004). Characterization of the first year mark allowed us to distinguish it from
supplemental marks that would otherwise interfere with age assignments. Closely spaced
LAGs have been interpreted differently in skeletochronology studies; we confirmed that
in Kemp’s ridleys, LAGs that appear very close to one another in humeri cross-sections
represent distinct years and indicate years of little or no growth.
Snover and Hohn (2004) also established that there is a constant proportionality
between dimensions of the humerus and carapace length. Regressions of seven
measurements of the humerus with carapace length for Kemp’s ridleys reveal strong
correlations. The best metric is between carapace length (L) and diameter of the humerus
(D) at the sectioning site for skeletochronology. This relationship is described by (r2 =
0.96, P < 0.005)
(1) 74.248.2
+
⋅
=
DL .
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3.3) APPLICATION TO KEMP’S RIDLEYS
Zug et al. (1997) were the first to apply skeletochronology to Kemp’s ridleys,
however they were not able to offer any validation in terms of justifying that the growth
marks observed in cross-sections of the humerus were annual and correctly interpreted.
Contrary to the findings of Schmid and Witzell (1997), the authors found some evidence
that Kemp’s in the Atlantic grow slower than similar-sized Kemp’s in the Gulf of Mexico
but cautioned that the size range of samples from the two regions may confound this
interpretation. They estimated age to maturity between 11 and 16 years.
In a further analysis of the size-at-age data from Zug et al. (1997), Chaloupka and
Zug (1997) identified a potential polyphasic relationship between size and age that could
be indicative of an ontogenetic shift in growth rates, where individual movement to new
feeding areas, a shift in diet, or physiological changes may increase growth rates for
particular age classes. The authors used nonparametric smoothing techniques to visualize
the functional form of the size-at-age data and subsequently fit a polyphasic parametric
function to the data. The resulting curve suggests different phases of growth, with a
slowing of growth rates followed by a surge in growth rates. The polyphasic curve can be
contrasted with parametric growth curves such as the von Bertalanffy curve, which
assume a constant, smooth increase in length with increasing age. Such curves will not
detect ontogenetic shifts in growth rates; however, the resulting estimate of age at
maturation may be comparable for the two techniques.
Using the findings from the validation study of Snover and Hohn (2004), we were
able to apply skeletochronology to Kemp’s ridley sea turtles with unknown histories. For
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this study, our samples were obtained primarily from the mid-Atlantic states of the USA.
To expand our size range and sample size of large sub-adults and adults, we included
samples from the Gulf of Mexico that were in these size categories.
3.3.1) Methods
We received front flippers from dead Kemp’s ridley sea turtles stranded along the
coasts of North Carolina, Virginia, and Maryland. For most of the turtles, straight
carapace length (SCL) was recorded, measured as standard straight-line length from the
nuchal notch to the posterior end of the posterior marginal. If only curved carapace length
(CCL) was recorded, it was converted to SCL using the regression equation (r2 = 0.99;
P<0.001)
(2) SCL = 0.957⋅CCL – 0.696.
This equation was generated from 309 paired measurements for Kemp’s between 18.4
and 66.2 cm SCL that were taken from the data sheet accompanying the flippers.
A subsample of 144 Kemp’s between 21.7 and 50.5 cm SCL and for which age
could be estimated with a high degree of confidence was used for this study. The sample
contained 21 males and 54 females with sex confirmed by visual examination of the
gonads during necropsy. The remaining 69 specimens in the sample did not have sex
identified. An additional 13 samples between 50.6 and 62.0 cm SCL were used to
estimate growth rates in large individuals for completion of a growth curve. These
animals were recovered from the mid-Atlantic states and Texas, and, due to resorption,
accurate ages could not be assigned to these animals.
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A complete description of the histological methods for bone preparation is
presented in Snover and Hohn (2004). We took a digital image of a stained cross-section
from each bone and compared the digital image to the actual cross-section viewed under
a stereoscopic microscope. Each LAG or annulus was identified on the digital image and
the total number of LAG and annuli were quantified to assign age (Fig. 1). Kemp’s ridley
nests hatch between late May and mid-July (Marquez, 1994). Such a narrow range of
hatch dates allowed for partial years to be incorporated in age assignments as follows;
animals that died from September to November were assigned ages equal to the LAG
count plus a quarter year, from December to February, LAG count plus one half year,
March to May, LAG count plus three quarters year and June to August were assigned
integer age numbers equal to the number of LAGs.
On the digital images, we measured the diameter of every LAG diameter for
which it was possible to measure the full lateral LAG diameter on each of the 144
specimens. Measurements were made of LAG diameters on the lateral axis of the bone,
parallel to the dorsal edge (Fig. 1; Snover et al., 2004). The first growth mark in Kemp’s
appeared as a diffuse annulus not always clearly visible (Fig. 1; Snover and Hohn, 2004).
