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GASTRIC EVACUATION AND DIGESTION STATE INDICES FOR GAG Mycteroperca microlepis CONSUMING FISH AND CRUSTACEAN PREY
By
ELIZABETH JOANNE BERENS
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2005
Copyright 2005
by
Elizabeth Joanne Berens
To my parents,
Charles and Joanne, For their support and encouragement
which has allowed me to pursue my interests in the marine environment
and the various organisms within.
ACKNOWLEDGMENTS
I would like to thank Dr. Mark Luttenton for introducing me to fisheries science
many years ago at Grand Valley State University. This study would not have been
possible without the help of many people and I especially thank Mark Butler, Jackie
Debicella, Rick Kline, Steve Larsen, Eddie Leonard, and Doug Marcinek for assistance
with laboratory set-up and specimen collection, and Doug Colle and Larry Tolbert for
advice and assistance with certain aspects of this study. In particular, I thank Day Cherry
and DJ White for their assistance with invertebrate collections and for giving me
invaluable insight into a commercial fishing industry. I thank the American Fisheries
Society, Florida Chapter, Florida Sea Grant, and the University of Florida’s Fisheries and
Aquatic Sciences Department for financial and logistical assistance during this study.
I thank my committee, Dr. Debra Murie, Dr. Daryl Parkyn, Dr. William Lindberg,
University of Florida, Department of Fisheries and Aquatic Sciences, and Dr. Karen
Bjorndal, University of Florida, Department of Zoology, for offering advice and
assistance along the way.
iv
TABLE OF CONTENTS page
ACKNOWLEDGMENTS ................................................................................................. iv
LIST OF TABLES............................................................................................................ vii
LIST OF FIGURES ........................................................................................................... ix
ABSTRACT....................................................................................................................... xi
CHAPTER
1 INTRODUCTION ........................................................................................................1
2 METHODS.................................................................................................................19
Gag Collections and Maintenance ..............................................................................19 Experimental Feeding Trials for Gastric Evacuation Rates .......................................21 Caloric Analysis of Prey and Stomach Contents........................................................22 Gastric Evacuation Models.........................................................................................25 Energetic Models ........................................................................................................26 Indices of Digestive States..........................................................................................28 Models of Average Digestion Codes..........................................................................29
3 RESULTS...................................................................................................................32
Gag and Prey Collections ...........................................................................................32 Gastric Evacuation Models for Gag Consuming Baitfish Prey ..................................32 Gastric Evacuation Models for Gag Consuming Crab Prey.......................................34 Comparative Gastric Evacuation Models for Gag Consuming Baitfish versus Crab
Prey ........................................................................................................................36 Caloric Values of Baitfish and Crab Prey...................................................................36 Energy Values for Recovered Gag Stomach Contents ...............................................37 Indices of Digestion for Baitfish Consumed by Gag..................................................40 Indices of Digestion for Crab Prey Consumed by Gag ..............................................41
4 DISCUSSION.............................................................................................................69
Gastric Evacuation Models.........................................................................................69 Effects of Prey Type............................................................................................74
v
Effects of Predator Size .......................................................................................77 Prey Composition .......................................................................................................79 Stomach Content Composition ...................................................................................83 Indices of Digestion....................................................................................................86 Consumption...............................................................................................................88 Conclusions.................................................................................................................90
LIST OF REFERENCES...................................................................................................92
BIOGRAPHICAL SKETCH .............................................................................................99
vi
LIST OF TABLES
Table page 1 Indices of baitfish Harengula jaguana digestion by gag over post-prandial time
(hr). ...........................................................................................................................30
2 Indices of crab Portunus gibbesii digestion by gag over post-prandial time (hr). ...31
3 Regression parameters of the gastric evacuation wet weight data of gag consuming baitfish prey fit to each model, for small gag (n = 13), medium gag (n = 16), and large gag (n = 11)................................................................................55
4 Regression parameters of the gastric evacuation dry weight data of gag consuming baitfish prey fit to each model, for small gag (n = 13), medium gag (n = 16), and large gag (n = 11)................................................................................56
5 Regression parameters of the pooled gastric evacuation data (n=40) of gag consuming baitfish prey on a wet and dry weight basis fit to the expanded power exponential models with either gag weight or TL scaling exponents ......................57
6 Regression parameters of the gastric evacuation wet weight data of gag consuming crab prey fit to each model, for small gag (n=8), medium gag (n=10), and large gag (n=8) ..................................................................................................58
7 Regression parameters of the gastric evacuation dry weight data of gag consuming crab prey fit to each model, for small gag (n=8), medium gag (n=10), and large gag (n=8) ..................................................................................................59
8 Regression parameters of the pooled gastric evacuation data (n=26) of gag consuming crab prey on a wet and dry weight basis fit to the expanded power exponential models with either gag weight or TL scaling exponents ......................60
9 Composition of representative baitfish Harengula jaguana and crab Portunus gibbesii prey types used in gastric evacuation trials of gag, values are means (±S.E.). .....................................................................................................................61
10 Regression parameters for models describing the gross energy (kcal/g dry weight) of the stomach contents as a function of post-prandial time (PPT) by all gag consuming baitfish prey fit to the linear, exponential, and square root models, for small gag (n=12), medium gag (n=15), and large gag (n=11) ..............62
vii
11 Regression parameters modeling the gross energy (kcal/g dry weight) present over PPT by all gag consuming crab prey fit to the linear, exponential, and square root models, small gag (n=7), medium gag (n=7), and large gag (n=5).......63
12 Regression parameters modeling the percent of gross energy digested over PPT by all gag consuming baitfish prey fit to each model, for small gag (n=11), medium gag (n=16), and large gag (n=8).................................................................64
13 Regression parameters modeling the percent of gross energy digested over PPT by all gag consuming crab prey fit to each model, small gag (n=7), medium gag (n=8), and large gag (n=7)........................................................................................65
14 Mean (±S.E.) and range of post-prandial times (PPT) in relation to digestion codes and % digestion for gag consuming baitfish Harengula jaguana versus crab prey Portunus gibbesi.......................................................................................66
15 Regression parameters of the average digestion code data of gag consuming baitfish prey fit to each model, for small gag (n=13), medium gag (n=16), and large gag (n=11) .......................................................................................................67
16 Regression parameters of the digestion code data of gag consuming crab prey fit to each model, for small gag (n=8), medium gag (n=11), and large gag (n=8) .......68
viii
LIST OF FIGURES
Figure page 1 Commonly used gastric evacuation models depicting the digestion processes of
different fish species.................................................................................................17
2 The relationship of gag weight (W) as a function of total length (TL) for gag between 300 and 750 mm TL...................................................................................44
3 The power exponential model describing the gastric evacuation processes of small, medium, and large gag consuming baitfish prey (scaled sardines): (a) wet weight basis and (b) dry weight basis. .....................................................................45
4 The power exponential model expanded to include weight (W) or TL as scalers describing the combined gastric evacuation data of all gag consuming baitfish prey (scaled sardines): (a) wet weight basis and (b) dry weight basis. ...................46
5 The power exponential model describing the gastric evacuation processes of small, medium, and large gag consuming crab prey (Portunus gibbesii): (a) wet weight basis and (b) dry weight basis. .....................................................................47
6 The power exponential model expanded to include weight (W) or TL as scalers describing the combined gastric evacuation data of all gag consuming crab prey (Portunus gibbesii): (a) wet weight basis and (b) dry weight basis. .......................48
7 The expanded power exponential model describing the combined gastric evacuation wet weight data of all gag consuming both baitfish (scaled sardines) and crab (Portunus gibbesii) prey incorporated with: (a) weight (W) and (b) total length (TL) scalers. ..........................................................................................49
8 Models describing the gross energy of recovered stomach contents from small, medium, and large gag consuming: (a) baitfish prey (scaled sardines), fit to a square-root model and (b) crab prey (Portunus gibbesii), fit to a linear model.......50
9 The power exponential model describing the percentage of stomach content energy digested over elapsed time for small, medium, and large gag consuming: (a) baitfish prey (scaled sardines) and (b) crab prey (Portunus gibbesii). ...............51
ix
10 The power exponential model: (a) expanded to include weight (W) or TL as scalers describing the combined stomach content energy digestion data for all gag consuming baitfish prey (scaled sardines) and (b) describing the combined stomach content energy digestion data for all gag consuming crab prey (Portunus gibbesii). ..................................................................................................52
11 The power exponential model describing the average digestion code values of gag consuming baitfish prey (scaled sardines) over elapsed time: (a) small, medium, and large gag and (b) all gag fit to the expanded model using weight (W) or TL as scalers. ................................................................................................53
12 The power exponential model describing the average digestion code values of gag consuming crab prey (Portunus gibbesii) over elapsed time: (a) small, medium, and large gag and (b) all gag fit to the expanded model using weight (W) or TL as scalers. ................................................................................................54
x
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
GASTRIC EVACUATION AND DIGESTION STATE INDICES FOR GAG Mycteroperca microlepis CONSUMING FISH AND CRUSTACEAN PREY
By
Elizabeth Joanne Berens
May 2005
Chair: Debra J. Murie Major Department: Fisheries and Aquatic Sciences
Gag Mycteroperca microlepis comprise one of the most valuable fisheries in the
Gulf of Mexico, especially off the west coast of Florida. Production of juvenile and pre-
reproductive female gag, as measured through growth, depends on the total amount of
surplus energy available to the fish for growth after losses due to metabolism and wastes.
Estimating consumption rates of gag, which feed primarily on fish prey and secondarily
on crustacean prey, requires prey-specific evacuation models.
To develop these models, the proportion of the gag meals, consisting of either
baitfish (scaled sardine Harengula jaguana) or crab (purple swimmer crab Portunus
gibbesii), that remained after a pre-determined post-prandial time (PPT) was fit to linear,
exponential, square root, logistic, and power exponential models, on a wet weight and dry
weight basis. The power exponential models were significant (p≤0.0008) and best fit the
wet weight and dry weight gastric evacuation data, the percentage of prey energy
digested over PPT, and the average digestion codes over PPT, regardless of prey type or
xi
gag size (R2≥0.81). Gag weight (W) or total length (TL) exponential scalers were
incorporated into the power exponential model and fit to the wet weight, dry weight, and
average digestion code data across all gag sizes by prey type, resulting in significant
(p<0.0001 for each model) and highly predictive (R2≥0.87) gastric evacuation models.
The expanded power exponential models with W and TL exponential scalers fit to the
wet weight baitfish and crab gastric evacuation data differed significantly (Maximum
Likelihood Ratio: n=66, Χ2=88.26, df=3, p<0.0001; n=66, Χ2=88.40, df=3, p<0.0001,
respectively), with lag phases of up to 5.0 hrs PPT in crab digestion only. The power
exponential model, with W or TL scalers, proved significant (p<0.0001) and highly
predictive (R2=0.98) for gag consuming baitfish when fit to the percentage of baitfish
energy digested over PPT; however, power exponential models fit to the crab data for
each size of gag were coincident. Therefore, these data were pooled and fit to an
unexpanded power exponential model, which also yielded a significant (p<0.0001) and
predictive model (R2=0.91).
After correcting for energy from chitin, which is unavailable to the gag, total crab
energy density (2.22 kcal/g dry wt) was significantly lower than that of the baitfish prey
(2-tailed Satterthwaite t-test for unequal variance; n=48, t=-31.05, p <0.0001). In
general, digestion of crab prey was associated with a 5-6 hr lag period, low prey energy
densities, and digestion over a longer period of time relative to fish prey. Therefore, for
gag consuming a mixture of fish and crab prey it will be necessary to develop a
multiplicative or additive consumption model that also incorporates mixed-prey gastric
evacuation models.
xii
CHAPTER 1 INTRODUCTION
Groupers (Pisces: Serranidae) support major commercial and recreational fisheries
throughout the world. Most species of groupers are demersal predators that generally
occupy a top niche in the food chain of tropical and subtropical marine waters and have
been recognized as slow-growing, late maturing reef fishes that consume a broad range of
prey, including fish, crustaceans, and cephalopods (Polovina & Ralston, 1987).
In recent years, grouper stocks have been overfished, in part due to their slow
growth and late maturity. Reef fish, such as groupers, tend to aggregate over specific
habitats characterized by a patchy distribution, thereby making the species vulnerable to
overfishing, and skewing traditional methods used to estimate their abundance (Federal
Register, 1998, Vol. 63 No. 208, p. 57660). One important grouper species in the Gulf of
Mexico, the gag Mycteroperca microlepis supports valuable recreational and commercial
fisheries. Gag range from New York to Brazil and through the Gulf of Mexico, but are
absent from most Caribbean waters (Smith, 1971). Commercial and recreational catches
in the southeastern U.S. have exceeded 2,268 metric tons (5 million lbs) annually, and in
2001, over 3,538 metric tons (7.8 million lbs) of gag were landed in the state of Florida
alone (Turner et al., 2001; Florida Fish and Wildlife Conservation Commission
[FFWCC], 2003).
Within the past decade, many studies have examined the life history of gag and the
effect of fishing on its populations (Hood & Schlieder, 1992; Ross & Moser, 1995;
Collins et al., 1998). Such studies are important for understanding the growth and
1
2
production of the gag fishery, and for helping managers cope with a variety of issues,
such as setting catch limits, size restrictions, and analyzing exploitation issues. One of
these topics, growth, is an integral part of a fish’s energy budget. An energy budget
relates all the energy an organism acquires through ingestion to its energy used in
metabolic processes, lost as wastes through excretion and egestion, or synthesized into
new somatic or reproductive tissue (Adams & Breck, 1990). Energy budgets can be used
to evaluate the importance of many different factors controlling individual growth,
including diet and activity demands, subject to different environmental conditions
(Adams and Breck, 1990; Jobling, 1993). A generalized energy budget can be modeled
as (Winberg, 1956; Warren & Davis, 1967)
( ) ( ) ( )rsar GGUFSDAMMC ++++++= (1)
where C = rate of energy consumption, Mr = standard metabolic rate, Ma = metabolic rate
increase above the standard rate due to activity, SDA = metabolic rate increase due to
specific dynamic action, F + U = waste losses due to egestion (feces) and excretion
(urine) rates, Gs = somatic growth rate due to protein synthesis [and lipid deposition], and
Gr = growth rate due to gonad (reproductive) synthesis. This generalized energy budget
is a balanced equation where all energy consumed by the animal (C), equals the energy
lost to metabolism (Mr + Ma + SDA), wastes (F + U), and growth (Gs + Gr). Hence,
energy for growth is only available after all metabolic and waste demands have been
subtracted from the total amount of energy consumed:
( ) ( )[ ]UFSDAMMCGG arrs ++++−=+ (2)
Due to the equation being theoretically balanced, researchers have the advantage of
estimating one component of the energy budget by subtracting it from the other measured
3
components, although this method tends to pool all error into that one component (Adams
& Breck, 1990). Total metabolism, waste, and growth all depend on the amount of
energy consumed, and hence accurate estimates of consumption rates are imperative.
Consumption rates, however, are often the least developed part of the energy budget,
even though they are one of the most direct “inter-links” between the trophic components
in an ecosystem (Klekowski & Duncan, 1975). Consumption rates are typically difficult
to estimate because of many complex factors that influence the amount of food that a fish
consumes, including: the fish’s size; the size, abundance and distribution of prey; the
types of prey consumed; the predator’s feeding history; and even physical parameters,
such as water temperature (Windell, 1978). In addition, these factors can affect the
number of meals consumed per day, the amount of food consumed in a single meal, and
the predator’s digestion rate, or the rate of gastric evacuation, which quantifies the rate at
which food passes out of the stomach (Adams & Breck, 1990; Bromley, 1994).
Consumption rates can partially affect gastric evacuation rates by causing an increase or
decrease in the amount of time food remains in the gut. For example, if a fish consumes
a large single meal of a certain prey type, that predator’s evacuation rate will tend to be
lower than if that fish had consumed a small single meal of the same food (Swenson &
Smith, 1973). Some of these complex factors are best studied in the field, others in the
lab, but some require the capture of large numbers of fish, which may or may not be
feasible depending on species. Considering the many different biotic and abiotic factors
that influence consumption and the fact that consumption rates are difficult to quantify
outside of the laboratory, most studies take into account only two or three of these
interrelated factors (Windell, 1978; Adams & Breck, 1990).
4
Most consumption models require the calculation of gastric evacuation rates and,
additionally, some models require estimates of the original prey weight at time of
capture. For example, the Continuous Feeding Model involves sampling a population
over a certain amount of time, calculating the amount of food in the gut at the beginning
and end of the sampling period, and then incorporating the length of the sampling period
and the instantaneous rate of gastric evacuation into the model (Adams & Breck, 1990).
Elliott and Persson (1978) developed a commonly used Continuous Feeding Model for
modeling consumption rate in brown trout Salmo trutta, which directly incorporates the
rate of gastric evacuation and can be modeled as
∑= tCC (3)
where C = daily ration (% body weight/day), or the sum all Ct values in each time block,
and Ct = the amount of food ingested in a block of time, or the time between two
sampling periods:
kt
ktot
ekteSS
tC −
−
−
−=1
)( (4)
where So = amount of food present at the beginning of the sampling interval, St = amount
of food present at the end of the sampling interval, t = length of the sampling interval,
and k = instantaneous rate of gastric evacuation, calculated as =k loge ( ) 1−tt
oS
S .
Although consumption affects rates of gastric evacuation, gastric evacuation rates
can also affect the amount of food a fish consumes (Brett & Higgs, 1970; Elliott &
Persson, 1978; Grove et al., 1978). Grove et al. (1978) found that an increase in the rate
of gastric evacuation occurred with the consumption of low-energy food and was
correlated to a rapid return of appetite and a high frequency of consumption, or more
5
meals consumed. Clearly, gastric evacuation rates impact consumption and therefore
they must be included in consumption models in order to calculate accurate estimates of
total energy intake.
The terms digestion rate and gastric evacuation rate are often used interchangeably
to denote the rate at which food passes from the stomach into the intestine even though
this is something of a misnomer (Windell, 1978). Digestion is the act of mechanical and
enzymatic breakdown in the fish’s stomach that converts food into soluble and diffusible
products capable of being absorbed, or assimilated, by cells in the fish’s stomach and
intestine (Knutsen & Salvanes, 1999). Materials that can’t be absorbed are simply passed
through the body, and upon defecation are often referred to as indigestible matter.
