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In an article (Guerrero and Tye, 2009) it talks about endochorous dispersers.
Guerrero, A. M., and A. Tye. 2009. Darwin's Finches as Seed Predators and Dispersers. The Wilson Journal of Ornithology 121:752-764.
The highest proportions of feces containing viable seeds were of Small Ground Finch (G. fuliginosa) and the"insectivorous"species Woodpecker Finch (Camarhynchus pallidus) and Warbler Finch (Certhidea olivacea). These two may be more important endochorous dispersers than other species that eat more fruit but are better seed predators. Intact seeds were found in 23% of fecal samples; 50% of the samples with intact seeds had viable seeds.
I've looked on google and found nothing…
Do you know what that means?
It seems it relates to mast-fruiting plant species.
I found the definition here, which is a cool package in R:
animal - all vertebrate dispersed seeds, without distinction between attached(epichorous) and ingested (endochorous) dispersal of seeds
scatter, disperse, dissipate, dispel mean to cause to separate or break up. scatter implies a force that drives parts or units irregularly in many directions. the bowling ball scattered the pins disperse implies a wider separation and a complete breaking up of a mass or group. police dispersed the crowd dissipate stresses complete disintegration or dissolution and final disappearance. the fog was dissipated by the morning sun dispel stresses a driving away or getting rid of as if by scattering. an authoritative statement that dispelled all doubt
Petals and sepals
The petals of the flower are modified leaves and serve as an advertisement of the plant to birds, insects and other animals to come and feed at the plant. They are often brightly colored to entice animals towards them.
The number of petals on a flower varies largely across angiosperms and can be used to help identify a monocot plant from the eudicots and basal angiosperms. Monocots tend to have flowers with petals in multiples of three whereas eudicots and basal angiosperms have flowers in fours or fives.
The amount of fusion between petals is useful in determining how evolutionarily advanced a plant species is. If the petals of a plant have a high level of fusion between them, they are likely to belong to a recently evolved lineage of plants. If, on the other hand, the petals show no level of fusion they are likely to belong to a more primitive group of plants. A lack of fusion between petals is common in basal angiosperms such as the magnolias.
The biology of button bucks
You could feel the tension in the air as the truck neared the property's deer check-in station. Word had spread that Billy had mistakenly harvested a second buck fawn for the season and disappointed club members were gathered in anticipation of his arrival back at camp.
Just one month earlier at the pre-season hunt club meeting, members were strongly encouraged to harvest does, but cautioned against harvesting buck fawns or "button bucks." In fact, this year the club even instituted a $100 fine for the first button buck and a $250 fine for the second to drive this point home. This sounded reasonable because the club was in its third year of their Quality Deer Management (QDM) program and wanted to limit the harvest of their "bucks of tomorrow."
This strategy is fine in principle, but is it based in biology?
Over the past two decades, whitetail researchers have conducted numerous studies on the movements of male whitetail deer with some interesting findings. Collectively, the results of these studies have significant implications for QDM programs attempting to maximize the number of adult bucks on their properties.
The primary justification for not harvesting button bucks is that they will remain on your property until they reach maturity and become eligible for harvest. Let's examine this premise in closer detail.
Studies show that a majority of bucks between 6-18 months of age will disperse some distance from their birth area before establishing a new home range.
A study conducted by Dr. Chris Rosenberry and others in Maryland provided some interesting findings. During this study, they captured and radio-collared 75 male whitetails ranging from six to 18 months of age. Of these, 51 were followed until death or the end of the study. Of these, 70 percent dispersed from the 3,300-acre study area with half dispersing more than 3.7 miles.
Dispersal distance varied greatly from 1.2 to 36 miles. A couple of these young bucks even swam a mile-wide river during dispersal.
A similar study conducted by Dr. Harry Jacobson and others in Mississippi reported that 42 percent of the 52 male whitetails captured as fawns died in excess of three miles from their original capture site. A Florida study by John Kilgo and others reported that all seven male fawns captured and followed in their study dispersed from their original capture area by 18 months of age.
Interestingly, the Jacobson study found that once the young bucks had dispersed, they generally remained within their new home range until death. In their study, 60 percent of bucks captured at two years of age or older died within one mile of their capture site and none died more than three miles from their original capture site.
Collectively, these studies show that a majority of bucks between 6-18 months of age will disperse some distance from their birth area before establishing a new home range. But, once their new home range is established, they will generally remain in this area until death.
These results have significant implications for QDM, especially on small properties.
Implications for small properties
It is believed that dispersal in young male whitetails (and many other mammals) may be a mechanism to prevent inbreeding.
Given that the average dispersal distance of young bucks in these studies was 1-4 miles, this means that even properties 3,000 acres and larger are potentially losing the majority of the button bucks produced on their properties. To a large degree, protecting button bucks on your property increases the number of bucks for the "neighborhood," but may do little to increase the number that will mature on your property.
This emphasizes the need for a cooperative approach to QDM. Since the button bucks being produced by your neighbors may be "your" adult bucks of tomorrow, the extent to which your neighbors protect their young bucks is at least as important as how well the hunters on your property protect theirs.
This also provides a possible explanation for why some properties that consistently pass all button bucks and yearling bucks never observe an increase in the number that reach 2.5 years of age or older. It could simply be that the young bucks passed on your property disperse to your neighbors and are harvested there.
In other words, your neighbors are not only harvesting their button bucks and yearling bucks, they are harvesting yours as well.
Many hunters practicing QDM fail to observe significant increases in body weight or antler development of yearling bucks despite monumental increases in high-quality forage through food plots and intensive habitat management. It is possible that the yearling bucks observed on your property actually spent their lives on your neighbor's property, where the habitat quality was lower, and only recently dispersed to your property.
