Antagonistic Interactions

CHAPTER OVERVIEW 16: Antagonistic Interactions Learning Objectives Differentiate between the various types of antagonistic interactions Understand how antagonistic interactions drive evolution in the positively impacts species (predator, parasite, herbivore) and in the negatively impacted species (prey, host, plant) Predict fluctuations in populations of predators and prey (or parasites and hosts) using a modification of the exponential growth model, the Lotka-Volterra predator-prey model. 16.1: Predation 16.2: Quantifying Predator-Prey Dynamics 16.3: Herbivory 16.4: Parasitism 16.5: Infection 16.6: Sources and Attributions Summary Antagonistic interactions involve interactions in which one individual or species benefits, while the other individual or species is harmed. These interactions include predation, herbivory, and parasitism. These type of interactions exert immense evolutionary pressure on the individuals involved as they are required to obtain energy (predators, parasites, herbivores) for one of the species involved and to avoid loss of energy or death (prey, host, plant) for the second species involved. As a result, antagonistic interactions often drive an "evolutionary arms race", a cycle of adaptations and counter-adaptations. Ecologists model interactions among predators and their prey using the Lotka-Volterra model, which builds on the exponential growth model to link predator population growth and prey population decline. 16: Antagonistic Interactions is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.

: Polar bears (top photo) are solitary predators, here shown feeding on a beaded seal. Other species, like meat ants

(photo below), cooperate to feed and are called social predators. Here, meat ants feed socially on a cicada far larger than

Predation is a biological interaction where one organism, the predator, kills and eats another organism, its prey. It is one of a family of common feeding behaviors that includes parasitism and micropredation (which usually do not kill the host) and parasitoidism (which always does, eventually). It is distinct from scavenging on dead prey, though many predators also scavenge. Predation and herbivory overlap because seed predators and destructive frugivores kill their "prey".

: The relationship of predation to other feeding strategies.

The concept of predation is broad, defined differently in different contexts, and includes a wide variety of feeding methods; and some relationships that result in the prey's death are not generally called predation. A parasitoid, such as an ichneumon wasp, lays its eggs in or on its host; the eggs hatch into larvae, which eat the host, and it inevitably dies. Zoologists generally call this a form of parasitism, though conventionally parasites are thought not to kill their hosts. A predator can be defined to differ from a

parasitoid in that it has many prey, captured over its lifetime, where a parasitoid's larva has just one, or at least has its food supply provisioned for it on just one occasion (Lafferty & Kuris, 2002; Gurr & Wratten, 2012).

: Spider wasps paralyze and eventually kill their hosts, but are considered parasitoids, not predators.

There are other difficult and borderline cases. Micropredators are small animals that, like predators, feed entirely on other organisms; they include fleas and mosquitoes that consume blood from living animals, and aphids that consume sap from living plants. However, since they typically do not kill their hosts, they are now often thought of as parasites (Poulin et al., 2011; Poulin & Randhawa, 2015). Animals that graze on phytoplankton or mats of microbes are predators, as they consume and kill their food organisms; but herbivores that browse leaves are not, as their food plants usually survive the assault (Bengston 2002). Scavengers, organisms that only eat organisms found already dead, are not predators, but many predators such as the jackal and the hyena scavenge when the opportunity arises (Kruuk, 1972; Kane et al., 2017). Among invertebrates, social wasps (yellowjackets) are both hunters and scavengers of other insects (Schmidt, 2009). Predators are adapted and often highly specialized for hunting, with acute senses such as vision, hearing, or smell. Many predatory animals, both vertebrate and invertebrate, have sharp claws or jaws to grip, kill, and cut up their prey. Other adaptations include stealth and aggressive mimicry that improve hunting efficiency. When prey is detected, the predator assesses whether to attack it. Predators may actively search for or pursue prey (pursuit predation) or sit and wait for prey (ambush predation), often concealed, prior to attack. If the attack is successful, the predator kills the prey, removes any inedible parts like the shell or spines, and eats it. Predation has a powerful selective effect on prey, and the prey develop antipredator adaptations such as warning coloration, alarm calls and other signals, camouflage, mimicry of well-defended species, and defensive spines and chemicals. Sometimes predator and prey find themselves in an evolutionary arms race, a cycle of adaptations and counter-adaptations.

: A basic foraging cycle for a predator, with some variations indicated (Kramer, 2001).

To feed, a predator must search for, pursue and kill its prey. These actions form a foraging cycle (Griffiths, 1980; Wetzel & Liken, 2000). The predator must decide where to look for prey based on its geographical distribution; and once it has located prey, it must assess whether to pursue it or to wait for a better choice. If it chooses pursuit, its physical capabilities determine the mode of pursuit (e.g., ambush or pursuit) (MacArthur, 1984; Pianka, 2011). Having captured the prey, it may also need to expend energy handling it (e.g., killing it, removing any shell or spines, and ingesting it) (Griffiths, 1980; Kramer, 2001).

Search Predators have a choice of search modes ranging from sit-and-wait to active or widely foraging (Perry, 1999; Kramer, 2011; Bell, 2012; Eastman & Thiel, 2015). The sit-and-wait method is most suitable if the prey are dense and mobile, and the predator has low energy requirements (Bell, 2012). Wide foraging expends more energy, and is used when prey is sedentary or sparsely distributed (Pianka, 2011; Bell, 2012). There is a continuum of search modes with intervals between periods of movement ranging from seconds to months. Sharks, sunfish, insectivorous birds and shrews are almost always moving while web-building spiders, aquatic invertebrates, praying mantises and kestrels rarely move. In between, plovers and other shorebirds, freshwater fish including crappies, and the larvae of coccinellid beetles (ladybirds), alternate between actively searching and scanning the environment (Bell, 2012).

: The black-browed albatross regularly flies hundreds of kilometers across the nearly empty ocean to find patches of food.

Prey distributions are often clumped, and predators respond by looking for patches where prey is dense and then searching within patches (Kramer, 2001). Where food is found in patches, such as rare shoals of fish in a nearly empty ocean, the search stage requires the predator to travel for a substantial time, and to expend a significant amount of energy, to locate each food patch (Bell, 2012). For example, the black-browed albatross regularly makes foraging flights to a range of around 700 kilometers (430 miles), up to a maximum foraging range of 3,000 kilometers (1,860 miles) for breeding birds gathering food for their young (Gremillet et al., 2000). With static prey, some predators can learn suitable patch locations and return to them at intervals to feed (Bell, 2012).

: Seven-spot ladybirds select plants of good quality for their aphid prey.

Having found prey, a predator must decide whether to pursue it or keep searching. The decision depends on the costs and benefits involved. A bird foraging for insects spends a lot of time searching but capturing and eating them is quick and easy, so the efficient strategy for the bird is to eat every palatable insect it finds. By contrast, a predator such as a lion or falcon finds its prey easily but capturing it requires a lot of effort. In that case, the predator is more selective (Pianka, 2011).

One of the factors to consider is size. Prey that is too small may not be worth the trouble for the amount of energy it provides. Too large, and it may be too difficult to capture. For example, a mantid captures prey with its forelegs and they are optimized for grabbing prey of a certain size. Mantids are reluctant to attack prey that is far from that size. There is a positive correlation between the size of a predator and its prey (Pianka, 2011).

Capture To capture prey, predators have a spectrum of pursuit modes that range from overt chase (pursuit predation) to a sudden strike on nearby prey (ambush predation) (Stevens, 2010; Kramer, 2011; Williams et al., 2012)

: A western green lizard ambushes its grasshopper prey (top photo) and a trapdoor spider waits in its burrow to ambush its prey (bottom photo).

Ambush or sit-and-wait predators are carnivorous animals that capture prey by stealth or surprise. In animals, ambush predation is characterized by the predator's scanning the environment from a concealed position until a prey is spotted, and then rapidly executing a fixed surprise attack (deVries et al., 2012; Moore & Biewener, 2015). Vertebrate ambush predators include frogs, fish such as the angel shark, the northern pike and the eastern frogfish (Canadian Wildlife Service, 2007; Bray, 2014; Moore & Biewener, 2015; Indiana Division of Fish and Wildlife, 2018). Among the many invertebrate ambush predators are trapdoor spiders and Australian Crab spiders on land and mantis shrimps in the sea (deVries et al., 2012; BBC, 2014; Arizona-Sonora Desert Museum). Ambush predators often construct a burrow in which to hide, improving concealment at the cost of reducing their field of vision. Some ambush predators also use lures to attract prey within striking range (Moore & Biewener, 2015). The capturing movement has to be rapid to trap the prey, given that the attack is not modifiable once launched (Moore & Biewener, 2015).

: Humpback whales (top photo) are lunge feeders, filtering thousands of krill from seawater and swallowing them

alive. Dragonflies (bottom photo), like this common clubtail with captured prey, are invertebrate pursuit predators.

In pursuit predation, predators chase fleeing prey. If the prey flees in a straight line, capture depends only on the predator's being faster than the prey (Moore & Biewener, 2015). If the prey maneuvers by turning as it flees, the predator must react in real time to calculate and follow a new intercept path as it closes on the prey (Moore & Biewener, 2015). Many pursuit predators use camouflage to approach the prey as close as possible unobserved (stalking) before starting the pursuit (Moore & Biewener, 2015). Pursuit predators include terrestrial mammals such as humans, African wild dogs, spotted hyenas and wolves; marine predators such as dolphins, orcas and many predatory fishes, such as tuna (Gazda et al., 2005; Tyus, 2011); predatory birds (raptors) such as falcons; and insects such as dragonflies (Combes et al., 2013). Pursuit predators may be social, like the lion and wolf that hunt in groups, or solitary (Lafferty & Kuris, 2002).

