How do aurelia move

However, moon jellies are the least dangerous jellyfish to humans. Moon jellyfish are found in oceans around the world. They prefer a warm environment and often live near coastlines in the Atlantic, Pacific, and Indian oceans.

These organisms can live in saltwater or brackish mixed salt- and freshwater environments, and waters between and degrees Fahrenheit offer them the best chance of survival. This species is not threatened,but can serve as an indicator that marine ecosystems are out of balance. Unlike other larger species, they can thrive in otherwise uninhabitable waters.

This means that as ocean health declines, moon jelly populations can actually increase! This is true in environments that experience human-induced phenomenon like overfishing, ocean warming, ocean acidification and pollution. In the Wild: Zooplankton that sticks to their short tentacles as they move through ocean environments. The Internet connection is missing right now, but you're able to browse previously opened pages offline. Mask wearing is now required indoors only, vaccinated or not, per county directive.

Moon Jellyfish Aurelia aurita. Conservation Status: Least Concern This species is not threatened,but can serve as an indicator that marine ecosystems are out of balance.

Diet In the Wild: Zooplankton that sticks to their short tentacles as they move through ocean environments. At the Zoo: Brine shrimp. However, the polyps formed when they reproduce can live up to 25 years awaiting favorable conditions to complete the stages of growing into a jellyfish.

Fun Facts about the Moon Jellyfish Jellyfish are the oldest multi-organ animal, with fossil evidence indicating they existed over million years ago, predating dinosaurs. Jellyfish have been studied widely by scientists, and moon jellies even went into space as an experiment aboard the Space Shuttle Columbia in Location: Aquariums.

Blood Star. Blood-Red Fire Shrimp. Blue Death-Feigning Beetle. Cabbage Leather Coral. Chilean Rose-Haired Tarantula. Colt Coral. Fish-Eating Anemone. Frogspawn coral. Giant African Millipede. Still, the DNN might influence the circular muscles by amplifying the impact of the MNN activity as it was measured in other jellyfish Passano, ; Passano, In addition, there is a small band of radial muscles on the marginal angles of Aurelia , which contract during a turning motion Gemmell et al.

The speed of the muscle activation and the position of the muscles indicate that they are innervated by the DNN. Some behavioral Horridge, ; Gemmell et al. These points indicate that each rhopalium is responsible for steering the animal by stimulating either one or both of the nerve nets. If and how the jellyfish can control its swimming motion beyond this is currently unknown. Oblate-shaped jellyfish like Aurelia are among the most efficient swimmers in the world. Their cost of transport energy consumption during movement per mass and movement distance is very low Gemmell et al.

Therefore, there has been a continuous effort to understand the hydrodynamics of their swimming motion. As described above, the jellyfish swim musculature is located solely on the subumbrella. Jellyfish do not have muscles that actively open the bell after a contraction. Instead their body is filled with mesoglea, a mixture of fluid and elastic fibers that create a hydrostatic skeleton. During a contraction the mesoglea stores elastic energy created by pushing the fluid to the center and stretching the fibers, which leads to relaxation of the bell when the muscle tension drops Alexander, ; Gladfelter, ; Gladfelter, Jellyfish that use the latter swimming mechanism produce most of their forward momentum during their contraction phase, and get pushed forward by propelling fluid out of their bell Villanueva et al.

In contrast rowers produce their forward momentum through a series of vortex rings at the bell margin. Since these vortices form both during the contraction and the relaxation of the bell, rowers are highly cost efficient swimmers Colin and Costello, ; Dabiri et al.

An important part of the insight into the swimming motion of animals has been gained through fluid dynamics simulations. Methods like the Immersed Boundary IB method have been applied to study the interactions of aquatic animals with the surrounding fluid Fauci and Peskin, ; Peskin, ; Cortez et al. This revealed for example that in anguilliform swimmers, the same muscle activation patterns can produce different swimming motions depending on body stiffness Tytell and Lauder, ; Tytell et al.