The average dispersal distance of young bucks is 1-4 miles, this means that even properties 3,000 acres and larger are potentially losing the majority of the bucks produced on their properties.
While dispersal is a common occurrence in whitetail deer populations, the causes for it are not fully understood. A study conducted by Stefan Holzenbein and Dr. R. Larry Marchinton in Georgia revealed that dispersal of young bucks was greatly reduced if the buck's mother was harvested prior to dispersal. Prior to this study, it was believed that adult bucks in the area were responsible for forcing young bucks to leave their birth area.
The Holzenbein study monitored 34 buck fawns divided into two groups 19 that were left with their mothers (non-orphans) and 15 whose mothers were harvested or removed (orphans). The results were surprising.
By 30 months of age, 87 percent of the non-orphans had dispersed from their birth areas, but only nine percent of the orphans had left theirs. In addition, the non-orphans died at more than twice the rate of the orphans.
They reasoned that dispersing bucks were less aware of their new surroundings and more likely to succumb to harvest by hunters as well as death from predation, accidents and other mortality factors.
This was supported by the Rosenberry study, which revealed that only 36 percent of yearling bucks that dispersed survived their first hunting season, whereas 66 percent of those that did not disperse survived.
The primary reason for death of the dispersers in this study was harvest by hunters on surrounding properties that were not practicing QDM.
The Rosenberry study also revealed another possible dispersal mechanism. They found that dispersers were more likely to associate with other yearling bucks and participate in breeding season behaviors more often than non-dispersers.
In addition, dispersers tended to be more subordinate in these interactions. They concluded that sexual competition among yearling bucks was a potential explanation for dispersal. Given that the social structure of a deer population may be affected by age structure (buck and doe), sex ratio, density, habitat quality, and more, it's not surprising that these studies reported different dispersal mechanisms.
Most deer researchers agree that dispersal in whitetail deer coincides with changes in a young buck's social position within the herd. In simple terms, yearling bucks are social outcasts recently expelled from their own family group and excluded from joining other family groups or associating with older males. Often, the only members of the herd that will "befriend" them are other yearling bucks, buck fawns, and occasionally yearling does. The actual dispersal "trigger" is likely a complex interaction of social pressures within a deer herd.
Reducing dispersal on your property
A study conducted in Georgia revealed that dispersal of young bucks was greatly reduced if the buck's mother was harvested prior to dispersal.
Given these findings, is there anything that can be done to reduce dispersal of button bucks on your property?
The Holzenbein study suggests that harvesting adult does with button bucks at their side may increase the number that remain on your hunting area and potentially reach maturity there.
It is believed that dispersal in young male whitetails (and many other mammals) may be a mechanism to prevent inbreeding. In other words, it prevents sons from breeding with their mothers, sisters, and other related females.
Given this, will preventing natural dispersal produce negative genetic impacts within your deer herd?
While no one can say for sure, it is not believed to be a problem, especially in areas with relatively high deer populations and high degrees of "genetic mixing" from deer on surrounding properties.
Should club members fine Billy for harvesting the button buck and maybe even expel him from the club? Not necessarily. Billy has demonstrated a willingness to harvest "antlerless" deer and this should be commended.
Often, reducing the total deer density on a property is the most important goal, even if a few button bucks are taken in the process.
The worst approach a QDM club can take is to make penalties for harvesting button bucks so severe that too few antlerless deer are harvested.
Should the club continue to aggressively protect button bucks? Of course, not all button bucks disperse and even those that do will help improve surrounding deer herds.
Additionally, buck fawns (doe fawns as well), provide useful data on habitat quality and herd condition. Because fawns grow rapidly their first year, their body weight is one of the best indicators of changes in habitat quality.
If penalties are too severe, the chances that these deer will not be reported or included in the harvest data are increased. As such, penalties, if imposed, should be sufficient to encourage hunters to look carefully before making harvest decisions, but not so severe that they refrain from harvest altogether or do not report their "mistakes."
The best approach is to provide the proper training and educational resources to your hunters to enable selective antlerless harvest decisions. Most importantly, target your efforts to protect button bucks and yearling bucks not just on your property, but on all properties within possible dispersal distance at least four miles. Otherwise, the success of your QDM program may be negatively impacted.
Seed dispersal is likely to have several benefits for different plant species. First, seed survival is often higher away from the parent plant. This higher survival may result from the actions of density-dependent seed and seedling predators and pathogens, which often target the high concentrations of seeds beneath adults.  Competition with adult plants may also be lower when seeds are transported away from their parent.
Seed dispersal also allows plants to reach specific habitats that are favorable for survival, a hypothesis known as directed dispersal. For example, Ocotea endresiana (Lauraceae) is a tree species from Latin America which is dispersed by several species of birds, including the three-wattled bellbird. Male bellbirds perch on dead trees in order to attract mates, and often defecate seeds beneath these perches where the seeds have a high chance of survival because of high light conditions and escape from fungal pathogens.  In the case of fleshy-fruited plants, seed-dispersal in animal guts (endozoochory) often enhances the amount, the speed, and the asynchrony of germination, which can have important plant benefits. 
Seeds dispersed by ants (myrmecochory) are not only dispersed short distances but are also buried underground by the ants. These seeds can thus avoid adverse environmental effects such as fire or drought, reach nutrient-rich microsites and survive longer than other seeds.  These features are peculiar to myrmecochory, which may thus provide additional benefits not present in other dispersal modes. 