When the boundary of the mark could be clearly distinguished, this annulus was
measured at the outer most points of the darkly stained area (Fig. 1). As this mark is not
always clearly visible, we were not able to measure it on every bone. Using the digital
images from the 13 large turtles for which age could not be assigned, we measured the
diameters of the two outermost LAGs.
To estimate carapace lengths from LAG diameters, we followed the methodology
presented in Snover et al. (2004). We used a back-calculation technique from the
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fisheries literature, the body proportional hypothesis (BPH; Francis, 1990) with equation
1
(3) ))/()(( 742482742482 .D.L.D.L finalfinalii
+
⋅
+
⋅
=
Here, Li is the estimated carapace length at the time the ith LAG was deposited, Di is the
diameter of the ith LAG and Lfinal and Dfinal are the carapace length and humerus diameter
at death. Final sample sizes for each age and for each sex varied (Table 2). In total, we
had 319 back-calculated lengths at age for 144 aged animals.
All of the resulting back-calculated lengths and one year growth increments from
the turtles were used to observe the mean and variance in size at age and age-specific
growth rates. To fit a growth curve to the data, we used only one yearly growth increment
from each turtle in order not to bias the curve with excessive measurements from
individual turtles. We used the growth increment estimated from the outer most two
LAGs or LAG and annuli (Fig. 1). In addition, we used the one-year growth increments
measured from the 13 large turtles.
As not every bone had two measurable LAG or annuli, the resulting sample size
for the one-year growth interval per turtle was 109. The von Bertalanffy growth interval
equation was fit to the data from 109 pairs of carapace lengths using the following
equation (Fabens, 1965)
(4) L
1 = L
∞
- (L
∞
- L2)e-k.
In this equation, L1 and L2 represent the carapace length estimated from the two outermost
LAG diameters with L1 being the last LAG and L2 the second to last LAG. Asymptotic
length and intrinsic rate of growth are represented by L
∞
and k, respectively. Once the L
∞
and k parameters were solved for by the curve-fit, the curve was plotted with size as a
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function of age using the von Bertalanffy growth function (VBGF; von Bertalanffy,
1938)
(5) Lx = L
∞
- (L
∞
- L0)e-kx.
In this equation, x is age in years and L0 is initial size. Chaloupka and Musick (1997)
caution against substituting parameters estimated from a growth-at-size function to a
growth-at-age function. Here we are able to compare how well the size-at-age VBGF
(Equation 5) using parameters estimated from the growth-at-size VBGF (Equation 4)
describe actual size-at-age data. We use the results to approximate age at maturity. In
addition, we fit cubic smoothing splines through the data. These smoothers do not assume
an a priori parametric function such as the VBGF, but rather fit a curve through data
locally; at any one point the curve depends only on the data at that point and specified
neighboring points. As such, smoothers can highlight underlying trends in data, such as
polyphasic growth, that are lost when parametric functions that specify a certain shape
are fit (Chaloupka and Zug, 1997). We visually compare the fitted VBGF with the cubic
smoothing splines.
3.3.2) Results and Discussion
3.3.2.1) Size at age
The size-at-age data for the 144 Kemp’s ridleys for which age could be assigned
with a high degree of confidence corresponded well to the size-at-age data where
carapace length was estimated from LAG diameters (Fig. 2). Using the average size of
Kemp’s at hatching (4.2 cm SCL; van Buskirk and Crowder, 1994), growth from age
zero to age one appeared to be a separate phase from growth after age one (mean = 16.9 ±
16
2.3 S.D.). A function such as the VBGF that models growth rates as declining
monotonically from birth to adult will not describe such a shift in growth rates.
Therefore, we set L0 in equation 5 at 21 cm SCL which was calculated as the average size
at age one from the back-calculation results (Table 3). This value compares favorably
with the mean length at age for the one-year-old turtles that were in the set of 144 turtles
with age assigned (Table 3; N=11; mean = 23.5 cm SCL). This mean is larger than the
back-calculated mean as these turtles died between June and August and have additional
growth beyond the first year mark.
The VBGF was fit to growth rate data only, similar to the type of data collected
from mark-recapture studies with the exception that all time intervals are one year. When
the parameters from the growth-at-size VBGF were applied to the size-at-age VBGF, the
resulting curve coincided well with the estimated size-at-age data (Fig. 2). The cubic
smoothing spline indicated a steady increase in length at size and coincided closely with
the fitted VBGF (Fig. 2), indicating that VBGFs generated from appropriate growth rate
data can be used to estimate average size at age. However, neither curve is capable of
capturing the high degree of variability in growth rates that can occur. This type of
variability can only be determined through the examination of actual size-at-age data.