Studies adopting an integrated view of neural circuitry and biomechanics Tytell et al. Herschlag and Miller , Park et al. Herschlag and Miller generated realistic jellyfish forward motion in 2D using a simple model of the bell kinematics. A related study, Park et al.

Hoover and Miller drove the bell of their model jellyfish at different frequencies. They found that frequencies around resonance, whose precise values depend on the contraction forces, are optimal for swimming speed and cost of transport. Few studies have so far attempted to pin down the mechanisms of directional steering in jellyfish locomotion. Jellyfish turn by creating an asymmetric bell contraction Gladfelter, In most scyphozoan jellyfish, the part of the bell on the inner side of the turn contracts stronger and earlier Gladfelter, ; Horridge, Jellyfish often use this turning to adjust their tilt.

The contraction wave then usually starts at the rhopalium on the inside of the turn Shanks and Graham, ; Horridge, To our knowledge, Hoover contains the so far only modeling study on turning in jellyfish.

Hoover created a 3D model of a jellyfish and tested the effect of a rectangular region of increased tension traveling in both directions around the bell. He found that the bell turns toward the direction of the origin of this traveling wave, as observed in real jellyfish. The amount of angular displacement depends strongly on the speed at which the activity travels around the bell.

Another component that is considered important for the swimming of jellyfish are the bell margins. During regular swimming, the margins of Aurelia are very flexible and follow the rest of the bell as it contracts and expands McHenry and Jed, As described above, the bell margins in Aurelia do not possess circular muscles but rather a set of loosely organized radial ones Figure 1. During turning maneuvers they stiffen the margins, starting at the origin of the activation wave Gemmell et al.

This, together with the observation that DNN activation creates no visible contraction of the circular muscles in Aurelia Horridge, , suggests that MNN and DNN each control one set of muscles and that this enables steering of the jellyfish. However, a mechanistic understanding how the activity of the two nerve nets determines turning is lacking. Furthermore, since the origin of nerve net activation waves is near the stimulus and apparently defines the inside of the turn, the hypothesis might only explain steering toward a stimulus.

Some observations in jellyfish, for example their ability to keep a certain distance from rock walls Albert, ; Albert, , may, however, suggest that jellyfish are capable of steering away from aversive stimuli. It is currently unknown how the through-conducting nerve nets could allow such a level of control.

We develop a biophysically plausible scyphozoan neuron model on the level of abstraction of Hodgkin-Huxley type single compartment models. These describe the actual voltage and current dynamics well and there is sufficiently detailed electrophysiological data available to fit such a model, obtained from Cyanea capillata Anderson, Furthermore, dynamical mechanisms are not obscured by the presence of too many variables and the models lend themselves to fast simulations of medium size neural networks, with several thousands of neurons.

We incorporate the voltage-dependent transmembrane currents observed for scyphozoan MNN neurons by Anderson and Anderson and fit the model parameters to the voltage-clamp data presented there see Materials and methods. The results of the fitting procedure are shown in Figure 2A.

The remaining unknown features of the model are the membrane capacitance and the synapse model. We choose them such that i the excitatory postsynaptic potentials resemble in their shape the experimentally found ones Anderson, , ii the inflection point of an AP is close to 0 mV Anderson and Schwab, and iii it takes approximately 2.

A Comparison of our model dynamics with the voltage-clamp data Anderson, that was used to fit its current parameters. The model follows the experimentally found traces. The model neuron generates an action potential similar in shape to experimentally observed ones.

C The disentangled transmembrane currents during an action potential. Our model generates APs similar to the ones observed experimentally by Anderson and Schwab It allows to quantitatively disentangle the contributions of the different transmembrane channel populations, see Figure 2.

Before an AP, the leak current dominates. After the voltage surpasses the inflection point, the fast transient in- and outward currents generate the voltage spike. During the spike, the steady-state outward current activates and stays active during repolarization. This is also visible in electrophysiological recordings Anderson and Schwab, ; Anderson, As a single AP evokes an AP in a resting postsynaptic neuron and synapses are bidirectional, one might expect that the postsynaptic AP or even the reflux in turn evokes further presynaptic APs.