Finally, at another scale, seed dispersal may allow plants to colonize vacant habitats and even new geographic regions.  Dispersal distances and deposition sites depend on the movement range of the disperser, and longer dispersal distances are sometimes accomplished through diplochory, the sequential dispersal by two or more different dispersal mechanisms. In fact, recent evidence suggests that the majority of seed dispersal events involves more than one dispersal phase. 
Seed dispersal is sometimes split into autochory (when dispersal is attained using the plant's own means) and allochory (when obtained through external means).
Long distance Edit
Long-distance seed dispersal (LDD) is a type of spatial dispersal that is currently defined by two forms, proportional and actual distance. A plant's fitness and survival may heavily depend on this method of seed dispersal depending on certain environmental factors. The first form of LDD, proportional distance, measures the percentage of seeds (1% out of total number of seeds produced) that travel the farthest distance out of a 99% probability distribution.   The proportional definition of LDD is in actuality a descriptor for more extreme dispersal events. An example of LDD would be that of a plant developing a specific dispersal vector or morphology in order to allow for the dispersal of its seeds over a great distance. The actual or absolute method identifies LDD as a literal distance. It classifies 1 km as the threshold distance for seed dispersal. Here, threshold means the minimum distance a plant can disperse its seeds and have it still count as LDD.   There is a second, unmeasurable, form of LDD besides proportional and actual. This is known as the non-standard form. Non-standard LDD is when seed dispersal occurs in an unusual and difficult-to-predict manner. An example would be a rare or unique incident in which a normally-lemur-dependent deciduous tree of Madagascar was to have seeds transported to the coastline of South Africa via attachment to a mermaid purse (egg case) laid by a shark or skate.     A driving factor for the evolutionary significance of LDD is that it increases plant fitness by decreasing neighboring plant competition for offspring. However, it is still unclear today as to how specific traits, conditions and trade-offs (particularly within short seed dispersal) effect LDD evolution.
Autochorous plants disperse their seed without any help from an external vector, as a result this limits plants considerably as to the distance they can disperse their seed.  Two other types of autochory not described in detail here are blastochory, where the stem of the plant crawls along the ground to deposit its seed far from the base of the plant, and herpochory (the seed crawls by means of trichomes and changes in humidity). 
Barochory or the plant use of gravity for dispersal is a simple means of achieving seed dispersal. The effect of gravity on heavier fruits causes them to fall from the plant when ripe. Fruits exhibiting this type of dispersal include apples, coconuts and passionfruit and those with harder shells (which often roll away from the plant to gain more distance). Gravity dispersal also allows for later transmission by water or animal. 
Ballistic dispersal Edit
Ballochory is a type of dispersal where the seed is forcefully ejected by explosive dehiscence of the fruit. Often the force that generates the explosion results from turgor pressure within the fruit or due to internal tensions within the fruit.  Some examples of plants which disperse their seeds autochorously include: Arceuthobium spp., Cardamine hirsuta, Ecballium spp., Euphorbia heterophylla,  Geranium spp., Impatiens spp., Sucrea spp, Raddia spp.  and others. An exceptional example of ballochory is Hura crepitans—this plant is commonly called the dynamite tree due to the sound of the fruit exploding. The explosions are powerful enough to throw the seed up to 100 meters. 
Witch hazel uses ballistic dispersal without explosive mechanisms by simply squeezing the seeds out at 28 mph. 
Allochory refers to any of many types of seed dispersal where a vector or secondary agent is used to disperse seeds. These vectors may include wind, water, animals or others.
Wind dispersal (anemochory) is one of the more primitive means of dispersal. Wind dispersal can take on one of two primary forms: seeds or fruits can float on the breeze or, alternatively, they can flutter to the ground.  The classic examples of these dispersal mechanisms, in the temperate northern hemisphere, include dandelions, which have a feathery pappus attached to their fruits (achenes) and can be dispersed long distances, and maples, which have winged fruits (samaras) that flutter to the ground.
An important constraint on wind dispersal is the need for abundant seed production to maximize the likelihood of a seed landing in a site suitable for germination. Some wind-dispersed plants, such as the dandelion, can adjust their morphology in order to increase or decrease the rate of germination.  There are also strong evolutionary constraints on this dispersal mechanism. For instance, Cody and Overton (1996) found that species in the Asteraceae on islands tended to have reduced dispersal capabilities (i.e., larger seed mass and smaller pappus) relative to the same species on the mainland.  Also, Helonias bullata, a species of perennial herb native to the United States, evolved to utilize wind dispersal as the primary seed dispersal mechanism however, limited wind in its habitat prevents the seeds to successfully disperse away from its parents, resulting in clusters of population.  Reliance on wind dispersal is common among many weedy or ruderal species. Unusual mechanisms of wind dispersal include tumbleweeds, where the entire plant (except for the roots) is blown by the wind. Physalis fruits, when not fully ripe, may sometimes be dispersed by wind due to the space between the fruit and the covering calyx which acts as an air bladder.
Many aquatic (water dwelling) and some terrestrial (land dwelling) species use hydrochory, or seed dispersal through water. Seeds can travel for extremely long distances, depending on the specific mode of water dispersal this especially applies to fruits which are waterproof and float on water.
The water lily is an example of such a plant. Water lilies' flowers make a fruit that floats in the water for a while and then drops down to the bottom to take root on the floor of the pond. The seeds of palm trees can also be dispersed by water. If they grow near oceans, the seeds can be transported by ocean currents over long distances, allowing the seeds to be dispersed as far as other continents.