An extreme form of pursuit is endurance or persistence hunting, in which the predator tires out the prey by following it over a long distance, sometimes for hours at a time. The method is used by human hunter-gatherers and by canids such as African wild dogs and domestic hounds. The African wild dog is an extreme persistence predator, tiring out individual prey by following them for many miles at relatively low speed (Hubel et al., 2016).

A specialized form of pursuit predation is the lunge feeding of baleen whales. These very large marine predators feed on plankton, especially krill, diving and actively swimming into concentrations of plankton, and then taking a huge gulp of water and filtering it through their feathery baleen plates (Goldbogen et al., 2006; Sanders et al., 2015).

: Catfish has sharp dorsal and pectoral spines which it holds erect to discourage predators such as herons which swallow prey whole (top photo). Osprey tears its fish prey apart, avoiding dangers such as sharp spines.

Once the predator has captured the prey, it has to handle it: very carefully if the prey is dangerous to eat, such as if it possesses sharp or poisonous spines, as in many prey fish. Some catfish such as the Ictaluridae have spines on the back (dorsal) and belly (pectoral) which lock in the erect position; as the catfish thrashes about when captured, these could pierce the predator's mouth, possibly fatally. Some fish-eating birds like the osprey avoid the danger of spines by tearing up their prey before eating it (Forbes, 1989). Some prey may also be time-consuming to handle, such as clams or other bivalves that are difficult for predators to crush or open.

16.1.2 Predator Adaptations Physical adaptations Under the pressure of natural selection, predators have evolved a variety of physical adaptations for detecting, catching, killing, and digesting prey. These include speed, agility, stealth, sharp senses, claws, teeth, filters, and suitable digestive systems (Bar-Yam, 2018). For detecting prey, predators have well-developed vision, smell, or hearing (Stevens et al., 2010). Predators as diverse as owls and jumping spiders have forward-facing eyes, providing accurate binocular vision over a relatively narrow field of view, whereas prey animals often have less acute all-round vision. Animals such as foxes can smell their prey even when it is concealed under 2 feet (60 cm) of snow or earth. Many predators have acute hearing, and some such as echolocating bats hunt exclusively by active or passive use of sound (Royal Saskatchewan Museum, 2012). Predators including big cats, birds of prey, and ants share powerful jaws, sharp teeth, or claws which they use to seize and kill their prey. Some predators such as snakes and fish-eating birds like herons and cormorants swallow their prey whole; some snakes can unhinge their jaws to allow them to swallow large prey, while fish-eating birds have long spear-like beaks that they use to stab and grip fast-moving and slippery prey (Royal Saskatchewan Museum, 2012). Fish and other predators have developed the ability to crush or open the armored shells of molluscs (Vermeij, 1993).

Many predators are powerfully built and can catch and kill animals larger than themselves; this applies as much to small predators such as ants and shrews as to big and visibly muscular carnivores like the cougar and lion (Lafferty & Kuris, 2002; Getz, 2011; Royal Saskatchewan Museum, 2012).

: a) Skull of brown bear has large pointed canines for killing prey, and self-sharpening carnassial teeth at rear for

cutting flesh with a scissor-like action, b) Large compound eyes, sensitive antennae, and powerful jaws (mandibles) of jack jumper

ant, c) Crab spider, an ambush predator with forward-facing eyes, catching another predator, a field digger wasp, d) Red-tailed

hawk uses sharp hooked claws and beak to kill and tear up its prey, e) Specialist: a great blue heron with a speared fish, f) Indian

python unhinges its jaw to swallow large prey like this chital.

: A camouflaged snow leopard in Ladakh (top photo). Striated frogfish uses camouflage and aggressive mimicry in the form of a fishing rod-like lure on its head to attract prey (bottom photo).

Camouflage is the use of any combination of materials, coloration, or illumination for concealment, either by making animals or objects hard to see, or by disguising them as something else. Predators may use camouflage to more effectively capture prey, while prey may use camouflage to more effectively evade predation. Members of the cat family such as the snow leopard (treeless highlands), tiger (grassy plains, reed swamps), ocelot(forest), fishing cat (waterside thickets), and lion (open plains) are camouflaged with coloration and disruptive patterns suiting their habitats (Cott, 1940). In aggressive mimicry, certain predators, including insects and fishes, make use of coloration and behavior to attract prey. Female Photuris fireflies, for example, copy the light signals of other species, thereby attracting male fireflies, which they capture and eat (Lloyd, 1965). Flower mantises are ambush predators; camouflaged as flowers, such as orchids, they attract prey and seize it when it is close enough (Forbes, 2009). Frogfishes are extremely well camouflaged, and actively lure their prey to approach using an esca, a bait on the end of a rod-like appendage on the head, which they wave gently to mimic a small animal, gulping the prey in an extremely rapid movement when it is within range (Bester, 2017).

Other Adaptations Venom - Many smaller predators such as the box jellyfish use venom to subdue their prey (Ruppert et al., 2005), and venom can also aid in digestion (as is the case for rattlesnakes and some spiders) (Cetaruk, 2005; Barceloux, 2008). The marbled sea snake that has adapted to egg predation has atrophied venom glands, and the gene for its three finger toxin contains a mutation (the deletion of two nucleotides) that inactives it. These changes are explained by the fact that its prey does not need to be subdued (Li et al., 2005). Electric fields - Several groups of predatory fish have the ability to detect, track, and sometimes, as in the electric ray, to incapacitate their prey by generating electric fields using electric organs (Castello et al., 2009; Feulner et al., 2009; Catania, 2015). The electric organ is derived from modified nerve or muscle tissue (Kramer, 1996). Physiology - Physiological adaptations to predation include the ability of predatory bacteria to digest the complex peptidoglycan polymer from the cell walls of the bacteria that they prey upon (Jurkevitch & Davidov, 2006). Carnivorous vertebrates of all five major classes (fishes, amphibians, reptiles, birds, and mammals) have lower relative rates of sugar to amino acid transport than either herbivores or omnivores, presumably because they acquire plenty of amino acids from the animal proteins in their diet (Karasov et al., 1988).

: Bats use echolocation to hunt moths at night.

To counter predation, prey have evolved defenses for use at each stage of an attack (Ruxton, 2004). They can try to avoid detection (Ruxton, 2004), such as by using camouflage and mimicry (Merilaita et al., 2017). They can detect predators (Caro, 2005) and warn others of their presence (Bergstrom & Lachmann, 2001; Getty, 2002). If detected, they can try to avoid being the target of an attack, for example, by signaling that they are toxic or unpalatable (Cott, 1940; Bowers et al., 1985), by forming groups (Beauchamp, 2012; Krause et al., 2002), or that a chase would be unprofitable (Ruxton et al., 2004; Caro, 2005). If they become a target, they can try to fend off the attack with defenses such as armor, quills, unpalatability, or mobbing (Dominey, 1983; Ruxton et al., 2004; Brodie, 2009) and they can escape an attack in progress by startling the predator (Cott, 1940; Merilaita et al., 2011; Edmunds, 2012), playing dead, shedding body parts such as tails, or simply fleeing (Caro, 2005).

: Dead leaf mantis's camouflage makes it less visible to both predators and prey (top photo). Syrphid hoverfly misdirects predators by mimicking a wasp, but has no sting (bottom photo).

Predators and prey are natural enemies, and many of their adaptations seem designed to counter each other. For example, bats have sophisticated echolocation systems to detect insects and other prey, and insects have developed a variety of defenses including the ability to hear the echolocation calls (Jacobs & Bastian, 2017; Barbosa & Castellanos, 2005). Many pursuit predators that run on land, such as wolves, have evolved long limbs in response to the increased speed of their prey (Janis & Wilhelm, 1993). Their adaptations have been characterized as an evolutionary arms race, an example of the coevolution of two species (Dawkins & Krebs, 1979). In a gene centered view of evolution, the genes of predator and prey can be thought of as competing for the prey's body (Dawkins & Krebs, 1979). Though criticized, the "life-dinner" principle of Dawkins and Krebs predicts that this arms race is asymmetric: if a predator fails to catch its prey, it loses its dinner, while if it succeeds, the prey loses its life (Dawkins & Krebs, 1979).

: Eastern coral snake, itself a predator, is venomous enough to kill predators that attack it, so when they avoid it, this behavior must be inherited, not learnt.

The metaphor of an arms race implies ever-escalating advances in attack and defense. However, these adaptations come with a cost; for instance, longer legs have an increased risk of breaking (Abrams, 1986), while the specialized tongue of the chameleon, with its ability to act like a projectile, is useless for lapping water, so the chameleon must drink dew off vegetation (Brodie, 1999).

16.1.3 Consequences of Predation Predators affect their ecosystems not only directly by eating their own prey, but by indirect means such as reducing predation by other species, or altering the foraging behavior of an herbivore, as with the biodiversity effect of wolves on riverside vegetation or sea otters on kelp forests. This may explain population dynamics effects such as the cycles observed in lynx and snowshoe hares (Preisser et al., 2005; Peckarsky et al., 2008; Sheriff et al., 2020). Biodiversity maintained by apex predation Predators may increase the biodiversity of communities by preventing a single species from becoming dominant. Such predators are known as keystone species and may have a profound influence on the balance of organisms in a particular ecosystem (Bond, 2012). Introduction or removal of this predator, or changes in its population density, can have drastic cascading effects on the

equilibrium of many other populations in the ecosystem. For example, grazers of a grassland may prevent a single dominant species from taking over (Bond, 2012).