Hence, food that has been broken down and is no longer present in the stomach (i.e., food
that has been evacuated from the stomach) does not necessarily represent food that has
been assimilated completely in the pyloric cecae (possibly) or intestine and utilized by
the fish (Pandian, 1967). In fisheries, research using consumption models has depended
more heavily on rates of gastric evacuation than research in other fields, such as animal
nutrition, which often focus more on the rate of transit through the entire gastrointestinal
track (Dorcas et al., 1997; Roxburgh & Pinshow, 2002; Sponheimer et al., 2003; Butler
et al., 2004; Henriques et al., 2004). In general, the alimentary tracts of fish are much
simplier than the alimentary tracts of other animals, such as mammals (Stevens & Hume,
1995) and food consumption estimates are usually determined through direct gut content
analysis using lavage techniques or by sacrificing large numbers of animals (Adams &
Breck, 1990). Conversely, estimating food consumption in many animals requires
indirect methods, such as inferring feeding rates of wild animals from feeding rates of
6
captive animals (Innes et al., 1987) or through bioenergetic modeling, because lavaging
certain types of animals or sacrificing large numbers of those animals may be logistically
difficult, prohibited, or simply undesirable (Winship et al., 2002).
There are several ways to estimate gastric evacuation rates, including using X-ray
and radioisotopic procedures and through serial slaughter (Beamish, 1972; Swenson &
Smith, 1973; Diana, 1979; Flowerdew & Grove, 1979). X-ray and radioisotopic
procedures have been used most often to monitor the movement of hard parts through the
entire digestion process (Jobling et al., 1977; Flowerdew & Grove, 1979). Serial
slaughter methods in situ are generally not feasible since it is very difficult to get large
groups of wild fish to simultaneously consume a measured amount of food, hold them in
pens, and at predetermined times collect their stomach contents (Windell, 1978).
Therefore, this method is usually used on captive fish where specific amounts of food can
be given to individual fish, the fish can be monitored, and their stomach contents easily
recovered by killing the fish or by pumping its stomach (Adams & Breck, 1990). The
fraction of original prey weight digested is plotted against hours post-feeding. The slope
of the resulting regression is then used to calculate the gastric evacuation rate and
determine the gastric evacuation model (Adams & Breck, 1990).
Linear, exponential, and square root gastric evacuation models have commonly
been used to quantify the gastric evacuation processes of fish species, and then are either
input directly into, or used to meet the assumptions of a consumption model (Figure 1).
A linear model often describes the gastric evacuation processes of top carnivores, or
piscivores, which tend to consume only a few fairly large prey items over a feeding cycle,
thereby causing relatively long digestion times compared to the length of their feeding
7
period (Adams & Breck, 1990). Jobling (1987) attributes the linear model to large food
particles with lower surface-to-volume ratios, low fragmentation rates, and high dietary
energy densities. These types of prey items tend to be evacuated from the stomach at a
constant rate. Previous work with piscivores and linear digestion processes have
included studies on black and yellow rockfish Sebastes chrysomelas (Hopkins & Larson,
1990), plaice Pleuronectes platessa (Jobling, 1980b), and walleye Stizostedion vitreum
vitreum (Swenson & Smith, 1973).
The exponential model generally illustrates the digestion process of herbivores,
detritivores, planktivores, and omnivores that feed at lower trophic levels on diets
composed of many small food items, such as zooplankton, and eat more or less
continuously throughout the day (Adams & Breck, 1990). These food items would be
expected to have high surface-to-volume ratios, high fragmentation rates, and tend to be
low in energy densities (Jobling, 1987). In these fish, the digestion rate increases
exponentially with time until some point at which refractory materials slow the rate and
basically level off the digestion process. Problems with the exponential model occur
when the lower portion of the curve levels off and reaches its lower asymptote because it
leads to overestimates of the amount of food remaining in the stomach at later stages of
evacuation (Brodeur & Pearcy, 1987; Adams & Breck, 1990). This slowing of gastric
evacuation means that the fish’s motivation to feed would return more slowly, thereby
increasing the amount of time between feedings, and lowering the animal’s total food
consumption rate (Rindorf, 2002). Due to the fact that the exponential model levels off,
Brodeur and Pearcy (1987) considered the active part of the curve to be between 0 and
90% evacuation.
8
The square root model has been used to describe digestion in predators such as
plaice, European perch Perca fluviatili, Atlantic cod Gadus morhua, and bluegill sunfish
Lepomis macrochirus (Jobling & Davies, 1979; Jobling, 1981). This volume-based
model assumes that the instantaneous rate of evacuation is dependent upon the amount of
food in the stomach; therefore, evacuation patterns of small meals correspond to the later
stages of large meal evacuation patterns and, theoretically, results in regression lines for
different meal sizes having the same slopes (Jobling, 1981).
Brodeur (1984) suggested that an alternate way to choose the most appropriate
evacuation model may be whether or not it is interpretable in terms of the inherent
biological processes that occur during digestion. The logistic model fits this idea
considering that it accounts for the lag phases often seen in the early stages of digestion
of many different fish species (Brodeur, 1984; Hopkins & Larson, 1990) (Figure 1). The
power exponential model has also been used to describe predators with and without lag
phases because it allows the shape of the evacuation curve to vary from sigmoidal to
concave, although it can not account for linear rates of digestion (Elashoff et al., 1982;
Hopkins & Larson, 1990; dos Santos & Jobling, 1992; Temming & Andersen, 1994)
(Figure 1). Recently, Temming and Andersen (1994) have developed a general gastric
evacuation model that integrates time after ingestion, the weight of the predator,
temperature, and meal size as predicting variables (R parameter) to determine values of a
shape parameter (B) as
BRSdtdS −=/ (5)
where S = residual stomach contents (g), t = elapsed time after ingestion, R = estimated
parameter(s), and B = shape parameter. This shape parameter describes the degree of
9
curvilinearity, whether it is convex, linear, exponential, or any curve in between
(Temming & Herrmann, 2001). If B=1, the evacuation model is exponential, but B=0
indicates a linear evacuation process (Temming & Andersen, 1994). For example,
Temming and Herrmann (2001) determined that B=0.7 on a dry weight basis for horse
mackerel Trachurus trachurus thereby indicating that the model was more exponential
than linear. This general model of gastric evacuation is only valid within a limited
temperature range because it assumes that the evacuation constant (R) increases
exponentially with temperature, when in fact studies have shown that at high
temperatures the evacuation rate will decrease (Tyler, 1970; Temming & Andersen,
1994).
As post-prandial time (PPT) increases, an increasing proportion of the fish with
faster digestion are typically excluded from the sample distribution. Empty stomachs are
normally dropped from the distribution because it cannot be determined when 100%
digestion occurred (Olson & Mullen, 1986). In effect, the distribution includes both
faster and slower digesting fish throughout most of the distribution but only the slower
digesting fish at the later time periods, thereby resulting in biased evacuation rate
estimates with exaggerated curvilinearity (Olson & Mullen, 1986). In part, excluding all
of the faster digesting fish from the gastric evacuation distribution may have led many
studies to choose exponential gastric evacuation models rather than square root or linear
models (Olson & Mullen, 1986). Considering that exact times of 100% evacuation can
not be determined from empty stomachs, gastric evacuation models often include times to
90% or 95% digestion only, which truncates the sample distribution and reduces bias
10
from fitting models to data that may include empty stomachs (Swenson & Smith, 1973;
Hopkins & Larson, 1990).
Many environmental, prey, and predator characteristics influence both consumption
and gastric evacuation rates by either speeding or slowing food digestion. Studies
dealing with these two different rates must take many different conditions into account,
including temperature, prey type, prey sizes, predator sizes, and meal sizes (Bromley,
1994). Temperature is among the most important environmental variables that influence
consumption and digestion rates in fish. Most fish are ectotherms, and, therefore, the
surrounding water temperature determines their body temperature (Hazel, 1993).
Metabolic rates of fish and their corresponding physical and chemical processes, such as
enzyme production and kinetics, are therefore directly related to water temperatures
(Diana, 1995). Previous studies have reported that consumption rates and digestion rates
increase with rising water temperatures (Brett & Higgs, 1970; Jobling, 1980b; He &
Wurtsbaugh, 1993). Body temperatures and digestion rates can therefore vary
considerably throughout the year, if temperature fluctuates seasonally.
Meal composition, or energy density, can vary greatly with prey type and prey size,
thereby having another very important influence on consumption and digestion rates.
Many studies on fish have found that meals high in energy result in an increase in time to
100% gastric evacuation (Flowerdew & Grove, 1979; Hopkins & Larson, 1990).
Additionally, diets with an added diluent, a non-digestible marker that lowers a meal’s
energy density, were evacuated more rapidly from fish stomachs than those with higher
energy content, thereby exhibiting an increased digestion rate (Flowerdew & Grove,
1979; Jobling, 1980a). High-energy fats tend to slow gastric emptying more than
11
proteins or carbohydrates (Jobling, 1980a). Therefore, mature prey fish high in lipid
content will slow digestion compared to juvenile prey fish, which may be high in protein,
but low in lipids. While, generally, lipid-rich mature prey are physically larger than
immature prey of the same species, any increase in prey size decreases the surface area to
volume ratio available for enzymatic digestion (Swenson & Smith, 1973). Therefore, not
only do mature individuals contain more lipid, which slows gastric evacuation rates, but
they also have a lower surface area to volume ratio which slows gastric evacuation rates
even more. The least digestible, hard skeletal elements of prey are generally low in
energy and are often the last part of a meal to be emptied from the stomach (Flowerdew
& Grove, 1979; Hopkins & Larson, 1990). Crustacean exoskeletons, in particular, have
been shown to remain in the stomach for long periods of time compared to food with
fewer indigestible hard parts, such as fish prey (Hopkins & Larson, 1990). Additionally,
crustacean exoskeletons contain chitin, a carbohydrate (polysaccharide) that most fish
breakdown to N-acetyl-D-glucosamine (NAG) and D-glucosamine and pass out of the
body (Jackson et al., 1992). However, fish are not known to assimilate chitin, NAG or
D-glucosamine, and therefore, chitin contains energy that is unavailable to the predator
(Battle, 1935; MacDonald et al., 1982; Lindsay & Gooday, 1985; Medved, 1985).
Gastric evacuation studies using baitfish and invertebrate prey, including crustaceans,
commonly report the means or the ranges of prey energy densities on an ash-free basis
because these values exclude all inorganic elements, or ash, which is not a source of
energy for the predator (Brett & Higgs, 1970; Beamish, 1972). Clearly, unlike baitfish
prey, unavailable energy locked up in the crustacean’s exoskeleton, as well as inorganic
ash, must be taken into account when determining the energy density available from
12
crustacean prey. In a form of self-regulation, fish often appear to maintain a relatively
constant energy intake for metabolic function and growth. For example, if food resources
are lower in energy then predators will tend to eat more, but if prey are high in energy
then they eat less and maintain similar energy consumption levels (Grove et al., 1978;
Jobling, 1980a). This idea originates from Optimal Foraging Theory, which states that
animals will maximize their food (energy) intake per unit of time or minimize the time
required to meet their energy requirements (Emlen, 1966; MacArthur & Pianka, 1966;
Schoener, 1971). Optimal Foraging Theory assumes that the rate of energy intake, or
foraging success, is a proxy for fitness (Krebs & Kacelnik, 1991). As the availability of a
gag’s food resources decreases, the gag’s dietary niche breadth should expand to include
lower energy prey and increase the chance of prey encounters.
Predator size is another factor that can affect consumption and digestion models.
Absolute gastric evacuation rates (grams of food leaving the stomach per hour) tend to
increase with increasing predator body size, but relative rates (per unit body weight)
either decrease with increasing predator size or stay the same (Flowerdew & Grove,
1979; Jobling, 1980b; Bromley, 1994). For example, a 500 g grouper fed a 15 g meal
would have a slower absolute gastric evacuation rate than a 1000 g grouper fed an
identical 15 g meal because the smaller grouper would be consuming a much larger meal
relative to its body size. On the other hand, a 500 g grouper fed a 10 g meal (meal = 2%
of grouper’s body weight) would have a faster relative gastric evacuation rate than a 1000
g grouper fed a 20 g meal (meal = 2% of grouper’s body weight). The effect of predator
size on gastric evacuation rates must be considered when using standardized meals to
prevent an underestimation of consumption for small fish and an overestimation of
13
consumption for large fish (Adams & Breck, 1990). Studies have determined relative
gastric evacuation rates most often (Beamish, 1972; Swenson & Smith, 1973;
MacDonald et al., 1982; Ruggerone, 1989; dos Santos & Jobling, 1992). Both predator
weight and length has been used when quantifying the influences of body size on gastric
evacuation rates; length because it is easily measured and predator weight because it can
vary much more than length, depending on season and individual growth (Koed, 2001).
Many studies have shown positive correlations between gastric evacuation rates
and meal size, although some have shown negative correlations or no correlations at all
(Bromley, 1994). Differing definitions of gastric evacuation rates, including absolute
rates and rates relative to body size, expressed as
Absolute Rate = timeFoodofWeight
(6)
Relative to Body Size = 1−× timeWeightBodyFishFoodofWeight (7)
have led to these conflicting conclusions and have made comparisons of different
evacuation results problematic (Bromley, 1994). In general, it has been shown that an
increase in meal size leads to an increase in the rate of gastric emptying and an increase
in time to 100% evacuation (Jobling et al., 1977).
Like gastric evacuation estimates, calculating the original time of prey ingestion by
a wild predator and incorporating this required variable into a consumption model has
been difficult. One method involves calculating the original prey weights based on
vertebral column length, standard length, and maximum length regressions, and back-
calculating the time of prey ingestion based on its stage of digestion (Minton et al., 1981;
Adams et al., 1982; Lindberg et al., 2002):
14
Stage of Digestion = 1 - Digested Weight of Prey (8) Original Weight of Prey
Another method involves creating a quantitative and qualitative visual index of digestion
stages using numerical codes and prey descriptions (MacDonald et al., 1982; Kao, 2000;
Lindberg et al., 2002). An index of digestion may be a faster method due to the fact that
a general time of prey ingestion can be estimated based on the prey’s appearance without
having to back-calculate its size through regression analysis, but it may not be as accurate
a method since it can only provide a general, rather than specific, time after feeding.
Both methods provide inaccurate estimates of ingestion times at later periods of digestion
due to the fact that the prey eventually become unrecognizable to species and tend to
have broken or missing vertebral columns.
Currently, gastric evacuation rates have only been estimated from field collections
of gag consuming baitfish from artificial reefs off the west coast of Florida during the
warmer months of the year (Lindberg et al., 2002). Through preliminary field estimates
based on back-calculated original prey weights and an assumed linear model, time to
90% evacuation has been estimated at 15 hours and 100% evacuation at 16 hours for
baitfish prey (Lindberg et al., 2002). Several factors affecting this assumption of linear
digestion include the type of prey gag consume and the amount of energy within that
prey. Gag are commonly considered to be highly piscivorous, with invertebrates
accounting for no more than 5% of the total food volume in their stomachs (Naughton &
Saloman, 1995). Weaver (1996), however, noted a 17.1% index of relative importance
for crabs in the stomachs of gag between 300 and 400 mm standard length (374-489 mm
TL). In addition, gag off the west coast of Florida were observed to have a significant
proportion of crabs in their diet (13-14%) during the warmer months of the year
15
(Lindberg et al., 2002). These proportions contribute between 7% and 24% of the total
diet on a gross energy basis, with whole crab caloric densities estimated as 1.000 kcal/g
dry weight (Lindberg et al., 2002). The hard chitinous material found in crab
exoskeletons should have a strong influence on the gag’s digestion rate. Hard materials,
such as crab exoskeleton, have been shown to cause significant lag times in digestion,
followed by a rapid increase in evacuation rate and a subsequent leveling off as meal
remnants are retained in the stomach (Hopkins & Larson, 1990). As discussed earlier,
changes in digestion rates can lead to a slower return of appetite, thereby lowering the
frequency with which gag would consume meals. Generally, crab prey are less calorie
dense than baitfish prey because they commonly contain less lipid but also because the
chitin in their exoskeletons contain energy that is unavailable to most fish (Battle, 1935;
MacDonald et al., 1982; Lindsay & Gooday, 1985; Medved, 1985). To date, there have
been no studies that have attempted to correct for unavailable energy contained within the
chitin of crab prey exoskeletons. Considering that crabs contain unabsorbable chitin in
their exoskeletons, more ash, and less energy than baitfish prey, gag consuming higher
percentages of crab in their diet should consume prey less frequently and may have less
energy available for growth after their metabolic and waste removal energy needs have
been met. To maintain growth rates similar to gag consuming baitfish, gag consuming
crab prey must compensate for their reduction in rates of gastric evacuation and feeding
frequency by consuming more crabs per meal.
Interestingly, Lindberg et al. (2002) have shown that gag on artificial reefs
(Suwannee Regional Reef System) off the west coast of Florida are consuming baitfish
prey (round scad Decapterus punctatus, juvenile tomtate Haemulon aurolineatum, scaled
16
sardine Harengula jaguana, Spanish sardine Sardinella aurita) that are abundant but not
necessarily high in energy (1.03-1.14 kcal/g wet weight, depending on species) compared
to relatively lipid-rich adult baitfish or other fish species that often are 2-3 times more
calorie dense. The organic composition of individual prey (in terms of energy content)
and the varying amounts of different prey items in the diet of wild gag during the warmer
months of the year may therefore influence digestion rates significantly. Lindberg et al.
(2002) suggested that estimates of consumption rates of wild gag may be improved with
additional knowledge of prey-specific evacuation models, especially for portunid crab
prey. However, evacuation models specific to prey type and fish size are currently
unavailable for gag.
The overall goal of this study was to develop gastric evacuation models for gag
consuming baitfish and crustacean prey. The specific objectives were: (1) experimentally
determine gastric evacuation rates for gag as a function of prey type, either baitfish or
crab prey, in relation to gag size; (2) compare the effect of prey type (baitfish versus
crab) on the gastric evacuation rates of gag; (3) create qualitative and quantitative indices
of prey digestive states in order to estimate consumption times of prey sampled from the
stomach contents of wild gag; and (4) model evacuation rates of gag and compare the
models for best fit by prey type and gag size.