However, experiments in two-neuron systems do not observe such repetitive firing but only bumps of depolarization after a spike Anderson, This is likely due to the long refractory period of scyphozoan neurons, which is initially absolute for about 30 ms and thereafter relative for about 70 ms Anderson and Schwab, In agreement with experimental findings, we do not observe repetitive firing in systems of two synaptically connected model neurons, but only bumps of depolarization after a spike.

This indicates that our model neurons have a sufficiently long refractory period, although it has not been explicitly inserted. Due to signal transmission delays, the neuron receives the second EPSC 7 ms after the first one. The first EPSC always generates a spike. The abscissa displays the time differences between its peak and the onset of the second EPSC. The ordinate displays the highest voltage reached after the end of the first spike, defined as reaching 0 mV from above. A plotted value of 0 mV means that the neuron did not exceed 0 mV after its first spike.

To determine the refractory period effective under arrival of synaptic inputs, we apply two EPSCs with increasing temporal distance see Figure 3C. We find a refractory period of about 20 ms. The longer refractory periods observed in scyphozoan neurons may be due to additional channel features that are not detectable from the voltage clamp data, such as delayed recovery from inactivation Kuo and Bean, ; French et al. The synaptic and AP traveling delay in our model at most 3.

We find that the synaptic reflux and the steady-state current are crucial for the long duration: without them the refractory period is reduced to about 5 ms purple trace in Figure 3C. It may therefore increase the reliability of signal conduction in the MNN. Given the described qualitative properties of its neurons and synapses, we can explain the main feature of the MNN, namely throughconductance without pathological firing: In fact, the properties of the MNN indicate that during the activation wave following an arbitrary initial stimulation of the network, every neuron spikes exactly once.

Generally, this is the case in a network where i the synapses are bidirectional, ii a presynaptic action potential evokes action potentials in all non-refractory postsynaptic neurons and iii the refractory period is so long that there is no repetitive firing in two neuron systems.

This becomes clear if we think of the nerve net as a connected undirected graph with neuron dynamics evolving in discrete time steps. The undirectedness of the graph reflects the synaptic bidirectionality, point i above. We assume that it takes a neuron one time step to generate an AP; its postsynaptic neurons that are resting generate an AP in the next time step, see point ii.

After an AP, a neuron is refractory for at least one time step and thereafter becomes resting, ensuring iii. More formally speaking, each vertex can be in one of three states in any time step: resting, firing, refractory. The state dynamics obey the following rules:.

If in such a graph a number of vertices fires at t 0 while the other vertices are resting initial stimulation , every vertex will subsequently fire exactly once: Obviously any vertex X will be firing at t x , where x is the minimum of the shortest path lengths to any of the vertices firing at t 0. This implies that at t x a vertex Y must be firing, with a path between X and Y of length j , along which the firing spreads from Y towards X.

There is, however, also a chain of firing traveling along this path from X to Y. After that both vertices are refractory and no other vertex along this path is firing. This vertex fires in the next time step, but since both neighboring vertices on this path are then refractory, no vertex along this path fires after that. We may thus conclude that X fires only once. To model the MNN in more detail, we uniformly distribute the developed Hodgkin-Huxley type neurons on a disc representing the subumbrella of a jellyfish with diameter 4 cm.

Its margin and a central disc are left void to account for margin and manubrium see Materials and methods for further details. Eight rhopalia are regularly placed at the inner edge of the margin. We model their pacemakers as neurons which we stimulate via EPSCs to simulate a pacemaker firing. The neurons are geometrically represented by their neurites, modeled as straight lines of length 5 mm Horridge, a.

All synapses are bidirectional and have the same strength, sufficient to evoke an AP in a postsynaptic neuron. We incorporate neurite geometry and relative position into our single compartment models by assuming that the delay between a presynaptic spike and the postsynaptic EPSP onset is given by the sum of i the traveling time of the AP from soma to synapse on the presynaptic side, ii the synaptic transmission delay and iii the traveling time of the EPSC from synapse to soma on the postsynaptic side.

