formed by the left tree and then proceeds along the ridge to that tree. Position the
troop close between the trees and you will see that the “ridge” effect is more
pronounced.
One other interesting result seems worth mentioning. If we plot the odor
concentration directly downwind of the tree, we find that, due to ground reflection
of the odor, the peak odor is at the canopy level only near the emitting tree. As one
moves away from that tree, the peak concentration falls both in intensity of course,
but also in height. So, for very distant fruit trees, the best place to be is not in the
canopy but down the trunk, perhaps half way to the ground. There is another
monkey species which travels with the Tamarin and which stays at about the
mid-height level of the tree, until the fruit tree is reached. These lower monkeys
are in a better position to direct the combined troop in the early stages of the search
for a distant tree with ripe fruit.
Although quite elaborate, our model has several shortcomings. The diffusion
equations are for a uniform medium, usually air. In the actual forest, we do not have
such a medium. The leaves and branches of the trees no doubt cause much more
rapid mixing than an air-only medium. The leaves are not uniformly distributed
vertically in the rain forest. Above the canopy, the model has a single medium
(clear air). How would this affect the readings in the forest? What about temperature inversions caused by the forest? How would the model include such effects?
Perhaps the monkeys use a mixture of odor tracking and memory. How would the
memory effects be included? It is assumed that the monkeys can detect the
difference between under and over ripe fruit. How can this be modeled?
28.2 Monkey Travels Model Equations
ALTERNATOR(t) ¼ ALTERNATOR(t À dt) + (ALTERNATOR_RESET) * dt
INIT ALTERNATOR ¼ 1 * RAND_1
INFLOWS:
ALTERNATOR_RESET ¼ À2 * ALTERNATOR
PHI_D1(t) ¼ PHI_D1(t À dt) + (PHI + N_SENSOR_RESET À T1) * dt
INIT PHI_D1 ¼ 0 {Phi delayed.}
INFLOWS:
PHI ¼ IF (TIME > 16) AND (N_X>¼0) THEN ARCTAN(N_Y/N_X) + ALTERNATOR * (RAND+DEVIATION_ANGLE) ELSE IF (TIME>16) AND
(N_X<0) THEN ARCTAN(N_Y/N_X) + PI + ALTERNATOR * (RAND
+DEVIATION_ANGLE) ELSE 0 {The chosen angle for the next step.}
N_SENSOR_RESET ¼ IF TIME ¼15 THEN THETA ELSE 0
DOCUMENT: (TIME ‚a ˆ• 14) AND (TIME ‚a ˆ§ 15)
OUTFLOWS:
T1 ¼ PHI_D1
PHI_D2(t) ¼ PHI_D2(t À dt) + (T1 À T2) * dt
INIT PHI_D2 ¼ 0
236
28 Monkey Travels
troop close between the trees and you will see that the “ridge” effect is more
pronounced.
One other interesting result seems worth mentioning. If we plot the odor
concentration directly downwind of the tree, we find that, due to ground reflection
of the odor, the peak odor is at the canopy level only near the emitting tree. As one
moves away from that tree, the peak concentration falls both in intensity of course,
but also in height. So, for very distant fruit trees, the best place to be is not in the
canopy but down the trunk, perhaps half way to the ground. There is another
monkey species which travels with the Tamarin and which stays at about the
mid-height level of the tree, until the fruit tree is reached. These lower monkeys
are in a better position to direct the combined troop in the early stages of the search
for a distant tree with ripe fruit.
Although quite elaborate, our model has several shortcomings. The diffusion
equations are for a uniform medium, usually air. In the actual forest, we do not have
such a medium. The leaves and branches of the trees no doubt cause much more
rapid mixing than an air-only medium. The leaves are not uniformly distributed
vertically in the rain forest. Above the canopy, the model has a single medium
(clear air). How would this affect the readings in the forest? What about temperature inversions caused by the forest? How would the model include such effects?
Perhaps the monkeys use a mixture of odor tracking and memory. How would the
memory effects be included? It is assumed that the monkeys can detect the
difference between under and over ripe fruit. How can this be modeled?
28.2 Monkey Travels Model Equations
ALTERNATOR(t) ¼ ALTERNATOR(t À dt) + (ALTERNATOR_RESET) * dt
INIT ALTERNATOR ¼ 1 * RAND_1
INFLOWS:
ALTERNATOR_RESET ¼ À2 * ALTERNATOR
PHI_D1(t) ¼ PHI_D1(t À dt) + (PHI + N_SENSOR_RESET À T1) * dt
INIT PHI_D1 ¼ 0 {Phi delayed.}
INFLOWS:
PHI ¼ IF (TIME > 16) AND (N_X>¼0) THEN ARCTAN(N_Y/N_X) + ALTERNATOR * (RAND+DEVIATION_ANGLE) ELSE IF (TIME>16) AND
(N_X<0) THEN ARCTAN(N_Y/N_X) + PI + ALTERNATOR * (RAND
+DEVIATION_ANGLE) ELSE 0 {The chosen angle for the next step.}
N_SENSOR_RESET ¼ IF TIME ¼15 THEN THETA ELSE 0
DOCUMENT: (TIME ‚a ˆ• 14) AND (TIME ‚a ˆ§ 15)
OUTFLOWS:
T1 ¼ PHI_D1
PHI_D2(t) ¼ PHI_D2(t À dt) + (T1 À T2) * dt
INIT PHI_D2 ¼ 0
236
28 Monkey Travels