Mangrove trees grow directly out of the water when their seeds are ripe they fall from the tree and grow roots as soon as they touch any kind of soil. During low tide, they might fall in soil instead of water and start growing right where they fell. If the water level is high, however, they can be carried far away from where they fell. Mangrove trees often make little islands as dirt and detritus collect in their roots, making little bodies of land.
Animals: epi- and endozoochory Edit
Animals can disperse plant seeds in several ways, all named zoochory. Seeds can be transported on the outside of vertebrate animals (mostly mammals), a process known as epizoochory. Plant species transported externally by animals can have a variety of adaptations for dispersal, including adhesive mucus, and a variety of hooks, spines and barbs.  A typical example of an epizoochorous plant is Trifolium angustifolium, a species of Old World clover which adheres to animal fur by means of stiff hairs covering the seed.  Epizoochorous plants tend to be herbaceous plants, with many representative species in the families Apiaceae and Asteraceae.  However, epizoochory is a relatively rare dispersal syndrome for plants as a whole the percentage of plant species with seeds adapted for transport on the outside of animals is estimated to be below 5%.  Nevertheless, epizoochorous transport can be highly effective if seeds attach to wide-ranging animals. This form of seed dispersal has been implicated in rapid plant migration and the spread of invasive species. 
Seed dispersal via ingestion by vertebrate animals (mostly birds and mammals), or endozoochory, is the dispersal mechanism for most tree species.  Endozoochory is generally a coevolved mutualistic relationship in which a plant surrounds seeds with an edible, nutritious fruit as a good food for animals that consume it. Birds and mammals are the most important seed dispersers, but a wide variety of other animals, including turtles, fish, and insects (e.g. tree wētā and scree wētā), can transport viable seeds.   The exact percentage of tree species dispersed by endozoochory varies between habitats, but can range to over 90% in some tropical rainforests.  Seed dispersal by animals in tropical rainforests has received much attention, and this interaction is considered an important force shaping the ecology and evolution of vertebrate and tree populations.  In the tropics, large animal seed dispersers (such as tapirs, chimpanzees, black-and-white colobus, toucans and hornbills) may disperse large seeds with few other seed dispersal agents. The extinction of these large frugivores from poaching and habitat loss may have negative effects on the tree populations that depend on them for seed dispersal and reduce genetic diversity.   A variation of endozoochory is regurgitation rather than all the way through the digestive tract.  The seed dispersal by birds and other mammals are able to attach themselves to the feathers and hairs of these vertebrates, which is their main method of dispersal. 
Seed dispersal by ants (myrmecochory) is a dispersal mechanism of many shrubs of the southern hemisphere or understorey herbs of the northern hemisphere.  Seeds of myrmecochorous plants have a lipid-rich attachment called the elaiosome, which attracts ants. Ants carry such seeds into their colonies, feed the elaiosome to their larvae and discard the otherwise intact seed in an underground chamber.  Myrmecochory is thus a coevolved mutualistic relationship between plants and seed-disperser ants. Myrmecochory has independently evolved at least 100 times in flowering plants and is estimated to be present in at least 11 000 species, but likely up to 23 000 or 9% of all species of flowering plants.  Myrmecochorous plants are most frequent in the fynbos vegetation of the Cape Floristic Region of South Africa, the kwongan vegetation and other dry habitat types of Australia, dry forests and grasslands of the Mediterranean region and northern temperate forests of western Eurasia and eastern North America, where up to 30–40% of understorey herbs are myrmecochorous.  Speed dispersal by ants is a mutualistic relationship and benefits both the ant and the plant. 
Seed predators, which include many rodents (such as squirrels) and some birds (such as jays) may also disperse seeds by hoarding the seeds in hidden caches.  The seeds in caches are usually well-protected from other seed predators and if left uneaten will grow into new plants. In addition, rodents may also disperse seeds via seed spitting due to the presence of secondary metabolites in ripe fruits.  Finally, seeds may be secondarily dispersed from seeds deposited by primary animal dispersers, a process known as diplochory. For example, dung beetles are known to disperse seeds from clumps of feces in the process of collecting dung to feed their larvae. 
Other types of zoochory are chiropterochory (by bats), malacochory (by molluscs, mainly terrestrial snails), ornithochory (by birds) and saurochory (by non-bird sauropsids). Zoochory can occur in more than one phase, for example through diploendozoochory, where a primary disperser (an animal that ate a seed) along with the seeds it is carrying is eaten by a predator that then carries the seed further before depositing it. 
Dispersal by humans (anthropochory) used to be seen as a form of dispersal by animals. Its most widespread and intense cases account for the planting of much of the land area on the planet, through agriculture. In this case, human societies form a long-term relationship with plant species, and create conditions for their growth.
Recent research points out that human dispersers differ from animal dispersers by having a much higher mobility, based on the technical means of human transport.  On the one hand, dispersal by humans also acts on smaller, regional scales and drives the dynamics of existing biological populations. On the other hand, dispersal by humans may act on large geographical scales and lead to the spread of invasive species. 
Humans may disperse seeds by many various means and some surprisingly high distances have been repeatedly measured.  Examples are: dispersal on human clothes (up to 250 m),  on shoes (up to 5 km),  or by cars (regularly
250 m, singles cases > 100 km).  Seed dispersal by cars can be a form of unintentional transport of seeds by humans, which can reach far distances, greater than other conventional methods of dispersal.  Cars that carry soil are able to contain viable seeds, a study by Dunmail J. Hodkinson and Ken Thompson found that the most common seeds that were carried by vehicle were broadleaf plantain (Plantago major), Annual meadow grass (Poa annua), rough meadow grass (Poa trivialis), stinging nettle] (Urtica dioica) and wild chamomile (Matricaria discoidea). 
