ABSTRACT:
The nudibranch mollusc Melibe leonina has been shown to be useful for
neurophysiological studies due to its relatively simple nervous system. As a
precursor to understanding nerve pathways for behavior lies the study of the
behaviors themselves. Spontaneously occurring behavior of Melibe was
observed and analyzed to determine patterns and predictability. Nine
individually occurring behaviors and copulat ion made up the behavior catalog
that was used. Results showed that there is an exponential transition rate
out of the feeding mode of Melibe that there is a correlation between time
spent feeding and size of the animal, and that the patterns of behavior can be
predicted by a stochastic, one-step transitional probability matrix. Two
behavior loops are also present in the Melibe which may demonstrate a
hormonal change between them. The predictability of behavior can assist
electrophysiological studies with live animal preparations by allowing the
researcher to predict and prepare for certain behaviors.
INTRODUCTION.
Curiosity about how the human brain governs the complexity of human
behavior has led many a scientist into the neurobiology field. Unfortunately,
the human brain is far too complicated for studies at the current level of
underständing. Invertebrate ganglia, with their significantly fewer neurons
and simpler structure, have provided a template for initial study with the
hope that relationships can be found between these animals and humans.
A great deal of time has been spent on trying to find the neural pathways
responsible for behavior and learning in invertebrates. In each study, a
behavioral paradigm is required in order to relate neurological findings to the
whole animal. A good example of the study of neuroethology, the combination
of behavior and its neurophysiological basis, is one of Alan Gelperin's
experiments on the terrestrial slug Limax maximus. The first part of his
study involved behavioral experiments to train the slug to respond positively
or negatively to certain food odors through associative learning. He then
examined recordings from lip-brain preparations of the slug to try and
understand the neurophysiological basis of training (Gelperin, 1983).
Marine mollusks have also been used for neuroethological studies. The
behavioral hierarchy of Pleurobranchaea californica a carnivorous mollusk,
has been described by Davis et.al. (1974), with the intent that an underlying
neurophysiological explanation could follow. The technique for deriving this
hierarchy involved limiting stimuli to the slug during non-experimental
periods, and then testing dominance between two initiated behaviors at a
time. Arepresentative model was interpreted from the results.
Because of the sensory deprivation involved in such a study, a general
idea of natural or "everyday" behavior of the animal cannot be interpreted
from a hierarchy. Äfter attempting a dominance study on the nudibranch
Melibe leonina  found that constant ly limiting the stimulus to the animal
would be difficult and actually detract from the animal's natural responses.
decided to attempt a less reductionistic approach to behavior by studying
behaviors occurring in a simulated natural habitat.
In spontaneous behavior, transitions between observable behaviors
become more important than dominance. At the neural level, transitions
between behaviors and decisions to make those transitions are probably
controlled by very different mechanisms than those involved in dominance.
while transitions may come in response to changing external and internal
states, à dominance hierarchy shows only where one mechanism can overrule
another.
The model developed to describe patterns of behavior involves stochastic
one-step transitions. A stochastic process is one in which a randomly
determined sequence of behaviors is considered a sample of one element from
a probability distribution (Random House Dictionary, 1987). Essentially, a
stochastic model involves transition probabilities between behaviors that
depend solely on the present state of the animal, reflecting no influence from
past behavioral states.
Melibe leonina provides many optimal conditions for this type of study.
It is a large, hermaphroditic nudibranch, with a very characteristic set of
behaviors. Transitions between behaviors are fairly slow, for easy
recognition, and its ganglia is large enough to facilitate neurological studies
(Thompson, personal communication). The three distinctive oral hood states
of the Melibe provide a physical apparatus for clearly observing behavioral
changes. Its environmental conditions are simple to replicate - a ratty blade
of Macrocystis kelp, flowing water, and a few snalls.
MATERIALS AND METHODS:
Melibe leonina were collected off of Del Monte Beach in Monterey during
free dives and SCUBA dives. They are found on solitary Macrocystis pyrifera
kelp plants, in crowded populations of hundreds of individuals. Tanks under
the shed at Hopkins were set up with a continuous sea water flow to hold the
animals for further experimentation. Kelp and snails in the tanks provided a
substrate and a natural cleaning service, respectively. A total of five holding
tanks were used and approximately 100 Melibe were collected over the
course of the ten weeks. All animals left at the end of the ten weeks were
returned to their original kelp bed.
Each of the five tanks for natural behavior observations, also set up
under the shed, contained one blade of Macrocystis kelp, one or two snails,
and two Melibe. As each animal was placed in a tank, it was measured for
volume by water displacement and studied to find unique characteristics for
later recognition. One animal from each study tank was injected with
methylene blue dye in its right first cerata to aid in differentiation.
All study animals were kept in as similar conditions as possible. They
were fed every other day with mysids collected off of the kelp in Hopkins
Marine Life Refuge. Tanks were cleaned out every two weeks, but never on ar
observational day. Melibe were never disturbed unless it became necessary
to change something in the tank, if a death occurred, or if the drain clogged.
Observations were made in six hour sessions, at fifteen minute intervals.
Daytime and nighttime behaviors were recorded for a total of sixty-six hours
of observations. Behaviors were recorded on tank sheets (figure 2) and
compiled into tables (figure 3).
The statistical analysis methods were mainly comprised of chi-square
tests for significance of different behavior patterns using the equation
Xa-2 (0.-E. P/E.
where O is the observed value and E is the expected value. Counting of the
various sequences of behaviors was done by an original program written in
Pascal on a Macintosh personal computer.
Transitional probabilities were assumed to be equal to the observed
transitional frequencies. They were used to calculate the probability of
occurrence of three behavior sequences using the equation
P(ABC)-P(A)P(BIA)P(CB)
where A, B, and Care any three behaviors (Oden, 1977).
RESULTS:
Initial observation of Melibe led to the distinction between nine
different behaviors, six of which are common, and three which are less
common in spontaneous behavior (figure 4). Copulation was observed to occur
during day and night hours, and all other behaviors, other than swimming,
were observed to occur spontaneously with copulation, as opposed to
observations made by Ageska (1972) which claimed that animals must remain
quiescent during copulation.
Analysis began with the study of casting/feeding behavior. Total time
spent in each behavior was tabulated for every animal. Plots were made of
total time spent in a behavior versus the volume of the animal (figure 5).
Feeding behavior was well correlated with size, with larger animals spending
more time feeding. Time spent resting showed a tendency to decrease as size
increased, but the correlation coefficient was low.
Transitional frequencies between consecutive behaviors were calculated
(figure 7). Using these frequencies, I diagramed all transitions with a
frequency greater than ten percent (figure 8). This diagram shows graphically
a tendency of the Melibe to remain in one of two behavior loops, F-OH-R/OH
or RST-AL-R/AL. The statistical reality of these loops was quantified and it
was found that transitions between these loops were significantly lower than
between any other combination of 3 or 4 behaviors.
Total time spent in each loop for every animal was counted, as
well as the average number of intervals within each loop. Volume of animals
was graphed against time spent in the F-ÖH-R/OH loop (figure 9). Larger
animals tend to spend more time in this feeding loop.
Four behaviors, F, OH, RST, and AL had a tendency to last longer than just
one interval before transition. I defined a session as a period in which a
single behavior continued consecutively. Feeding was the behavior with the
most data to support an analysis of the length of its sessions. Sessions were
tabulated for each animal and the results graphed (figure 10). An exponential
describes the best fit of the data. The equation y-C10 (-kt) calculated for
all animals with enough data to support analysis, showed that each antmal
had a different decay rate. When volume was graphed versus the mean feeding
session length, derived from the k-constant, there was shown to be no
significant correlation (figure 6). The k-constant can be converted to mean