: Riparian willow recovery at Blacktail Creek, Yellowstone National Park, after reintroduction of wolves, the local keystone species and apex predator (Ripple & Beschta, 2004). Left, in 2002; right, in 2015

The elimination of wolves from Yellowstone National Park had profound impacts on the entire ecosystem's structure and function. In Yellowstone, wolves are both keystone species and apex predators. Without predation, herbivores began to over-graze many woody browse species, affecting the area's plant populations. In addition, wolves often kept animals from grazing near streams, protecting the beavers' food sources. The removal of wolves had a direct effect on the beaver population, as their habitat became territory for grazing. Increased browsing on willows and conifers along Blacktail Creek due to a lack of predation caused channel incision because the reduced beaver population was no longer able to slow the water down and keep the soil in place. The predators were thus demonstrated to be of vital importance in the ecosystem (Ripple & Beschta, 2004).

Population dynamics In the absence of predators, the population of a species can grow exponentially until it approaches the carrying capacity of the environment (Neal, 2004). Predators limit the growth of prey both by consuming them and by changing their behavior (Nelson et al., 2004). Increases or decreases in the prey population can also lead to increases or decreases in the number of predators, for example, through an increase in the number of young they bear. Cyclical fluctuations have been seen in populations of predator and prey, often with offsets between the predator and prey cycles. A well-known example is that of the snowshoe hare and lynx. Over a broad span of boreal forests in Alaska and Canada, the hare populations fluctuate in near synchrony with a 10-year period, and the lynx populations fluctuate in response. This was first seen in historical records of animals caught by fur hunters for the Hudson Bay Company over more than a century (Krebs et al., 2001; Peckarsky et al., 2008; Krebs, 2014; BBC, 2015).

: Predator-prey population cycles in a Lotka-Volterra model.

A simple model of a system with one species each of predator and prey, the Lotka-Volterra equations, predicts population cycles (Goel et al., 1971). However, attempts to reproduce the predictions of this model in the laboratory have often failed; for example, when the protozoan Didinium nasutum is added to a culture containing its prey, Paramecium caudatum, the latter is often driven to extinction (Levin et al., 2009). The Lotka-Volterra equations rely on several simplifying assumptions, and they are structurally unstable, meaning that any change in the equations can stabilize or destabilize the dynamics (Murdoch et al., 2009; Nowak & May, 2000). For example, one assumption is that predators have a linear functional response to prey: the rate of kills increases in proportion to the rate of encounters. If this rate is limited by time spent handling each catch, then prey populations can reach densities above which predators cannot control them (Levin et al., 2009). Another assumption is that all prey individuals are identical. In reality, predators tend to select young, weak, and ill individuals, leaving prey populations able to regrow (Genovart et al., 2010). Many factors can stabilize predator and prey populations (Rockwood, 2009). One example is the presence of multiple predators, particularly generalists that are attracted to a given prey species if it is abundant and look elsewhere if it is not (Rockwood, 2009). As a result, population cycles tend to be found in northern temperate and subarctic ecosystems because the food webs are simpler (Rockwood, 2009). The snowshoe hare-lynx system is subarctic, but even this involves other predators, including coyotes, goshawks and great horned owls, and the cycle is reinforced by variations in the food available to the hares (Rockwood, 2009). A range of mathematical models have been developed by relaxing the assumptions made in the Lotka-Volterra model; these variously allow animals to have geographic distributions, or to migrate; to have differences between individuals, such as sexes and an age structure, so that only some individuals reproduce; to live in a varying environment, such as with changing seasons (Cushing, 2005; Thieme, 2003); and analyzing the interactions of more than just two species at once. Such models predict widely differing and often chaotic predator-prey population dynamics (Cushing, 2005; Kozlov & Vakulenko, 2013). The presence of refuge areas, where prey are safe from predators, may enable prey to maintain larger populations but may also destabilize the dynamics (Sih, 1987; McNair, 1986; Berryman et al., 2006; Ross & Garay, 2009).

Alternative Stable States and Predator-Prey Dynamics Case study based on the CC-BY Paper By: Glaspie, C. N., Seitz, R. D., & Lipcius, R. N. (2020). Are predator-prey model predictions supported by empirical data?

Evidence for a storm-driven shift to an alternative stable state in a crab-clam system. Marine Ecology Progress Series, 645, 8390. Predators play a key role in ecosystem stability and function by consuming dominant competitors (Lubchenco & Gaines, 1981; Boudreau & Worm. 2012). Predators can also destabilize ecosystems or collapse food webs if they become too abundant (Estes et al., 2009), or if their prey do not have natural defenses against predation (Johnston et al., 2015). One of the ways the balance between predator and prey adaptations manifests itself in nature is through density-dependent predation. Predators can exhibit a numerical response to prey densities by increasing reproduction rates due to an overabundance of prey (demographic response) or by gathering in areas with relatively high densities of prey (aggregative response) (Holling, 1959). An individual predator may also adjust its predation rate to prey density through a `functional response' (changes in a predator's consumption rate in response to prey density). Many mathematical models can be used to predict predator-prey dynamics (Briggs & Hoopes, 2004). These models contain nonlinear functions describing the density-dependent interactions between predator and prey. Due to nonlinearities, model behavior often includes shifts to alternative stable states (Drake & Griffen, 2010). The states may include extinction of one or both species, or coexistence steady states where both predator and prey are able to coexist at densities predicted by the model. The theory of alternative stable states predicts that ecosystems can exist under multiple "states" (sets of unique biotic and abiotic conditions). These alternative states are non-transitory and therefore considered stable over ecologically-relevant timescales. Ecosystems may transition from one stable state to another, in what is known as a state shift or regime shift, when perturbed. Due to ecological feedbacks, ecosystems display resistance to state shifts and therefore tend to remain in one state unless perturbations are large enough. In their study, Glaspie et al. (2020) examined evidence for a storm-driven shift to a low-density state for the soft-shell clam Mya arenaria, which was once a biomass-dominant species in Chesapeake Bay, USA, in the face of predation by the blue crab Callinectes sapidus. Tropical Storm Agnes, which reached and remained in the Chesapeake Bay watershed from 21 to 23 June 1972, has long been suspected of causing long-term changes to the Bay (Orth & Moore, 1983). Tropical Storm Agnes was a `100-year storm' that caused sustained, extremely low salinities and increased sedimentation throughout Chesapeake Bay (Schubel, 1976, Schubel et al., 1976). This storm has been blamed for accelerating the loss of seagrass Zostera marina (Orth & Moore, 1983) and oysters Crassostrea virginica (Haven et al. 1976) in Chesapeake Bay, although these losses were already in motion before the storm occurred. Unlike seagrass and oysters, declines in abundance of the soft-shell clam M. arenaria were uniquely related to the storm. M. arenaria was abundant enough to support a major commercial fishery throughout Chesapeake Bay prior to 1972 (Haven, 1970). Widespread mass mortality of M. arenaria occurred after the storm (Cory & Redding, 1976), and the fishery never recovered in lower Chesapeake Bay (Virginia) (Glaspie et al., 2018).

: Though the boom-and-bust cycle of the species continued after the Tropical Storm Agnes, the storm clearly impacted the abundance of the populations and disrupted the pattern of their population cycles.

Using a 30-year dataset on soft-shell clams and blue crabs, Glaspie et al. (2020) found that Mya arenaria was subjected to a

storm-driven shift to a low-density alternative stable state, which has been maintained by blue crab predation in Chesapeake

Bay. Before the storm, clams were likely prey for juvenile crabs that entered the fishery at 1 yr of age, resulting in a positive

correlation between crab and clam abundance with a lag of 1 yr (Figure

). After the storm, crabs were likely consuming

juvenile clams that would have been large enough to enter the fishery a year later, resulting in a negative correlation between

clams and crab abundance with a lag of 1 yr (Figure

). Both M. arenaria and Callinectes sapidus enter their respective

fisheries after 1-1.5 yr (Newell & Hidu, 1986; Lipcius & van Engel, 1990), providing an explanation for the 1 yr lag. This is

consistent with a shift from a system controlled from the bottom up (i.e., by the abundance of prey resources), to a system

controlled from the top down by predation pressure on bivalves.

: The change in trendlines from a positive correlation before the storm to a negative correlation after the storm again shows the impact natural disasters can have on the patterns of population cycles.

Sources Abrams, P.A. (1986). Adaptive responses of predators to prey and prey to predators: The failure of the arms-race analogy. Evolution, 40(6), pp. 1229-1247. doi:10.1111/j.1558-5646.1986.tb05747.x. PMID 28563514. S2CID 27317468. Barbosa, P., & Castellanos, I. (2005). Ecology of predator-prey interactions. Oxford University Press, p. 78. ISBN 9780199874545. Barceloux, D.G. (2008). Medical toxicology of natural substances: Foods, fungi, medicinal herbs, plants, and venomous animals. Wiley, p. 1028. ISBN 978-0-470-33557-4. Bar-Yam. Predator-prey relationships. New England Complex Systems Institute. Retrieved 7 September 2018. Bengtson, S. (2002). Origins and early evolution of predation. In Kowalewski, M., & Kelley, P.H. (Eds.), The fossil record of predation. The Paleontological Society, pp. 289-317. Bergstrom, C.T., & Lachmann, M. (2001). Alarm calls as costly signals of antipredator vigilance: The watchful babbler game. Animal Behaviour, 61(3), 535-543. CiteSeerX 10.1.1.28.773. doi:10.1006/anbe.2000.1636. S2CID 2295026. Berryman, A.A., & Hawkins, Bradford A. (2006). The refuge as an integrating concept in ecology and evolution. Oikos, 115(1), 192-196. doi:10.1111/j.0030-1299.2006.15188.x. Bester, C. (2017). Antennarius striatus. University of Florida. Retrieved 31 January 2018. Bond, W.J. (2012). Keystone species. In Schulze, E.D., & Mooney, H.A. (Eds.), Biodiversity and Ecosystem Function. Springer. p. 237. ISBN 978-3642580017.