When mean length at age was considered, lengths increased in a relatively linear
manner up to age six (Fig. 3). After age six sample sizes are too low to make any
inferences (Table 2). Though not a perfect fit, the VBGF described the trend in increasing
mean length at age relatively well though it appears to overestimate length in older
animals (Fig. 3). It is possible that the older animals for which age could be assigned
17
were growing slower than the average in the population as the interior of the humerus
was not yet remodeled to an extent where all of the LAG could not be identified.
One benefit of fitting a VBGF to growth data that span all lengths is to use the
curve to estimate age at maturity. This parameter is very poorly understood in sea turtles
and is critical to studies of population dynamics. Hence, if approximations of this
parameter can be generated from VBGF it is important to do so. In this case we had
growth data ranging from 14 cm SCL to 61 cm SCL with which to fit the curve. Size of
adult Kemp’s ridleys is defined as >60 cm SCL (TEWG, 2000). Based on the 95%
confidence intervals of the VBGF curve, this size was reached between age 9.9 and 16.7
yrs, with a mean of 12.0 yrs.
We used the size-at-age data from the 144 aged Kemp’s ridleys to estimate age
based on length for Kemp’s ridley turtles (Table 4). The probabilities were calculated
directly from the number of turtles of a given age in each size class using all of the back-
calculated lengths. Given that growth rates are linked to environmental conditions, these
results are applicable only to Kemp’s from the mid-Atlantic coast of the USA. We also
estimated mean lengths at age from each of the three methods used in this study, direct
count of LAGs, back-calculated length at age, and the size-at-age VBGF (Table 3).
3.3.2.2) Growth rates
We were able to assign ages to turtles from 1-8 yrs. Within that range, the
variability in size at age made it difficult to detect a trend in growth rates at age (Fig. 4a).
However, when mean growth rates at age were considered, growth after age one was high
18
followed by reduced growth at age two. Mean growth rates appeared relatively constant
from ages three to five (Fig.4b).
As with the size-at-age data, there was a great deal of variability in annual growth
rate at size (Fig. 5), with a declining trend as length at maturity is approached (Fig. 5).
The derivative of the size-at-age VBGF described the data well. The cubic smoothing
spline through the data indicated more dynamics in the growth rates of small turtles than
the VBGF and was likely a result of the decrease in growth rates noted between ages two
and three (Fig. 4b).
In their first year of life, Kemp’s from the mid-Atlantic grew an average of 16.7
cm SCL based on a mean hatchling length of 4.2 cm (Van Buskirk and Crowder, 1994).
Subsequent annual growth rates are much lower, and we found a mean of 4.4 cm/yr ± 0.3
SE for 20-30 cm SCL, 5.1 cm/yr ±0.5 SE for 30-40 cm SCL, and 5.0 cm/yr ± 1.0 SE for
40-50 cm SCL.
3.3.2.3) Individual growth patterns
One striking result from the plotted size-at-age data is the high level of variability
that can occur in size at a given age (Fig. 2). Bjorndal et al. (2003) suggest that
compensatory growth in small juvenile loggerheads (Caretta caretta) minimizes
variability in size at age. Compensatory growth is a mechanism by which individuals that
are small for their age due to poor resources or environmental conditions exhibit rapid
growth when exposed to more favorable conditions, ‘catching-up’ in size to their
conspecifics that have experienced more favorable conditions (Bjorndal et al., 2003). One
way Bjorndal et al. (2003) demonstrated compensatory growth was by comparing length
19
at age with the amount of subsequent annual growth. They compared the slopes of
regression lines through each age class to a regression line through all age classes. When
the regression line through an age class declined more steeply than the regression through
all of the data (exclusive of the data from the age class being compared), compensatory
growth was inferred. As in this study, Bjorndal et al. (2003) back-calculated lengths from
LAG dimensions to assess length at age. The researchers found that the slopes through
the first two ages of loggerheads were significantly different from the slope through all of
the data, which indicates that turtles that were small for their age grew more than turtles
that were large for their age. From this they inferred that during the early post-hatchling
and juvenile years, loggerheads exhibit compensatory growth.
We applied the same method to our Kemp’s data, first looking at all of the data,
and then at a singe cohort for which there was a large sample size (Fig. 6). None of the
regressions were significantly different from 0 (Fig. 6). Only one regression through the
individual age classes showed a declining trend, age one for all of the data combined,
however, the slope of this regression is not significantly different from the slope of the
regression through ages two through four (P>0.20; Zar, 1999). To ensure that these
results were not confounded by inter-annual variability in growth rates, we looked at just
the cohort from 1997. We had the most data from this cohort, however it was comprised
only of age classes one and two. Slopes of regression lines through both ages one and two
were positive, though not significantly different from 0 (Fig. 6). Hence, we found no
indication of compensatory growth in Kemp’s ridleys that would act as a mechanism to
reduce variability in size at age. This apparent difference in early growth between
loggerheads and Kemp’s ridleys is evidenced by the greater overlap in size at age that we
20
observed in Kemp’s ridleys as compared to that observed by Bjorndal et al. (2003) for
similar-aged loggerheads.