The traveling times depend linearly on the distances between synapse and somata; for simplicity, we assume that AP and EPSC propagation speeds are equal. In agreement with Anderson , the total delays vary between 0. Interestingly, the preferred spatial orientations of MNN neurites along the subumbrella are related to neuron position. Horridge a reports the following observations:. Near the rhopalia, most neurites run radially with respect to the jellyfish center.

Near the outer bell margin and between two rhopalia, most neurites follow the edge of the bell. Closer to the center of the subumbrella there is no obvious preferred direction. To incorporate these observations, we draw the neurite directions from distributions whose mean and variance depend appropriately on neuron position. Specifically, we use von Mises distributions for the angle, which are a mathematically simple approximation of the wrapped normal distribution around a circle Mardia and Jupp, The neurite orientation structure may emerge due to ontogenetic factors: In the complex life cycle of scyphozoans, juvenile jellyfish start to swim actively during the ephyra stage.

In this stage, the jellyfish has some visual similarity to a starfish, with a disc in the center containing the manubrium, and eight or more arms, one per rhopalium, extending from it. The motor nerve net is already present in the ephyra and extends into its arms Nakanishi et al. As the jellyfish matures, the arms grow in width until they fuse together to form the bell. MNN neurites simply following the directions of growth would thus generate a pattern as described above: Neurites in the center disc may not have a growth direction or constraints to follow, therefore there is no preferred direction.

When the ephyral arms grow out, neurites following the direction of growth run radially. Also the geometric constraints allow only for this direction. Neurons that develop in new tissue as the arms grow in width to form the bell orient circularly, following the direction of growth.

There are, to our knowledge, no estimates on the number of neurons in a scyphozoan MNN; only some measurements for hydrozoans and cubozoans exist Bode et al. For a neuron of 5 mm length, this translates to roughly 70 synapses placed along its neurites.

To obtain an estimate for the number of MNN neurons from this, we generate model networks with different neuron numbers, calculate their average synaptic distances and compare them with the experimentally observed values see Figure 4A.

We find that in a von Mises MNN, about neurons yield the experimentally measured synaptic density, while the uniform MNN requires about neurons. In general, for a fixed number of neurons, a von Mises MNN is more sparsely connected than a uniform MNN: The biased neurite direction at the bell margin of a von Mises MNN see Figure 18 in Materials and methods implies that neurons in close proximity have a high probability of possessing similarly oriented neurites. This decreases their chance of overlap and thus the number of synapses.

B Delay between the spike times of the pacemaker initiating an activation wave and the opposing one, for different MNN neuron numbers. Displayed are results for model jellyfish with 3 cm and 4 cm diameter. The dashed line indicates the experimentally measured average delay of 30 ms between muscle contractions on the initiating and the opposite side of Aurelia aurita Gemmell et al.

C Delays measured in B for the 4 cm jellyfish, plotted against the average number of synapses in MNNs with identical size. Measurement points are averages over 10 MNN realizations; bars indicate one standard deviation. Our numerical simulations confirm that firing of a pacemaker initiates a wave of activation where every MNN neuron generates exactly one AP see Figures 5 and 6 for an illustration.

The activity propagates in two branches around the bell. These cancel each other on the opposite side. During the wave, all other pacemakers fire as well, which presumably resets them in real jellyfish.

In a uniform MNN the wave spreads rather uniformly Figure 6. In a von Mises MNN, the signal travels fastest around the center of the jellyfish and spreads from there, sometimes traveling a little backwards before extinguishing Figure 5.

A Activity of each neuron at different times after stimulation of a single pacemaker neuron. Color intensity increases linearly with neuron voltage.

B Spike times of the same network. Neurons are numbered by their position on the bell. Red dots represent the pacemakers inside one of the eight rhopalia.

The neurite orientations are distributed according to location-dependent von Mises distributions. Setup similar to Figure 5 , but the neurite orientations are uniformly distributed. Gemmell et al. This delay should directly relate to the propagation of neural activity.