17
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18
Elapsed Time Since Feeding
% R
emai
ning
in th
e St
omac
h
Linear Model BtAY −= Where,
Y = % prey remaining in the stomach
A = Y-intercept B = gastric evacuation rate t = elapsed time after ingestion
BtAeY −= Where,
Y = % prey remaining in the stomach
A = Y-intercept B = gastric evacuation rate t = elapsed time after ingestion
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18
Elapsed Time Since Feeding
% R
emai
ning
in th
e St
omac
h
Exponential Model
BtAY −= Where,
Y = % prey remaining in the stomach
A = Y-intercept B = gastric evacuation rate t = elapsed time after ingestion 0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18
Elapsed Time Since Feeding
% R
emai
ning
in th
e St
omac
h
Square Root Model
Figure 1. Commonly used models depicting the gastric evacuation processes of different fish species.
18
( )[ ]CtBeAY ++
−=1
100 Where,
Y = % prey remaining in the stomach
A = estimated parameter B = scale parameter C = x-ordinate of the point of inflection of the curve
t = elapsed time after ingestion
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18
Elapsed Time Since Feeding
% R
emai
ning
in th
e S
tom
ach
Logistic Model
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18
Elapsed Time Since Feeding
% R
emai
ning
in th
e St
omac
h
Power Exponential
Model
( )BAtY −= 2
Where, Y = % prey remaining in the
stomach A = half life of decaying prey B = shape coefficient t = elapsed time after ingestion and
dashed lines show the potential variability in the shape coefficient
Figure 1. (continued)
CHAPTER 2 METHODS
Gag Collections and Maintenance
Gag were collected from the Suwannee Regional Reef System, a series of artificial
reefs off the west coast of Florida (28°59.06 N, 83°19.10 W to 29°20.91 N, 83°31.71 W),
at a depth of 13 m (40 feet) (Lindberg et al., 2002) between February 2003 and January
2004. Small (300-449 mm total length, TL), medium (450-599 mm TL), and large (600-
750 mm TL) gag were trapped by SCUBA divers or caught underwater on hook-and-line
using baited hooks. Grouper traps measured approximately 1.0 m by 0.9 m, were
constructed out of plastic-coated wire, and featured an opening approximately 0.46 m
high by 0.30 m wide. The gag were lifted to the surface in the traps, their air bladders
were vented (Lindberg et al., 2002), and they were transported 101 km to the University
of Florida’s (UF) Fisheries and Aquatic Science’s aquatic facility using aerated coolers
with 100% diffused oxygen supplied through carbon stones. While in transport to UF,
water changes, and ChlorAm-X (an ammonia, chloramines, and chlorine neutralizer)
were used as necessary to ease stress and bring the gag back in the best possible
condition.
Gag were held in 378 L (87-cm diameter) to 473 L (147-cm diameter) fiberglass
tanks in a recirculating saltwater system of approximately 4,163 L. Filtration equipment
included a 187 L sand filter, a 189.3 L bead filter, and a mesh bag filter covering the
tanks’ drainage pipe within the lower sump tank. Light regimes averaged 13.5 hrs of
light and 10.5 hrs of darkness per day, following the photoperiod of the eastern Gulf of
19
20
Mexico during the warmer months of the year (~May to November). Small transitional
lights mimicked dusk and dawn periods, turning on or off 1 hr before or after the main
lights. Gag were maintained individually or in small groups of 2-3 individuals at a mean
water temperature of 28.0oC (+ 1.0 oC), which corresponded to the average temperature
during the warmer months of the year at 12.2 m (40 ft) depth where the gag were
collected (Bledsoe & Phlips, 2000; Phlips & Bledsoe, 2002). Pieces of PVC cut 45-cm
long with 20-cm diameters were placed in the tanks to provide shelter for the gag. All
gag were measured for maximum TL, fork length (FL), and weighed within 2 weeks of
capture. Fish exhibiting any health problems were anesthetized with Tricaine-S
(Methanesulfonate, or MS-222), their bodies scraped and their gills and fins clipped for
microscopic analysis and possible bacterial, fungal, or parasitic identification. Ammonia
(0.0-0.3 mg/L), nitrite (0.0-0.5 mg/L), pH (8.0-8.3), and salinity (30-35 ppt) parameters
were kept within acceptable limits for marine fish by weekly tests, while nitrate levels (0-
30 mg/L) were tested monthly (Stickney & Kohler, 1990). Monthly water changes
replaced between 10-15% of the system’s total water capacity. ChlorAm-X was used as
needed, along with supplemental water changes, to neutralize any ammonia spikes. Gag
were fed on maintenance rations of thawed, whole baitfish or crab prey at 3.0% body
weight every other day in the morning or evening, based on current estimates of baitfish
prey average daily consumption during the warmer months being between 1.2 and 1.8%
body weight (Lindberg et al., 2002). Gag that did not feed within 1 week of initial
capture were anesthetized with Tricaine-S and tube-fed a slurry of ground fish to
stimulate subsequent natural feeding. Only fish feeding voluntarily on the maintenance
diet were used in feeding experiments.
21
Experimental Feeding Trials for Gastric Evacuation Rates
The model pelagic baitfish species and potential gag prey, the scaled sardine, were
caught by underwater cast nets towed by SCUBA divers over reef areas occupied by gag
during the Fall of 2002 and 2003. They were immediately placed on ice for transport to
UF, vacuum-sealed with a FoodSaver 550, and frozen until use. Portunid crab prey
Portunus gibbesii, also a potential gag prey item (Lindberg et al., 2002), were caught in
standard shrimp trawls at night off Horseshoe Beach or Keaton Beach, FL (29°25.10 N,
83°15.30 W to 29°30.50 N, 83°25.60 W), in the eastern Gulf of Mexico during the spring
of 2003 and 2004, with the help of commercial bait-shrimp fishermen. Crabs were
immediately placed on ice for transport, vacuum-sealed while being covered with bubble
wrap to prevent their spines from cutting the sealing bag, and frozen.
Acclimation periods for fish in previous studies have varied from 2 to 6 weeks
(Brodeur & Pearcy, 1987; Hopkins & Larson, 1990). Gag were acclimated for a
minimum of 2 weeks or until they voluntarily fed on baitfish or crab prey. Only
completely whole scaled sardines or portunid crabs were used in the feeding trials (e.g.,
all skin and fins intact in the sardine prey and all legs attached in the crab prey). Prior to
consumption by the gag, sardine prey were thawed and then measured for TL, FL,
vertebral column length (VCL: measured from the atlas/axis to the hypural plate), and
weighed. Crab prey were measured for carapace length (CL), carapace width (CW), and
weighed. Food was withheld for 1-2 days before baitfish feeding trials and for 2 days
before crab trials to insure that all maintenance rations had been evacuated from the gag’s
stomach. Meals weighing approximately 1.5% of the grouper’s body weight on a wet
weight basis were fed to individual small, medium, and large gag. Only trials where all
of the prey were consumed were included in this study. Stomach contents were
22
recovered via serial slaughter after various time intervals, ranging between 0.18 hrs (5
min) and 24 hrs. A serial slaughter method was necessary since previous lavage
techniques have only been between 65% and 77% complete, leaving whole crabs, pieces
of crab exoskeleton, large whole fish, and pieces of fish vertebrae and spines behind
(Lindberg et al., 2002). After a predetermined time interval, gag were sacrificed by
applying a brain-spinal pith (American Fisheries Society [AFS], 2004). The stomach
contents were recovered by removing the entire gastrointestinal tract from the esophagus
to the anus, opening up the stomach from the esophageal opening to the pylorus, and
gently scraping the contents out, without scraping so hard as to remove large amounts of
mucus off the stomach lining. Wet weight of the recovered stomach contents was taken
immediately by placing the contents on a damp sponge covered with a damp Kimwipe
(paper tissue) to remove excess water, transferring the stomach contents from the
Kimwipe to a preweighed weigh boat, and then weighing the contents to the nearest
0.0001 g. The % wet weight of the stomach contents remaining in the gag’s stomach was
calculated as
% Wet Weight = Wet Weight of Stomach Contents (g) x 100 (9) Remaining Wet Weight of Items Consumed (g)
Caloric Analysis of Prey and Stomach Contents
To determine the total caloric density of representative prey types, samples of
whole scaled sardines and portunid crabs were measured for TL, FL, VCL, and weighed
(g), or CL, CW, and weighed (g), respectively. Each individual prey was then chopped
up, freeze-dried to a constant weight, and % moisture content determined (methods
926.08 and 925.09, Association of Official Analytical Chemists [AOAC] 1990). Freeze
dried prey were then individually ground in a Braun coffee grinder until homogenized.
23
Additionally, some crab prey had to be chopped or cut by hand using small dissecting
scissors to fully homogenize the remaining hard pieces of exoskeleton. Energy densities
in kcal/g dry weight of each individual prey type (0.70-0.99 g sample) were then
determined using an isoperibol bomb calorimeter (Parr 1261; Moline, IL). Individual
prey weighing <0.70 g dry weight were spiked using benzoic acid tablets to facilitate
burning and gross heat determinations. Standardized corrections to gross energy were
made for fuse wire burn (15.0 mm) and acid production (10.0 ml). To correct for
inorganic materials within each individual, the percentage of ash was determined by
ashing a subsample of individual freeze-dried baitfish and crabs (0.04-1.57 g dry weight
sample) in a muffle furnace for 12 hours at 450oC to determine their % ash-free dry
weight (g) (method 923.03, AOAC 1990):
% Ash-Free = (Tissue Dry Weight – Tissue Ash Dry Weight) x 100 (10) Dry Weight Dry Tissue Weight
Differences in % moisture content, mean energy densities, and % ash content between
baitfish and crab prey were determined using a 2-tailed Satterthwaite t-test for unequal
variances after a determination of homogeneity of variance was made using Levene’s
Test (α=0.05). The ash-free caloric densities of the initial whole baitfish and crab prey
were calculated in kcal/g dry weight by dividing the total available dry weight caloric
value by the ash-free dry weight:
Kcal/g Ash-Free = Total Energy kcal/g Dry Weight (11) Dry Weight Ash-Free Dry Weight
Due to the fact that portunid crabs contain chitin in their exoskeletons, energy
density determinations were used to correct for the unavailable energy contained within
the chitin component. For this correction, whole crab prey, throughout a size range, were
measured for CL and CW and weighed (g). Soft body tissue was removed by cutting the
24
body and legs length-wise and placing the crab in a Pyrex beaker containing 15% KOH
solution. The KOH solution digested all protein completely through alkaline hydrolysis
while excluding chitin, a large beta-1,4-linked polysaccharide (Pandian, 1967). After
approximately 1 week, the residual chitinous exoskeleton from each crab was placed
against a light box (a lit background) to check for any remaining tissue. If any crab tissue
was remaining the crab was placed back in the 15% KOH solution for another 2-3 days or
until it was free of all tissue, after which the exoskeletons were rinsed with distilled
water. To determine the caloric density of the individual chitinous exoskeleton, the
exoskeleton was weighed for damp wet weight (as before), frozen (-80ºC), and freeze-
dried to a constant weight. The exoskeleton was then ground in a coffee grinder and
chopped or cut by hand using small dissecting scissors until homogenized. The energy
density of each exoskeleton was determined in the same manner as the whole baitfish and
crab prey. Initial whole crab wet weights (W), before the KOH treatments, were
regressed as a function of their exoskeleton’s total energy density (kcal/g dry weight) and
the resulting linear regression was used to estimate the amount of unavailable energy in
the chitin from the initial whole crab prey that had been used for % moisture and ash
determinations. The regression estimates of energy in the chitin exoskeleton of the initial
whole portunid crab were then subtracted from the crab’s total energy density, resulting
in the total available energy density in kcal/g dry weight of each initial individual whole
crab.
To determine the caloric densities of the individually recovered stomach contents,
the recovered stomach contents were freeze-dried to a constant weight. The stomach
contents were then homogenized and a subsample was analyzed for caloric density (as
25
per previous samples). For energetic regression analyses, the average caloric content of
each meal type (i.e., fish or crab) (kcal/g dry weight) was multiplied by the estimated dry
weight of the meal fed to determine the original caloric energy content of each meal fed
to each gag. For stomach contents containing crab prey, the total available crab energy
density per meal in kcal/g dry weight was estimated by back-calculating and then
subtracting the average exoskeletal energy density of the entire meal (using the previous
regression of individual whole crab W plotted as a function of gross exoskeletal energy)
from that meal’s total energy density.
Gastric Evacuation Models
Linear, exponential, square-root, logistic, and power exponential evacuation
models were fit to the wet weight and dry weight gastric evacuation data (% remaining in
the stomach) separately for each prey type and gag size in order to model the percentage
of food remaining with PPT (Hopkins & Larson, 1990). In the power exponential model,
the percentage of food remaining in the stomach was divided by 100 in order to fit the
proportion of food remaining in the stomachs to the model and, therefore, all Y-intercepts
had to be multiplied by 100 to be comparable with the outputs of other models (Elashoff
et al., 1982; Hopkins & Larson, 1990). Only initial zeroes, or the first feeding trials
resulting in 0% remaining at PPT, were included in the data set for each gag size class in
order to prevent biases associated with excluding all zeros, and thereby, increasing the
proportion of fish with faster digestion that are eliminated from the distribution (Olson &
Mullen, 1986), as well as to prevent biases from including zeros past endpoints. A model
was considered adequate if it: (1) showed homoscedasticity of variances; (2) had y-
intercept values (estimates of the % prey remaining at time 0) between 95-105% prey
remaining; and (3) had lower asymptotes showing less than 5% prey remaining (Hopkins
26
& Larson, 1990; Zar, 1999). For models that fit these initial selection criteria, r2 or R2
values were compared to determine which model best explained the gastric evacuation
data (Brodeur & Pearcy, 1987). All nonlinear R2 values were calculated as (Elashoff et
al., 1982; SAS v. 8.1, SAS Institute Inc., 1999)
( )TotalCSS
RSSR −= 12 (12)
where RSS = residual sums of squares and CSSTotal = corrected sums of squares total.
Differences among gastric evacuation models for small, medium, and large gag were
compared using Kimura’s Likelihood Ratio test (α=0.05) (Kimura, 1980; Haddon, 2001).
For curves that were not coincident (i.e., differed significantly),among gag size groups,
the data were pooled by prey type and modeled with either a gag weight (W) or TL
scaling exponent to create prey-specific models of gastric evacuation that account for
differences in gag size as
CWfunctionY ×= (13)
CTLfunctionY ×= (14)
where Y = % prey remaining in the stomach, function = evacuation model, and C = W or
TL scaling exponent.
Energetic Models
The linear, exponential, square-root, logistic, and power exponential models were
fit to the gross energy (kcal/g dry weight) of the stomach content data, as determined by
bomb calorimetry, for each size of gag consuming either baitfish or crab prey in order to
model the gross energy of the stomach contents as a function of PPT. The baitfish and
crab prey data sets were truncated as all recoverable stomach content energy values
remained constantly high and variation tended to increase as zero points, or empty
27
stomachs, began to show up. A model was considered adequate if it showed
homogeneity of variances. Again, the highest r2 or R2 values determined which model
best explained the gross energy data (Brodeur & Pearcy, 1987) and all nonlinear R2
values were calculated using Eqn (12).
Next, each of the models fit to the gastric evacuation data (i.e., the linear,
exponential, square-root, logistic, and power exponential models) were fit to the
percentage of energy (kcal/g dry weight) digested as a function of PPT for each prey type
and gag size, based on the average energy density of each prey type. Again, only initial
zeroes were included in the data set for each gag size class in order to prevent biases
(Olson & Mullen, 1986). Like the gastric evacuation data, the percentage of energy
digested in the stomach was divided by 100 and all Y-intercepts multiplied by 100 to fit
the proportion of energy digested to the power exponential model and to allow
comparisons between Y-intercept estimates (Elashoff et al., 1982; Hopkins & Larson,
1990). Adequate models showed: (1) homoscedasticity of variances; (2) Y-intercept
values (estimates of the % energy digested at time 0) between -0.5 and 0.5% energy
digested; and (3) upper asymptotes greater than 85% energy digested (Hopkins & Larson,
1990; Zar, 1999). As per previous analyses, the model of best fit was determined to be
the model that fit all selection criteria and had the highest r2 or R2 value (Brodeur &
Pearcy, 1987), with non-linear R2 values calculated using Eqn (12). Kimura’s Likelihood
Ratio test was used to compare the energy digestion models among gag sizes (α=0.05)
(Kimura, 1980; Haddon, 2001). Again, W and TL exponential scalers were given to
models that differed with gag size to create prey-specific models of the percentage of
energy digested over time [as per Eqns (13) and (14)].
28
Indices of Digestive States
Gag stomach contents recovered for evacuation rate models were immediately
analyzed for the state of prey digestion using the presence, absence, and appearance of
prey skin/carapace, eyes, muscle, and bones/exoskeleton. Numeric values, or codes,
were given to each individual prey item based on the approximate percentage of the prey
remaining (Tables 1 and 2) (modified from Lindberg et al., 2002). Digestion codes
correlated to prey descriptions, thereby creating both a quantitative (codes and
percentages of prey remaining) and qualitative (prey descriptions) index of prey digestive
states over time. For example, assigning a digestion index value of 0 to baitfish prey
would correlate to stomach contents that had complete prey items (intact eyes, skulls,
skin, and gut tracts) that were less than 5% digested (Lindberg et al., 2002). On the other
hand, an index value of 1 would indicate 5 to 10 % digestion and was correlated with
stomach contents that had prey with pieces of skin and muscle removed by digestion
(Lindberg et al., 2002). The maximum digestion index of 6 indicated well-digested prey,
or prey digested over 90%. Prey digestive states were evaluated for each prey item
recovered in the digestion rate feeding trials, then the mean digestion code per gag was
determined to create indices of scaled sardine and portunid crab digestive states after
PPT. These average code values for each size of gag consuming either baitfish or crab
prey were plotted as a function of PPT. Again, the first feeding trials in each gag size
class that resulted in 0% remaining at time were included in the model analyses but all
other 0 codes were excluded because it could not be determined exactly when 100%
evacuation occurred.