Boudreau, S.A., & Worm, B. (2012). Ecological role of large benthic decapods in marine ecosystems: A review. Mar. Ecol. Prog. Ser., 469, pp. 195-213 Bowers, M.D., Brown, I.L., & Wheye, D. (1985). Bird predation as a selective agent in a butterfly population. Evolution, 39(1), pp. 93-103. doi:10.1111/j.1558-5646.1985.tb04082.x. PMID 28563638. S2CID 12031679. Bray, D. Eastern frogfish, Batrachomoeus dubius. Fishes of Australia. Archived from the original on 14 September 2014. Retrieved 14 September 2014. Briggs, C.J., & Hoopes, M.F. (2004). Stabilizing effects in spatial parasitoid-host and predator-prey models: A review. Theor. Popul. Biol., 65, pp. 299-315 Brodie, E.D. (2009). Toxins and venoms. Current Biology, 19(20), R931-R935. doi:10.1016/j.cub.2009.08.011. PMID 19889364. S2CID 9744565. Brodie, E.D. (1999). Predator-prey arms races. BioScience, 49(7), pp. 557-568. doi:10.2307/1313476. JSTOR 1313476. Castello, M.E., Rodriguez-Cattaneo, A., Aguilera, P.A., Iribarne, L., Pereira, A.C., & Caputi, A.A. (2009). Waveform generation in the weakly electric fish Gymnotus coropinae (Hoedeman): The electric organ and the electric organ discharge. Journal of Experimental Biology, 212(9), pp. 1351-1364. doi:10.1242/jeb.022566. PMID 19376956. Catania, K.C. (2015). Electric eels use high-voltage to track fast-moving prey. Nature Communications, 6(1), pp. 8638. Bibcode:2015NatCo...6.8638C. doi:10.1038/ncomms9638. ISSN 2041-1723. PMC 4667699. PMID 26485580. Cetaruk, E.W. (2005). Rattlesnakes and other Crotalids. In Brent, J. (Eds.), Critical care toxicology: Diagnosis and management of the critically poisoned patient. Elsevier Health Sciences. p. 1075. ISBN 978-0-8151-4387-1. Cory, R.L., & Redding, M.J. (1976). Mortalities caused by Tropical Storm Agnes to clams and oysters in the Rhode River area of Chesapeake Bay. In Anderson, A.M. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, p 478-487. Cott. (1940). pp. 368-389 Cougar. Hinterland Who's Who. Canadian Wildlife Service and Canadian Wildlife Federation. Archived from the original on 18 May 2007. Retrieved 22 May 2007. Cushing, J.M. (2005). Book reviews | Mathematics in population biology, by Horst R. Thiene. Bulletin of the American Mathematical Society, 42(4), pp. 501-505. doi:10.1090/S0273-0979-05-01055-4. Dawkins, R., & Krebs, J.R. (1979). Arms races between and within species. Proceedings of the Royal Society B: Biological Sciences, 205(1161), pp. 489-511. Bibcode:1979RSPSB.205..489D. doi:10.1098/rspb.1979.0081. PMID 42057. S2CID 9695900. deVries, M.S., Murphy, E.A.K., & Patek S.N. (2012). Strike mechanics of an ambush predator: The spearing mantis shrimp. Journal of Experimental Biology, 215(Pt 24), pp. 4374-4384. doi:10.1242/jeb.075317. PMID 23175528. Dominey, W.J. (1983). Mobbing in colonially nesting fishes, especially the bluegill, Lepomis macrochirus. Copeia, (4), pp. 1086- 1088. doi:10.2307/1445113. JSTOR 1445113. Drake, J.M., & Griffen, B.D. (2010). Early warning signals of extinction in deteriorating environments. Nature, 467, pp. 456-459 Eastman, L.B., & Thiel, M. (2015). Foraging behavior of crustacean predators and scavengers. In Thiel, M., & Watling, L. (Eds.), Lifestyles and feeding biology. Oxford University Press. pp. 535-556. ISBN 9780199797066. Edmunds, M. (2012). Deimatic Behavior. Springer. Retrieved 31 December 2012. Estes, J.A., Doak, D.F., Springer, A.M., & Williams, T.M. (2009). Causes and consequences of marine mammal population declines in southwest Alaska: A food-web perspective. Philos. Trans. R. Soc. B., 364, pp. 1647-1658 Feulner, P. G., Plath, M., Engelmann, J., Kirschbaum, F., & Tiedemann, R. (2009). Electrifying love: Electric fish use speciesspecific discharge for mate recognition. Biology Letters, 5(2), pp. 225-228. doi:10.1098/rsbl.2008.0566. PMC 2665802. PMID 19033131.

Forbes, L.S. (1989). Prey defenses and predator handling behaviour: The dangerous prey hypothesis. Oikos, 55(2), pp. 155-158. doi:10.2307/3565418. JSTOR 3565418. Forbes, P. (2009). Dazzled and deceived: Mimicry and camouflage. Yale University Press, p. 134. ISBN 978-0-300-17896-8. Gazda, S.K., Connor, R.C., Edgar, R.K., & Cox, F. (2005). A division of labour with role specialization in group-hunting bottlenose dolphins (Tursiops truncatus) off Cedar Key, Florida. Proceedings of the Royal Society, 272(1559), pp. 135-140. doi:10.1098/rspb.2004.2937. PMC 1634948. PMID 15695203. Genovart, M., Negre, N., Tavecchia, G., Bistuer, A., Parpal, L., & Oro, D. (2010). The young, the weak and the sick: Evidence of natural selection by predation. PLOS ONE, 5(3): e9774. Bibcode:2010PLoSO...5.9774G. doi:10.1371/journal.pone.0009774. PMC 2841644. PMID 20333305. Getty, T. (2002). The discriminating babbler meets the optimal diet hawk. Anim. Behav., 63(2), pp. 397-402. doi:10.1006/anbe.2001.1890. S2CID 53164940. Glaspie, C.N., Seitz, R.D., Ogburn, M.B., Dungan, C.F., & Hines, A.H. (2018). Impacts of habitat, predators, recruitment, and disease on soft-shell clams Mya arenaria and stout razor clams Tagelus plebeius in Chesapeake Bay. Mar. Ecol. Prog. Ser., 603, pp. 117-133 Goel, N.S., Maitra, S.C., & Montroll, E.W. (1971). On the Volterra and other non-linear models of interacting populations. Academic Press. ISBN 978-0122874505. Goldbogen, J.A., Calambokidis, J., Shadwick, R.E., Oleson, E.M., McDonald, M.A., & Hildebrand, J.A. (2006). Kinematics of foraging dives and lunge-feeding in fin whales. Journal of Experimental Biology, 209(7), pp. 1231-1244. doi:10.1242/jeb.02135. PMID 16547295. S2CID 17923052. Archived from the original (PDF) on 13 August 2011. Retrieved 4 September 2018. Gremillet, D., Wilson, R.P., Wanless, S., & Chater, T. (2000). Black-browed albatrosses, international fisheries and the Patagonian Shelf. Marine Ecology Progress Series, 195, pp. 69-280. Bibcode:2000MEPS..195..269G. doi:10.3354/meps195269. Griffiths, D. (1980). Foraging costs and relative prey size. The American Naturalist, 116(5), pp. 743-752. doi:10.1086/283666. JSTOR 2460632. S2CID 85094710. Gurr, G.M., Wratten, S.D., & Snyder, W.E. (2012). Biodiversity and insect pests: Key issues for sustainable management. John Wiley & Sons, pp. 105. ISBN 978-1-118-23185-2. Haven, D.S. (1970). A study of the hard and soft clam resources of Virginia: Annual contract report for the period 1 July 1969 through 30 June 1970. Virginia Institute of Marine Science, Gloucester Point, VA. Haven, D.S., Hargis, W.J., Loesch, J.G., & Whitcomb, J.P. (1976). The effect of Tropical Storm Agnes on oysters, hard clams, soft clams, and oyster drills in Virginia. In Anderson, A.M. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, pp. 488-508. Holling, C. (1959). The components of predation as revealed by a study of small mammal predation of the European pine sawfly. Can. Entomol., 91, pp. 293-320 Hubel, T.Y., Myatt, J.P., Jordan, N.R., Dewhirst, O.P., McNutt, J.W., & Wilson, A.M. (2016). Energy cost and return for hunting in African wild dogs. Nature Communications, 7: 11034. doi:10.1038/ncomms11034. PMC 4820543. PMID 27023457. Janis, C.M., & Wilhelm, P.B. (1993). Were there mammalian pursuit predators in the Tertiary? Dances with wolf avatars. Journal of Mammalian Evolution, 1(2), pp. 103-125. doi:10.1007/bf01041590. S2CID 22739360. Jurkevitch, E., & Davidov, Y. (2006). Phylogenetic diversity and evolution of predatory prokaryotes. Predatory Prokaryotes. Springer. pp. 11-56. doi:10.1007/7171_052. ISBN 978-3-540-38577-6. Kane, A., Healy, K., Guillerme, T., Ruxton, G.D., & Jackson, A.L. (2017). A recipe for scavenging in vertebrates - the natural history of a behaviour. Ecography, 40(2), pp. 324-334. doi:10.1111/ecog.02817 Karasov, W.H., & Diamond, J.M. (1988). Interplay between physiology and ecology in digestion. BioScience, 38(9), pp. 602-611. doi:10.2307/1310825. JSTOR 1310825.