feeding session length using the equation (mean t) = 0.3017K.
To see if a one-step transition frequency matrix is enough to describe
longer sequences of behavior, I used the equation presented in the methods to
calculate predicted frequencies of sequences of three behaviors (or two-step
transitions), starting with the behaviors F, OH, AL, and RST. The one-step
probabilities were taken from figure 7. The observed and predicted matrices
of occurrences of triplet sequences are in figure 11, with the corresponding
chi-square values in figure 12. These matrices were graphed 3-dimensionally
in order to visualize the similarities between the predicted and observed
frequencies (figure 13) using the computer software "Surfer
DISCUSSION:
Iwo main conclusions can be drawn from the results of this research.
One entails the relationship of physical characteristics to the individual
animal's behavior and the other is the relationship among different behaviors.
This research also reforms some previously published observations about
Melibe behavior in general (Ageska, 1972).
Analysis shows that larger animals will spend more time feeding and
more time within the "active" feeding loop. Further experimentation could
show how a larger animal may have a greater concentration of a "feeding
hormone, or that certain neural pathways are stronger that control feeding.
Individual animals will tend to remain in the behaviors F, OH, AL, and RST for
longer than one fifteen minute interval, yet transitions in and out of roaming
behaviors (R/AL, R/OH) are much faster. There may be different bases for the
different types of behavior, one "static" and one "transitionary", which can be
detected through electrophysiological examination.
The importance to the animal of choosing between behavior loops is very
interesting. Although transition between loops is infrequent, all animals
spend some time in each, presumably because all animals need resting time as
well as feeding time. There is probably some hormonal basis that increases
the probability of switching from one loop to another, considering the
different physiological ramifications involved in the behaviors. The feeding
loop allows the animal to be ready to detect food and/or eat it, whereas the
resting loop is less energy consuming. Future study involving applied stimuli
could show if the defense responses, either crumple or shrug (Scott, 1990),
are greater in one of the two loops.
The transition frequencies among all of the behaviors led to the
formulation of a stochastic model dependent on those frequencies. The use of
a stochastic model in descriptions of animal behavior is not new (Bekoff,
1977). Often the process so described is referred to as a Markov process, in
reference to its designer. I have chosen to avoid the term "Markov" due to its
connotative requirement of stationarity in the data. Stationarity is the
property that behavior or behavioral sequences do not change when comparing
different time periods. Previous studies that claimed to have found
Markovian analyses of animal behavior have been criticized on their lack of
stationarity (Bekoff, 1977). In my research, I assumed that stationarity was
irrelevant: first, because ! could not determine a proper time interval in
which to assume stationarity in ten weeks; second, because 1 pooled the
behavior of all animals in each set, effectively neutralizing any substantial
motivational changes that would be considered non-stationary
The equation for testing a one-step transition model (pg. 5) was taken
from an analysis on hermit crab agonistic behavior that was assumed to be
non-stationary (Oden, 1977). Using this equation, the frequency of two-step
transitions (or 3-behavior sequences) was estimated. Comparing the
matrices of predicted and observed occurrences of each sequence yields
chi-square values that for thirty-six degrees of freedom show some
significant differences. Fortunately, there are several problems with the
chi-square as a statistical test in this case. First, the data set in this study
is co-variant, leading to an unclear decrease in the degrees of freedom. Also,
large six-by-six matrices result in some cells with low expected counts.
This will tend to overinflate the chi-square statistic (Steinberg, 1977).
Assuming that the chi-square values are overinf lated, the amazing
similarities between the predicted and observed sequence frequencies become
apparent. This similarity demonstrates that spontaneous behavior sequences
in Melibe are stochastic. An animal's transition between its present state
and the next future state, within my imposed interval of fifteen minutes,
depends only on its present state and not on any previous states. This gives