Inflection points in individual growth curves are usually related to ontogenetic
shifts, either behavioral or physiological, (von Bertalanffy, 1960; Eisen, 1975; Atchley,
1984). Surges in growth rates often coincide with ontogenetic shifts to more profitable
habitats (Werner and Gilliam, 1984; Gilliam and Fraser, 1988) and a decline in the
profitability of a habitat for a given size-class results in increased variance in size at age
and growth rates (Lomnicki, 1988). On the basis of an analysis of the age data from Zug
et al. (1997), Chaloupka and Zug (1997) fit a polynomial regression to the size-at-age
data and detected a polyphasic nature in growth rates through time. With our larger data
set, we could not identify a similar polyphasic growth pattern that was consistent among
individuals (Fig. 3, 6 a, b, and c). It was relatively common for Kemp’s in our study to
experience decreased growth between ages two and three, as evidenced by the lower
mean growth rate for that age (Fig. 4b) as compared to the spread of the data (Fig. 4a).
The only time zero growth was detected was between two and three years. However,
decreased growth between those ages was not observed in all animals (Fig. 4a). Growth
trajectories for animals that had at least four years of data demonstrate that decreased
growth between ages two and three is common but not universal (Fig. 7). Growth after
age three was most often linear, however, some animals displayed decreased growth over
a year, giving the appearance of polyphasic growth (Fig. 7). These shifts in growth rates
did not appear to occur at a specific size or age in these turtles. When the data are
considered by sex, mean lengths at age suggest polyphasic growth trajectories with
different ages for the growth rate shift (Fig. 8). These generalities are not corroborated by
21
the individual growth trajectories of males and females (Fig. 7); the apparent polyphasic
growth profile may be a function of small sample size.
4.0) CONCLUSIONS AND FUTURE RESEARCH NEEDS
In general, size at age is very poorly understood in sea turtles, however, more is
known about the Kemp’s ridley than any of the other sea turtle species, especially with
the addition of this new skeletochronology data. Our advanced understanding of Kemp’s
age and growth is due to in part to extensive conservation efforts, including the head-start
and coded wire tag projects (Klima and McVey, 1995; Caillouet et al., 1997; Chapter 15),
which have resulted in the release of tens of thousands of tagged, known-age Kemp’s into
the wild. When these animals are identified, measurements give valuable size-at-age data.
Animals recovered as strandings from both of these projects have proven vital in
validation studies for skeletochronology techniques that can then be applied to untagged
animals (Snover and Hohn, 2004).
That growth rates and size at age are highly variable in sea turtles has long been
assumed. The skeletochronology work from this study highlights how extreme the
variability can be. Hatchling Kemp’s ridleys are believed to follow the currents of the
Gulf of Mexico and either remain within the Gulf of Mexico or enter Florida Current and,
eventually, the Gulf Stream (Collard, 1990; Collard and Ogren, 1990; Chapter 11?).
Subsequently, juvenile Kemp’s ridleys utilize the coastal benthic habitats of either the
Gulf of Mexico or the Atlantic Ocean. This study focused on stranded Kemp’s from the
mid-Atlantic coast region of the USA and the growth rates and potential ages at
maturation were representative of small to large juvenile turtles that entered the Gulf
22
Stream. We observed a great deal of variability in annual growth rates in animals from
the same habitats. Habitats as different as the Gulf of Mexico versus the mid- and
northern Atlantic coast are likely to present different environments for growth and the
differences in size at age between the two habitats is likely great. For a more complete
understanding of Kemp’s life history, similar analyses need to be done for juvenile
Kemp’s that strand in the Gulf. There was not general trend, higher or lower, between our
skeletochronology growth rates for juveniles from the mid-Atlantic and those reported by
Schmid (1998) for the Gulf of Mexico; 5.1 versus 4.6 for 30-40 cm SCL (Atlantic vs.
Gulf of Mexico) and 5.0 versus 6.2 for 40-50 cm SCL. So the question remains as to
whether growth rates in the Gulf of Mexico are higher, lower, or similar to growth rates
for animals of similar sizes and ages utilizing coastal habitats along the Atlantic coast.
Our estimate of age at sexual maturity occurring between 9.9 and 16.7 years
coincides with previous estimates of 10-15.7 years which were generated from both
mark-recapture and skeletochronology studies (Table 1). It is not yet known what
proportion of Kemp’s hatchlings remain in the Gulf of Mexico and what proportion
emigrate to the Gulf Stream. Therefore, to gain a complete understanding of growth and
age at maturity for the population as a whole, information on what fraction of a cohort is
utilizing each habitat is necessary. Results of this study emphasize the value of
skeletochronology, especially when combined with other techniques, for developing a
better understanding of the life history of the Kemp’s ridley and of sea turtles in general.