Kozlov, V., & Vakulenko, S. (2013). On chaos in Lotka-Volterra systems: An analytical approach. Nonlinearity, 26(8), pp. 2299- 2314. Bibcode:2013Nonli..26.2299K. doi:10.1088/0951-7715/26/8/2299. Kramer, D.L. (2001). Foraging behavior. In Fox, C.W., Roff, D.A., & Fairbairn, D.J. (Eds.), Evolutionary ecology: Concepts and case studies. Oxford University Press, pp. 232-238. ISBN 9780198030133. Archived from the original (PDF) on 12 July 2018. Retrieved 20 September 2018. Krause, J., & Ruxton, G.D. (2002). Living in groups. Oxford University Press, pp. 13-15. ISBN 9780198508182. Krebs, C.J., Boonstra, R., Boutin, S., & Sinclair, A.R.E. (2001). What drives the 10-year cycle of snowshoe hares?. BioScience, 51(1), pp. 25. doi:10.1641/0006-3568(2001)051[0025:WDTYCO]2.0.CO;2. Krebs, C., & Myers, J. (2014). The snowshoe hare 10-year cycle - a cautionary tale. Ecological rants. University of British Columbia. Retrieved 2 October 2018. Kruuk, H. (1972). The spotted hyena: A study of predation and social behaviour. University of California Press, pp. 107-108. ISBN 978-0226455082. Lafferty, K.D., & Kuris, A.M. (2002). Trophic strategies, animal diversity and body size. Trends in Ecology and Evolution, 17(11), pp. 507-513. doi:10.1016/s0169-5347(02)02615-0. Archived from the original (PDF) on 3 October 2019. Levin, S.A., Carpenter, S.R., Godfray, H.C.J., Kinzig, A.P., Loreau, M., Losos, J.B., Walker, B., & Wilcove, D.S. (2009). The Princeton guide to ecology. Princeton University Press, pp. 204-209. ISBN 9781400833023. Li, M., Fry, B.G., & Kini, R.M. (2005). Eggs-only diet: Its implications for the toxin profile changes and ecology of the marbled sea snake (Aipysurus eydouxii). Journal of Molecular Evolution, 60(1), pp. 81-89. Bibcode:2005JMolE..60...81L. doi:10.1007/s00239-004-0138-0. PMID 15696370. S2CID 17572816. Lipcius, R.N., & van Engel, W.A. (1990). Blue crab population dynamics in Chesapeake Bay: Variation in abundance (York River 1972-1988) and stock-recruit functions. Bull Mar. Sci., 46, pp. 180-194 Lloyd J.E. (1965). Aggressive mimicry in Photuris: Firefly femmes fatales. Science, 149(3684), pp. 653-654. Bibcode:1965Sci...149..653L. doi:10.1126/science.149.3684.653. PMID 17747574. S2CID 39386614. Lubchenco, J., & Gaines, S.D. (1981). A unified approach to marine plant-herbivore interactions. I. Populations and communities. Annu. Rev. Ecol. Syst., 12, pp. 405-437 MacArthur, R.H. (1984). The economics of consumer choice. Geographical ecology: Patterns in the distribution of species. Princeton University Press, pp. 59-76. ISBN 9780691023823. McNair, J.N. (1986). The effects of refuges on predator-prey interactions: A reconsideration. Theoretical Population Biology, 29, (1), pp. 38-63. doi:10.1016/0040-5809(86)90004-3. PMID 3961711. Merilaita, S., Scott-Samuel, N.E., & Cuthill, I.C. (2017). How camouflage works. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1724): 20160341. doi:10.1098/rstb.2016.0341. PMC 5444062. PMID 28533458. Merilaita, S., Vallin, A., Kodandaramaiah, U., Dimitrova, M., Ruuskanen, S., & Laaksonen, T. (2011). Number of eyespots and their intimidating effect on naïve predators in the peacock butterfly. Behavioral Ecology, 22(6), pp. 1326-1331. doi:10.1093/beheco/arr135. Moore, T.Y., & Biewener, A.A. (2015). Outrun or outmaneuver: Predator-prey interactions as a model system for integrating biomechanical studies in a broader ecological and evolutionary context. Integrative and Comparative Biology, 55(6), pp. 1188-97. doi:10.1093/icb/icv074. PMID 26117833. Neal, D. (2004). Introduction to population biology. Cambridge University Press, pp. 68-69. ISBN 9780521532235. Newell, C.R., & Hidu, H. (1986). Species profiles: Life histories and environmental requirements of coastal fishes and invertebrates (North Atlantic)--softshell clam. US Fish Wildl. Serv. Biol. Rep., 82(11.53). US Army Corps of Engineers, TR EL82-4. https: //apps.dtic.mil/dtic/tr/fulltext/ u2/ a174837. Pdf Nowak, M., & May, R.M. (2000). Virus dynamics: Mathematical principles of immunology and virology. Oxford University Press, p. 8. ISBN 9780191588518.

Orth, R.J., & Moore, K.A. (1983). Chesapeake Bay: An unprecedented decline in submerged aquatic vegetation. Science, 222, pp. 51-53 Peckarsky, B.L., Abrams, P.A., Bolnick, D.I., Dill, L.M., Grabowski, J.H., Luttbeg, B., Orrock, J.L., Peacor, S.D., Preisser, E.L., Schmitz, O.J., & Trussell, G.C. (2008). Revisiting the classics: Considering nonconsumptive effects in textbook examples of predator-prey interactions. Ecology, 89(9), pp. 2416-2425. doi:10.1890/07-1131.1. PMID 18831163. Perry, G. (1999). The evolution of search modes: Ecological versus phylogenetic perspectives. The American Naturalist, 153(1), pp. 98-109. doi:10.1086/303145. PMID 29578765. S2CID 4334462. Pianka, E.R. (2011). Evolutionary ecology (7th (eBook) ed.). pp. 78-83. Pikes (Esocidae). Indiana Division of Fish and Wildlife. Retrieved 3 September 2018. Predator & prey: Adaptations. (2012). Royal Saskatchewan Museum. Archived from the original (PDF) on 3 April 2018. Retrieved 19 April 2018. Predators and their prey. BBC. Retrieved 7 October 2015. Poulin, R. (2011). The many roads to parasitism: A tale of convergence. In Rollinson, D., & Hay, S.I. (Eds.), Advances in parasitology, 74. Academic Press. pp. 27-28. doi:10.1016/B978-0-12-385897-9.00001-X. ISBN 978-0-12-385897-9. PMID 21295676. Poulin, R., & Randhawa, H.S. (2015). Evolution of parasitism along convergent lines: From ecology to genomics. Parasitology, 142(Suppl 1): S6-S15. doi:10.1017/S0031182013001674. PMC 4413784. PMID 24229807. Preisser, E.L., Bolnick, D.I., & Benard, M.F. (2005). Scared to death? The effects of intimidation and consumption in predator- prey interactions. Ecology, 86(2), pp. 501-509. doi:10.1890/04-0719. ISSN 0012-9658. Ruppert, E.E., Fox, R.S., & Barnes, R.D. (2004). Invertebrate zoology, 7th edition. Cengage Learning. pp. 153-154. ISBN 978-81315-0104-7. Ruxton, Sherratt & Speed 2004, pp. vii-xii Sanders, J.G., Beichman, A.C., Roman, J., Scott, J.J., Emerson, D., McCarthy, J.J., & Girguis, P.R. (2015). Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nature Communications, 6, pp. 8285. Bibcode:2015NatCo...6.8285S. doi:10.1038/ncomms9285. PMC 4595633. PMID 26393325. Schmidt, J.O. (2009). Wasps. ScienceDirect. Encyclopedia of Insects (Second ed.). pp. 1049-1052. doi:10.1016/B978-0-12374144-8.00275-7 Schubel, J.R. (1976). Effects of Agnes on the suspended sediment on the Chesapeake Bay and contiguous shelf waters. In Schubel, J.R. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, p 179-200 Schubel, J.R., Carter, H.H., & Cronin, W.B. (1976). Effects of Agnes on the distribution of salinity along the main axis of the Bay and in contiguous shelf waters. In Ruzecki, E.P. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, p 33-65 Sheriff, M.J., Peacor, S.D., Hawlena, D., Thaker, M., & Gaillard, J.M. (2020). Non-consumptive predator effects on prey population size: A dearth of evidence. Journal of Animal Ecology, 89(6), pp. 1302-1316. doi:10.1111/1365-2656.13213. PMID 32215909. Sih, A. (1987). Prey refuges and predator-prey stability. Theoretical Population Biology, 31, pp. 1-12. doi:10.1016/00405809(87)90019-0. Stevens, A.N.P. (2010). Predation, herbivory, and parasitism. Nature Education Knowledge, 3(10), pp. 36. Retrieved 12 February 2018. Trapdoor spider. (2014). Arizona-Sonora Desert Museum. Retrieved 12 December 2014. Trapdoor spiders. BBC. Retrieved 12 December 2014.