Deliberate seed dispersal also occurs as seed bombing. This has risks, as unsuitable provenance may introduce genetically unsuitable plants to new environments.
Seed dispersal has many consequences for the ecology and evolution of plants. Dispersal is necessary for species migrations, and in recent times dispersal ability is an important factor in whether or not a species transported to a new habitat by humans will become an invasive species.  Dispersal is also predicted to play a major role in the origin and maintenance of species diversity. For example, myrmecochory increased the rate of diversification more than twofold in plant groups in which it has evolved because myrmecochorous lineages contain more than twice as many species as their non-myrmecochorous sister groups.  Dispersal of seeds away from the parent organism has a central role in two major theories for how biodiversity is maintained in natural ecosystems, the Janzen-Connell hypothesis and recruitment limitation.  Seed dispersal is essential in allowing forest migration of flowering plants. It can be influenced by the production of different fruit morphs in plants, a phenomenon known as heterocarpy.  These fruit morphs are different in size and shape and have different dispersal ranges, which allows seeds to be dispersed for varying distances and adapt to different environments. 
In addition, the speed and direction of wind are highly influential in the dispersal process and in turn the deposition patterns of floating seeds in the stagnant water bodies. The transportation of seeds is led by the wind direction. This effects colonization situated on the banks of a river or to wetlands adjacent to streams relative to the distinct wind directions. The wind dispersal process can also affect connections between water bodies. Essentially, wind plays a larger role in the dispersal of waterborne seeds in a short period of time, days and seasons, but the ecological process allows the process to become balanced throughout a time period of several years. The time period of which the dispersal occurs is essential when considering the consequences of wind on the ecological process.
Biology & Behavior
Use this quick guide to learn the basics of wolf biology and behavior.
Usually only the dominant pair breeds, however in areas where there is a high ratio of prey per wolf, such as in Yellowstone National Park, there can be multiple litters per pack. In the western Great Lakes area wolves breed in February through March and after a gestation period of 63 days, four to six pups are born in late-April or early-May. However, the higher the latitude, the later the breeding. For instance, wolves in northern Canada living at a latitude of 71 degrees breed in late March through April.
Pup survival is directly related to prey availability. Prey availability is generally higher in areas that are being newly colonized by wolves, where wolves have been recently reintroduced, or where adult wolves are harvested.
Adult Survival and Longevity
The overall survival of yearling and adult wolves in the western Great Lakes area has been documented to vary between 60% and 80%. Gray wolves are known to live up to 13 years in the wild and 16 years in captivity. However, averages vary based on geographic location.
Learn about the wolf skeletal system with this 3-Dimensional Virtual Wolf, an Interactive Wolf Skeleton created for the Zooarchaeology classes at the University of Wyoming.
Pack and Territory Size
The number of individuals per pack can be highly variable, but averages four to eight during winter in the western Great Lakes area with records of up to 16. Pack size can be as high as 30 or more in parts of Canada and Alaska. A wolf pack will roam and defend a territory of between 25 and 100 miles in the western Great Lakes area. Territories can reach hundreds of square miles where prey densities are in low density such as in northwestern Canada.
Dispersal and Ability to Colonize New Areas
Dispersal is the primary way wolves colonize new areas and maintain genetic diversity. Wolves have been known to disperse up to 550 miles, but more commonly disperse 50 – 100 miles from their natal pack. Generally wolves disperse when 1 – 2 years old as they reach sexual maturity although some adults disperse also. At any one time 5 – 20 percent of the wolf population may be dispersing individuals. Usually a wolf disperses to find an individual of the opposite sex, find a territory, and start a new pack. Some dispersers join packs that are already formed.
Wolves can occur wherever there is a sufficient number of large ungulates such as deer, moose, elk, caribou, bison, and musk ox. Wolves were once considered a wilderness animal, however if human-caused mortality is kept below certain levels, wolves can live in most areas. Historically, they once occupied every habitat that had sufficient prey in North America from mid Mexico to the polar ice pack.
Wolves can survive on 2.5 pounds of food per day, but require about five to seven pounds per day to reproduce successfully. Wolves are estimated to eat 10 pounds of food per day on average. Wolves don’t actually eat every day, however as they live a feast or famine lifestyle. They may go several days without a meal and them gorge on over 20 pounds of meat when a kill is made. Wolves primarily feed on prey animals larger than themselves as this provides food for many individuals. However, wolves will prey upon smaller mammals such as beaver and hare. Because wolves as a species inhabit a much wider area than its prey species, different populations of wolves prey upon different animals. Wolves located in the Western Great Lakes region typically prey upon whitetail deer whereas wolves in central Canada prey primarily on caribou.
Hunting and Feeding Behavior
Impacts on Prey
Wolf kill rates vary in relation to winter severity. Young, old, and sick prey animals are often nutritionally stressed and have difficulty traveling in deep snow. Wolf kill rates are highest during severe winters and the following spring. Sometimes wolf predation can keep prey populations at low levels for extended periods, but habitat alterations like forest cutting or fire, improved weather conditions, and human management practices allow prey populations to quickly recover.
One example of the predator-prey dynamic is that the reductions in ungulate herds caused by wolves increases habitat quality and helps rid the herd of genetically unfit and diseased individuals. This results in long term maintenance of a healthier ungulate herd. For example, deer and wolves have evolved together and wolf predation has played a crucial role in making the deer what it is today.