interesting insights into the neurophysiological bases of Me/ibe behavior. It
is probable that memory is limited and that circuits that control behavioral
transitions are relatively simple.
Spontaneous behavior of Melibe leonina is an observable and analyzable
subject of study. The information that the study has provided shows that
behavior does not have to be elicited in order to be interesting. Possibilities
for future research include examining learning in Melibe determining the
physiological changes of the animal between behavioral loops and determining
the usefulness of the predictive capability of the one-step transition model
for whole animal electrophysiological preparations.
ACKNOWLEDGEMENTS:
First of all I would like to thank my effervescent advisor, Stuart
Thompson, for his encouragement, enthusiasm, and patience this quarter. A
big thanks to all of the people at Hopkins who are friendly and helpful and
always willing to assist a flailing undergraduate, especially Nicole Crane,
Bruce Hopkins, Carla (for the super Melibe tanks)), Chris Patton, and Alan
Baldridge. To everyone in the 175H class, I never thought 1 could meet such an
amazing, talented, friendly group of people in one placel Thanks to Chris
Mathes for all of his help, and to his wife and son, Libby and Jonathan for
adding a touch of home to the lab. Thanks, Agnieszka and Nat, for always
keeping the laughter in the lab and becoming two dear friends. Special thanks
to Shari for long mysid collecting mornings and diving trips which became
wonderful heart-to-heart talks (Why ask why? Drink a beerl). Thanks to my
awesome roomie, Saween, for keeping me on track, making me be social, and
teaching me that ! don't have to eat Noodle-Roni every night. And last but
absolutely not least, I want to express my gratitude to my mentor, Sam Wang,
for hours and hours of generous encouragement and help. You are truly an
amazing person and friend.
LITERATURE CITED
1. Ageska, R.A. and Nybakken, J. 1972. Contributions to the Biology of Melibe
leonina(Gould, 1852), The Veliger, 19: 19-26.
2. Bekoff, M. 1977. Quantitative Studies of Three Areas of Classical
Ethology: Social Dominance, Behavioral Taxonomy, and Behavioral
Variability. pp 1-46. 7: Hazlett,B.A. (ed.), Quantitative Methods in the
Study of Animal Behavior. Academic Press: San Francisco.
3. Davis, W.J., Mpitsos, G.J., and Pinneo, JM. 1974. The Behavioral Hierarchy
of the Mollusk Pleurobranchaea, 1. The Dominant Position of the Feeding
Behavior. Journal of Comparative Physiology 90: 207-224.
4. Gelperin, A. 1983. Neuroethological Studies of Associative Learning in
Feeding Control Systems. pp 189-205 77: Huber,F. and Markl,H. (eds.),
Neuroethology and Behavioral Physiology - Roots and Growing Points.
Springer-Verlag: New York.
5. Oden, N. 1977. Partitioning Dependence in Nonstationary Behavioral
Sequences. pp 203-220. 7n: Hazlett,B.A. Quantitative Methods in the
Study of Animal Behavior. Academic Press: San Francisco.
6. The Random House Dictionary of the English Language, second edition,
unabridged. Random House: New York, 1987
7. Scott, D.L. 1990. The Shrug Response in Melibe leonina: Behavioral and
Neurophysiological Observations. HMS Biology 175H papers.
e
8. Senter, R.J. 1969. Analysis of Data: Introductory Statistics for the
Behavioral Sciences. Scott, Foresman and Company : Glenview, IIlinois.
509 pp.
9. Steinberg, J.B. 1977. Information Theory as an Ethological Tool. pp 47-74
/n: Hazlett, B.A. (ed.), Quantitative Methods in the Study of Animal
Behavior. Academic Press: San Francisco.
10. Taylor, HM. and Karlin,S. 1984. An Introduction to Stochastic Modeling.
Academic Press: San Francisco. 398 pp.
1. Thompson, S.H., Personal communications.
LIST OF FIGURES
1. Schematic of a Melibe leonina specimen, showing important morphology.
2. Example of a tank sheet, used for recording Melibe behavior.
3. Example of a compiled behavior table from one six-hour observational day,
4. List of behaviors and their physical characteristics,
5. Total time spent feeding/casting and resting versus volume of the animal.
6. Mean feeding session length versus volume of the animal.
7. One-step transitional probabilities for each set of ten Melibe.
8. Diagram of transition probabilities greater than ten percent for both sets
of animals.
9. Time spent in the feeding loop versus volume of the Melibe
10. Exponential decay rate of feeding session lengths for two animals.
II. Example of observed and predicted matrices for three behavior sequences.
12. Significance of X-square scores calculated from the predicted and
observed three behavior sequence matrices.
13. 3-D representation of predicted and observed matrices of sequences of
three behaviors.
0