Some of the individual growth trajectories demonstrated polyphasic growth while
others did not. Whether these shifts in growth rates are due to extrinsic (environmental)
factors or intrinsic (genetic or developmental) factors remains unclear. When separated
23
by sex, mean growth rates appeared to decline between ages two and three in males and
between ages three and four in females (Fig. 8); however, observations of sex-specific
individual growth trajectories (Fig. 7) suggests that this pattern is not universal. The
accumulation of additional individual growth trajectories from animals in both habitats
will help to clarify any general patterns in growth rates that are due to sex and
environment.
Skeletochronology has proven to be a valuable tool in understanding growth and
size-at-age in Kemp’s ridley sea turtles, thanks in large part to the availability of known-
age animals. We can take what we have learned and apply similar techniques to other
species, including the olive ridley in order to increase our understanding of growth rates
and age at sexual maturity in sea turtles.
5.0) ACKNOWMEDGEMENTS
We thank L. Avens, A. Read, and D. Rittschof and C. Sotka for their valuable
comments on earlier versions of this manuscript. A. Gorgone, B. Brown and J. Weaver
provided assistance with the preparation of the humeri. Most of the humeri were received
through the Sea Turtle Stranding and Salvage Network, a cooperative endeavor between
the National Marine Fisheries Service, other federal and state agencies, many academic
and private entities, and innumerable volunteers. Special thanks go to R. Boettcher and
W. Teas for their assistance with the sample collections. In addition, humeri were
received from B. Higgins at the National Marine Fisheries Service, Galveston Lab, the
Virginia Marine Science Museum Stranding Program, the Maryland Department of
Natural Resources, and the Massachusetts Audubon Society in Wellfleet. Funding was
24
provided by the National Marine Fisheries Service. All work was done under and
complied with the provisions of the Sea Turtle Research Permit TE-676379-2 issued by
the U.S. Fish and Wildlife Service. SSH was supported in part through the Oregon
Agricultural Experiment Station under project ORE00102 and a contract from the
National Marine Fisheries Service, Southeast Fisheries Science Center.
6.0) LITERATURE CITED
Atchley, W.R. 1984. Ontogeny, timing of development, and genetic variance-covariance
structure. The American Naturalist 123:519-540.
Balaz, G.H. and Chaloupka, M.Y. 2004. Spatial and temporal variability in somatic
growth of green sea turtles (Chelonia mydas) resident in the Hawaiian
Archipelago. Marine Biology in press.
Banerjee, R., Nandi, N.C., Raut, S.K. 1987. Growth rate in infant ridley turtle
Lepidochelys olivacea. Environmental Ecology 5:388-390.
Bjorndal, K.A. 2003. Roles of loggerhead sea turtles in marine ecosystems. In: Bolten,
A.B. and Witherington, B.E. (Eds.). Loggerhead sea turtles. Washington, D.C.:
Smithsonian Institution, pp.235-254.
Bjorndal, K.A., Bolten, A.B., Dellinger, T., Delgado, C., and Martins, H.R. 2003.
Compensatory growth in oceanic loggerhead sea turtles: response to a stochastic
environment. Ecology 84:1237-1249.
Caillouet, C.W., Jr., Fontaine, C.T., Manzella-Tirpak, S.A., and Williams, T.D. 1995.
Growth of head-started Kemp’s ridley sea turtles (Lepidochelys kempi) following
release. Chelonian Conservation Biology 1:231-234.
25
Caillouet, C.W., Jr., Robertson, B.A., Fontaine, C.T., Williams, T.D., Higgins, B.M., and
Revera, D.B. 1997. Distinguishing captive-reared from wild Kemp’s ridleys.
Marine Turtle News Letter. 77:1-6.
Castanet, J. 1994. Age estimation and longevity in reptiles. Gerontology 40:174-192.
Castanet, J., Francillon-Viellot, H., Meunier, F.J., and De Ricqles, A. 1993. Bone and
individual aging. In: Hall, B.K. (Ed.). Bone, Volume 7: Bone Growth-B. Boca
Raton, Florida: CRC Press, pp. 245-283.
Castanet, J., Meumier, F. J., and de Ricqles, A. 1977. L’enregistrement de la croissance
cyclique par le tissu osseux chez les vertébrés poïkilothermes: données
comparatives et essai de synthése. Bulletin Biologique de la France et de la
Belgique 111:183-202.
Chaloupka, M.Y. and Limpus, C.J. 1997. Robust statistical modeling of hawksbill sea
turtle growth rates (southern Great Barrier Reef). Marine Ecology Progress Series
146:1-8.
Chaloupka, M.Y. and Musick, J.A. 1997. Age, growth, and population dynamics. In:
Lutz, P.L. and Musick, J.A. (Eds.). The biology of sea turtles. Boca Raton,
Florida: CRC Press, pp.233-276.
