Basic behaviour patterns of animals for a modeling project?

Basic behaviour patterns of animals for a modeling project?

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I am working on a project (a tool) for my GIS class that models natural habitat of an animal based on landcovers, distance to the nearest watersource and distance from developed area in Kansas, USA. I need these numerical values for at least a couple of animal species (larger herbivores that can occur in Kansas) so the user can enter these values and visualize the natural habitat.

Now, I know it is very difficult to determine such values (and modeling natural habitat is much much more complex than this) but these values don't need to be very accurate as it aims at showing the capabilities of the GIS software rather then producing real-world output (again, its a school project).

I've contacted a professor at the Department of Biology but she only provided me with tons of over-detailed information and links to scientific research which are of no use for me.

For example: a deer goes to drink water twice a day and doesn't move more than 10 miles a day -> it is most likely to occur within 5 miles from a water source. I know it is a crude simplification but it really doesn't matter in this case.

Is there a source of such information?

STARFLAG: a project on collective animal behaviour

Collective animal behaviour is a fascinating phenomenon. How does global co-ordination emerge in a flock of thousands starlings swirling at dusk? How does a school of sardines organize in a circular pattern? What is the evolutionary function of collective behaviour? Is it purely defensive, or has it some other purpose, perhaps social? INFM-CNR was the leader node of a European project – FP6-NEST 12682 STARFLAG (2005-2008) – dedicated to the study of collective animal behaviour.

The STARFLAG project is now ended, but its research continues in the COBBS lab.

In order to learn from nature the fundamental mechanisms of self-organization it is essential to have a strong feedback between quantitative empirical observations on one side, and theories and models on the other side. The STARFLAG project was born exactly with this aim: scientists from many different disciplines, including biology, physics and economics, joined forces to tackle the problem of collective behaviour. By focusing on one paradigmatic case, namely the motion of large flocks of European starlings (Sturnus vulgaris), STARFLAG aimed to quantitatively understand the mechanisms of self-organization in natural systems, to develop models as accurate as possible, and finally to explore the role of collective animal behaviour in other disciplines. STARFLAG includes seven research institutes and it was coordinated at the European level by Giorgio Parisi.

The backbone of STARFLAG was to obtain empirical data, and this was the task of the INFM-CNR node, coordinated by Andrea Cavagna. Measuring the three-dimensional (3D) position of each individual within a large group of animals is tricky. The first measurements on small fish schools were done in the ‘60s. These early studies considered not more than 20 animals, a number that is very far from the natural case, where groups can range up to tens of thousands individuals. Surprisingly, forty years later the field of 3D observations on animal collective behaviour had done little progress, and as recently as 1998 scientists could only study groups of few tens of animals. The bottleneck was not technology, nor equipment, but something called the correspondence (or matching) problem (See Fig.1).

Figure 1.
In order to obtain the 3D positions of individual birds within the flock, STARFLAG used stereometry, which consists in taking synchronous picture of the same target from two different positions. Images a and b are the left and right photographs of the stereometric pair, taken at the same instant of time, from two cameras 25 meters apart. This flock consists of about 1300 starlings, flying at 11ms-1. To perform the 3D reconstruction, each bird’s image on the left photo must be matched to its corresponding image on the right photo. This is the correspondence, or matching, problem. Five matched pairs of birds are visualized by the red squares. In previous studies the matching was performed manually, and this severely limited the number of animals, as well as the cohesion of the group. STARFLAG developed new algorithms, inspired by statistical physics and computer vision, that can perform the matching automatically in flocks of several thousands birds. This has been the breakthrough that allowed STARFLAG to make the first large-scale quantitative study of collective animal behaviour in the field. c,d,e,f: 3D reconstruction of the flock under 4 different points of view. Panel d shows the reconstructed flock under the same perspective as the right photograph (b).

Using new techniques, inspired by computer vision and statistical physics, the STARFLAG project managed to solve the correspondence problem and to reconstruct for the first time the 3D positions of individual birds in flocks of several thousands starlings in the field. Compared to former studies, this is an advance of two orders of magnitude, which put STARFLAG in the unique position of making a detailed analysis of the rules of interaction in collective animal behaviour.