Thieme, H.R. (2003). Mathematics in population biology. Princeton University Press. ISBN 978-0-691-09291-1. Tyus, H.M. (2011). Ecology and conservation of fishes. CRC Press, p. 233. ISBN 978-1-4398-9759-1. Vermeij, G.J. (1993). Evolution and escalation: An ecological history of life. Princeton University Press, pp. 11 and passim. ISBN 978-0-691-00080-0. Wetzel, R.G., & Likens, G.E. (2000). Predator-prey interactions. Limnological Analyses. Springer. pp. 257-262. doi:10.1007/9781-4757-3250-4_17. ISBN 978-1-4419-3186-3. Williams, A.C., & Flaxman, S.M. (2012). Can predators assess the quality of their prey's resource? Animal Behaviour, 83(4), pp, 883-890. doi:10.1016/j.anbehav.2012.01.008. S2CID 53172079. 16.1: Predation is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.

: Numbers of snowshoe hare (Lepus americanus) (yellow background) and Canada lynx (black line, foreground) furs sold to the Hudson's Bay Company from 1845 to 1935. Image by Lamiot is licensed under CC BY-SA 4.0.

In the classic Lotka-Volterra model of predator-prey dynamics, both predators and prey are modeled using a modified version of the equation for exponential growth, so neither population has an explicit carrying capacity. However, either or both may have an implicit carrying capacity imposed by the interaction between the two populations.

To model the prey population, we begin with a basic exponential model with some additional terms. Here, the prey population is growing at their intrinsic growth rate rprey, but is also declining due to predation.

The number of prey killed will depend on the number of predators: the greater the number of predators, the more prey they will kill (Npred). It will also depend on the number of prey available: the more prey, the more successful the predators (Nprey). Finally, it will depend on the attack rate: the ability of a predator to find and consume prey (p). The number of prey killed in one time interval will be the product of these, pNpreyNpred. The resulting equation for prey growth is:

In words, the prey population grows according to its per capita growth rate minus losses to predators. Losses are determined by attack rate, predator population, and prey population. To model the predator population, we also begin with an exponential model, in concept. However, there is a wrinkle in this model, because we cannot assume a constant per capita rate of population growth. There is no simple r for the predator population because its growth rate will depend on how many prey are caught. As in the prey model, the number of prey caught will be pNpreyNpred. The growth of the predator population will depend on this number, and on the efficiency with which predators convert consumed prey into predator offspring (c for conversion).

We will represent this conversion efficiency with the parameter c, so the per capita population growth of predators will be cpNpreyNpred. We should reduce this predator population growth by some quantity to represent the starvation rate of predators who fail to consume prey. This will be the product of the per capita starvation rate times the predator population: dNpred. Taking all this into account, we can write an equation for the predator population:

In words, the predator population grows according to the attack rate, conversion efficiency, and prey population, minus losses to starvation. Predator-Prey Model Parameters dNprey/dt = rate of change in prey population (change in number over change in time) dNpred/dt = rate of change in predator population (change in number over change in time) c = rate at which prey are converted into offspring (a slope: predators produced per predator per time as a function of prey consumed per unit time) p = attack rate efficiency (a slope: the change in prey consumed per predator per time as a function of the number of prey); higher search or handling time leads to a lower p dpred = predator death rate rprey = prey per capita rate of increase Nprey = number of prey Npred = number of predators

It's important to note that the prey and predator equations above are coupled equations. In other words, the equation for prey includes a term for Npred and the equation for predators includes the term Nprey and changes in one population will always impact the other population. Specifically, these equations lead to oscillations between the populations of predators and their prey. We can ask several questions about the interaction between predators and their prey using these equations: · Under what conditions (i.e., parameter values) will the predator population drive the prey to extinction? · Under what conditions will the predator population die off, leaving the prey population to expand unhindered? · Under what conditions will predator and prey populations both persist indefinitely? What will be their population dynamics while they coexist? In other words, will one or both populations stabilize, or will they continue to change over time? Equilibrium Solutions We will examine these questions by seeking equilibrium solutions to the coupled predator and prey equations we introduced above. For the prey population, we want to find values of predator and prey population sizes at which the prey population remains stable.

In other words, we want to solve for dNpred/dt = 0 and dNprey/dt = 0. Though we will not go through the derivations here, you can try them out on your own by replacing these terms with 0 then solving for Nprey and Npred, respectively. In words, the prey population reaches equilibrium when the predator population equals the prey's per capita growth rate divided by the predator's attack rate. Note that this is a constant. Strangely, the equilibrium size of the prey population is not determined by this solution, which says, in effect, that the prey population can be stable at any size as long as the predator population is at the specified size. The isocline of Zero Growth for Prey occurs at Npred = rprey/p

: The dotted line (r/p) shows the isocline of zero growth for prey. They purple and orange lines show the direction of the prey and predator populations.

In words, the predator population reaches equilibrium when the prey population equals the predator's starvation rate over the product of attack rate times conversion efficiency. Note that this is also a constant, and like the solution for the prey population, it does not specify the equilibrium size of the predator population, only the size of the prey population at which the predators are at equilibrium.

The isocline of Zero Growth for Predator occurs at Nprey = d/cp

: The dotted line shows the isocline of zero growth for predators (d/cp), while the purple and orange lines describe the direction the predator and prey populations will trend towards.

Now that we have solved for these isoclines, we can plot the population sizes of the two interacting populations on the two axes of a graph. The equilibrium solutions then become straight-line zero net growth isoclines (ZNGIs).

On this graph, the ZNGI for the prey population is a horizontal line at rprey/p (orange line), below which the prey population increases, and above which it decreases. The ZNGI for the predator population is a vertical line at d/cp (blue line), to the left of which the predator population decreases, and to the right of which it increases (dashed arrows).

Where the two lines cross--at the point [d/cp, rprey/p]--the two populations are at equilibrium. The continuous-time Lotka-Volterra model predicts that the point representing the two populations will cycle endlessly around the point where the two ZNGIs cross.

: To the left, the isoclines for zero predator and prey growth are plotted. The vector arrows in each quadrant describe

the counter-clockwise trends of the populations based on the movement from their initial populations to the point of equilibrium

created by the isoclines. To the right, the boom-and-bust cycle of the populations are shown.

A Quick Summary - Coupled Predator-Prey Models Based on the Exponential Model: · Predators decline exponentially without food (prey) · Prey increase exponentially without predation

Outcomes Depend on: · Predator functional (p) and numerical (c) response · Predator and prey birth/death rate · Predator and prey initial population size

· Find and plot d/cp · Find and plot and r/p · Plot initial population · At isoclines, either predator or prey change trajectory 16.2: Quantifying Predator-Prey Dynamics is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.

16.3: Herbivory Herbivory is a form of consumption in which an organism principally eats autotrophs (Abraham 2006) such as plants, algae and photosynthesizing bacteria. More generally, organisms that feed on autotrophs are known as primary consumers. Herbivory is usually limited to animals that eat plants. Fungi, bacteria, and protists that feed on living plants are usually termed plant pathogens (plant diseases), while fungi and microbes that feed on dead plants are described as saprotrophs. Flowering plants that obtain nutrition from other living plants are usually termed parasitic plants.

16.3.1 Feeding Strategies Two herbivore feeding strategies are grazing (e.g. cows) and browsing (e.g. moose). For a terrestrial mammal to be called a grazer, at least 90% of the forage has to be grass, and for a browser at least 90% tree leaves and twigs. An intermediate feeding strategy is called "mixed-feeding" (Janis, 1990). In their daily need to take up energy from forage, herbivores of different body mass may be selective in choosing their food (Belovsky, 1997). "Selective" means that herbivores may choose their forage source depending on, e.g., season or food availability, but also that they may choose high quality (and consequently highly nutritious) forage before lower quality. The latter especially is determined by the body mass of the herbivore, with small herbivores selecting for highquality forage, and with increasing body mass animals are less selective (Belovsky, 1997).

Feeding Strategy Algivores Frugivores Folivores Nectarivores Granivores Graminivores Palynivores Mucivores Xylophages

Diet Algae Fruit Leaves Nectar Seeds Grass Pollen Plant fluids, i.e. sap Wood

Example krill, crabs, sea snail, sea urchin, parrotfish, surgeonfish, flamingo Ruffed lemurs, orangutans Koalas, gorillas, red colobuses Honey possum, hummingbirds Hawaiian honeycreepers Horses Bees Aphids Termites

: Herbivores employ numerous types of feeding strategies. Many herbivores do not fall into one specific feeding strategy, but employ several strategies and eat a variety of plant parts.