-4

C
goe 1
4

.
-
DAE:S
2
6




9
TIME
a

et

O
7
20
-
29110
Figure 3
Behavior
6 COMON BEHAVIORS.
casting/feeding
open hood
alert posture
roaming with open hood
roaming with alert posture
resting
3 INFREQUENT BEHAVIORS:
swimming
crumple
roamng
NATURALLY OCCURRING BEHAVIORS
Abbreviation Oneletter
Charactert
-casting of hood.
expulsion of water
from hood
-ho motion, hood and
O
tentacles extended,
cerata upright
-hood up, tentacles in,
Al
cerata upright
-motion of foot on
R/OH
substrate with open
nood
-otion of foot with
R/AL
alert posture
-hood,tentacles tucked
RST
in, cerata down, no
motion
-side to side body
contractions, no
4
contact with subsuat
-tight contraction of
CR
whole body
-in of fot
substrate with hood
down and tucked under
Figure 4
.. —

SIZE OF MELBE VS.
% TIME FEEDING AND RESTING
80
5 60
y -- 15.71+0.4684 R-0.91
.
O FEEDM
y-3.1465-0.1275x R-0.40

v 20

2

100
200
VOUT &a REIIEE (I)
Figure 5
Mean Feeding Session Length vs. Volume of Melibe
y -3.4222 - 0.0023x P =0.07

100
50
200
volume (mi)
Figure 6
E
2
Figure
0.
IL
47.3


21.8
1.5

R/ON
25.3

4.2
2.2
R/AL
30.2
5.
RST8
65.5
BEHAVIOR TRANSITIONS (by %)



22.7
20.0
A
R/OH

126.8
12
3.!
0.
—
R/1
44.6
52.6
2.2
97
BEHAVIOR TRANSITIONS (by %)
igure 8
VOLUME VS. TIME SPENT IN F-OH-R/OH LOOP
100
6.7537 +0.3101X R-0.87
80
60
40
9
20
o
100
200
300
VOL (mI)
FigureS
Ezponential Decay of Feeding Session Length in 1A
30 -
y - 18.5833 * 10•(-0 0966x) R -098
20
D 1A sessions

* intervals (15 min)
Exponential Decay of Feeding Session Length in 11A
y - 18.5833 * 10°(-0.0966x) R -0.98

intervals
Figure 10
8
1
F
0
C3
R
11
Observed matrix
P(A,BC
1
I
27
Figure
A,-A
8
A
1
1
1
C3
0
88
4
21
26
5310.3
23
73
10 14.2
81.2
6.2
32
23
12
26
41
132
4
03 1.21132 06
Predicted matrix
PCA,DPCB, IA,PCC,IB.
15
2
15.5
15
35
26
O.3
12
09
12
33.7
XSOUARE VALUES FOR DIFFERENCE BETWEEN NUMBER OF
EDICTED AND ÖBSERVED 3-BEHAVIOR SEQUENCES (ABC)
Behavior A
Kequare value
Significance pe(df-36
SET1
Feeding/casting
62.9
0.005
Open hood
453
O.5
58.2
Alert posture
0025
Resting
55.0
0.025
SET 2
Feeding/casting
0.05
52.3
46.3
Open hood
0.5
O.5
42.5
Alert posture
43.6
Resting
0.5
Figure
A




Figure 13



BEHAVIOR AEF