A description of the STARFLAG empirical methods and computer vision techniques can be found in these two papers: Part I and Part II

The results were striking. By measuring the anisotropic structure of birds within the flock we were able to track the interaction among the birds. At variance with previous models and theories, we found that the strength of the interaction binding two individuals does not decay proportionally to the metric distance between them, as previously assumed, but rather to the topological distance. An example may clarify this result: whenever we take the bus, we do not measure the distance between two stops in meters, or kilometres (metric distance), but rather in number of stops (topological distance). Birds do something similar: they measure the distance in units of individuals, not meters. This implies that each bird interacts with a fixed number of neighbours, rather than interacting with all neighbours within a fixed metric distance, as assumed by all current models. In particular, we discovered that each bird always interacts with 6-7 neighbours, independently of the metric distance of these neighbours.

Learn more about metric vs. topological interaction by looking at this page.

Why 6-7 neighbours? This range is significantly smaller than the number of visually unobstructed neighbours around each bird. We conclude that this specific value, 6-7, must derive from the cortical elaboration of the visual input, rather than from a limitation of the input itself. In order to keep under control a fixed number of neighbours, irrespective of their metric distance, it is necessary for the individuals to have some pre-numeric ability, or, more precisely, an object-tracking, or ‘subitizing’, ability. This capability decays beyond a certain number, and such perceptual limit defines the range of interaction. Emmerton & Delius show that trained pigeons can discriminate sets of different numerosities provided that these sets have less than 7 objects. In our field study we find a range of 6-7 neighbours. Such a striking agreement suggests that the same tracking ability at the basis of numerical discrimination may be used for interacting with a fixed number of neighbours, and then be an essential ingredient of collective animal behaviour. An alternative interpretation of the interaction range we find is that the specific value 6-7 may be functional to optimize anti-predatory response: if each individual interacts with too few neighbours, information is non-noisy, but too short-ranged conversely, if the interaction involves too many neighbours, information is averaged over several ill-informed individuals, and it is too noisy.

A flock of 3000 starlings together with its 3D reconstruction. Previous studies could deal with up to few tens of animals, in loose, non-cohesive arrangements, and very seldon in the field. Starling flocks often display a rather thin shape, similar to a pan-cake. Surprisingly enough, the aspect ratio of the flocks is quite constant, even though flocks differ considerably in number of birds. This kind of feature, as many others, would be impossible to detect without a full 3D reconstruction of the individual birds positions. From these 3D data it was possible to investigate the nature of the interaction among birds.

Why a topological, and not a metric interaction? Animal collective behaviour is staged in a troubled natural environment. Hence, the interaction mechanism shaped by evolution must keep cohesion in the face of strong perturbations, of which predation is the most relevant. We believe that topological interaction is the only mechanism granting such robust cohesion, and therefore higher biological fitness. A metric interaction is inadequate to cope with this problem: whenever the inter-individual distance became larger than the metric range, interaction would vanish, cohesion would be lost, and stragglers would ‘evaporate’ from the aggregation. A topological interaction, on the opposite, is very robust, since its strength is the same at different densities. By interacting within a fixed number of individuals, rather than meters, the aggregation can be either dense or sparse, change shape, fluctuate and even split, yet maintaining the same degree of cohesion.

A simple numerical model confirmed our hypothesis that the use of a topological rather than metric distance is beneficial for the biological fitness of the flock. Under strong perturbation, as the attack of a predator, numerical simulations show that the cohesion of the group is far more robust for a topological interaction, than a metric one. STARFLAG is also trying to find out what are the behavioural rules at the basis of the anisotropic structure observed empirically within the flock. To this aim, new models taking into account the anisotropic visual apparatus of birds and fishes are being developed.

You can find all the INFM-CNR STARFLAG papers here.

Understanding Animal Behavior

Animal behavior research is particularly relevant to the study of human behavior when it comes to the preservation of a species, or how an animal’s behavior helps it survive. The behavior of animals in stressful or aggressive situations can be studied to help find solutions for humans in similar circumstances it may also provide insight for dealing with depression, anxiety, or similar mental health disorders.