16.3.2 Plant-herbivore interactions Interactions between plants and herbivores can play a prevalent role in ecosystem dynamics such community structure and functional processes (Sandsen & Klaassen, 2008; Descombes et al., 2016). Plant diversity and distribution is often driven by herbivory, and it is likely that trade-offs between plant competitiveness and defensiveness, and between colonization and mortality allow for coexistence between species in the presence of herbivores (Lubchenco, 1978; Gleeson & Wilson, 1986; Olff & Ritchie, 1998; Hidding et al., 2009). However, the effects of herbivory on plant diversity and richness is variable. For example, increased abundance of herbivores such as deer decrease plant diversity and species richness (Arcese et al., 2014), while other large mammalian herbivores like bison control dominant species, which allows other species to flourish (Collins 1998). Plant-herbivore

interactions can also operate so that plant communities mediate herbivore communities (Pellissier et al., 2013). Plant communities that are more diverse typically sustain greater herbivore richness by providing a greater and more diverse set of resources (Tilman, 1997). Coevolution and phylogenetic correlation between herbivores and plants are important aspects of the influence of herbivore and plant interactions on communities and ecosystem functioning, especially in regard to herbivorous insects (Descombes et al., 2016; Pellissier et al., 2013; Mitter et al., 1991). This is apparent in the adaptations plants develop to tolerate and/or defend from insect herbivory and the responses of herbivores to overcome these adaptations. The evolution of antagonistic and mutualistic plantherbivore interactions are not mutually exclusive and may co-occur (de Mazancourt et al., 2001). Plant phylogeny has been found to facilitate the colonization and community assembly of herbivores, and there is evidence of phylogenetic linkage between plant beta diversity and phylogenetic beta diversity of insect clades such as butterflies. These types of eco-evolutionary feedbacks between plants and herbivores are likely the main driving force behind plant and herbivore diversity (Pellissier et al., 2013; Mitter et al., 1991). Abiotic factors such as climate and biogeographical features also impact plant-herbivore communities and interactions. For example, in temperate freshwater wetlands herbivorous waterfowl communities change according to season, with species that eat above-ground vegetation being abundant during summer, and species that forage below-ground being present in winter months (Sansten & Klaassen, 2008; Hidding et al., 2009). These seasonal herbivore communities differ in both their assemblage and functions within the wetland ecosystem (Hidding et al., 2009). Such differences in herbivore modalities can potentially lead to trade-offs that influence species traits and may lead to additive effects on community composition and ecosystem functioning (Sansten & Klaassen, 2008; Hidding et al., 2009). Seasonal changes and environmental gradients such as elevation and latitude often affect the palatability of plants which in turn influences herbivore community assemblages and vice versa (Descombes et al., 2016; Hidding et al., 2009). Examples include a decrease in abundance of leaf-chewing larvae in the fall when hardwood leaf palatability decreases due to increased tannin levels which results in a decline of arthropod species richness (Futuyma & Gould, 1979) and increased palatability of plant communities at higher elevations where grasshoppers abundances are lower (Descombes et al., 2016). Climatic stressors such as ocean acidification can lead to responses in plant-herbivore interactions in relation to palatability as well (Poore et al., 2013). Herbivore Offense

The myriad defenses displayed by plants means that their herbivores need a variety of skills to overcome these defenses and obtain food. These allow herbivores to increase their feeding and use of a host plant. Herbivores have three primary strategies for dealing with plant defenses: choice, herbivore modification, and plant modification.

Feeding choice involves which plants a herbivore chooses to consume. It has been suggested that many herbivores feed on a variety of plants to balance their nutrient uptake and to avoid consuming too much of any one type of defensive chemical. This involves a tradeoff however, between foraging on many plant species to avoid toxins or specializing on one type of plant that can be detoxified (Dearing et al., 2000). Herbivore modification is when various adaptations to body or digestive systems of the herbivore allow them to overcome plant defenses. This might include detoxifying secondary metabolites (Karban & Agrawal, 2002), sequestering toxins unaltered (Nishida, 2002), or avoiding toxins, such as through the production of large amounts of saliva to reduce effectiveness of defenses. Herbivores may also utilize symbionts to evade plant defenses. For example, some aphids use bacteria in their gut to provide essential amino acids lacking in their sap diet (Douglas, 1998). Plant modification occurs when herbivores manipulate their plant prey to increase feeding. For example, some caterpillars roll leaves to reduce the effectiveness of plant defenses activated by sunlight (Sagers, 1992). Plant Defense A plant defense is a trait that increases plant fitness when faced with herbivory. This is measured relative to another plant that lacks the defensive trait. Plant defenses increase survival and/or reproduction (fitness) of plants under pressure of predation from herbivores. Defense can be divided into two main categories, tolerance and resistance. Tolerance is the ability of a plant to withstand damage without a reduction in fitness (Call & St. Clair, 2018). This can occur by diverting herbivory to non-essential plant parts, resource allocation, compensatory growth, or by rapid regrowth and recovery from herbivory (Hawkes & Sullivan, 2001). Resistance refers to the ability of a plant to reduce the amount of damage it receives from herbivores (Call & St. Clair, 2018). This can occur via avoidance in space or time (Milchunas & Noy-Meir, 2002), physical defenses, or chemical defenses. Defenses can either be constitutive, always present in the plant, or induced, produced or translocated by the plant following damage or stress (Edwards & Wratten, 1985). Physical, or mechanical, defenses are barriers or structures designed to deter herbivores or reduce intake rates, lowering overall herbivory. Thorns such as those found on roses or acacia trees are one example, as are the spines on a cactus. Smaller hairs known as trichomes may cover leaves or stems and are especially effective against invertebrate herbivores (Pillemer & Tingey, 1976). In addition, some plants have waxes or resins that alter their texture, making them difficult to eat. Also the incorporation of silica into cell walls is analogous to that of the role of lignin in that it is a compression-resistant structural component of cell walls; so that plants with their cell walls impregnated with silica are thereby afforded a measure of protection against herbivory (Epstein, 1994). Chemical defenses are secondary metabolites produced by the plant that deter herbivory. There are a wide variety of these in nature and a single plant can have hundreds of different chemical defenses. Chemical defenses can be divided into two main groups, carbon-based defenses and nitrogen-based defenses. 1. Carbon-based defenses include terpenes and phenolics. Terpenes are derived from 5-carbon isoprene units and comprise essential oils, carotenoids, resins, and latex. They can have several functions that disrupt herbivores such as inhibiting adenosine triphosphate (ATP) formation, molting hormones, or the nervous system (Langenheim, 1994). Phenolics combine an aromatic carbon ring with a hydroxyl group. There are several different phenolics such as lignins, which are found in cell walls and are very indigestible except for specialized microorganisms; tannins, which have a bitter taste and bind to proteins making them indigestible; and furanocumerins, which produce free radicals disrupting DNA, protein, and lipids, and can cause skin irritation. 2. Nitrogen-based defenses are synthesized from amino acids and primarily come in the form of alkaloids and cyanogens. Alkaloids include commonly recognized substances such as caffeine, nicotine, and morphine. These compounds are often bitter and can inhibit DNA or RNA synthesis or block nervous system signal transmission. Cyanogens get their name from the cyanide stored within their tissues. This is released when the plant is damaged and inhibits cellular respiration and electron transport. Plants have also changed features that enhance the probability of attracting natural enemies to herbivores. Some emit semiochemicals, odors that attract natural enemies, while others provide food and housing to maintain the natural enemies'

presence, e.g. ants that reduce herbivory (Heil et al., 2001). A given plant species often has many types of defensive mechanisms, mechanical or chemical, constitutive or induced, which allow it to escape from herbivores. Sources Abraham, M.A.A. (2006). Sustainability science and engineering. Elsevier, 1, pp. 123. ISBN 978-0444517128 Arcese, P., Schuster, R., Campbell, L., Barber, A., & Martin, T.G. (2014). Deer density and plant palatability predict shrub cover, richness, diversity and aboriginal food value in a North American archipelago. Diversity and Distributions, 20(12), pp. 1368-1378. doi:10.1111/ddi.12241. ISSN 1366-9516. Belovsky, G.E. (1997). Optimal foraging and community structure: The allometry of herbivore food selection and competition. Evolutionary Ecology, 11(6), pp. 641-672. doi:10.1023/A:1018430201230. S2CID 23873922. Call, A., & St. Clair, S.B. (2018). In Ryan, M. (Eds.), Timing and mode of simulated ungulate herbivory alter aspen defense strategies. Tree Physiology, 38(10), pp. 1476-1485. doi:10.1093/treephys/tpy071. ISSN 1758-4469. PMID 29982736. Collins, S.L. (1998). Modulation of diversity by grazing and mowing in native tallgrass prairie. Science, 280(5364), pp. 745-747. Bibcode:1998Sci...280..745C. doi:10.1126/science.280.5364.745. ISSN 0036-8075. PMID 9563952. de Mazancourt, C., Loreau, M., & Dieckmann, U. (2001). Can the evolution of plant defense lead to plant-herbivore mutualism? The American Naturalist, 158(2), pp. 109-123. doi:10.1086/321306. ISSN 0003-0147. PMID 18707340. S2CID 15722747. Dearing, M.D., Mangione, A.M., & Karasov, W.H. (2000). Diet breadth of mammalian herbivores: Nutrient versus detoxification constraints. Oecologia, 123(3), pp. 397-405. Bibcode:2000Oecol.123..397D. doi:10.1007/s004420051027. PMID 28308595. S2CID 914899. Descombes, P., Marchon, J., Pradervand, J.N., Bilat, J., Guisan, A., Rasmann, S., & Pellissier, L. (2016). Community-level plant palatability increases with elevation as insect herbivore abundance declines. Journal of Ecology, 105(1), pp. 142-151. doi:10.1111/1365-2745.12664. ISSN 0022-0477. Douglas, A.E. (1998). Nutritional interactions in insect-microbial symbioses: Aphids and their symbiotic bacteria buchnera. Annual Review of Entomology, 43, pp. 17-37. doi:10.1146/annurev.ento.43.1.17. PMID 15012383. Epstein, E. (1994). A review. PNAS, 91. Edwards, P.J., & Wratten, S.D. (1985). Induced plant defenses against insect grazing: Fact or artefact?. Oikos, 44(1), pp. 70-74. doi:10.2307/3544045. JSTOR 3544045. Futuyma, D.J., & Gould, F. (1979). Associations of plants and insects in deciduous forest. Ecological Monographs, 49(1), pp. 33- 50. doi:10.2307/1942571. ISSN 0012-9615. JSTOR 1942571. Gleeson, S.K., & Wilson, D.S. (1986). Equilibrium diet: Optimal foraging and prey coexistence. Oikos, 46(2), pp. 139. doi:10.2307/3565460. ISSN 0030-1299. JSTOR 3565460. Hawkes, C.V., & Sullivan, J.J. (2001). The impact of herbivory on plants in different resource conditions: A meta-analysis. 82, pp. 2045-2058 - via Wiley. Heil, M., Koch, T., Hilpert, A., Fiala, B., Boland, W., & Linsenmair, K.E. (2001). Extrafloral nectar production of the antassociated plant, Macaranga tanarius, is an induced, indirect, defensive response elicited by jasmonic acid. Proceedings of the National Academy of Sciences, 98(3), pp. 1083-1088. Bibcode:2001PNAS...98.1083H. doi:10.1073/pnas.031563398. PMC 14712. PMID 11158598. Hidding, B., Nolet, B.A., de Boer, T., de Vries, P.P., & Klaassen, M. (2009). Above- and below-ground vertebrate herbivory may each favour a different subordinate species in an aquatic plant community. Oecologia, 162(1), pp. 199-208. doi:10.1007/s00442009-1450-6. ISSN 0029-8549. PMC 2776151. PMID 19756762. Janis, C. (1990). Correlation of cranial and dental variables with body size in ungulates and macropodoids. In Damuth, J., & MacFadden, B.J. (Eds.), Body size in mammalian paleobiology: Estimation and biological implications. Cambridge University Press, pp. 255-299.