Wolf density often changes with the density of their primary prey. For example, in the northern Great Lakes region, the severe winters of 1995-96 and 1996-97 resulted in substantial numbers of deer being stressed and many starved or were killed by wolves. This provided a readily available food supply to wolves and increased their survival.
However, wolf numbers usually decline a year or two following the decline of primary prey. In addition to other factors, the mild winters since 1997 have been favorable to deer populations by increasing the winter survival of deer and in turn increasing the number of fawns being born.
Potential for Population Change
With abundant food and low human-caused mortality, wolves have a high capacity for population growth. In fact, in the right conditions, wolf populations can double in two to three years. From 1997 to 2000 the wolf population in the Northern Rocky states doubled from 200 to 400. Wolf populations can decline, however, if human-caused mortality is consistently greater than 28-50% of the fall wolf population.
The concept of the keystone species was introduced in 1969 by the zoologist Robert T. Paine.   Paine developed the concept to explain his observations and experiments on the relationships between marine invertebrates of the intertidal zone (between the high and low tide lines), including starfish and mussels. He removed the starfish from an area, and documented the effects on the ecosystem.  In his 1966 paper, Food Web Complexity and Species Diversity, Paine had described such a system in Makah Bay in Washington.  In his 1969 paper, Paine proposed the keystone species concept, using Pisaster ochraceus, a species of starfish generally known as ochre starfish, and Mytilus californianus, a species of mussel, as a primary example.  The ochre starfish is a generalist predator and feeds on chitons, limpets, snails, barnacles, echinoids and even decapod crustacea. The favourite food for these starfish is the mussel which is a dominant competitor for the space on the rocks. The ochre starfish keeps the population numbers of the mussels in check along with the other preys allowing the other seaweeds, sponges and anemones to co-exist that ochre starfish do not consume. When Paine removed the ochre starfish the mussels quickly outgrew the other species crowding them out. The concept became popular in conservation, and was deployed in a range of contexts and mobilized to engender support for conservation, especially where human activities had damaged ecosystems, such as by removing keystone predators.  
A keystone species was defined by Paine as a species that has a disproportionately large effect on its environment relative to its abundance.  It has been defined operationally by Davic in 2003 as "a strongly interacting species whose top-down effect on species diversity and competition is large relative to its biomass dominance within a functional group." 
A classic keystone species is a predator that prevents a particular herbivorous species from eliminating dominant plant species. If prey numbers are low, keystone predators can be even less abundant and still be effective. Yet without the predators, the herbivorous prey would explode in numbers, wipe out the dominant plants, and dramatically alter the character of the ecosystem. The exact scenario changes in each example, but the central idea remains that through a chain of interactions, a non-abundant species has an outsized impact on ecosystem functions. For example, the herbivorous weevil Euhrychiopsis lecontei is thought to have keystone effects on aquatic plant diversity by foraging on nuisance Eurasian watermilfoil in North American waters.  Similarly, the wasp species Agelaia vicina has been labeled a keystone species for its unparalleled nest size, colony size, and high rate of brood production. The diversity of its prey and the quantity necessary to sustain its high rate of growth have a direct impact on other species around it. 
The keystone concept is defined by its ecological effects, and these in turn make it important for conservation. In this it overlaps with several other species conservation concepts such as flagship species, indicator species, and umbrella species. For example, the jaguar is a charismatic big cat which meets all of these definitions: 
The jaguar is an umbrella species, flagship species, and wilderness quality indicator. It promotes the goals of carnivore recovery, protecting and restoring connectivity through Madrean woodland and riparian areas, and protecting and restoring riparian areas. . A reserve system that protects jaguars is an umbrella for many other species. . the jaguar [is] a keystone in subtropical and tropical America .
The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.
Early developments Edit
The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then built upon by von Neumann's friend Stanislaw Ulam, also a mathematician Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of the devices later termed cellular automata. Another advance was introduced by the mathematician John Conway. He constructed the well-known Game of Life. Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional checkerboard.
The Simula programming language, developed in the mid 1960s and widely implemented by the early 1970s, was the first framework for automating step-by-step agent simulations.
1970s and 1980s: the first models Edit
One of the earliest agent-based models in concept was Thomas Schelling's segregation model,  which was discussed in his paper "Dynamic Models of Segregation" in 1971. Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome.
In the early 1980s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner. Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from ethnocentrism to the dissemination of culture.  By the late 1980s, Craig Reynolds' work on flocking models contributed to the development of some of the first biological agent-based models that contained social characteristics. He tried to model the reality of lively biological agents, known as artificial life, a term coined by Christopher Langton.
The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory",  based on an earlier conference presentation of theirs.
At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory (CMOT). This field grew as a special interest group of The Institute of Management Sciences (TIMS) and its sister society, the Operations Research Society of America (ORSA).
1990s: expansion Edit
The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by Joshua M. Epstein and Robert Axtell to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.  Other notable 1990s developments included Carnegie Mellon University's Kathleen Carley ABM,  to explore the co-evolution of social networks and culture. During this 1990s timeframe Nigel Gilbert published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established a journal from the perspective of social sciences: the Journal of Artificial Societies and Social Simulation (JASSS). Other than JASSS, agent-based models of any discipline are within scope of SpringerOpen journal Complex Adaptive Systems Modeling (CASM). 
Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems (CASOS)—incorporated more and more agent-based modeling. Samuelson (2000) is a good brief overview of the early history,  and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.  