Animal-assisted therapy, in which dogs, horses, and other domestic animals help facilitate different forms of therapy, can be helpful for individuals who are socially isolated, living with a diagnosis on the autism spectrum, or suffering from a mood disorder or post-traumatic stress. Interacting with animals has been found to increase humans' levels of oxytocin, a hormone that enhances social bonding. Animal behaviorists are also interested in the ways in which animals themselves may benefit from relationships with humans.

What causes animal behavior?

Animal behavior is a result of biology and environment. Behavioral changes are triggered by an internal or external cue, such as the appearance of a threat nearby. Animal responses are driven by the primal urges to survive and reproduce. While animal behavior can vary widely based on the individual, certain behavioral traits, like attention seeking and chasing prey, are genetically inherited, as with dog behavior.

How do you observe animal behavior?

While some animal behavior scholars perform experiments and study animals in a laboratory setting, others advocate watching animals in their natural habitats to get a clearer sense of what they do and how they allocate their time.

How are animal behaviors classified?

Innate behaviors are genetically hardwired and can be performed in response to stimuli without any prior experience. Learned behaviors are acquired by social learning, often by watching and imitating adult members of their species. Through natural selection, animals are more likely to pass on skills that will help their young survive and thrive.

What are some examples of animal behaviors?

Animals are motivated to fulfill basic survival needs for shelter, food, warmth, and community. Through a combination of genetics and social learning, they acquire skills based on their species’ preferences (e.g., some animals forage, while others hunt). Other animal behaviors include migrating to warmer climates during the winter, establishing a group pecking order, and imprinting on a parental figure.

Why is the study of animal behavior important?

Humans share planet Earth with other non-human animals, many of whom are in danger of going extinct. Learning more about animal behavior can help people conserve nature and better coexist with animals. Additionally, observations about animal behavior may provide fresh insights on why people behave the way they do, and how they can change for the better.

What is animal learning?

Like humans, animals acquire the necessary skills to survive by watching and imitating adult members of their species. Social learning is quicker and more effective than having to figure out how to do something through trial-and-error, and it gives individuals and the species as a whole a better chance at survival.

What behaviors are inherited?

Inherited behaviors may vary between species and even among individuals. In dogs, for example, many behaviors are strongly inherited, including trainability, aggression towards strangers, attachment, and attention-seeking.

What is learned animal behavior?

Animals learn from the behavior of more experienced individuals in their family or social group to figure out which behaviors are likely to be punished and which rewarded. They are motivated to avoid pain and seek out pleasure. They can also be conditioned by people to behave in a certain way using a system of rewards and punishments.

What is animal sampling?

Animal sampling is taking a group of animals from a larger population for measurement. The findings are then used to make generalized conclusions about the whole population. Smaller sample sizes tend to be more problematic and prone to error than larger ones.

Work in the chemical ecology and tropical diversity laboratory focuses on direct and indirect trophic interactions in complex biotic communities with emphases on global change. The research includes field and laboratory work, as well as research with specimens in the Museum of Natural History at the University of Nevada, Reno.


‡ Present address: Radcliffe Institute for Advanced Study, Harvard University, Cambridge MA 02138, USA.

Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.


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Basic behaviour patterns of animals for a modeling project? - Biology

Machine learning (ML) offers a hypothesis-free approach to modelling complex data.

We present a review of ML techniques pertinent to the study of animal behaviour.

Key ML approaches are illustrated using three different case studies.

ML offers a useful addition to the animal behaviourist's analytical toolbox.

In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them. These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods. The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data. In this review, we aim to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data. We start by describing the rationale behind ML and review a number of animal behaviour studies where ML has been successfully deployed. The ML framework is then introduced by presenting several unsupervised and supervised learning methods. Following this overview, we illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address. The first uses a large number of spectral and morphological characteristics that describe the appearance of pheasant, Phasianus colchicus, eggs to assign them to putative clutches. The second takes a continuous data stream of feeder visits from PIT (passive integrated transponder)-tagged jackdaws, Corvus monedula, and extracts foraging events from it, which permits the construction of social networks. Our final example uses aerial images to train a classifier that detects the presence of wildebeest, Connochaetes taurinus, to count individuals in a population. With the advent of cheaper sensing and tracking technologies an unprecedented amount of data on animal behaviour is becoming available. We believe that ML will play a central role in translating these data into scientific knowledge and become a useful addition to the animal behaviourist's analytical toolkit.