29
Models of Average Digestion Codes
Linear, exponential, square-root, logistic, and power exponential models were fit to
the average digestion code data for each prey type by gag size in order to model the
average digestion code of food remaining with PPT after feeding (MacDonald et al.,
1982). As with the evacuation models, the power exponential model used the proportions
of average digestion codes and, therefore, all Y-intercepts were multiplied by 6 (a
maximum digestion code of 6 equated to prey being ≥90% digested at PPT) to facilitate
comparison with the other models (Elashoff et al., 1982; Hopkins & Larson, 1990). In
addition, only initial codes of 6 were included in the data set for each gag size class. A
model was considered adequate if it: (1) showed homoscedasticity of variances; (2) had
y-intercept values (estimates of the average digestion code at time 0) between -0.5 and
0.5; and (3) had upper asymptotes showing greater than 82.5% prey digested, or a
digestion code=5.5. For models that fit the initial selection criteria, the highest r2 or R2
value determined which model best fit the gastric evacuation data (Brodeur & Pearcy,
1987). All nonlinear R2 values were calculated using Eqn (12). As with evacuation rates,
differences among models for small, medium, and large gag gastric evacuation rates were
tested using the maximum likelihood ratio test (α=0.05) (Kimura, 1980; Haddon, 2001).
30
Table 1. Indices of digestion for baitfish Harengula jaguana by gag over post-prandial time (hr).
Code Percent of Total
Fish Digested
Description
0 <5 Whole fish, complete VCL, most skin, head, skull, otoliths present, all meat, all guts, all bones present, most finrays, no chyme/digesta
1 5 -10 Mostly whole fish, complete VCL, most skin, head, skull, otoliths present, most meat but maybe bits missing, all guts and all bones present, some finrays maybe present, no chyme/digesta
2 10 - 25
Recognizable fish but maybe not complete, complete VCL, most skin but more missing than in code 1, complete or partial head, skull and otoliths present, most meat but more missing than code 1, most guts present, most bones present, most or all finrays gone, very little chyme/digesta
3 25 - 50 Mostly recognizable fish, complete VCL, some skin, partial head, complete or partial skull and otoliths present, some meat, some guts present, most bones present, no finrays, little chyme/digesta
4 50 - 75 May or may not be a recognizable fish, complete or incomplete VCL, little or no skin, no head, partial or no skull, otoliths present or absent, some meat, some guts present, bones present, no finrays, some chyme/digesta
5 75 - 90 Not a recognizable fish, incomplete VCL, bits of or no skin, no head, no skull, otoliths absent, little meat, no guts present, bones present, no finrays, more chyme/digesta than code 4
6 >90 Not a recognizable fish, incomplete VCL, no skin, no head, no skull, no otoliths, bits of or no meat, no guts, some bones present, no finrays, much chyme/digesta
31
Table 2. Indices of digestion for crab Portunus gibbesii by gag over post-prandial time (hr).
Code Percent of Total
Crab Digested
Description
0 <5 Whole crab recognizable to species, complete and hard carapace, all spines, all meat, all guts, all legs, no chyme/digesta
1 5 -10 Whole crab recognizable to species, complete carapace but getting soft and folding, spines getting soft, all meat, all guts, most legs, no chyme/digesta
2 10 - 25 Partial crab, partial soft carapace, carapace usually folded in, spines soft if present, all meat, all guts, few or no legs, no chyme/digesta
3 25 - 50 Partial crab, possibly recognizable to species, partial soft carapace, carapace usually folded in, spines soft if present, most meat, most guts, few or no legs, no chyme/digesta
4 50 - 75 Partial crab, possibly recognizable to species, partial soft carapace, carapace folded in or top/bottom missing, no spines, some meat, some guts, no legs, little chyme/digesta
5 75 - 90 Partial crab, partial very soft carapace, carapace folded in or top/bottom missing, no spines, some meat present but exposed, some guts, no legs, more chyme/digesta than code 4
6 >90 Mostly still recognizable as a crab based on shell parts and color, partial very soft carapace, carapace anterior/posterior missing, no spines, little exposed meat present, few guts, no legs, more chyme/digesta than code 5
CHAPTER 3 RESULTS
Gag and Prey Collections
A total of 66 gag were collected for gastric evacuation experiments; 21 small gag
(300-449 mm TL, 370-940 g), 26 medium gag (450-599 mm TL, 988-2295 g), and 19
large gag (600-750 mm TL, 2153-4702 g). The relationship between gag weight (W, in
g) as a function of gag maximum total length (TL, in mm) was given by (Figure 2):
W = 2E-05 x TL2.8865 R2 = 0.98 n=71 (15)
Baitfish fed to gag for use in the experimental feeding trials were on average 78.6 mm TL
(±0.31 mm S.E., range 64-93 mm, n=231) and weighed an average of 5.08 g (±0.06 g
S.E., range 2.9-8.5 g). Crabs fed to gag averaged 19.4 mm CL (±0.15 mm S.E., range
15.0-23.5 mm, n=111) and had an average mass of 5.93 g (±0.16 g S.E., range 2.77-11.68
g). Individual gag consumed meals of whole baitfish averaging 1.51% (±0.02% S.E.) of
their body weight, whereas meals fed to gag consuming meals of whole crabs averaged
1.45% (±0.02% S.E.) of their body weight. The small difference in average size of fish
and crab meals (0.06%) was attributed to gag being fed prey items that were completely
whole, and therefore individual prey items were not “pruned” in order to feed meals that
were exactly 1.5% of the fish’s body weight.
Gastric Evacuation Models for Gag Consuming Baitfish Prey
Baitfish feeding trials were completed on 40 gag, including 13 small gag, 16
medium gag, and 11 large gag. Based on the model selection criteria (homoscedasticity
of variances, y-intercepts between 95-105% prey remaining; and lower asymptotes
32
33
showing less than 5% prey remaining), the power exponential models best described the
gastric evacuation processes of small, medium, and large gag consuming baitfish prey
when using recovered stomach contents on a wet weight basis (Table 3, Figure 3a). All
power exponential models fit to the wet weight gastric evacuation data for gag
consuming baitfish prey were significant (p<0.0001). Initial zero points from fish
digesting their meals faster were included in the wet weight analysis while all zero points
(n=3) following this were dropped to prevent model bias. Additionally, one baitfish point
was dropped as it was more than 2 standard deviations away from the mean. There was
no apparent lag phase in the digestion of baitfish by gag as 5-30 min trials showed
between 99.9-98.5% of the prey remaining, with 92.5% remaining 1.5 hr after feeding.
The active parts of the evacuation curves, or times to 5% remaining, on a wet weight
basis for small, medium, and large gag were calculated to be 14.7, 19.5, and 17.4 hrs
PPT, respectively. On a dry weight basis, the power exponential models also best
described the gastric evacuation processes of small, medium, and large gag consuming
baitfish prey (Table 4, Figure 3b). All power exponential models fit to the dry weight
gastric evacuation data were also significant (p<0.0001). Again, the three initial zero
points were dropped to prevent model bias. Times to 5% remaining for small, medium,
and large gag on a dry weight basis were calculated as 9.4, 16.5 and 12.5 hours PPT,
respectively. Among the three different size classes of gag consuming baitfish, the power
exponential evacuation models were not coincident on either a wet weight or dry weight
basis (Maximum Likelihood Ratio [ML]: n=40, Χ2=21.03, df=2, p<0.0001 and n=40,
Χ2=11.18, df=2, p=0.0040, respectively), and size-specific models were therefore
retained. Scaling factors for gag W or TL incorporated into the wet weight and dry
34
weight power exponential models were significant (p<0.0001 for both expanded models
fit to the wet or dry weight data) and each met model selection criteria and had high R2
values (≥0.87) (Table 5, Figure 4). Although gag W and TL scaling exponents were
small (≤0.00134), the models were highly predictive (R2=0.87-0.97).
On a wet weight basis, the linear model met selection criteria for each size of gag,
was significant (p<0.0001), and explained between 93% and 95% of the variation in the
data (Table 3), as opposed to 90-96% of the wet weight variation explained by the power
exponential model. However, the linear model provided a much poorer fit (R2=0.76-
0.88) when using the gastric evacuation data on a dry weight basis. The square root and
logistic models were significant (p<0.0001) but did not meet selection criteria at every
gag size class using either the wet weight or dry weight data. Only small gag met criteria
when fit to the square root model using both the wet weight and dry weight data and was
significant (p<0.0001, R2=0.98 and 0.78, respectively). Although highly predictive and
significant (p<0.0001), the logistic models could only meet the selection criteria for small
gag when modeling the wet weight data (R2=0.99). The exponential models did not meet
selection criteria for any gag size using the wet weight or dry weight data.
Gastric Evacuation Models for Gag Consuming Crab Prey
Crab feeding trials were completed on 26 gag, including 8 small gag, 10 medium
gag, and 8 large gag. As with gag consuming baitfish, the power exponential models best
described the gastric evacuation processes of small, medium, and large gag consuming
crab prey on a wet weight basis (Table 6, Figure 5a). All power exponential models fit to
the crab wet weight gastric evacuation data were significant (p<0.0001). Again, initial
zero points from the faster digesting fish were included in the model analyses, however
one zero point following these fish was dropped to prevent model bias. The crab prey
35
caused long lag phases in digestion with noticeable breakdown only starting
approximately 6 hrs after ingestion. The active parts of the curves, or times to 5% prey
remaining, for small, medium, and large gag were calculated as 21.0, 19.6, and 24.5
hours PPT, respectively. On a dry weight basis, the power exponential models were also
the most adequate models to describe the gastric evacuation processes of small, medium,
and large gag consuming crab prey, although lag phases were approximately ≤3.0 hrs
PPT (Table 7, Figure 5b). As with the crab wet weight data, all power exponential
models fit to the crab dry weight gastric evacuation data were significant (p≤0.0007).
Again, one initial zero point was dropped to prevent model bias. Times to 95% gastric
evacuation were calculated as 20.1, 17.2, and 26.2 hrs PPT for small, medium, and large
gag consuming crab prey on dry weight basis, respectively. Models of gastric evacuation
for crab prey among the three different gag size classes differed significantly for both wet
weight and dry weight crab data (ML: n=26, Χ2=7.36, df=2, p=0.025 and n=26, Χ2=8.48,
df=2, p=0.014, respectively). Because gag size had a significant effect on the gastric
evacuation rate, predator size-specific models of crab evacuation rates were retained.
Power exponential models with scaling factors for gag W or TL incorporated were
significant (p<0.0001 for both expanded models fit to the wet weight or dry weight data)
and met selection criteria using both the wet weight and dry weight data (Table 8, Figure
6). As with the baitfish gastric evacuation data, gag W and TL scaling exponents were
small (≤0.00123) but the models were highly predictive (R2≥0.94).
The logistic models also met selection criteria, were significant (p≤0.0003), and fit
the crab prey wet weight data well (R2=0.96) (Table 6). However, on a dry weight basis,
the logistic model could only meet selection criteria using the medium gag data (R2=0.96)
36
(Table 7). In addition, the linear model adequately met selection criteria and was
significant (p≤0.0001) when using the medium gag dry weight data (R2=0.90), although
the model could not meet criteria when data were expressed in wet weight. The
exponential and square root models did not meet the selection criteria for any gag size
class.
Comparative Gastric Evacuation Models for Gag Consuming Baitfish versus Crab Prey
The expanded power exponential model with either W or TL exponential scalers fit
to the baitfish or crab wet weight data, pooled across all gag size classes by prey type,
differed significantly from one another (ML: n=66, Χ2=88.26, df=3, p<0.0001; n=66,
Χ2=88.40, df=3, p<0.0001, respectively) (Figure 7). Gastric evacuation of fish prey in
gag occurred earlier than crab prey on both a wet weight and dry weight basis, with no
lag period obvious with fish prey and at least a 5 hr lag period evident with crab prey.
Caloric Values of Baitfish and Crab Prey
Scaled sardine used in the prey composition analysis ranged from 67 to 111 mm TL
and had an average mass of 6.09 g (Table 9). Crabs ranged from 11.9 to 32.4 mm
maximum CL and had a mean mass of 7.03 g. At a mean of 73.47 % moisture, the
baitfish prey had a significantly greater moisture content than the mean for crab prey at
69.59% moisture (Levene: p=0.001, 2-tailed Satterthwaite t-test for unequal variance;
n=49, t=-4.77, p≤0.0001). As expected, the ash content of crabs (49.6%) was
significantly higher than the ash content of the baitfish prey (24.2%) (Levene: p<0.0001,
2-tailed Satterthwaite t-test for unequal variance; n=38, t=23.98, p<0.0001). The mean
caloric energy density of the baitfish prey was significantly greater than that of the crab
prey (Levene: p=0.009, 2-tailed Satterthwaite t-test for unequal variance; n=48, t=-31.05,
37
p≤0.0001), being approximately double (4.24 kcal/g dry weight versus 2.22 kcal/g dry
weight, respectively). Baitfish mean ash-free caloric energy density (5.60 kcal/g dry wt)
was also greater than the mean ash-free caloric energy density of the crab prey (4.55
kcal/g dry wt).
A representative size range of crabs (15.1–28.0 mm CL, mean mass=6.71 g) from
both 2003 and 2004 were soaked in 15% potassium hydroxide (KOH) to dissolve away
all soft tissues and proteins (Table 9). The remaining chitinous exoskeletons had an
average mass of 2.57 g and were used to correct for energy unavailable to the gag for
assimilation. The mean caloric energy density of the remaining chitinous crab
exoskeletons was 0.76 kcal/g dry weight. When the mean energy density of the crab
exoskeletons was subtracted from the mean total energy density of the initial whole crabs,
the mean caloric energy density available to the gag for assimilation was 2.03 kcal/g dry
weight. The mean available crab energy density was significantly lower than the mean
energy density of the baitfish prey (Levene: p=0.006, 2-tailed Satterthwaite t-test for
unequal variance; n=48, t=-40.50, p≤0.0001).
Energy Values for Recovered Gag Stomach Contents
The square-root model best described the total gross energy (kcal/g dry weight) of
the recovered baitfish stomach contents from each size of gag when plotted as a function
of PPT (Table X, Figure 8a). The square-root and exponential models fit to the baitfish
gross energy data were significant, for each size of gag (p<0.0001). However, the square
root model’s R2 values were identical to the exponential model’s R2 values for each size
of gag (R2=0.01-0.29) and both had very low R2 values, indicating that the square-root
and exponential models fit to the baitfish data were descriptive rather than predictive.
The baitfish data set was truncated at 16.5 hrs PPT because stomach content energy
38
densities remained constantly high (2.42-3.53 kcal/g dry weight, respectively) resulting in
the exclusion of three baitfish data points. Additionally, one baitfish data point was
dropped because it had a gross energy of zero at 16.5 hrs PPT. Y-intercept energy
densities for the stomach contents from baitfish trials were 4.41, 4.21, and 4.59 kcal/g dry
weight for small, medium, and large gag at time zero and remained fairly constant,
ending at 4.31, 4.71, and 4.95 kcal/g dry weight at 16.5 hrs PPT, respectively (Figure 8a).
The linear model met the selection criterion of homogeneity of variances but was
insignificant at each gag size (p=0.7962, 0.0627, and 0.0907 for small, medium, and large
gag, respectively). Conversely, the linear model best fit the total gross energy (kcal/g dry
weight) of the crab prey stomach contents consumed by each size of gag when plotted as
a function of PPT due to its simplicity (Table 11, Figure 8b). The linear, exponential, and
square-root models were significant (p≤0.0159) regardless of gag size and R2 values were
indistinguishable (R2=0.89-0.92, for each model, regardless of gag size). Model R2
values only differed by 0.01-0.02 when small gag were fed crab (R2=0.92, 0.90, and 0.91
for the linear, exponential, and square-root models, respectively. Crab data sets for total
gross energy recovered were truncated at 20.0 hrs PPT, because again, stomach content
energy densities remained constantly high (3.53-5.24 kcal/g dry weight), resulting in the
exclusion six crab data points. Additionally, one crab data point was dropped at 20.0 hrs
PPT because it had a gross energy of zero. Energy values for the stomach contents from
crab trials using the linear model were 2.29, 2.51, and 2.24 kcal/g dry weight for small,
medium, and large gag at PPT=0 and increased to 3.58, 3.38, and 3.39 kcal/g dry weight
at 20.0 hrs PPT, respectively (Figure 8b). The logistic and power exponential models
39
could not converge on parameter estimates on either the baitfish or the crab gross energy
data, and these models were therefore excluded.
When the percentage of gross energy digested (energy passed out of stomach) was
regressed against PPT, the power exponential models best fit the evacuation processes of
each size of gag consuming either baitfish or crab prey (Tables 12 and 13, Figure 9). All
power exponential models were significant (p<0.0001) at each size of gag, regardless of
prey type. Six gag fed baitfish and three gag fed crab were excluded from the analysis
due to analytical problems. While initial points representing 100% gross energy digested
at PPT were included in the regression analyses, three gag fed baitfish and one gag fed
crab were dropped to prevent model bias. No other models met both the Y-intercept
selection criteria of ≤5% energy digested and an upper asymptote of ≥85% energy
digested for small, medium, and large gag consuming baitfish prey. Both the power
exponential and linear models met model criteria and were significant (p<0.0003) when
gag were consuming crab prey, however r2 values for the linear model were lower (Table
13). Both the baitfish and crab prey energy digested data exhibited lag phases. Baitfish
prey energy did not begin to digest until 2-3 hrs PPT, while crab prey energy did not
begin to digest until approximately 4.5 hrs PPT.
The power exponential models describing the percentage of baitfish energy density
digested over time for each gag size differed significantly (ML: n=35, Χ2=13.22, df=2,
p=0.0013). Exponential scaling factors incorporating gag W and TL were added to the
original power exponential model to retain the size-specific models of baitfish energy
digestion (Figure 10a). Both expanded models with W and TL scaling factors met the
selection criteria and were significant (p<0.0001). The W and TL scaling exponents were
40
small (C=0.0180 and 0.0403, respectively) but highly predictive (R2=0.91 and 0.92,
respectively). The power exponential models describing the percentage of crab energy
digested over time for each gag size were coincident (ML: n=22, Χ2=2.23, df=2, p=0.33),
therefore, the data were pooled and fit to the original model (Figure 10b). The power
exponential model fit to the combined percentage data of crab energy digestion fit the
selection criteria, was significant (p<0.0001), and was highly predictive (R2=0.91,).
Indices of Digestion for Baitfish Consumed by Gag
All gag consumed the baitfish and crab prey whole with very minimal, if any, loss
of scales and skin of fish prey, or the cracking of carapaces and limb removal of crab
prey. Digestion was observed to be most rapid in prey located in the pylorus of the
stomach. Baitfish prey digested continuously after their initial consumption (Table 14).