We thus compare it to the delay between spiking of the initiating pacemaker and the opposing one in our model MNNs. We find that both our von Mises and uniform MNNs can generate delays within one standard deviation of the measurements, see Figure 4B.

Our simulations indicate that MNN networks typically have neurons or more, as the propagation delays obtained for jellyfish with 3 and 4 cm diameter start to clearly bracket the experimentally found average at this size.

Figure 4B shows that the delay decreases with neuron density. On the one hand, this is because in denser networks among the more synaptic partners of a neuron there will be some with better positions for fast wave propagation; in other words, the fastest path from the initiating pacemaker to the opposing one will be better approximated, if the neurons have more synaptic partners to which the activity propagates. On the other hand, there is a decrease of delay due to stronger stimulation of neurons in denser networks: a postsynaptic neuron fires earlier if more presynaptic neurons have fired, since their EPSCs add up.

This implies that von Mises MNNs create more optimal paths of conduction. Indeed, neurons near the pacemaker preferably orient themselves radially towards the center of the subumbrella, and thus quickly direct the activity toward the opposite side. Since transmitter release consumes a significant amount of energy Niven, , we conclude that von Mises networks are more efficient for fast through-conduction than uniform ones.

To further illustrate that the nerve net is through-conducting even when its structure is heavily damaged, we replicate some of the cutting experiments by Romanes In these experiments, Romanes cut the umbrella of the jellyfish several times and observed that the activity is able to spread through small bottlenecks created by these cuts.

To test if our MNN model reproduces this behavior, we simulate cuts by straight line segments, assuming that if a neuron intersects with that line segment, the larger part containing the soma will survive and still transmit and receive potentials via the leftover intact synapses, while the smaller part without the soma dies off. In the first cutting experiment, an inner disc on the subumbrella is almost completely cut off from an outer ring. The two sections are only connected by a small patch Figure 7.

In the second experiment, 16 cuts are placed radially in an interdigitating fashion around the umbrella. The signal has to travel between the interleaving cuts Figure 8. In both cases, we find that the excitation wave is able to travel through the whole nerve net, with von Mises or uniform neurite orientation Figure 9. This again confirms our analytical result: the through-conducting property is preserved and every neuron in the network fires once, no matter how the neurons are connected.

Setup similar to Figure 5 , but black line segments indicate cuts through the nervous system where neurites are severed.

Cuts are placed along the outline of an octagon with a small gap through which the signal can propagate to the central neurons.

Setup similar to Figure 7 , but the cuts are placed radially creating a zig-zag patterned bell. Setup similar to Figures 7 and 8 , but the neurite orientations are uniformly distributed.

To analyze the swimming behavior, we employ a 2D hydrodynamics simulation of a cross section of the jellyfish bell. We assume that MNN neurons synaptically connect to muscles that lie in the same region see Materials and methods for details.

APs in the neurons evoke stereotypical contractions of the muscles. These add up to large muscle forces contracting the bell. Their interaction with the elastic forces of the bell and the hydrodynamics of the media in- and outside the bell determines the dynamics of the swimming stroke.

Figure 10 shows a representative time series of such a stroke. The left hand side pacemaker initiates a wave of MNN activation, which in turn triggers a wave of contraction around the subumbrella.

Because the MNN activation wave is fast compared to muscle contraction and swimming movement, the motion is highly symmetrical. As a result, the jellyfish hardly turns within a stroke. The panels show the dynamics of the bell surface black and internal and surrounding media grey , in steps of ms. In this and all following figures, it is the pacemaker on the left hand side of the bell that initiates MNN activation.

Further, if not stated otherwise, the MNN has 10, neurons. We can qualitatively compare the simulated swimming motion to that of real jellyfish by considering the formation of vortex rings. Earlier research suggests that the formation of two vortex rings pushes oblate jellyfish, such as Aurelia , forward Dabiri et al.