When the first amphibians evolved from their tetrapod ancestors 400 million years ago, they quickly became the dominant vertebrates on Earth. However, their reign wasn't destined to last the frogs, toads, salamanders, and caecilians (legless amphibians) that make up this group have long since been surpassed by reptiles, birds, and mammals. Amphibians are characterized by their semi-aquatic lifestyles (they must stay near bodies of water to maintain the moisture of their skin and to lay eggs), and today they are among the most endangered animals in the world.

Hunting and Feeding behavior

The wolf is a carnivore, an animal suited for catching, killing and eating other creatures. Wolves prey primarily on large, hoofed mammals called ungulates. For example: In Michigan, Wisconsin and Minnesota, the white-tailed deer is the wolf’s primary prey, with moose, beaver, snowshoe hare and other small mammals also being taken. Elsewhere, wolves prey on caribou, musk-oxen, bison, Dall sheep, elk and mountain goats.

All of these ungulates have adaptations for defense against wolves, including a great sense of smell, good hearing, agility, speed, and sharp hooves. As these prey are so well adapted to protecting themselves, wolves feed upon vulnerable individuals, such as weak, sick, old, or young animals, or healthy animals hindered by deep snow. By killing the inferior animals, wolves help increase the health of their prey population a tiny bit at a time. When inferior animals are removed, the prey population is kept at a lower level and there is more food for the healthy animals to eat. Such “culling” also ensures that the animals which reproduce most often are healthy and well suited for their environment. Over many generations, this selection helps the prey become better adapted for survival.

Wolves require at least 3.7 pounds of meat per day for minimum maintenance. Reproducing and growing wolves may need 2-3 times this much. It has been estimated that wolves consume around 10 pounds of meat per day, on average. However, wolves don’t actually eat everyday. Instead, they live a feast or famine lifestyle they may go several days without a meal and then gorge on over 20 pounds of meat when a kill is made.

Wolves and deer

In Minnesota, for example, each wolf eats an average of 15-20 adult-sized deer or their equivalent per year to meet their nutritional requirements. Based on this average, and the estimate of 2,400 wolves in Minnesota, wolves kill the equivalent of about 36,000 to 48,000 adult-sized deer per year. In comparison, Minnesota hunters take around 52,500 deer per year in wolf range (over 250,000 for the entire state) and several thousand are killed during collisions with vehicles.

Wolf predation on ungulates varies seasonally. It is highest during mid to late winter, when animals are suffering from poor nutrition and the snow is deep, making them easier to kill. It is also quite high in early summer when prey animals have their young, as wolves prey heavily on vulnerable young.

Additive or Compensatory Predation?

The question of whether wolf predation is additive (the number of animals killed are in addition to those which would die otherwise) or compensatory (animals wolves kill would die anyway) is a complicated one, as wolf predation effects vary with the prey species, time of year, area, and system. It is quite probable that wolf predation is both additive and compensatory, and the real question is how much of it is additive.

For example, wolf predation on deer is moderated by the severity of the winters. In a severe winter, wolves may kill healthy deer which would have survived the winter had they not had been made vulnerable by the deep snow. This would be an example of wolf predation as an additive factor. Conversely, in a mild winter, when the snow levels are low, healthy deer easily escape wolves. Therefore, the deer captured are primarily sick or weak. This would be an example of compensatory mortality, as most of these deer probably would not have survived the winter. This is why it is rare to find a starving deer in Minnesota wolf range.

Reciprocally, prey populations may limit wolf numbers. When considering the examples above, the potential for prey numbers or conditions to regulate wolf numbers is observable. In a mild winter, deer will be healthier and wolves may not be able to catch enough animals to feed themselves. This may cause a decrease in the wolf population. It is also possible that several severe winters in a row would decrease deer populations and wolves may not be able to kill enough food to eat, so again wolf numbers would decrease.