At 9 hrs PPT, incomplete baitfish vertebral columns began to appear, and heads, skulls,
otoliths, guts, skin, and fin rays were often absent (code>4) . At 12 hrs PPT the baitfish
were unrecognizable with only incomplete vertebral columns and very small bits of meat
present (code=6). Between 16 and 18 hrs after consumption, the baitfish consisted only
of chyme, loose bones, and digesta (code=6). Due to the fact that the baitfish became
unrecognizable and indistinguishable from one another after being 90% digested, all
recovered stomach contents ≥90% digested were given a digestion code=6.
The power exponential model best described the relationship between the average
digestion code for each gag and PPT for small, medium, and large gag consuming
baitfish prey (R2≥0.91) (Table 15, Figure 11a). All power exponential models fit to the
average digestion code data for gag consuming baitfish prey were significant (p<0.0001).
Initial points with digestion codes of 6 were included in the analyses, however, three data
points with a code of 6 were dropped to prevent model bias. There were slight lag phases
41
detected as 2.5 hr trials resulted in average digestion codes of 1.12 or less. For each size
of gag, the power exponential model’s upper asymptote equaled or exceeded an average
digestion code of 5.5, which equates to approximately 82.5% digestion. While the
logistic models met selection criteria, were significant (p<0.0001), and had high R2
values for each size of gag (all R2≥0.93), the model’s upper asymptotes did not reach an
average digestion code of 5.5 for large gag. The linear, exponential, and square root
models could not meet selection criteria for any gag size (Y-intercepts≥0.63). The
average digestion code data differed significantly between the three different gag size
classes (ML: n=40, Χ2=7.54, df=2, p=0.0231) when fit to the power exponential model.
Because predator size had a significant effect on the gag’s average digestion code values
over PPT, the gastric evacuation data for gag consuming baitfish prey were pooled and
scaling factors for gag W or TL incorporated (Figure 11b). The expanded models scaled
for W or TL met selection criteria and were significant (p<0.0001), and, although the
scaling exponents were small (C=0.0202 and 0.0355, respectively), the expanded models
were highly predictive (both R2=0.91).
Indices of Digestion for Crab Prey Consumed by Gag
Crab prey stomach content analyses showed that several legs were commonly
detached from the crab carapace located toward the pyloric end of the stomach after the
lag phase, approximately 6-8 hrs PPT. However, the exoskeleton was not yet softened
nor had it been noticeably digested at 6-8 hrs PPT. Subsequently, the carapace began to
soften and fold in on itself while the carapace spines began to erode. Digestion continued
with all appendages detaching from the carapace(s) and with further softening. The
carapace softened, folded, and eroded until holes formed and allowed digestive enzymes
access to the body cavity around 12 hrs PPT. This pattern continued until around 15 hrs
42
PPT when the crab prey was recognizable only by scattered pigmented carapace piece(s)
and small bits of partial carapace, void of any meat, gut, or appendages. From 18 to 24
hrs PPT, the crabs were often unrecognizable because only tiny bits of carapace were
present in the digesta.
The power exponential model best described the average digestion code values as a
function of PPT for small, medium, and large gag consuming crab prey (R2≥0.95) (Table
16, Figure 12a). As with the baitfish data, all power exponential models fit to the average
digestion code data for small, medium, and large gag consuming crab were significant
(p≤0.0008). Again, initial points with a digestion code of 6 were included in the
regression analyses but one point with a code of 6 was dropped to prevent model bias.
The average digestion code data clearly showed a lag in crab digestion for each size of
gag as codes at 6 hrs PPT were still <1.0. For all sizes of gag, the power exponential
models’ average digestion code estimates equaled or exceeded code 5.5 at 24 hrs PPT,
which equates to approximately 82.5% of the total prey digested at time. Average
digestion codes for each size of gag consuming crab prey differed significantly (ML:
n=26, Χ2=10.5601, df=2, p=0.0051). Because gag size had a significant effect on the
average digestion code values of crab prey over PPT, size-specific models were retained
and scaling factors for either W (C=0.0188) or TL (C=0.0258) were added (Figure 12b).
The resulting expanded power exponential models met selection criteria, were significant
(p<0.0001), and were highly predictive (R2=0.95).
The square root models were significant (p<0.0001) and had high R2 values (0.86-
0.92) for each size of gag consuming crab prey, but slightly overestimated the average
digestion code at the earliest time intervals (Y-intercepts = 0.12 -0.41 hrs PPT). The
43
linear model met selection criteria and was significant (p=0.0002) when fit to the large
gag data, however this model projected the Y-intercept of the small gag size class at -2.04
hrs and medium gag at -0.80 hrs PPT, both of which are well below acceptable criteria.
In addition, the logistic models were significant (p≤0.0008), fit the small, medium, and
large gag size classes well, estimating Y-intercepts at 0.01, 0.06, and 0.15, respectively,
and had R2 values at 0.94, 0.96, and 0.99, respectively. However, the logistic models
could not adequately estimate the average digestion code at time of large gag consuming
baitfish, as the upper asymptote estimate could not reach an average code of 5.5 at 24 hrs.
Again, the exponential model could not meet selection criteria for any size of gag
consuming crab prey.
44
y = 2E-05x2.8865
R2 = 0.9878
0
1000
2000
3000
4000
5000
300 400 500 600 700 800
Total Length (TL)
Wei
ght (
g)
Figure 2. The relationship of gag weight (W) as a function of total length (TL) for gag between 300 and 750 mm TL.
45
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100
120Pe
rcen
t Foo
d R
emai
ning
in S
tom
ach
L
L
L
L
L
(a)
L
M
M
M
M
M
M
S
SS
S
SS
S
S
S
S
S
S
S
L
L
L
L
L
MM
M
M
M
M
M
M
M
M
…. Small Gag (S) ( ) 68.116.62 tY −=
____ Medium Gag (M) ( ) 52.1
44.72 tY −=
---- Large Gag (L) ( ) 89.101.82 tY −=
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100
120
Perc
ent F
ood
Rem
aini
ng in
Sto
mac
h
(b)
LLM
M
M
M
M
M
S
SS
S
S S
S
S
S
S
S
S
S
L
L
L
L
L
L
L
L
M
M
M
M
M
M
M
M
M
M
L
…. Small Gag (S) ( ) 64.326.62 tY −=
____ Medium Gag (M) ( ) 34.1
54.52 tY −=
---- Large Gag (L) ( ) 85.247.72 tY −=
Figure 3. The power exponential model describing the gastric evacuation processes of
small, medium, and large gag consuming baitfish prey (scaled sardines) on a: (a) wet weight basis (see Table 3) and (b) dry weight basis (see Table 4).
46
(a)
0 5 10 15 20 25
Time Elapsed
0
20
40
60
80
100
120
Per
cent
Foo
d R
emai
ning
in S
tom
ach
…. Weight ( ) 0013.064.1
12.72 WY t×= −
____ TL ( ) 0008.065.1
16.72 TLY t×= −
Post-Prandial Time (PPT) (hr)
(b)
0 5 10 15 20 25
Time Elapsed
0
20
40
60
80
100
120
Per
cent
Foo
d R
emai
ning
in S
tom
ach
…. Weight ( ) 0024.085.140.62 −− ×= WY t
____
TL ( ) 0044.088.146.62 −− ×= TLY t
Post-Prandial Time (PPT) (hr)
Figure 4. The power exponential model expanded to include weight (W) or TL as scalers
describing the combined gastric evacuation data of all gag consuming baitfish prey (scaled sardines) on a: (a) wet weight basis and (b) dry weight basis (see Table 5).
47
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100
120P
erce
nt F
ood
Rem
aini
ng in
Sto
mac
h L
L
L
L
L
L
L
M
M
M
M
M M
MM
M
M
M
(a)
S
S
S
S
S
SS
S
…. Small Gag (S) ( ) 85.249.122 tY −=
____ Medium Gag (M) ( ) 07.3
17.122 tY −=
---- Large Gag (L) ( ) 28.291.122 tY −=
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100
120
Per
cent
Foo
d R
emai
ning
in S
tom
ach
L
(b)
L
L
LM
M
S
S
S
S
S
SS
S
L
L
L
L
M
MM M
MM
M
M
…. Small Gag (S) ( ) 80.192.82 tY −=
____ Medium Gag (M) ( ) 32.2
19.92 tY −=
---- Large Gag (L) ( ) 23.100.82 tY −=
Figure 5. The power exponential model describing the gastric evacuation processes of
small, medium, and large gag consuming crab prey (Portunus gibbesii) on a: (a) wet weight basis (see Table 6) and (b) dry weight basis (see Table 7).
48
(a)
0 5 10 15 20 25
Time Elapsed
0
20
40
60
80
100
120
Per
cent
Foo
d R
emai
ning
in S
tom
ach
…. Weight ( ) 0008.065.247.122 WY t
×= − ____
TL ( ) 0012.064.246.122 TLY t
×= −
Post-Prandial Time (PPT) (hr)
(b)
0 5 10 15 20 25
Time Elapsed
0
20
40
60
80
100
120
Per
cent
Foo
d R
emai
ning
in S
tom
ach
…. Weight ( ) 0037.088.130.92 −− ×= WY t
____
TL ( ) 0037.086.126.92 −− ×= TLY t
Post-Prandial Time (PPT) (hr)
Figure 6. The power exponential model expanded to include weight (W) or TL as scalers
describing the combined gastric evacuation data of all gag consuming crab prey (Portunus gibbesii) on a: (a) wet weight basis and (b) dry weight basis (see Table 8).
49
(a)
0 5 10 15 20 25
Time Elapsed
0
20
40
60
80
100
120
Per
cent
Foo
d R
emai
ning
in S
tom
ach C
F
F
F
F
F
F
F
F
F
F
F
FF
F
F
F
F
F
F
F
F
F
F
FF
F
F
F
FF
F
FF
F
F
F
F
F
F
F C
C
C
C
C
C
C
C
C
C
C C
CC
C
C
C
C
C
C
C
C
CC
C
____ Baitfish (F) ( ) 0013.064.1
12.72 WY t×= −
…. Crab (C) ( ) 0008.065.247.122 WY t
×= −
Post-Prandial Time (PPT) (hr)
(b)
0 5 10 15 20 25
Time Elapsed
0
20
40
60
80
100
120
Per
cent
Foo
d R
emai
ning
in S
tom
ach C
F
F
F
F
F
F
F
F
F
F
F
FF
F
F
F
F
F
F
F
F
F
F
FF
F
F
F
FF
F
FF
F
F
F
F
F
F
F C
C
C
C
C
C
C
C
C
C
C C
CC
C
C
C
C
C
C
C
C
CC
C
____ Baitfish (F) ( ) 0008.065.1
16.72 TLY t×= −
…. Crab (C) ( ) 0012.064.246.122 TLY t
×= −
Post-Prandial Time (PPT) (hr)
Figure 7. The expanded power exponential model describing the combined gastric
evacuation wet weight data of all gag consuming both baitfish (scaled sardines) and crab (Portunus gibbesii) prey incorporated with: (a) weight (W) and (b) total length (TL) scalers (see Tables 5 and 8).
50
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
1.0
2.0
3.0
4.0
5.0
6.0G
ross
Ene
rgy
(kca
l/g d
ry w
t)
L LLL L
LL
L
L LL
MMM
M
M
M
MMM
M
MMMM
M SS
S
S
SS
S
SS
S
S
S
(a)
…. Small Gag (S) tY 0015.04139.4 +=____
Medium Gag (M) tY 0073.02053.4 −=
---- Large Gag (L) tY 0051.05871.4 −=
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
0.7
1.4
2.1
2.8
3.5
4.2
Gro
ss E
nerg
y (k
cal/g
dry
wt)
(b)
L
MS
S
S
S
S
SL
L
LM
M
M
ML
…. Small Gag (S) tY 0645.02935.2 += ____
Medium Gag (M) tY 0436.05073.2 +=
---- Large Gag (L) tY 0578.02388.2 +=
Figure 8. Models describing the gross energy of recovered stomach contents from small,
medium, and large gag consuming: (a) baitfish prey (scaled sardines), fit to a square-root model (see Table 10) and (b) crab prey (Portunus gibbesii), fit to a linear model (see Table 11).
51
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100P
erce
nt G
ross
Ene
rgy
Dig
este
d (k
cal/g
dry
wt)
L
L
L
L
L
L
L
L
L
LM
M
M
M
M
M
M
M
M
M
M
M
MM
MM
M
M
M
S
SS
S
S
SS
S
S
S
S
(a)
…. Small Gag (S) ( ) 98.164.52
−−=tY
____ Medium Gag (M) ( ) 46.1
33.62−−=
tY
---- Large Gag (L) ( ) 89.161.72
−−=tY
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100
Per
cent
Gro
ss E
nerg
y D
iges
ted
(kca
l/g d
ry w
t)
L
(b)
L
M
M
S
S
S
S
S
S
S
L
L
L
L
M
MM M
M
M
L
…. Small Gag (S) ( ) 73.107.92
−−=tY
____ Medium Gag (M) ( ) 78.2
64.92−−=
tY
---- Large Gag (L) ( ) 98.123.102
−−=tY
Figure 9. The power exponential model describing the percentage of stomach content
energy digested over elapsed time for small, medium, and large gag consuming: (a) baitfish prey (scaled sardines) (see Table 12) and (b) crab prey (Portunus gibbesii) (see Table 13).
52
(b)
____ All Gag ( ) 51.2
21.102−−=
tY
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100
Per
cent
Gro
ss E
nerg
y D
iges
ted
(kca
l/g d
ry w
t)
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
20
40
60
80
100
Per
cent
Gro
ss E
nerg
y D
iges
ted
(kca
l/g d
ry w
t)
…. Weight ( ) 0180.032.139.72 WY t
×=−−
____ TL ( ) 0403.012.1
69.82 TLY t×=
−−
(a) Figure 10. The power exponential model: (a) expanded to include W or TL as scalers
describing the combined stomach content energy digestion data for all gag consuming baitfish prey (scaled sardines) and (b) describing the combined stomach content energy digestion data for all gag consuming crab prey (Portunus gibbesii).
53
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
1
2
3
4
5
6Av
erag
e D
iges
tion
Cod
e
(a)
L
MM
M
M
S
SS
S
S
S
S
S
S
S
S
S
S
L
L
L
L
L
L
L
L
L
L
MM
M
M
M
M
M
M
M
M
M
M
…. Small Gag (S) ( ) 66.118.42
−−=tY
____ Medium Gag (M) ( ) 84.1
56.42−−=
tY
---- Large Gag (L) ( ) 97.179.52
−−=tY
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
1
2
3
4
5
6
Ave
rage
Dig
estio
n C
ode
(b)
…. Weight ( ) 0202.030.171.52 WY t
×=−−
____ TL ( ) 0355.030.1
33.62 TLY t×=
−−
Figure 11. The power exponential model describing the average digestion code values of
gag consuming baitfish prey (scaled sardines) over elapsed time: (a) small, medium, and large gag (see Table 15) and (b) all gag fit to the expanded model using W or TL as scalers.
54
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
1
2
3
4
5
6Av
erag
e D
iges
tion
Cod
e
(a)
L
LLM S
S
S
S
S
SS
S
L
L
L
L
L
M
M
M
M
M
M
MM
M
M
…. Small Gag (S) ( ) 80.417.112
−−=tY
____ Medium Gag (M) ( ) 47.3
29.112−−=
tY
---- Large Gag (L) ( ) 38.272.92
−−=tY
0 5 10 15 20 25
Post-Prandial Time (PPT) (hr)
0
1
2
3
4
5
6
Ave
rage
Dig
estio
n C
ode
(b)
…. Weight ( ) 0188.059.297.112 WY t
×=−−
____TL ( ) 0258.047.2
19.122 TLY t×=
−−
Figure 12. The power exponential model describing the average digestion code values of
gag consuming crab prey (Portunus gibbesii) over elapsed time: (a) small, medium, and large gag (see Table 16) and (b) all gag fit to the expanded model using W or TL as scalers.
Table 3. Regression parameters of the gastric evacuation wet weight data of gag consuming baitfish prey fit to each model, for small gag (n=13), medium gag (n=16), and large gag (n=11). Models meeting selection criteria are highlighted in bold; assumptions met for heterogeneity of variance and characteristics of the lower asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p<0.0001) (see Figure 3).
55
Model Gag Size A B C Y-intercept MSE R2 Homogeneity of Variance
Lower Asymptote
<5%
Linear Small 95.50 6.32 95.50 85.81 0.95 Y
Medium
99.85 6.03 99.85 52.15 0.95 Y
Large 103.19 6.18 103.19 83.44 0.93 YExponential Small 106.50 0.14 106.50 68.49 0.96 N N
Medium 114.00 0.12 114.00 70.54 0.93 N N
Large 115.70 0.11 115.70 120.70 0.90 N YSquare Root Small 103.80 0.52 103.80 29.42 0.98 Y Y
Medium 108.40 0.45 108.40 44.09 0.96 Y Y
Large 111.30 0.44 111.30 77.63 0.94 Y YLogistic Small 92.89 -0.54 -5.93 96.32 11.47 0.99 Y Y
Medium 91.23 -0.38 -7.02 93.90 69.43 0.94 Y N
Large 86.84 -0.53 -7.20 98.16 40.14 0.97 Y N
Small 6.16 1.68 100.00a 0.0012 0.99 Y YPower Exponentiala
Medium 7.44 1.52 100.00a 0.0015 0.96 Y Y
Large 8.01 1.89 100.00a 0.0047 0.96 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=1 (100%) and not uniquely estimated.
Table 4. Regression parameters of the gastric evacuation dry weight data of gag consuming baitfish prey fit to each model, for small gag (n=13), medium gag (n=16), and large gag (n=11). Models meeting selection criteria are highlighted in bold; assumptions met for homogeneity of variance and characteristics of the lower asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p<0.0001) (see Figure 3).
56
Model Gag Size A B C Y-intercept MSE R2 Homogeneity of Variance
Lower Asymptote
<5%
Small 90.21 6.11 90.21 460.60 0.76 YMedium
90.59 6.05 90.59 133.16 0.88 Y
Linear
Large 106.44 6.84 106.44 206.65 0.88 YSmall 99.38 0.14 99.38 489.00 0.74 Y NMedium 114.60 0.16 114.60 50.47 0.96 Y N
Exponential
Large 124.50 0.13 124.50 200.50 0.88 Y NSmall 97.86 0.52 97.86 425.20 0.78 Y YMedium 106.30 0.57 106.30 51.63 0.96 Y Y
Square Root
Large 119.70 0.54 119.70 141.00 0.92 Y YSmall 90.23 -24.88 -5.98 100.00 303.00 0.84 N NMedium 91.00 -0.49 -5.27 93.46 78.40 0.94 Y N
Logistic
Large 91.41 -0.66 -7.16 99.21 50.86 0.97 Y N
Small 6.26 3.64 100.00a 0.0368 0.81 Y Y
Medium 5.54 1.34 100.00a 0.0050 0.95 Y Y
Power Exponentiala
Large 7.47 2.85 100.00a 0.0060 0.96 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=1 (100%) and not uniquely estimated.