In the late 1990s, the merger of TIMS and ORSA to form INFORMS, and the move by INFORMS from two meetings each year to one, helped to spur the CMOT group to form a separate society, the North American Association for Computational Social and Organizational Sciences (NAACSOS). Kathleen Carley was a major contributor, especially to models of social networks, obtaining National Science Foundation funding for the annual conference and serving as the first President of NAACSOS. She was succeeded by David Sallach of the University of Chicago and Argonne National Laboratory, and then by Michael Prietula of Emory University. At about the same time NAACSOS began, the European Social Simulation Association (ESSA) and the Pacific Asian Association for Agent-Based Approach in Social Systems Science (PAAA), counterparts of NAACSOS, were organized. As of 2013, these three organizations collaborate internationally. The First World Congress on Social Simulation was held under their joint sponsorship in Kyoto, Japan, in August 2006. [ citation needed ] The Second World Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with George Mason University taking the lead role in local arrangements.
2000s and later Edit
More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation.  Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at UCLA have also made significant contributions in organizational behavior and decision-making. Since 2001, UCLA has arranged a conference at Lake Arrowhead, California, that has become another major gathering point for practitioners in this field. [ citation needed ]
Most computational modeling research describes systems in equilibrium or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior. The three ideas central to agent-based models are agents as objects, emergence, and complexity.
Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity. Typically agents are situated in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in algorithmic form in computer programs. In some cases, though not always, the agents may be considered as intelligent and purposeful. In ecological ABM (often referred to as "individual-based models" in ecology), agents may, for example, be trees in forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water). The modeling process is best described as inductive. The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions. Sometimes that result is an equilibrium. Sometimes it is an emergent pattern. Sometimes, however, it is an unintelligible mangle.
In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Agent-based models can explain the emergence of higher-order patterns—network structures of terrorist organizations and the Internet, power-law distributions in the sizes of traffic jams, wars, and stock-market crashes, and social segregation that persists despite populations of tolerant people. Agent-based models also can be used to identify lever points, defined as moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency.
Rather than focusing on stable states, many models consider a system's robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions.
Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models.    describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:
- Complex Network Modeling Level for developing models using interaction data of various system components.
- Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers.
- Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines.
- Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner.
Other methods of describing agent-based models include code templates  and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol. 
The role of the environment where agents live, both macro and micro,  is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generates diversity of behaviour. 
In modeling complex adaptive systems Edit
We live in a very complex world where we face complex phenomena such as the formation of social norms and emergence of new disruptive technologies. To better understand such phenomena, social scientists often use a reductionism approach where they reduce complex systems to lower-lever variables and model the relationships among them through a scheme of equations such as partial differential equation (PDE) [ citation needed ] . This approach that is called equation-based modeling (EBM) has some basic weaknesses in modeling real complex systems. EBMs emphasize nonrealistic assumptions, such as unbounded rationality and perfect information, while adaptability, evolvability, and network effects go unaddressed [ citation needed ] . In tackling deficiencies of reductionism, the framework of complex adaptive systems (CAS) has proven very influential in the past two decades [ citation needed ] . In contrast to reductionism, in the CAS framework, complex phenomena are studied in an organic manner where their agents are supposed to be both boundedly rational and adaptive [ citation needed ] . As a powerful methodology for CAS modeling, agent-based modeling (ABM) has gained a growing popularity among academics and practitioners. ABMs show how agents’ simple behavioral rules and their local interactions at micro-scale can generate surprisingly complex patterns at macro-scale. 
In biology Edit
Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics,  and the threat of biowarfare, biological applications including population dynamics,  stochastic gene expression,  plant-animal interactions,  vegetation ecology,  landscape diversity,  sociobiology,  the growth and decline of ancient civilizations, evolution of ethnocentric behavior,  forced displacement/migration,  language choice dynamics,  cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis,  the effects of ionizing radiation on mammary stem cell subpopulation dynamics,  inflammation,   and the human immune system.  Agent-based models have also been used for developing decision support systems such as for breast cancer.  Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible a priori.  Military applications have also been evaluated.  Moreover, agent-based models have been recently employed to study molecular-level biological systems.   
In epidemiology Edit
Agent-based models now complement traditional compartmental models, the usual type of epidemiological models. ABMs have been shown to be superior to compartmental models in regard to the accuracy of predictions.   Recently, ABMs such as CovidSim by epidemiologist Neil Ferguson, have been used to inform public health (nonpharmaceutical) interventions against the spread of SARS-CoV-2.  Epidemiological ABMs have been criticized for simplifying and unrealistic assumptions.   Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated. 
In business, technology and network theory Edit
Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include marketing,  organizational behaviour and cognition,  team working,  supply chain optimization and logistics, modeling of consumer behavior, including word of mouth, social network effects, distributed computing, workforce management, and portfolio management. They have also been used to analyze traffic congestion. 
Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science domain (journals versus conferences).  In addition, ABMs have been used to simulate information delivery in ambient assisted environments.  A November 2016 article in arXiv analyzed an agent based simulation of posts spread in Facebook.  In the domain of peer-to-peer, ad hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown.  The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated. 
Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems. 
In economics and social sciences Edit
Prior to, and in the wake of the 2008 financial crisis, interest has grown in ABMs as possible tools for economic analysis.   ABMs do not assume the economy can achieve equilibrium and "representative agents" are replaced by agents with diverse, dynamic, and interdependent behavior including herding. ABMs take a "bottom-up" approach and can generate extremely complex and volatile simulated economies. ABMs can represent unstable systems with crashes and booms that develop out of non-linear (disproportionate) responses to proportionally small changes.  A July 2010 article in The Economist looked at ABMs as alternatives to DSGE models.  The journal Nature also encouraged agent-based modeling with an editorial that suggested ABMs can do a better job of representing financial markets and other economic complexities than standard models  along with an essay by J. Doyne Farmer and Duncan Foley that argued ABMs could fulfill both the desires of Keynes to represent a complex economy and of Robert Lucas to construct models based on microfoundations.  Farmer and Foley pointed to progress that has been made using ABMs to model parts of an economy, but argued for the creation of a very large model that incorporates low level models.  By modeling a complex system of analysts based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – financial markets were simulated to high accuracy. Results showed a correlation between network morphology and the stock market index.  However, the ABM approach has been criticized for its lack of robustness between models, where similar models can yield very different results.  
ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment  and the examination of public policy applications to land-use.  There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network. 
Organizational ABM: agent-directed simulation Edit
The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems."  Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others. Agents for Systems are divided in two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analyses).
Self-driving cars Edit
Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents.  Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars.   It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior. The basic idea of using agent-based modeling to understand self-driving cars was discussed as early as 2003. 
Many ABM frameworks are designed for serial von-Neumann computer architectures, limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size,  scalability restrictions may hinder model validation.  Such limitations have mainly been addressed using distributed computing, with frameworks such as Repast HPC  specifically dedicated to these type of implementations. While such approaches map well to cluster and supercomputer architectures, issues related to communication and synchronization,   as well as deployment complexity,  remain potential obstacles for their widespread adoption.
A recent development is the use of data-parallel algorithms on Graphics Processing Units GPUs for ABM simulation.    The extreme memory bandwidth combined with the sheer number crunching power of multi-processor GPUs has enabled simulation of millions of agents at tens of frames per second.
Integration with other modeling forms Edit
Since Agent-Based Modeling is more of a modeling framework than a particular piece of software or platform, it has often been used in conjunction with other modeling forms. For instance, agent-based models have also been combined with Geographic Information Systems (GIS). This provides a useful combination where the ABM serves as a process model and the GIS system can provide a model of pattern.  Similarly, Social Network Analysis (SNA) tools and agent-based models are sometimes integrated, where the ABM is used to simulate the dynamics on the network while the SNA tool models and analyzes the network of interactions. 
Verification and validation (V&V) of simulation models is extremely important.   Verification involves making sure the implemented model matches the conceptual model, whereas validation ensures that the implemented model has some relationship to the real-world. Face validation, sensitivity analysis, calibration, and statistical validation are different aspects of validation.  A discrete-event simulation framework approach for the validation of agent-based systems has been proposed.  A comprehensive resource on empirical validation of agent-based models can be found here. 
The predator-prey relationship is one of the most common examples of coevolution. In this respect, there is a selective pressure on the prey to avoid capture and thus, the predator must evolve to become more effective hunters. In this manner, predator-prey coevolution is analogous to an evolutionary arms race and the development of specific adaptations, especially in prey species, to avoid or discourage predation.
Herbivores and plants
Similar to the predator-prey relationship, another common example of coevolution is the relationship between herbivore species and the plants that they consume. One example is that of the lodgepole pine seeds, which both red squirrels and crossbills eat in various regions of the Rocky Mountains. Both herbivores have different tactics for extracting the seeds from the lodgepole pine cone the squirrels will simply gnaw through the pine cone, whereas the crossbills have specialized mandibles for extracting the seeds. Thus, in regions where red squirrels are more prevalent, the lodgepole pine cones are denser, contain fewer seeds, and have thinner scales to prevent the squirrels from obtaining the seeds. However, in regions where crossbills are more prevalent, the cones are lighter and contain thick scales, so as to prevent the crossbills from accessing the seeds. Thus, the lodgepole pine is concurrently coevolving with both of these herbivore species.
Acacia ants and Acacias
An example of coevolution that is not characteristic of an arms race, but one which provides a mutual benefit to both a plant species and insect is that of the acacia ants and acacia plants. In this relationship, the plant and ants have coevolved to have a symbiotic relationship in which the ants provide the plant with protection against other potentially damaging insects, as well as other plants which may compete for nutrients and sunlight. In return, the plant provides the ants with shelter and essential nutrients for the ants and their growing larvae (shown below).
Flowering Plants and Pollinators
Another example of beneficial coevolution is the relationship between flowering plants and the respective insect and bird species that pollinate them. In this respect, flowering plants and pollinators have developed co-adaptations that allow flowers to attract pollinators, and insects and birds have developed specialized adaptations for extracting nectar and pollen from the plants (shown below).
Research indicates that there are at least three traits that flowering plants have evolved to attract pollinators:
- Distinct visual cues: flowering plants have evolved bright colors, stripes, patterns, and colors specific to the pollinator. For example, flowering plants seeking to attract insect pollinators are typically blue an ultraviolet, whereas red and orange are designed to attract birds.
- Scent: flowering plants use scents as a means of instructing insects as to their location. Since scents become stronger closer to the plant, the insect is able to hone-in and land on that plant to extract its nectar.
- Some flowers use chemical and tactile means to mimic female insect species to attract the male species. For example, orchids secrete a chemical that is the same as the pheromones of bee and wasp species. When the male insect lands on the flower and attempts to copulate, the pollen is transferred to him.
1. Which of the following statements is TRUE regarding coevolution?
A. Coevolution can result in a symbiotic or mutually beneficial relationship between two species.
B. Coevolution can be the result of selective pressures between two species, resulting in an arms race between them.
C. Both A and B are correct
D. None of the above
2. Which of the following is NOT an example of coevolution?
A. Acacia ants and lodgepole pines
B. Acacia ants and acacia plants
C. Crossbills and lodgepole pines
D. Red squirrels and lodgepole pines
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