Chaloupka, M.Y. and Zug, G.R. 1997. A polyphasic growth function for the endangered
Kemp’s ridley sea turtle, Lepidochelys kempii. Fishery Bulletin 95:849-856.
Collard, S.B. 1990. The influence of oceanographic features in post-hatching sea turtle
distribution and dispersion in the pelagic environment. In: Richardson, T.H.,
Richardson, J.I., and Donnely, M. (Compilers). Proceedings of the 10th annual
26
workshop on sea turtle biology and conservation. NOAA Technical Memorandum
NMFS-SEFC-278, p. 111.
Collard, S.B. and Ogren, L.H. 1990. Dispersal scenarios for pelagic post-hatchling sea
turtles. Bulletin of Marine Science 47:233-243.
Epperly, S.P., Braun, J., and Veishlow, A. 1995. Sea turtles in North Carolina waters.
Conservation Biology 9:384-394.
Eisen, E.J. 1975. Results of growth curve analysis in mice and rats. Journal of Animal
Science 42:1008-1023.
Fabens, A.J. 1965. Properties and fitting of the von Bertalanffy growth curve. Growth
29:265-289.
Francis, R.I.C.C. 1990. Back-calculation of fish length: a critical review. Journal of Fish
Biology 36:883-902.
Frazer, N.B. 1987. Preliminary estimates of survivorship for wild juvenile loggerhead sea
turtles (Caretta caretta). Journal of Herpetology 21:232-235.
Gilliam, J.F. and Fraser, D.F. 1988. Resource depletion and habitat segregation by
competitors under predation hazard. In: Ebenman, B. and Persson, L. (Eds.). Size-
structured populations. New York: Springer-Verlag, pp. 173-184.
Heppell, S.S., Snover, M.L., and Crowder, L.B. 2003. Sea turtle population ecology. In:
Lutz, P.L., Musick, J.A., and Wyneken, J. (Eds.). The biology of sea turtles
Volume II. Boca Raton, Florida: CRC Press, pp. 275-306.
Higgins, B.M., Robertson, B.A., and Williams, T.D. 1997. Manual for mass wire tagging of
hatchling sea turtles and the detection of internal wire tags. NOAA Technical
Memorandum NMFS-SEFSC-402, 75pp.
27
Klevezal, G. A. 1996. Recording structures of mammals: Determination of age and
reconstruction of life history. Rotterdam: A.A. Balkema, 274pp.
Klima, E. F. and McVey, J. P. 1995. Headstarting the Kemp’s ridley turtle, Lepidochelys
kempi. In: Bjorndal, K.A. (Ed.). The Biology and Conservation of Sea Turtles.
Washington, D.C.: Smithsonian Institution, pp. 481-487.
Limpus, C. and Chaloupka, M.Y. 1997. Nonparametric regression modeling of green sea
turtle growth rates (southern Great Barrier Reef). Marine Ecology Progress Series
149:23-34.
Lomnicki, A. 1988. Population ecology of individuals. Princeton, New Jersey: Princeton
University Press, 223 pp.
Marquez, R. 1994. Synopsis of biological data on the Kemp’s ridley turtle, Lepidochelys
kempi (Garman, 1880). NOAA Technical Memorandum NMFS-SEFSC-343, 91
pp.
Schmid, J.R. 1995. Marine turtle populations on the east-central coast of Florida: results
of tagging studies at Cape Canaveral, Florida, 1986-1991. Fishery Bulletin 93:
139-151.
Schmid, J.R. 1998. Marine turtle populations on the west-central coast of Florida: results
of tagging studies at the Cedar Keys, Florida, 1986-1995. Fishery Bulletin
96:589-602.
Schmid, J.R. and Witzell, W.N. 1997. Age and growth of wild Kemp’s ridley turtles
(Lepidochelys kempi): cumulative results from tagging studies in Florida.
Chelonian Conservation Biology 2:532-537.
28
Seminoff, J.A., Resindiz, A., Nichols, W.J., and Jones, T.T. 2002. Growth rates of wild
green turtles (Chelonia mydas) at a temperate foraging area in the Gulf of
California, México. Copeia 2002:610-617.
Simmons, D.J. 1992. Circadian aspects of bone biology. In: Hall, B.K. (Ed.). Bone,
Volume 6: Bone Growth-A. Boca Raton, Florida: CRC Press, pp. 91-128.
Snover, M.L., Avens, L., and Hohn, A.A. 2004. Estimating growth rates of loggerhead
sea turtles (Caretta caretta) using skeletal growth marks. Marine Ecology
Progress Series In review.
Snover, M.L. and Hohn, A.A. 2004. Validation and interpretation of the annual
deposition of skeletal marks in loggerhead (Caretta caretta) and Kemp’s ridley
(Lepidochelys kempii) sea turtles. Fishery Bulletin 102:in press.