Table 5. Regression parameters of the pooled gastric evacuation data (n=40) of gag consuming baitfish prey on a wet and dry weight
basis fit to the expanded power exponential models with either gag weight or TL scaling exponents. Assumptions met for constant variance and characteristics of the lower asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p<0.0001) (see Figure 4).
57
Stomach Content Conditiona
Scaler for Gag A B C Y-intercept MSE R2 Homogeneity
of Variance Lower
Asymptote <5%
Wet Weight Weight 7.12 1.64 0.00134 100.00a 0.00537 0.96 Y Y
Wet Weight TL 7.16 1.65 0.00081 100.00a 0.00538
0.96 Y Y
Dry Weight Weight 6.40 1.85 -0.00240 100.00a 0.01900 0.87 Y Y
Dry Weight TL 6.46 1.88 -0.00444 100.00a 0.01900 0.87 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=1 (100%) and not uniquely estimated.
Table 6. Regression parameters of the gastric evacuation wet weight data of gag consuming crab prey fit to each model, for small gag (n=8), medium gag (n=10), and large gag (n=8). Models meeting selection criteria are highlighted in bold; assumptions met for homogeneity of variance and characteristics of the lower asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p<0.0001) (see Figure 5).
58
Model Gag Size A B C Y-intercept MSE R2 Homoogeneity
of Variance Lower
Asymptote <5%
Small 125.75 5.88 125.75 113.32 0.93 YMedium
112.41 5.04 112.41 114.04 0.94 Y
Linear
Large 111.17 4.81 111.17 56.94 0.96 YSmall 173.70 0.11 173.70 171.30 0.88 N Y
Medium 130.40 0.09 130.40 301.80 0.63 N N
Exponential
Large 118.80 0.08 118.80 207.50 0.87 N NSmall 149.50 -0.43 149.50 102.70 0.93 Y N
Medium 124.90 -0.38 124.90 155.20 0.93 Y Y
Square Root
Large 115.90 -0.31 115.90 124.30 0.92 Y YSmall 87.76 -0.49 -11.42 99.69 54.39 0.96 Y Y
Medium 101.30 -0.37 -12.31 98.96 14.82 0.99 Y Y
Logistic
Large 129.90 -0.19 -16.23 94.70 77.68 0.96 Y YSmall 12.49 2.85 100.00a 0.0066 0.96 Y Y
Medium 12.17 3.07 100.00a 0.0015 0.99 Y Y
Power Exponentiala
Large 12.91 2.28 100.00a 0.0050 0.96 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=1 (100%) and not uniquely estimated.
Table 7. Regression parameters of the gastric evacuation dry weight data of gag consuming crab prey fit to each model, for small gag (n=8), medium gag (n=10), and large gag (n=8). Models meeting selection criteria are highlighted in bold; assumptions met for homogeneity of variance and characteristics of the lower asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p≤0.0007) (see Figure 5).
59
Model Gag Size A B C Y-intercept MSE R2 Homogeneity of Variance
Lower Asymptote
≤5%
Small 90.47 4.46 90.47 117.22 0.87 YMedium
102.60 4.85 102.60 212.60 0.90 Y
Linear
Large 89.47 4.16 89.47 38.49 0.97 YSmall 137.10 0.13 137.10 128.60 0.86 Y NMedium 134.80 0.12 134.80 181.20 0.91 Y Y
Exponential
Large 103.10 0.10 103.10 63.90 0.95 Y NSmall 114.00 0.42 114.00 88.74 0.90 N YMedium 125.60 0.46 125.60 89.84 0.96 N Y
Square Root
Large 97.24 0.34 97.24 36.07 0.97 N YSmall 98.91 -0.29 -9.07 93.40 76.90 0.92 Y YMedium 96.98 -0.45 -8.88 98.18 84.31 0.96 Y Y
Logistic
Large 105.00 -0.19 -9.77 86.04 64.25 0.96 Y Y
Small 8.92 1.80 100.00a 0.0081 0.91 Y Y
Medium 9.19 2.32 100.00a 0.0058 0.97 Y Y
Power Exponentiala
Large 8.00 1.23 100.00a 0.0052 0.96 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=1 (100%) and not uniquely estimated.
Table 8. Regression parameters of the pooled gastric evacuation data (n=26) of gag consuming crab prey on a wet and dry weight basis fit to the expanded power exponential models with either gag weight or TL scaling exponents. Assumptions met for constant variance and characteristics of the lower asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p<0.0001) (see Figure 6).
60
Stomach Content Conditiona
Scaler A B C Y-intercept MSE R2 Homogeneity of Variance
Lower Asymptote
<5%
Wet Weight Weight 12.47 2.65 0.00084 100a 0.00528 0.97 Y Y
Wet Weight TL 12.46 2.64 0.00123 100a 0.00528
0.97 Y Y
Dry Weight Weight 9.30 1.88 -0.00370 100a 0.00752 0.94 Y Y
Dry Weight TL 9.26 1.86 -0.00371 100a 0.00755 0.94 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=1 (100%) and not uniquely estimated.
Table 9. Composition of representative baitfish Harengula jaguana and crab Portunus gibbesii prey types used in gastric evacuation trials of gag, values are means (±S.E.).
61
Parameters Baitfish Crab ExoskeletonCrab
Number 25 24 23
TL or Carapace Length (mm) 85.04 (1.99) 19.76 (0.93) 19.96 (0.78)
TL or Carapace Length (mm) Range 67 - 111 11.9 - 32.4 15.1 - 28.0
Mass (g) 6.09 (0.36) 7.03 (1.22) 2.57 (0.36)
Mass Range (g) 3.2 - 10.7 1.0 - 29.3 1.9 - 19.0
Exoskeleton Mass Range After KOH Treatment (g) 0.7 - 7.2
% Moisture 73.47 (0.18) 69.62 (0.79)
% Ash 24.22 (0.27)) 49.61 (1.03)
Caloric Density (kcal/g dry weight) 4.24 (0.02) 2.22 (0.06) 0.76 (0.03)
Caloric Ash-Free Energy Density (kcal/g ash-free dry weight) 5.60 (0.03) 4.55 (0.07)
Caloric Energy Density Available for Assimilation (kcal/g dry weight) 4.24 (0.02) 2.03 (0.00)
Table 10. Regression parameters for models describing the gross energy (kcal/g dry weight) of the stomach contents as a function of post-prandial time (PPT) by all gag consuming baitfish prey fit to the linear, exponential, and square root models, for small gag (n=12), medium gag (n=15), and large gag (n=11). All models met selection criterion; assumptions met for homogeneity of variance are given by Y (yes) or N (no). Exponential and square-root models were significant at the α=0.05 level (p<0.0001) (see Figure 8).
62
Model Gag Size A B Y-intercept MSE R2 Homogeneity of Variance
Linear Small 4.4140 -0.0062 4.4140 0.18757 0.01 Y
Medium
4.2058 0.0303 4.2058 0.06630 0.24 Y
Large 4.5871 0.0221 4.5871 0.03790 0.29 Y
Exponential Small 4.4137 0.0014 4.4137 0.18758 0.01 Y
Medium 4.2048 -0.0070 4.2048 0.06575 0.25 Y
Large 4.5871 -0.0047 4.5871 0.03769 0.29 Y
Square Root Small 4.4139 -0.0015 4.4139 0.18757 0.01 Y
Medium 4.2053 0.0073 4.2053 0.06603 0.25 Y
Large 4.5871 0.0051 4.5871 0.03780 0.29 Y
Table 11. Regression parameters modeling the gross energy (kcal/g dry weight) present over PPT by all gag consuming crab prey fit to the linear, exponential, and square root models, small gag (n=7), medium gag (n=7), and large gag (n=5). All models met selection criterion; assumptions met for homogeneity of variance are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p≤0.0159) (see Figure 8).
63
Model Gag Size A B Y-intercept MSE R2 Homogeneity of Variance
Linear Small 2.2935 0.0645 2.2935 0.01265 0.92 Y
Medium
2.5073 0.0436 2.5073 0.00894 0.91 Y
Large 2.2388 0.0578 2.2388 0.01602 0.89 Y
Exponential Small 2.3852 -0.0205 2.3852 0.01598 0.90 Y
Medium 2.5274 -0.0149 2.5274 0.00899 0.91 Y
Large 2.2908 -0.0202 2.2908 0.01560 0.89 Y
Square Root Small 2.3423 0.0182 2.3423 0.01428 0.91 Y
Medium 2.5176 0.0127 2.5176 0.00895 0.91 Y
Large 2.5654 0.0171 2.5654 0.01571 0.89 Y
Table 12. Regression parameters modeling the percent of gross energy digested over PPT by all gag consuming baitfish prey fit to each model, for small gag (n=11), medium gag (n=16), and large gag (n=8). Models meeting selection criteria are highlighted in bold; assumptions met for homogeneity of variance and characteristics of the upper asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p<0.0001) (see Figure 9).
64
Model Gag Size A B C Y-intercept MSE R2 Homogeneity of Variance
Upper Asymptote >85%
Small 12.38 5.70 12.38 97.75 0.92 Y
Medium
3.37 5.88 3.37 114.24 0.89 Y
Linear
Large -0.68 5.90 -0.68 62.73 0.93 Y
Small 28.62 -0.08 28.62 207.70 0.82 Y Y
Medium 20.07 -0.10 20.07 223.10 0.79 Y Y
Exponential
Large 22.20 -0.09 22.20 149.00 0.83 Y Y
Small 22.03 0.35 22.03 150.60 0.87 Y Y
Medium 14.02 0.39 14.02 169.30 0.84 Y Y
Square Root
Large 14.60 0.37 14.60 108.00 0.88 Y Y
Small 94.75 -0.46 -5.88 6.01 24.21 0.98 Y Y
Medium 86.05 -0.46 -6.08 4.83 104.30 0.91 Y Y
Logistic
Large 88.24 -0.42 -7.32 3.79 47.37 0.96 Y Y
Small 5.64 -1.98 0.00a 0.0057 0.95 Y Y
Medium 6.33 -1.46 0.00a 0.0094 0.91 Y Y
Power Exponentiala
Large 7.61 -1.89 0.00a 0.0033 0.96 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=0 (0%) and not uniquely estimated.
Table 13. Regression parameters modeling the percent of gross energy digested over PPT by all gag consuming crab prey fit to each model, small gag (n=7), medium gag (n=8), and large gag (n=7). Models meeting selection criteria are highlighted in bold; assumptions met for homogeneity of variance and characteristics of the upper asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p≤0.0003) (see Figure 9).
65
Model Gag Size A B C Y-intercept MSE R2 Homogeneity of Variance
Upper Asymptote >85%
Small 2.05 4.55 2.05 44.10 0.95 Y
Medium
-3.44 4.71 -3.44 125.83 0.90 Y
Linear
Large -2.53 4.57 -2.53 31.62 0.97 Y
Small 24.35 -0.07 24.35 121.30 0.87 Y Y
Medium 26.02 -0.06 26.02 257.30 0.80 Y Y
Exponential
Large 18.66 -0.08 18.66 52.19 0.95 Y Y
Small 16.13 0.28 16.13 78.86 0.91 Y Y
Medium 16.22 0.27 16.22 191.30 0.85 Y Y
Square Root
Large 11.32 0.31 11.32 37.27 0.96 Y Y
Small 97.42 -0.27 -10.32 5.80 15.21 0.99 Y Y
Medium 101.60 -0.32 -11.00 3.05 36.74 0.98 Y Y
Logistic
Large 152.30 -0.14 -17.35 13.17 53.51 0.96 Y Y
Small 9.07 -1.73 0.00a 0.0089 0.90 Y Y
Medium 9.64 -2.78 0.00a 0.0053 0.96 Y Y
Power Exponentiala
Large 10.23 -1.98 0.00a 0.0138 0.87 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=0 (0%) and not uniquely estimated.
Table 14. Mean (±S.E.) and range of post-prandial times (PPT) in relation to digestion codes and % digestion for gag consuming baitfish Harengula jaguana (n=44) versus crab prey Portunus gibbesii(n=27). Digestion code descriptions are given in Tables 1 and 2.
PPT (hr)
Baitfish Prey Crab Prey
Digestion Code % Digestion Mean(+S.E.) Range Mean(+S.E.) Range
0 <5 0.2 (0.09) 0.08 – 0.5 3.6 (1.08) 0.8 – 8.0
1 5 - 10 2.36 (0.53) 0.5 - 4.5 7.3 (0.75) 6.5 - 8.0
2 10 - 25 4.5 (1.50) 3.0 – 6.0 10.0 (0.00) 10.0
3 25 - 50 4.0 (0.50) 3.0 – 4.5 11.0 (1.00) 9.0 – 12.0
4 50 - 75 8.0 (0.67) 6.0 – 12.0 15.0 (1.73) 12.0 – 18.0
5 75 - 90 10.5 (2.03) 7.5 - 16.5 16.0 (0.00) 16.0
6 >90 14.8 (0.71) 9.0 – 18.0 20.9 (0.84) 16.0 – 24.0
66
Table 15. Regression parameters of the average digestion code data of gag consuming baitfish prey fit to each model, for small gag (n=13), medium gag (n=16), and large gag (n=11). Models meeting selection criteria are highlighted in bold; assumptions met for homogeneity of variance and characteristics of the upper asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p<0.0001) (see Figure 11).
67
Model Gag Size A B C Y-intercept MSE R2 Homogeneity of Variance
Upper Asymptote > Code 5.5
Small 0.74 0.36 0.74 0.90 0.86 Y
Medium
1.05 0.32 1.05 0.82 0.81 Y
Linear
Large 0.63 0.35 0.63 0.66 0.84 Y
Small 1.63 -0.09 1.63 1.67 0.75 Y
Medium 1.84 -0.08 1.84 1.33 0.70 Y
Exponential
Large 1.62 -0.08 1.62 1.22 0.72 Y
Small 1.22 0.10 1.22 1.25 0.81 Y
Medium 1.47 0.09 1.47 1.08 0.76 Y
Square Root
Large 1.23 0.09 1.23 0.94 0.79 Y
Small 6.02 -0.46 -5.32 0.47 0.52 0.93 Y YMedium 5.62 -0.57 -4.77 0.34 0.34 0.93 Y Y
Logistic
Large 5.43 -0.50 -5.66 0.30 0.28 0.94 Y N
Small 4.18 -1.66 0.00a 0.02 0.91 Y Y
Medium 4.56 -1.84 0.00a 0.01 0.93 Y Y
Power Exponentiala
Large 5.79 -1.97 0.00a 0.01 0.92 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=0 (0%) and not uniquely estimated.
Table 16. Regression parameters of the digestion code data of gag consuming crab prey fit to each model, for small gag (n=8), medium gag (n=11), and large gag (n=8). Models meeting selection criteria are highlighted in bold; assumptions met for homogeneity of variance and characteristics of the upper asymptote are given by Y (yes) or N (no). All models were significant at the α=0.05 level (p≤0.0008) (see Figure 12).
68
Model Gag Size A B C Y-intercept MSE R2 Homogeneity of Variance
Upper Asymptote > Code 5.5
Small -2.04 0.42 -2.04 0.74 0.91 Y
Medium
-0.80 0.31 -0.80 0.48 0.93 Y
Linear
Large -0.43 0.30 -0.43 0.36 0.95 Y
Small 0.78 -0.10 0.78 1.66 0.79 Y
Medium 1.03 -0.08 1.03 1.35 0.81 Y
Exponential
Large 0.83 -0.09 0.83 0.87 0.88 Y
Small 0.12 0.11 0.12 1.13 0.86 YMedium 0.40 0.08 0.40 0.83 0.89 Y
Square Root
Large 0.41 0.09 0.41 0.60 0.92 YSmall 6.01 -0.56 -11.72 0.01 0.28 0.96 Y YMedium 6.22 -0.37 -12.55 0.06 0.07 0.99 Y Y
Logistic
Large 5.61 -0.36 -9.99 0.15 0.59 0.94 Y Y
Small 11.18 -4.80 0.00a 0.01 0.97 Y Y
Medium 11.29 -3.47 0.00a 0.01 0.96 Y Y
Power Exponentiala
Large 9.72 -2.38 0.00a 0.01 0.95 Y Y
a Due to model specifications, the Y-intercept of the power exponential model is fixed at Y=0 (0%) and not uniquely estimated.
CHAPTER 4 DISCUSSION
Gastric Evacuation Models
The power exponential model (Elashoff et al., 1982) provided the best description
of the relationship of evacuated digesta as a function of PPT for gag, regardless of prey
type or method of measuring stomach contents, based on consuming meals at 1.5% of the
gag’s body weight (Tables 3, 4, 6 and 7, Figures 3 and 5). In all cases, R2 values were
very high (0.81-0.99), explaining >81% of the variation in both the baitfish and crab
digestion data. The gastric evacuation rates of several piscivorous fish species have
previously been described using the power exponential model, including black and
yellow rockfish consuming whitebait smelt Allosmerus elongatusus and purple shore crab
Hemigrapsus nudus (Hopkins & Larson, 1990), and Atlantic cod consuming capelin
Mallotus villosus and Atlantic herring Clupea harengus (dos Santos & Jobling, 1992).
Other studies have fit gastric evacuation data using the linear model (Swenson & Smith,
1973; Jones, 1974; Olson & Boggs, 1986), the exponential model (Brett & Higgs, 1970;
Tyler, 1970; Persson, 1979; MacDonald et al., 1982), the square root model (Jobling,
1981), the logistic model (Hopkins & Larson, 1990; Nelson & Ross, 1995), and with
expanded power exponential models incorporating time after ingestion, predator weight,
temperature, and meal size as predicting variables (Temming & Andersen, 1994;
Andersen, 1999; Koed, 2001). In particular, temperature has one of the strongest
influences on rates of gastric evacuation in fish, increasing gastric evacuation rates as
temperature increases (Bromley, 1994). In the present study, gastric evacuation rates
69
70
were quantified at a mean temperature found on artificial reefs in the eastern Gulf of
Mexico during the warmer months of the year (mean=28°C from May-November)
because baitfish are only present on these reefs during these warmer months.