In a 2D cross-section, a vortex ring is reflected by a vortex pair with opposing spin. We find indeed that two such vortex pairs are shed off near the bell margin see Figure The first pair is shed off during the contraction and the second one during the relaxation. The second pair slips under the jellyfish bell, which provides additional forward push Gemmell et al.

After the swimming stroke, the vortex rings in real jellyfish leave the bell and tend to stretch out Dabiri et al. In contrast, in our 2D model, the vortex pairs move further into the bell and interact with it for a longer time.

This has been observed in previous 2D models of oblate jellyfish, even with prescribed bell deformation and is likely due to the different behavior of 2D and 3D vortices Herschlag and Miller, Simulations of more prolate jellyfish show less discrepancy. McHenry and Jed measured changes in the bell geometry of Aurelia aurita during its swimming motion.

When tracking the same data in our simulations for our standard parameters, we find qualitatively similar time series see Figure 11 blue. In particular, the sequence of changes in the bell geometry agrees with that of real jellyfish Figure 11A,B. During the contraction phase, the bell diameter shrinks and the bell height increases. The bell margin begins to bend outward as the jellyfish contracts and folds inward during the relaxation of the bell. The margins of the real jellyfish bend less than those of our model jellyfish Figure 11C.

Their higher stiffness may originate from passive resistance of the probably inactive radial muscles. The speed profile in the experiments shows broader peaks and a longer continuation of forward movement after bell relaxation compared to our Figure 11D and previous 2D models Herschlag and Miller, In particular, the models produce negligible forward momentum during the relaxation phase in oblate jellyfish.

This may again be due to differences in vortex dynamics in 2D and 3D, as a 3D model does not show this discrepancy Park et al. To test if the quantitative agreement of our model with the measurements can be improved, we adjusted the bell size and spring parameters Figure 11 orange. While this leads to a better agreement of the margin bending, the speed profile does not improve, unless we switch to a more prolate bell shape not shown.

This supports the idea that a 2D model of oblate jellyfish is unable to reproduce the real rowing mechanism. Dynamics of A bell diameter, B bell height and C the orientation of the margin of the bell relative to the orientation of the bell as a whole, during a sequence of swimming strokes as in Figure 10A , initialized in intervals of 1.

D Corresponding speed profile. Shown are models with our standard parameters blue and manually adjusted parameters orange to match the experimentally found traces gray in McHenry and Jed Fig. To quantify the effects of MNN size on swimming, we evaluate travel distances and changes in orientation, see Figure We find that the typical total distance traveled by individual jellyfish increases with network size Figure 12A,B , while the variance and thus the typical distance traveled sideways and the typical angular movement decrease Figure 12A,C A,D.

This can be explained by the higher temporal and spatial coherence in the activation waves of larger MNNs.

They arise from larger throughconductance speed, see Figure 4 , and from more uniform neuron density and muscle innervation: Since neurons are distributed uniformly in space, the fluctuations of local neuron density relative to its mean decreases with increasing neuron number.

This implies that the relative fluctuation in the number of neurons innervating the different muscle segments decreases. With small MNNs, random fluctuations in the number of innervating neurons are likely to lead to a spatial imbalance of contraction force that is sufficient to generate marked sideways movement and turning.

Generally, the variance of a characteristic sampled over different MNN realizations decreases as the number of neurons increases, because the decrease of relative local density fluctuations implies that the network ensembles become more homogeneous. Once the egg is fertilized, a larva hatches and lives in the pelagic environment for some time.

As it grows, the larva searches for a suitable place in shallow water and eventually attaches to the sea floor where it grows into an upside down medusa known as a polyp. During the polyp phase, an individual asexually buds off several clones of itself that swim away as medusae and eventually grow into sexually mature moon jellies. This alternation of sexual and asexual reproduction may be a means of quickly increasing numbers while preserving the importance of mixing genes with other individuals.

Scientists believe that moon jellies and other jellies thrive in areas that are particularly affected by human activity. These results provide a more favorable environment for this species. As people continue to increase our ocean activities, the Moon Jelly may become one of the more successful species in the open ocean.