Turtle Expert Working Group. 2000. Assessment update for the Kemp’s ridley and
loggerhead sea turtle populations in the Western North Atlantic. U.S. Department
of Commerce NOAA Technical Memorandum NMFS-SEFSC-444, 115 pp.
Van Buskirk, J. and Crowder, L.B. 1994. Life-history variation in marine turtles. Copeia
1994:66-81.
von Bertalanffy, L. 1938. A quantitative theory of organic growth (inquiries on growth
laws II). Human Biology 10:181-213.
von Bertalanffy, L. 1960. Principles and theory of growth. In: Nowinski, W. (Ed.).
Fundamental aspects of normal and malignant growth. Amsterdam:Elsevier, pp.
137-259.
29
Werner, E.E. and Gilliam, J.F. 1984. The ontogenetic niche and species interactions in
size-structured populations. Annual Review of Ecology and Systematics 15:393-
425.
Zar, J.H. 1999. Biostatistical analysis, fourth edition. Upper Saddle River, New Jersey:
Prentice Hall, pp. 360-364.
Zug, G.R., Kalb, H.J., and Luzar, S.J. 1997. Age and growth in wild Kemp’s ridley sea
turtles Lepidochelys kempii from skeletochronological data. Biological
Conservation 80:261-268.
Zug, G.R., Wynn, A.H., and Ruckdeschel, C. 1986. Age determination of loggerhead sea
turtles, Caretta caretta, by incremental growth marks in the skeleton. Smithsonian
Contributions to Zoology 427 34 pp.
30
Table1. von Bertalanffy parameter and age at sexual maturity estimates in Kemp’s ridleys. Region indicated where tagging studies
occurred or where skeletochronology samples were taken. Under methods, M-R indicates mark-recapture and S indicates
skeletochronology. For the studies by Schmid (1995, 1998) numbers indicate curves where all recapture data was used.
1Gulf of Mexico
2Not Estimated
Region Size at
Maturation
Time to
Maturation
Asymptotic
length
Growth
coefficient k
Method Reference
GOM1 60.0 cm SCL 10 yrs 62.3 cm SCL 0.317 M-R Caillouet et al., 1995
Atlantic - N.E.2 61.1 cm SCL 0.577 M-R Schmid, 1995
Atlantic/GOM1 65.0 cm SCL 15.7 yrs 79.4 cm SCL 0.130 S Zug et al., 1997
GOM1 - N.E.2 91.4 cm SCL 0.085 M-R Schmid, 1998
Atlantic 64.0 cm SCL 12-13yrs 73.2 cm SCL 0.167 M-R, S TEWG, 2000
GOM1 64.0 cm SCL 10-11 yrs 71.1 cm SCL 0.210 M-R, S TEWG, 2000
Atlantic 60.0 cm SCL 10-17 yrs 74.9 cm SCL 0.115 S Present Study
31
Table 2. Resulting sample sizes for back-calculated lengths in the skeletochronology study. Size-at-age values indicate the number of
turtles for which we could back-calculate a length at that age. Growth increments indicate the number of turtles for which we could
estimate the amount of growth for the year following the assigned age from back-calculating length the following year.
Age Size-at-Age Growth Increment
All Male Female All Male Female
1 90 14 28 56 13 26
2 105 19 50 66 13 30
3 68 14 29 30 6 14
4 30 6 14 16 4 6
5 16 4 6 7 2 3
6 7 2 3 2 1 0
7 2 1 0 1 1 0
8 1 1 0 0 0 0
32
Table 3. Average lengths at age for the three methods used in the skeletochronology study. Age based on LAG count are the 144
turtles for which age could be assigned be direct count of LAGs. Back-calculated lengths are the 319 back-calculated lengths at age.
The last three columns are the results of the VBGF with the upper and lower 95% confidence intervals around the fitted parameters. N
indicates sample size, SD indicates standard deviation and CI indicates confidence interval.