Goodness of fit evaluations among different gastric evacuation models has often involved
using a combination of Y-intercept values, residual plots, standard errors, residual mean
square values, and/or the coefficient of determination (R2) (Swenson & Smith, 1973;
Eleshoff, 1982; Brodeur & Pearcy, 1987; Ruggerone, 1989). Using the R2 value, which
represents the total variability in y that can be explained by the fitted regression (Zar,
1999), may not by itself reliably evaluate regression models (Draper, 1984; Healy, 1984;
Rao, 1998). Specifically, small R2 values can be statistically significant while large R2
values can be insignificant (Rao, 1998). In addition, equal values of the residual sums of
squares can result in different R2 values depending on the steepness of the regression,
with steeper regressions resulting in higher R2 values (Barrett, 1974; Rao, 1998). Using
R2 values to compare among different models can also be problematic if the models use
different numbers of predictor variables, since R2 will increase with an increasing number
of predictor variables (Healy, 1984). While in the present study goodness of fit was
determined by multiple critieria, including Y-intercepts, homogeneity of variance,
upper/lower asymptote criteria, and R2 values, using adjusted R2 values to determine
model fit may evaluate regression models more reliably (Healy, 1984; Rao, 1998).
Adjusted R2 values comprise the proportion of variance accounted for in the data and
takes into account both the number of predictor variables and the sample size, and is
calculated as (Healy, 1984)
Total
ResidualMS
MSadjR −= 12
(16)
71
where MSResidual = residual mean squares and MSTotal = total mean squares. Since
adjusted R2 values are not influenced by the number of predictor variables within each
model, they should better evaluate fit between models with differing numbers of
variables, such as the logistic and power exponential models in this study.
Wet weight measurement error had the potential to affect this study due to the fact
that prey items recovered at later time periods could not be easily blotted prior to
weighing, and so water, enzymes, or body fluids may have remained and contributed to
an overestimation of the amount of prey remaining over PPT (Hopkins & Larson, 1990).
Because of this measurement error, gastric evacuation rate estimates and the shape of the
evacuation curve have often depended on whether the stomach contents were measured
by wet weight, dry weight, or volume (Brodeur, 1984; Olson & Boggs, 1986; Hopkins &
Larson, 1990; dos Santos & Jobling, 1992). Theoretically, dry weight measures of prey
remaining over PPT should have corrected for measurement error but it also introduced
new error because the original baitfish and crab dry weights had to be back-calculated
based on predictive regressions for wet weight versus dry weight of whole representative
prey. In this study, model selection was only minimally influenced by the way in which
the stomach contents were measured considering that the power exponential model best
fit both baitfish and crab wet weight and dry weight data, although this likely reflects the
curvilinear flexibility of the power exponential model. Because wet weight
measurements of the amount of food remaining in the stomach are the most common
measurement taken in the field (Bromley, 1994), the wet weight gastric evacuation curves
generated from gag feeding trials in this study may be the most useful.
72
Both baitfish and crab prey used in all gastric evacuation feeding trials were
vacuum-sealed fresh, frozen immediately, and stored frozen because live baitfish were
only present on artificial reefs in the eastern Gulf of Mexico in the warmer months
(capturing and maintaining scaled sardines was not feasible) and collections of crab prey
were only successful during February and March. Previous work has shown that prey
(whole Cape anchovy Engraulis capensis, pieces of squid Loligo vulgaris, pieces of hake
Merluccius sp. and detached rock lobster tails Jasus lalandii) that had been frozen and
then thawed before use in in vitro digestion rate experiments digested faster in pepsin
than controls of fresh prey (Jackson et al., 1987). Using previously frozen prey in gastric
evacuation feeding trials may therefore result in underestimating the amount of food
remaining in the stomach at time and consequently lead to overestimating the gastric
evacuation rate. MacDonald et al. (1982) tested the effects of frozen versus live prey on
digestion indices and found that Atlantic cod and ocean pout Macrozoarces americanus
had significantly lower indices of digestion when consuming pellets of fresh bivalves
Yoldia sapotilla or polychaete worms Nephtys incise at 5 hrs and 20 hrs PPT,
respectively, as compared to thawed pellets. Conversely, MacDonald et al. (1982) also
found that ocean pout had lower indices of digestion at 20 hrs PPT when fed pellets of
thawed Y. sapotilla. Additionally, Atlantic cod and ocean pout had lower digestion
indices when fed thawed versus fresh polychaete worms N. incise at 5 and 20 hrs PPT,
respectively. In contrast, field and laboratory experiments with tagged, free-swimming
black and yellow rockfish indicated that the evacuation rates of thawed purple shore crab
determined in the laboratory gave acceptable approximations of evacuation rates in situ
using freshly fed purple shore crabs (Hopkins & Larson, 1990). Results from the present
73
study indicate that thawed baitfish prey are 95% evacuated from the stomachs of gag
(300-750 mm TL) between 14.7–19.5 hrs PPT. These results correspond very closely
with preliminary field estimates on the stomach contents of wild gag, with times to 90%
gastric evacuation being approximately 15 hrs PPT (Lindberg et al., 2002).
Most gastric evacuation experiments have described predator digestion patterns
based on single-meals of a single prey type, even though multiple prey types may be
incorporated into a multivariate model (Tyler, 1970; MacDonald et al., 1982; Brodeur,
1984; Hopkins & Larson, 1990; He & Wurtsbaugh, 1993; Temming & Andersen, 1994;
Nelson & Ross, 1995; Singh-Renton & Bromley, 1996; Andersen, 1999; Koed, 2001).
Applying single-meal models to sequential-meal experimental conditions assumes that
stomach contents are homogenous, and considering this may not be the case, models may
tend to underestimate the evacuation rates of early meals and overestimate the rates of
later meals (Persson, 1984; Ruggerone, 1989; dos Santos & Jobling, 1992). Even so,
evidence suggests that the extrapolation of single-meal models can still yield reasonable
estimates of total daily ration (Persson, 1984; dos Santos & Jobling, 1992). Singh-
Renton and Bromley (1996) determined that the gastric evacuation rates of Atlantic cod
and whiting Merlangius merlangus fed mixed-prey meals of brown shrimp Crangon
vulgaris and Atlantic herring, or meals of brown shrimp and lugworm Arenicola marina
were not significantly different than the gastric evacuation rates of single-prey meals.
Further work is necessary to determine if the single-meal gastric evacuation rates
determined in this study are biased when applied to sequential-meal, mixed-meal, and
sequential-meal/mixed-meal situations, and whether or not their extrapolation will yield
acceptable estimates of total daily ration. Evacuation trials for meals of combined fish
74
and crab prey were not addressed in the present study but would be useful to develop for
gag, since some stomach contents recovered in the field contain mixed prey types
(personal observation).
Effects of Prey Type
For all sizes of gag consuming baitfish or crab prey, on both a wet weight and dry
weight basis, the power exponential model best fit the gastric evacuation data (Tables 3,
4, 6, and 7, Figures 3 and 5). Other studies have used the power exponential model,
including those on black and yellow rockfish consuming whitebait smelt (Hopkins &
Larson, 1990), and on Atlantic cod consuming capelin, herring, prawn Pandalus borealis,
and krill Meganyctiphanes norvegica (dos Santos & Jobling, 1992). The power
exponential model in the present study accounted for the lack of a lag phase seen in the
gastric evacuation of scaled sardine prey based on wet weight and with the slight lag
phase observed in the dry weight data (0-3 hrs PPT) (Figure 3). The power exponential
model best fit black and yellow rockfish gastric evacuation data based on the wet weight
and dry weight measurements of recovered whitebait smelt, both measurement types
showed lag phases in digestion, and 80-90% prey remaining at 2-5 hrs PPT, likely due to
the lower temperature (14.5°C) (Hopkins & Larson, 1990). Likewise, dos Santos and
Jobling (1992) used the power exponential model and found that on a dry weight basis
the gastric evacuation rates of Atlantic cod fed capelin, Atlantic herring, prawn, or krill
were initially faster that rates of gastric evacuation considered on a wet weight basis.
As with most fish predators, gag capture and swallow their prey whole and
therefore, along with mechanically macerating the prey, digestive enzymes must work
through the prey’s scales and skin or carapace before the muscle, gut, and other tissues
can be easily digested (Diana, 1995). The scaled sardine prey chosen for this study were
75
immature and therefore, relatively small and low in energy density compared to mature
sardines. However, they were of a similar size to prey found in the stomach contents of
gag during warmer months on reefs in the eastern Gulf of Mexico (Lindberg et al., 2002).
Because of their size, the sardine prey had fairly small scales and thin skin, therefore
these sardines were considered highly friable compared to mature sardines of the same
species. The gag’s digestive enzymes were likely better able to quickly and continuously
break down these comparatively low-energy, immature sardines. In fact, the linear model
described the continuous gastric evacuation of baitfish prey on a wet weight basis for
each gag size class relatively well (r2>0.93) (Table 3). Although low in energy compared
to mature sardines, these immature sardines are high-energy prey when compared to
other prey types such as portunid crabs (Table 9). Therefore, the fact that a linear model
adequately describes the gastric evacuation process of baitfish prey is still consistent with
the continuous digestion of high energy prey types over PPT as described by Jobling
(1987).
The power exponential model also provided the best fit for both the wet weight and
dry weight gastric evacuation data for all sizes of gag consuming crab prey (Tables 6 and
7, Figure 5). Similarly, the wet weight and volume gastric evacuation data from black
and yellow rockfish fed purple shore crabs were modeled using a power exponential
model, although the dry weight gastric evacuation data was better fit using a linear model
(Hopkins & Larson, 1990). Few studies have modeled the gastric evacuation of crab prey
fed to piscivorous fish species. Most studies have concentrated on shrimp, krill,
amphipods, or polychaete prey, which all tend to possess thinner exoskeletons and
therefore less chitin material than most crab exoskeletons (Tyler, 1970; MacDonald et al.,
76
1982; dos Santos & Jobling, 1992; Temming & Andersen, 1994; Andersen, 1999). The
power exponential model was able to capture the large digestive lag phases seen in the
gastric evacuation patterns of the crab prey (Figure 5). Generally, when gag consumed
crab prey on a wet weight basis, lag phases of approximately 0-6.5 hrs with little or no
digestion occurred, followed by a rapid increase in digestion for approximately the next
12-15 hrs. Digestion was then slowed again as residual hard parts persisted in the
stomach, accounting for the model’s lower asymptote estimates. At these later time
intervals, digestion-resistant exoskeleton material and other residual hard parts are known
to cause evacuation curves to level off and form the lower asymptotes of various gastric
evacuation models (Battle, 1935; Windell, 1966; dos Santos & Jobling, 1992). Basing
models on the dry weight data tended to decrease the lag phases (0-3 hrs), thereby
appearing more similar to the dry weight digestion curves of gag consuming baitfish
prey. This trend was likely caused by measurement error because the crabs may have
more fluid within their exoskeleton normally, as compared to baitfish prey, and in
addition, this fluid retention may vary greatly with crab molt stage. The crabs could not
be easily damp-blotted for weighing and therefore water and body fluids may have
remained within the crabs’ exoskeletons, thereby causing the large lag phases seen in the
wet weight data (Figure 5). Similarly, Hopkins and Larson (1990) found that modeling
the gastric evacuation data from black and yellow rockfish consuming the purple shore
crab on a dry weight basis reduced the digestive lag phase, as compared to modeling the
data on a wet weight basis.
Lags in digestion after prey consumption have been attributed to impaired enzyme
secretion, force-feeding, and starvation, and have the potential to cause bias by
77
overestimating the percentages of prey remaining at PPT (Swenson & Smith, 1973;
Jones, 1974; MacDonald et al., 1982; Jackson et al., 1987). In the present study,
however, food was withheld from gag for <48 hr prior to testing and the gag voluntarily
consumed thawed baitfish or crab prey. In addition, most gag either gained a very slight
amount of weight or maintained their weight while in captivity. Therefore, the observed
lag phase was not due to force-feeding or starvation. More likely, the lag was due to the
gastric softening and digestive break down of whole crustaceans, through the secretion of
hydrochloric acid to decalcify the calcium carbonate material found in their exoskeletons,
plus chitinase, chitobiase, and mechanical peristalsis necessary to breakup the chitinous
material in the softened exoskeleton (Pandian, 1967; Lindsay, 1984; Hopkins & Larson,
1990; Lovell, 1998). In addition, the pyloric sphincter may limit the size of items that
pass into the intestine, thereby playing a role in the retention of exoskeletal material and
the formation of digestive lag phases.
Effects of Predator Size
Gag size had a significant effect on their rates of gastric evacuation, whether they
were consuming baitfish prey or crab prey, and whether stomach contents were measured
on a wet weight or dry weight basis. Similarly, studies on haddock Melanogrannus
aeglefinus, Atlantic cod, and whiting fed saithe Pollachius virens (Jones, 1974), turbot
Scophthalmus maximus fed processed pellets (Flowerdew & Grove, 1979), and Atlantic
cod fed capelin, Atlantic herring, prawn, or krill (dos Santos & Jobling, 1995), found that
predator size had a significant effect on the rate of gastric evacuation, with smaller
predators having faster rates of gastric evacuation. In contrast, other studies on Atlantic
cod that were fed shrimp Pandalus montagui (Tyler, 1970), dab Limanda limanda fed an
artificial paste diet (Jobling et al., 1977), and plaice Pleuronectes platessa fed fish-paste
78
(Jobling, 1980a) did not observe size-specific evacuation rates. Previous work has shown
that feeding predators meal sizes relative to their body weight accounts for the variability
associated with gastric evacuation rates in different sized predators (Swenson & Smith,
1973). Swenson and Smith (1973) found that large and small fish evacuate meals at
approximately the same rate when meals where fed relative to their body weight, whereas
their evacuation rates differed when they were fed equal-sized meals (Swenson & Smith,
1973). In contrast, the present study shows that gag size significantly affects rates of
gastric evacuation despite feeding meals of baitfish or crab relative to each gag’s body
weight. Meal size was not included as a predicting factor in the gastric evacuation
models because initial meal sizes cannot be estimated in wild gag caught in the field.
Scaling factors were incorporated into the power exponential models for both prey
types and measures of stomach contents (wet weights and dry weights) to account for
significant differences in either gag weight or total length, based on the assumption that
the effects of predator size on gastric evacuation times are multiplicative (Andersen,
1999) (Tables 5 and 8; Figures 4 and 6). Previous studies have incorporated scaling
factors such as temperature, meal size, prey size, prey energy density, predator weight
(W), and predator total length (TL) based on this assumption (dos Santos & Jobling,
1992; Temming & Andersen, 1994; Andersen, 1999; Andersen, 2001; Koed, 2001). In
the present study, all W and TL coefficients were ≤0.00134. Therefore, while gag size
significantly affected the gastric evacuation rates of both baitfish and crab prey, values
for W and TL raised to a power approaching 0.0 created multiplicative scaling factors for
the evacuation models that were very close to 1.0 (e.g., W=1). These small but
significant scaling factors are reasonable considering that the gag were fed meals on a
79
relative weight basis, i.e., a percentage of gag body weight, because using the percentage
of meal recovered has been known to take out much of the variation in the weight of
meals recovered from different sized predators (Bromley, 1994). Incorporating W or TL
scaling factors into the power exponential model with data on gag <200 mm TL and >750
mm TL should further improve the R2 values of the power exponential model, better
determine the extent to which the W and TL exponential scalers account for gag size, and
better predict the percentages of prey remaining in the stomachs of all sizes of gag.
Prey Composition
Many fish secrete chitinase but it has been commonly accepted that piscivorous
fish, especially marine fish, are not efficient at converting carbohydrates, such as chitin,
into an absorbable form and therefore can not utilize chitin as an energy source because
the β-linkages of glucose within the chitin molecule cannot be broken by amylase
enzymes (which only breaks α-linkages) (Battle, 1935; MacDonald et al., 1982; Lindsay
& Gooday, 1985, Medved, 1985, Vollhardt & Schore, 1987). However, the role of
chitinase in the gastric processes of fish has been somewhat controversial. Lindsay
(1984) hypothesized that the primary function of chitinase in the gut of fish may be to
chemically disrupt the outer chitinous material of prey, such as crabs. Lower levels of
chitinase activity were found in fish that mechanically disrupted prey before ingestion
compared to fish that swallowed prey whole and likely required more chitinase to break-
up chitinous material (Lindsay, 1984). Conversely, other work has shown that high
levels of gastric N-acetyl-D-glucosamine (NAG) were caused by high levels of
chitinolytic enzymes (chitinase and chitobiase) and resulted in significantly lower growth
rates in fish fed diets containing 4, 10, and 25% chitin, as compared to diets containing
starch (Lindsay et al., 1984). Jackson et al. (1992) suggested that absorbing NAG and D-
80
glucosamine (Gln) (i.e., the products of chitin digestion) may actually be inhibiting
growth. Kohn et al. (1962) reported that mammals metabolize NAG and Gln more
slowly than D-glucose, which possibly inhibited individual growth because NAG and Gln
could not be utilized as quickly as D-glucose for metabolic, waste removal, and growth
processes (Jackson et al., 1992). Jackson et al. (1992) hypothesized that, although chitin
may be available to assimilate, fish that consume chitin may have adapted to inefficiently
absorb chitin end-products because of the possible costs associated with reduced growth.
One advantage of chitinolysis seems to be the increase in gastric evacuation rates
associated with the increase in mechanical breakdown of the exoskeleton, thereby
allowing easier access to more readily digestible tissues and allowing proteolytic
enzymes better access to cuticle proteins (Jackson et al., 1992). It has been commonly
accepted that crustaceans with their chitinous exoskeletons allow piscivorous predators to
assimilate only between 70–80% of the total crustacean energy consumed, versus
approximately 89-96% of the total energy when fish prey are consumed (Diana, 1995).
In the present study, the mean energy densities of both baitfish and crab prey were
determined, along with the mean energy density of the crab shells by themselves, to
better estimate the mean energy density available for assimilation by individual gag
consuming crab prey. However, estimations of exoskeletal energy densities may have
been biased. Potassium hydroxide treatments for tissue removal of the crab prey did not
decalcify the exoskeleton or extract the cuticle’s pigments (Pandian, 1967). Compere et
al. (2002) has shown, however, that many proteins can be extracted from the exoskeleton
without decalcification and that many proteins in the exoskeleton have relatively high
apparent molecular weights. The presence of calcium carbonate, and potentially,
81
pigments, remaining in the crab exoskeletons may have lowered the estimates of
exoskeletal energy density.