Multiple predator ecosystems

Another factor complicating our ability to determine the precise effect of wolf predation, is that it is difficult to tease out the effects wolves have on their prey populations in areas where there are many different predators. For example, in Yellowstone National Park, in addition to wolves, there are grizzly bears, coyotes, mountain lions, bobcats, lynx, wolverines, and black bears which all prey on Yellowstone ungulates.

In summary, we cannot generalize about what kind of effect wolves have on their prey populations, because their effect is dependent on so many factors. It is possible to get an indication of wolf and prey population trends in a small area or system, but generalizing from one to the other is not always valid.

Literature Cited

Mech, L. David (1970, reprint 1981)
The Wolf: The Ecology and Behavior of an Endangered Species
University of Minnesota Press.

Mech, L. David and Luigi Boitani, Editors (2003)
Wolves: Behavior, Ecology, and Conservation
University of Chicago Press

Animal behaviour

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Animal behaviour, the concept, broadly considered, referring to everything animals do, including movement and other activities and underlying mental processes. Human fascination with animal behaviour probably extends back millions of years, perhaps even to times before the ancestors of the species became human in the modern sense. Initially, animals were probably observed for practical reasons because early human survival depended on knowledge of animal behaviour. Whether hunting wild game, keeping domesticated animals, or escaping an attacking predator, success required intimate knowledge of an animal’s habits. Even today, information about animal behaviour is of considerable importance. For example, in Britain, studies on the social organization and the ranging patterns of badgers (Meles meles) have helped reduce the spread of tuberculosis among cattle, and studies of sociality in foxes (Vulpes vulpes) assist in the development of models that predict how quickly rabies would spread should it ever cross the English Channel. Likewise in Sweden, where collisions involving moose (Alces alces) are among the most common traffic accidents in rural areas, research on moose behaviour has yielded ways of keeping them off roads and verges. In addition, investigations of the foraging of insect pollinators, such as honeybees, have led to impressive increases in agricultural crop yields throughout the world.

Even if there were no practical benefits to be gained from learning about animal behaviour, the subject would still merit exploration. Humans (Homo sapiens) are animals themselves, and most humans are deeply interested in the lives and minds of their fellow humans, their pets, and other creatures. British ethologist Jane Goodall and American field biologist George Schaller, as well as British broadcaster David Attenborough and Australian wildlife conservationist Steve Irwin, have brought the wonders of animal behaviour to the attention and appreciation of the general public. Books, television programs, and movies on the subject of animal behaviour abound.

Ecological and ethological approaches to the study of behaviour

The natural history approach of Darwin and his predecessors gradually evolved into the twin sciences of animal ecology, the study of the interactions between an animal and its environment, and ethology, the biological study of animal behaviour. The roots of ethology can be traced to the late 19th and early 20th centuries, when scientists from several countries began exploring the behaviours of selected vertebrate species: dogs by the Russian physiologist Ivan Pavlov rodents by American psychologists John B. Watson, Edward Tolman, and Karl Lashley birds by American psychologist B.F. Skinner and primates by German American psychologist Wolfgang Köhler and American psychologist Robert Yerkes. The studies were carried out in laboratories, in the case of dogs, rodents and pigeons, or in artificial colonies and laboratories, in the case of primates. These studies were oriented toward psychological and physiological questions rather than ecological or evolutionary ones.

It was not until the 1930s that field naturalists—such as English biologist Julian Huxley, Austrian zoologist Konrad Lorenz, and Dutch-born British zoologist and ethologist Nikolaas Tinbergen studying birds and Austrian zoologist Karl von Frisch and American entomologist William Morton Wheeler examining insects—gained prominence and returned to broadly biological studies of animal behaviour. These individuals, the founders of ethology, had direct experience with the richness of the behavioral repertoires of animals living in their natural surroundings. Their “return to nature” approach was, to a large extent, a reaction against the tendency prevalent among psychologists to study just a few behavioral phenomena observed in a handful of species that were kept in impoverished laboratory environments.