Method: A
g
e based on LAG Count Bac
k
-calculated len
g
th Von Bertalanff
y
g
rowth curve
A
g
e N Mean SD N Mean SD Mean 95% CI
0-1.0 11 23.5 2.08 90 20.9 2.1 - - -
1.25-2.0 31 27.6 2.08 105 26.7 2.6 26.9 26.4 27.2
2.25-3.0 37 35.4 3.05 68 30.1 3.7 32.1 31.2 32.9
3.25-4.0 36 36.5 3.71 30 34.1 5.2 36.7 35.4 38.0
4.25-5.0 15 42.0 5.42 16 36.1 3.8 40.9 39.1 42.6
5.25-6.0 9 41.5 4.53 7 41.2 4.4 44.6 42.4 46.8
6.25-7.0 3 48.0 1.62 2 40.1 3.3 47.9 45.2 50.6
7.25-8.0 1 43.1 - 1 46.5 - 50.8 47.7 54.0
8.25-9.0 1 50.5 - 0 53.4 49.9 57.1
10.0 55.8 51.8 59.9
11.0 57.8 53.5 62.5
12.0 59.7 55.0 64.8
13.0 61.3 56.3 66.8
33
Table 4. Probability of age assignments for Kemp’s ridley from the Atlantic coast of the USA
based on length. These data are based on 144 stranded Kemp’s for which carapace length was
recorded and age could be assigned from direct count of LAG
Age: 1 2 34567
Size Range Probability
15-19.99 0.90 0.07 0.030000
20-24.99 0.67 0.30 0.030000
25-29.99 0.01 0.63 0.300.06000
30-34.99 0 0.18 0.48 0.21 0.11 0.02 0
35-39.99 0 0 0.23 0.32 0.32 0.09 0.04
40-44.99 0 0 0.1 0.3 0.3 0.2 0.1
34
Figure Legends
Figure 1. Image of a thin-sectioned and stained Kemp’s ridley humerus cross-section. This
animals was 3.25 years old; age was estimated from LAG count and stranding date. Diagram
demonstrates how LAG and annuli diameters were measured and ages estimated; a total of four
diameters were measured on this humerus including the full outer diameter. The one year growth
rate used in the estimation of von Bertalanffy parameters from this animal was the difference
between the carapace lengths estimated from the diameters of LAGs 3 and 2. The black line at
the lower left represents 1mm in length.
Figure 2. Relationship between carapace length and age for Kemp’s ridley sea turtles
(Lepidochelys kempii). Filled diamonds are the length and skeletochronology age estimates for
144 stranded Kemp’s recovered along the mid-Atlantic coast of the USA. Open squares are the
back-calculated carapace lengths from all LAG that could be measured on the 144 humeri cross-
sections. See Table 2 for sample sizes. The solid line is the von Bertalanffy growth curve
prepared by using the last full year growth increment from 109 humeri cross-sections, including
increments from large turtles consistent with length at maturity. The dashed line is a cubic
smoothing spline fit through the 144 size at age data.
Figure 3. Relationship between mean carapace length at age for Kemp’s ridley sea turtles. Data
were generated from back-calculated lengths at age based on LAG diameters (see Table 2 for
sample sizes). Error bars indicate standard error. The solid line is the von Bertalanffy growth
curve prepared by using the last full year growth increment from 109 humeri cross-sections,
including increments from large turtles consistent with length at maturity.
35
Figure 4. Relationship between age and the amount of growth that occurred the following year.
All data are from the back-calculated lengths at age based on LAG diameters (N=178). a) All of
the data; b) mean and standard error of the data.
Figure 5. Relationship between initial length and the subsequent amount of annual growth
(N=191). Data are from all of the back-calculated lengths at age based on LAG diameters. The
solid line is the derivative of the von Bertalanffy growth curve prepared by using the last full
year growth increment from 109 humeri cross-sections, including increments from large turtles
consistent with length at maturity. The dashed line is a cubic smoother through the data.
Figure 6. Relationships between initial length at age and the subsequent annual growth. Symbols
represent different ages; filled diamonds are age 1, open squares are age 2, open triangles are age
3 and crosses are age 4. a) Ages 1 (N = 55) and 3 (N = 29) for all of the data from the back-
calculated lengths at age. Thick solid line is the regression through all of the data (ages 1-4; r2 =
0.006; P = 0.30), thin lines are the regressions through age 1 (r2 = 0.05; P = 0.11) and age 3 (r2 =
0.11; P = 0.20). b) Ages 2 (N = 65) and 4 (N = 16) for all of the data from the back-calculated
lengths age age. Thick solid line is the regression through all of the data (same as shown in (a)),
thin lines are the regressions through age 2 (r2 = 0.03; P = 0.20) and age 4 (r2 = 0.01; P = 0.65).
c) Ages 1 (N = 25) and 2 (N = 13) for the 1997 cohort. Thick solid line is the regression through
all of the data (ages 1 & 2; r2 = 0.08; P = 0.07), thin lines are the regressions through age 1 (r2 =
0.003; P = 0.78) and age 2 (r2 = 0.006; P = 0.79).
36
Figure 7. Individual growth trajectories of Kemp’s ridley sea turtles. Profiles of growth
trajectories are shown for turtles that had at least four LAG that could be measured. Carapace
lengths were back-calculated based on LAG diameters.
Figure 8. Relationship between mean length and age for male (dashed line) and female (solid
line) Kemp’s ridley sea turtles. Bars on data point indicate standard error.
Diameter 1st
year annulus
Diameter LAG 2
Diameter LAG 3
Chapter 5, Figure 1
0
10
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Age (yrs.)
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