All representative baitfish and crab prey used in the prey analyses reflected size
(TL and CL) ranges that mirrored those normally found in the stomach contents of wild
gag (Murie, unpublished data). The representative baitfish prey had lower mean mass,
higher mean % moisture, lower mean % ash, higher mean available caloric energy
density, and higher mean available ash-free caloric energy density estimates than the crab
prey (Table 9). Quantifying baitfish energy densities in terms of their ash-free dry weight
corrects for the inorganic materials of their bones and other parts, such as scales. The
crab prey used in the regression analysis to correct for the known unavailable energy in
crab fed to gag, specifically unabsorbable chitin energy, were within the size range (CL)
and mass (g) of the whole crabs used for the prey analyses. The composition of food
prey, in terms of energy density and specifically, fat and ash content, is known to affect
rates of gastric emptying (Elliott & Persson, 1978; Jobling, 1980a). Diets containing
unabsorbable materials tend to lower a meal’s energy density and increase rates of gastric
evacuation (Flowerdew & Grove, 1979; Jobling, 1980a). On the other hand, digestion-
resistant materials have been shown to slow rates of gastric evacuation due to the
relatively large amounts of residual matter and ash that must be digested and passed from
the body (Battle, 1935; Windell, 1966; Hopkins & Larson, 1990; dos Santos & Jobling,
1992). Therefore, prey with high amounts of digestion-resistant materials, such as crabs
with exoskeletal coverings, would be expected to be lower in energy density and have
slower gastric evacuation rates (Flowerdew & Grove, 1979; Jobling, 1980a). In fact, in
the present study, times to 95% evacuation at 28.0oC for small, medium, and large gag
82
consuming crab prey on a wet weight basis took 4.3 hrs longer than gag consuming
baitfish (Figure 7). In a previous study, black and yellow rockfish evacuated whitebait
smelt to 95% in 27 hrs, while purple shore crabs were not evacuated to 95% until 49.5 hrs
PPT, a 22.5 hr difference (Hopkins & Larson, 1990). Similarly, there was no lag phase in
digestion for black and yellow rockfish fed whitebait smelt, as 46.8% of the meal was
remaining at 10 hrs PPT, compared to a large lag phase seen when rockfish were fed the
purple shore crab and 99.7% of the meal was remaining 10 hrs PPT, a 52.9% difference
(Hopkins & Larson, 1990). While these earlier findings are corroborated by the present
study, it is important to recognize that the whitebait smelt used in the black and yellow
rockfish feeding trials were thin-skinned and almost scaleless, while the purple shore
crabs were smaller, relative to the scaled sardine and portunid crab prey used in the
present study.
Most digestion and consumption models do not account for fluctuating prey energy
densities between species and seasons (Koed, 2001). In the present study, immature
scaled sardines were collected during late October or November of 2002 and 2003, and
all sardines collected or observed on artificial reefs in the eastern Gulf of Mexico
(Lindberg et al., 2002) were of a similar size range. Due to the fact that all baitfish,
including immature scaled sardines, are only present on artificial reefs in the eastern Gulf
of Mexico in the warmer months, fluctuating baitfish prey energy densities between
seasons did not need to be determined. Crab prey, however, were collected in February
and March of 2003 and 2004. Spring samples of crab could have biased the rates of
gastric evacuation and estimates of energy density determined in this study. Crab
samples collected in the spring may have contained higher percentages of pre-molting
83
and post-molted individuals that have no gonad development, resulting in greatly reduced
energy densities. However, considering that all crabs used in the prey composition
determinations were similar in energy density (mean=2.22, S.E.=0.06) and more than
81% of the variation in the gastric evacuation data was explained by the power
exponential model, any biases caused by pre-molt or post-molt individuals were likely
small and within error. Comparing the energy of crabs sampled in the present study to
crabs sampled from the stomachs of wild gag could determine the extent to which pre-
molt and post-molt crabs potentially biased results from the present study. Determining
the extent of fluctuating crab prey energy densities by season may be necessary
depending on when they begin to show up in the gags’ diet, or if they are continuously
found in their diets, and their seasonal molting patterns. Considering that prey
composition and energy densities greatly affect both gastric evacuation and consumption
rates, it is important to know the true energy densities of the prey consumed by season.
Stomach Content Composition
Both the scaled sardines and portunid crabs used in the gastric evacuation and
average digestive code regressions were within the size range of the subsampled prey
used in the whole prey regression analyses, although the measurements of mean mass for
both prey types was slightly smaller (Tables 9 and 10). The baitfish stomach contents
had higher mean % moisture and mean available caloric energy density estimates but
lower mean % ash and mean available ash-free caloric energy density estimates than the
representative baitfish prey. All values are very close to those of the representative
baitfish prey and likely reflect the natural variation seen in the scaled sardine population
residing on artificial reefs in the eastern Gulf of Mexico. The crab prey stomach contents
had higher mean % moisture, mean available caloric energy density, and mean available
84
ash-free caloric energy density estimates but a lower mean % ash estimate than the
representative crab prey. Again, all values are very close to those of the representative
crab prey, most likely reflecting the natural variation seen in the crab population. The
fact that mean % moisture differed significantly between whole baitfish and crab prey but
did not differ between baitfish and crab stomach contents may reflect wet weight
measurement error ( i.e., the ability to damp-blot excess water off recovered prey), or the
addition of secreted digestive juices (hydrochloric acid, pepsin, and mucous).
The square-root and linear models provided adequate fits for the gross energy
(kcal/g dry wt) of baitfish and crab prey stomach contents, respectively, regressed as a
function of PPT (Tables 11 and 12, Figure 8). However, the square-root model fit to the
baitfish data was more descriptive than predictive, likely reflecting the friable nature of
the baitfish prey that was easily broken down through mechanical and enzymatic action
in the gag’s stomach. This likely resulted in a homogenous mixture of baitfish with a
relatively consistent energy density in the gag’s stomach. In contrast, the crab prey may
not comprise such a homogenous mixture because the crabs contain higher amounts of
indigestible matter, such as chitin, that may preferentially remain in the stomach for
longer periods. Few studies have looked at stomach content gross energy as a function of
PPT. Jobling (1980a) fed plaice meals of fish paste with kaolin (inert material) and
regressed the square-root of the meal’s energy density by time with a linear model and
found that energy decreased over PPT. A linear model assumes that energy turnover is
maintained at a constant level, therefore, non-nutritive bulk , such as chitin, should be
instantaneously evacuated from the stomach (Jobling, 1980a). Jobling (1980a)
hypothesized that as the energy density of meals increased, muscular activity and rates of
85
gastric peristalsis may restrain energy turnover. One study by Beamish (1972) on
largemouth bass Micropterus salmoides fed emerald shiners Notropis atherinoides noted
that the caloric energy density of the stomach contents increased over time when
expressed as cal/g dry weight, and that protein (i.e., nitrogen) digested more quickly than
lipid, but did not fit a quantitative function to the data. Conversely, Bromley (1988)
found that the gross energy density (kcal/g dry weight) of sandeel Ammodytes sp. in the
stomach contents of whiting declined over time and attributed the decline to changes in
the ratio of readily digestible soft tissue to skeletal material but again, the function was
not modeled. In the present study, the caloric energy density of the stomach contents for
both prey types increased over PPT for each size of gag, except small gag consuming
baitfish (Figure 8). These results most likely reflect energy:weight ratio changes over
PPT, that is, a larger portion of low energy unabsorbable skeletal or exoskeletal material
remains in the stomach longer compared to proteins or lipids but, at the same time, large
portions of digesta and heavy macromolecules are evacuated from the gut. Relatively
heavy macromolecules, such as calcium carbonate and some proteins within the
exoskeleton, may be extracted from the exoskeleton with the addition of digestive
enzymes (including HCl) more quickly then lighter macromolecules. A greater weight
change in the energy:weight ratio compared to the energy change due to low energy
skeletal or exoskeletal material remaining in the gut may be causing the increase in the
gross caloric energy densities of the recovered gag stomach contents over time.
The power exponential model best described the percentage of stomach content
energy digested over PPT for both prey types and in all sizes of gag, with predictive
models explaining over 87% of the variation (R2 = 0.87-0.96) (Tables 13 and 14, Figure
86
9). Previous work, such as that of Beamish (1972) and Bromley (1988), have determined
gross energy densities over time, others have estimated the amount of energy consumed,
absorbed, converted, and residual energy densities per day as a function fish body weight
for estimations of absorption efficiency (Pandian, 1967). In the present study, the power
exponential model fit the percentage of digested baitfish energy in a pattern most similar
to the gastric evacuation of the wet weight data for baitfish (A ranging from 5.64 to 7.61
and from 6.16 to 8.01, and B ranging from -1.46 to -1.98 and from 1.52 to 1.89, for the
digested energy data and wet weight gastric evacuation data, respectively) (Tables 3 and
8, Figures 3 and 9). However, the lag phases present in the baitfish energy digested data
(2-3 hrs PPT) were more similar to the lag phases seen in the dry weight gastric
evacuation data of small and large gag (2-2.5 hrs PPT). Conversely, the power
exponential model fit the percentage of digested crab energy from the gag stomach
contents in a pattern most similar to the gastric evacuation of the dry weight crab prey
data, with A ranging from 9.07 to 10.23 and from 8.00 to 9.19, and B ranging from -1.73
to -2.78 and from 1.23 to 2.32, for the digested energy data and dry weight gastric
evacuation data, respectively (Tables 7 and 14, Figures 5 and 9). Lag phases were more
similar to the wet weight gastric evacuation crab data, with the lag in the percentage of
digested crab prey energy being approximately 5 hrs PPT and the wet weight gastric
evacuation lag phase being 6 hrs PPT.
Indices of Digestion
Visual indices of prey digestive stages over PPT are one way that researchers can
estimate times of prey consumption in the field based on the average digestion codes
given to recovered stomach contents instead of quantitative measurements (i.e., back-
calculation of original prey sizes and weights). When each size class of gag consumed
87
either baitfish or crab prey the power exponential model best described their average
digestive codes over PPT (Tables 15 and 16, Figures 11 and 12). Again, little or no lag
phase was seen in the average digestion code data when gag consumed baitfish prey due
to the fact that the gag’s digestive enzymes and mechanical peristalsis of the stomach
could easily breakup the individual prey items. Lag phases of up to 6.5 hrs in digestion
of crab were seen in both the average digestion code data and the wet weight gastric
evacuation data, as the crab’s exoskeleton acts as a barrier to prevent quick enzymatic
and mechanical digestion of the crab tissues (MacDonald et al., 1982; Hopkins & Larson,
1990; dos Santos & Jobling, 1992; Bromley, 1994). These visual indices of scaled
sardine and portunid crab digestive stages clearly support gastric evacuation results from
the present study and the contention that prey containing higher percentages of chitin will
remain recognizable for longer periods in the gut (MacDonald et al., 1982). Evacuation
studies on Atlantic cod, ocean pout, winter flounder Pseudopleuronectes americanus, and
American plaice Hippoglossoides platessoide fed bivalves Y. sapotilla, amphipods
Tmetonyx cicada, and polychaetes N. incisa have also found that visual indices of prey
digestion are correlated with quantitative differences in evacuation rates between prey
types (MacDonald et al., 1982). Using the visual indices of prey digestion qualified in
this study, along with their corresponding models of average digestion codes at time, will
allow researchers to approximate the wet weight and dry weight percentages of prey
remaining in a gag’s stomach and estimate an approximate time of prey consumption for
wild gag, based on preliminary average daily consumption estimates of between 1.2-1.8%
body weight per day (Lindberg et al., 2002). Therefore, only average digestion codes of
prey at time will need to be estimated, thereby saving measurement effort and processing
88
time for stomach contents collected in the field. Most importantly, these back-calculated
times of prey consumption can be used to determine the frequency with which gag feed
and their diurnal pattern of feeding. Both feeding frequency and the diurnal feeding
pattern (synchronous or asynchronous) of gag will affect which type of consumption
model is most appropriate for wild gag (Adams & Breck, 1990).
Consumption
Based on results from this study, gag will likely require a multiplicative
consumption model, such as that of Atlantic cod feeding on Atlantic herring in the
Barents Sea (Johansen et al., 2004) or an additive model, with a delay for gag consuming
crab. The multiplicative or additive model chosen must incorporate gastric evacuation
models explicit to the prey types consumed by gag, i.e., both fish and crustacean prey.
Such models can account for the differing gastric evacuation rates of various
representative prey items, such as baitfish, crab, squid, and possibly larger pelagic prey.
Water temperature influences on gag gastric evacuation rates must be determined on a
species-specific basis. Similarly, each species’ seasonal energy density fluctuations
should be incorporated into a comprehensive gastric evacuation model for gag, taking
into account the seasonality of prey quality and quantity
Predators consuming prey that contain less available energy and higher amounts of
indigestible materials that slow rates of gastric evacuation, such as crab prey, will need to
increase their consumption of that prey to grow at rates similar to predators consuming
more energy-rich prey, such as baitfish. If baitfish and crab energy densities were equal,
the rate of crab prey exploitation would likely better approximate a Type II Functional
Response curve (Holling, 1959) compared to baitfish prey as gag became satiated.
Specifically, handling time, which includes the pursuit, capture, handling, and gastric
89
evacuation of prey, is taken into account, resulting in the number of prey consumed per
predator initially rising as prey density increases and then leveling off with further
increases in prey density (Holling, 1959). Results from the present study clearly show
that crab prey require more handling time (at a minimum, more gastric evacuation time)
than baitfish prey, with times to 95% gastric evacuation of baitfish and crab prey between
14.7-19.5 hrs and 19.6-24.5 hrs PPT, respectively . Based on the Type II Functional
Response curve, the consumption of crab prey by gag will level off at a maximum rate
that is lower than the maximum rate of baitfish consumption because crabs require more
handling time in digesting food. Therefore, even when gag are fed to satiation on crab,
energy available for gag growth will be limited to the time required for individual gag to
handle the crab prey. Gag fed to satiation on friable baitfish prey should be limited less
by prey handling times, resulting in more energy available for individual gag growth.
Based on preliminary analyses of gag stomach contents, it is unclear why some gag may
be consuming significant amounts of energy-poor crab prey (Lindberg et al., 2002).
Different prey have different vulnerabilities, in terms of encounter and capture success
rates, therefore, differences in consumption rates of baitfish and crab prey may be a
function of their differing vulnerabilities (Sih & Christensen, 2001). Optimal Foraging
Theory predicts that prey are detected and consumed based on their energy density, in
addition to the time, effort and risk involved in capture of prey (Emlen, 1966; MacArthur
and Pianka, 1966; Gill, 2003), i.e., if the time and effort needed to capture baitfish and
crab prey were similar, a gag should preferentially choose energy-rich baitfish prey over
lower energy crab prey. However, density-dependent effects may reduce a gag’s
successful foraging episodes due to increased competition with a reduction in prey
90
numbers, thereby causing an increase in the gag’s optimal diet breadth to include lower
energy prey, such as crabs (Emlen, 1966; MacArthur and Pianka, 1966). Interestingly,
some gag seemed to naturally take to crab prey much more readily in the laboratory than
others (personal observation). This feeding disparity may be evidence of differing prey
recognition capabilities, previous experience, individual food preferences in gag,
previous density-dependant effects on foraging successes, or differing portunid crab and
baitfish abundances in the environment affecting predator-prey encounter rates.
Conclusions
In conclusion, the power exponential model best fit both the wet weight and dry
weight gastric evacuation data irrespective of gag size and prey type. Gag gastric
evacuation rates as a function of PPT were significantly affected by baitfish and crab prey
composition, in terms of the curve’s shape and total prey retention times, most probably a
result of the crab exoskeleton acting as a barrier to digestion, differing levels of
unabsorbable material, and energy density. Gag size (300-750 mm TL) significantly
affected both the scaled sardine and portunid crab gastric evacuation models when using
either the wet weight or dry weight data. This range in gag size reflects the stage-specific
size range of gag normally encountered on coastal reefs in the eastern Gulf of Mexico
(Lindberg et al., 2002). Results from the present study independently verify preliminary
gastric evacuation rate estimates of wild gag consuming baitfish prey on artificial reefs
off the west coast of Florida (Lindberg et al. 2002). Based on field estimates, wild gag
evacuated 90% of a baitfish meal in 15 hrs and 100% in 16 hrs, compared to 95% gastric
evacuation between 14.7-17.4 hrs PPT determined from experimental feeding trials of
captured gag in this study. The flexibility of the power exponential model also best fit
the percentage of stomach content energy digested over time and the average scaled
91
sardine and portunid crab digestion code data for each size of gag. Additional work on
quantifying the gastric evacuation rates of gag < 300 mm TL and >750 mm TL, at
temperatures <28°C and >28°C, with different relative meal sizes, mixed meals,
sequential meals, and differing prey types will improve the ability of the best-fit power
exponential model to predict the percentages of prey remaining over PPT, the
percentages of prey energy digested over PPT, and the average digestion codes over PPT
of wild gag. The power exponential gastric evacuation models for gag determined in the
present study have shown that prey type and gag size are not only significant parameters
when determining rates of gastric evacuation but they, along with back-calculated times
of prey consumption by wild gag, are variables that must be included in any future
consumption model for wild gag.
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BIOGRAPHICAL SKETCH
Born on April 15, 1975, in Grand Rapids, Michigan, the author showed a great
curiosity for various aquatic organisms and their environments at an early age. Her
curiosity developed into an interest in fisheries science at Grand Valley State University
in Allendale, Michigan, and resulted in a Bachelor of Science in biology with an aquatic
emphasis and minors in Russian studies and studio art.
After working as a Lake Management Assistant on Kiawah Island, South Carolina,
the author decided to pursue a graduate-level education to further her interest in marine
fisheries. While pursuing a Master of Science in biology at the University of Florida, she
served as Treasurer for Students United in the Research of Fisheries (SURF). During
September of 2004, she began work at Mote Marine Laboratory in Sarasota, Florida, as a
staff biologist with the Sarasota Dolphin Research Program. She plans to continue work
on commercially important marine species, species of special concern, predator-prey
relationships, and the various human influences affecting these relationships.
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