The goal of the psychologists was to formulate behavioral hypotheses that claimed to have general applications (e.g., about learning as a single, all-purpose phenomenon). Later they would proceed using a deductive approach by testing their hypotheses through experimentation on captive animals. In contrast, the ethologists advocated an inductive approach, one that begins with observing and describing what animals do and then proceeds to address a general question: Why do these animals behave as they do? By this they meant “How do the specific behaviours of these animals lead to differential reproduction?” Since its birth in the 1930s, the ethological approach—which stresses the direct observation of a broad array of animal species in nature, embraces the vast variety of behaviours found in the animal kingdom, and commits to investigating behaviour from a broad biological perspective—has proved highly effective.

One of Tinbergen’s most important contributions to the study of animal behaviour was to stress that ethology is like any other branch of biology, in that a comprehensive study of any behaviour must address four categories of questions, which today are called “levels of analysis,” including causation, ontogeny, function, and evolutionary history. Although each of these four approaches requires a different kind of scientific investigation, all contribute to solving the enduring puzzle of how and why animals, including humans, behave as they do. A familiar example of animal behaviour—a dog wagging its tail—serves to illustrate the levels of analysis framework. When a dog senses the approach of a companion (dog or human), it stands still, fixates on the approaching individual, raises its tail, and begins swishing it from side to side. Why does this dog wag its tail? To answer this general question, four specific questions must be addressed.

With respect to causation, the question becomes: What makes the behaviour happen? To answer this question, it becomes important to identify the physiological and cognitive mechanisms that underlie the tail-wagging behaviour. For example, the way the dog’s hormonal system adjusts its responsiveness to stimuli, how the dog’s nervous system transmits signals from its brain to its tail, and how the dog’s skeletal-muscular system generates tail movements need to be understood. Causation can also be addressed from the perspective of cognitive processes (that is, knowing how the dog processes information when greeting a companion with tail wagging). This perspective includes determining how the dog senses the approach of another individual, how it recognizes that individual as a friend, and how it decides to wag its tail. The dog’s possible intentions (for example, receiving a pat on the head), feelings, and awareness of self become the focus of the investigation.

With respect to ontogeny, the question becomes: How does the dog’s tail-wagging behaviour develop? The focus here is on investigating the underlying developmental mechanisms that lead to the occurrence of the behaviour. The answer derives from understanding how the sensory-motor mechanisms producing the behaviour are shaped as the dog matures from a puppy into a functional adult animal. Both internal and external factors can shape the behavioral machinery, so understanding the development of the dog’s tail-wagging behaviour requires investigating the influence of the dog’s genes and its experiences.

With respect to function: How does the dog’s tail-wagging behaviour contribute to genetic success? The focus of this question is rooted in the subfield called behavioral ecology the answer requires investigating the effects of tail wagging on the dog’s survival and reproduction (that is, determining how the tail-wagging behaviour helps the dog survive to adulthood, mate, and rear young in order to perpetuate its genes).

Lastly, with respect to evolutionary history, the question becomes: How did tail-wagging behaviour evolve from its ancestral form to its present form? To address this question, scientists must hypothesize evolutionary antecedent behaviours in ancestral species and attempt to reconstruct the sequence of events over evolutionary time that led from the origin of the trait to the one observed today. For example, an antecedent behaviour to tail wagging by dogs might be tail-raising and tail-vibrating behaviours in ancestral wolves. Perhaps when a prey animal was sighted, such behaviours were used to signal other pack members that a chase was about to begin.

Both the biological and the physical sciences seek explanations of natural phenomena in physicochemical terms. The biological sciences (which include the study of behaviour), however, have an extra dimension relative to the physical sciences. In biology, physicochemical explanations are addressed by Tinbergen’s questions on causation and ontogeny, which taken together are known as “proximate” causes. The extra dimension of biology seeks explanations of biological phenomena in terms of function and evolutionary history, which together are known as “ultimate” causes. In biology, it is legitimate to ask questions concerning the use of this life process today (its function) and how it came to be over geologic time (its evolutionary history). More specifically, the words use and came to be are applied in special ways, namely “promoting genetic success” and “evolved by means of natural selection.” In physics and chemistry, these types of questions are out of bounds. For example, questions concerning the use of the movements of a dog’s tail are reasonable, whereas questions regarding the use of the movements of an ocean’s tides are more metaphysical.

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