Karban, R., & Agrawal, A.A. (2002). Herbivore offense. Annual Review of Ecology and Systematics, 33, pp. 641-664. doi:10.1146/annurev.ecolsys.33.010802.150443. Langenheim, J.H. (1994). Higher plant terpenoids: A phytocentric overview of their ecological roles. Journal of Chemical Ecology, 20,(6), pp. 1223-1280. doi:10.1007/BF02059809. PMID 24242340. S2CID 25360410. Lubchenco, J. (1978). Plant species diversity in a marine intertidal community: Importance of herbivore food preference and algal competitive abilities. The American Naturalist, 112(983), pp. 23-39. doi:10.1086/283250. ISSN 0003-0147. S2CID 84801707. Milchunas, D.G., & Noy-Meir, I. (2002). Grazing refuges, external avoidance of herbivory and plant diversity. Oikos, 99(1), pp. 113-130. doi:10.1034/j.1600-0706.2002.990112.x. Mitter, C., Farrell, B., & Futuyma, D.J. (1991). Phylogenetic studies of insect-plant interactions: Insights into the genesis of diversity. Trends in Ecology & Evolution, 6(9), pp. 290-293. doi:10.1016/0169-5347(91)90007-k. ISSN 0169-5347. PMID 21232484. Nishida, R. (2002). Sequestration of defensive substances from plants by Lepidoptera. Annual Review of Entomology, 47, pp. 57- 92. doi:10.1146/annurev.ento.47.091201.145121. PMID 11729069. Olff, H., & Ritchie, M.E. (1998). Effects of herbivores on grassland plant diversity. Trends in Ecology & Evolution, 13(7), pp. 261- 265. doi:10.1016/s0169-5347(98)01364-0. hdl:11370/3e3ec5d4-fa03-4490-94e3-66534b3fe62f. ISSN 0169-5347. PMID 21238294. Pellissier, L., Ndiribe, C., Dubuis, A., Pradervand, J.N., Salamin, N., Guisan, A., & Rasmann, S. (2013). Turnover of plant lineages shapes herbivore phylogenetic beta diversity along ecological gradients. Ecology Letters, 16(5), pp. 600-608. doi:10.1111/ele.12083. PMID 23448096. Pillemer, E.A., & Tingey, W.M. (1976). Hooked trichomes: A physical plant barrier to a major agricultural pest. Science, 193(4252), pp. 482-484. Bibcode:1976Sci...193..482P. doi:10.1126/science.193.4252.482. PMID 17841820. S2CID 26751736. Poore, A.G.B., Graba-Landry, A., Favret, M., Sheppard Brennand, H., Byrne, M., & Dworjanyn, S.A. (2013). Direct and indirect effects of ocean acidification and warming on a marine plant-herbivore interaction. Oecologia, 173(3), pp. 1113-1124. Bibcode:2013Oecol.173.1113P. doi:10.1007/s00442-013-2683-y. ISSN 0029-8549. PMID 23673470. S2CID 10990079. Sagers, C.L. (1992). Manipulation of host plant quality: Herbivores keep leaves in the dark. Functional Ecology, 6(6), pp. 741-743. doi:10.2307/2389971. JSTOR 2389971. Sandsten, H., & Klaassen, M. (2008). Swan foraging shapes spatial distribution of two submerged plants, favouring the preferred prey species. Oecologia, 156(3), pp. 569-576. Bibcode:2008Oecol.156..569S. doi:10.1007/s00442-008-1010-5. ISSN 0029-8549. PMC 2373415. PMID 18335250. 16.3: Herbivory is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.

: A fish parasite, the isopod Cymothoa exigua, replacing the tongue of a Lithognathus.

Parasitism is a close relationship between species, where one organism, the parasite, lives on or inside another organism, the host, causing it some harm, and is adapted structurally to this way of life (Poulin, 2007). Like predation, parasitism is a type of consumer-resource interaction (Getz, 2011), but unlike predators, parasites, with the exception of parasitoids, are typically much smaller than their hosts, do not kill them, and often live in or on their hosts for an extended period. Parasites of animals are highly specialized, and reproduce at a faster rate than their hosts. Classic examples include interactions between vertebrate hosts and tapeworms, flukes, the malaria-causing Plasmodium species, and fleas. Parasites reduce host fitness by general or specialized pathology, from parasitic castration to modification of host behavior. Parasites increase their own fitness by exploiting hosts for resources necessary for their survival, in particular by feeding on them and by using intermediate (secondary) hosts to assist in their transmission from one definitive (primary) host to another. Although parasitism is often unambiguous, it is part of a spectrum of interactions between species, grading via parasitoidism into predation, through evolution into mutualism, and in some fungi, shading into being saprophytic. Within that scope are many possible strategies. Taxonomists classify parasites in a variety of overlapping schemes, based on their interactions with their hosts and on their life-cycles, which are sometimes very complex. An obligate parasite depends completely on the host to complete its life cycle, while a facultative parasite does not. Parasite life-cycles involving only one host are called "direct"; those with a definitive host (where the parasite reproduces sexually) and at least one intermediate host are called "indirect" (Garcia, 1999). An endoparasite lives inside the host's body; an ectoparasite lives outside, on the host's surface (Australian Society of Parasitology, 2010). Mesoparasites--like some copepods, for example--enter an opening in the host's body and remain partly embedded there (Vecchione & Aznar, 2008). Some parasites can be generalists, feeding on a wide range of hosts, but many parasites, and the majority of protozoans and helminths that parasitise animals, are specialists and extremely hostspecific (Australian Society of Parasitology, 2010). The microorganisms and viruses that can reproduce and complete their life cycle within the host are known as microparasites. Macroparasites are the multicellular organisms that reproduce and complete their life cycle outside of the host or on the host's body (Poulin, 2011; Blackwell Science, 2018).

16.4.1 Strategies There are six major parasitic strategies, namely parasitic castration; directly transmitted parasitism; trophically-transmitted parasitism; vector-transmitted parasitism; parasitoidism; and micropredation. These apply to parasites whose hosts are plants as

well as animals (Poulin, 2011; Poulin, 2015). These strategies represent adaptive peaks; intermediate strategies are possible, but organisms in many different groups have consistently converged on these six, which are evolutionarily stable (Poulin, 2011). A perspective on the evolutionary options can be gained by considering four key questions: the effect on the fitness of a parasite's hosts; the number of hosts they have per life stage; whether the host is prevented from reproducing; and whether the effect depends on intensity (number of parasites per host). From this analysis, the major evolutionary strategies of parasitism emerge, alongside predation (Lafferty & Kuris, 2002).

Host fitness Able to reproduce (fitness > 0) Unable to reproduce (fitness = 0)

Single host, stays alive Conventional parasite Pathogen ----Parasitic castrator

Trophically-transmitted parasite Trophically-transmitted pathogen

Trophically-transmitted castrator Parasitoid

: Major parasitic strategies categorized by host fitness and survival and by the number of hosts.

: The parasitic castrator Sacculina carcini (highlighted) attached to its crab host.

Parasitic castrators partly or completely destroy their host's ability to reproduce, diverting the energy that would have gone into reproduction into host and parasite growth, sometimes causing gigantism in the host. The host's other systems remain intact, allowing it to survive and to sustain the parasite (Poulin, 2015; Poulin, 2007). Parasitic crustaceans such as those in the specialized barnacle genus Sacculina specifically cause damage to the gonads of their many species (Elumalai et al., 2013) of host crabs. In the case of Sacculina, the testes of over two-thirds of their crab hosts degenerate sufficiently for these male crabs to develop female secondary sex characteristics such as broader abdomens, smaller claws and egg-grasping appendages. Various species of helminth castrate their hosts (such as insects and snails). This may happen directly, whether mechanically by feeding on their gonads, or by secreting a chemical that destroys reproductive cells; or indirectly, whether by secreting a hormone or by diverting nutrients. For example, the trematode Zoogonus lasius, whose sporocysts lack mouths, castrates the intertidal marine snail Tritia obsoleta chemically, developing in its gonad and killing its reproductive cells (Poulin, 2007; Cheng, 2012).

: Human head-lice exemplify directly transmitted obligate ectoparasites.

Directly transmitted parasites, not requiring a vector to reach their hosts, include such parasites of terrestrial vertebrates as lice and mites; marine parasites such as copepods and cyamid amphipods; monogeneans; and many species of nematodes, fungi, protozoans, bacteria, and viruses. Whether endoparasites or ectoparasites, each has a single host-species. Within that species, most individuals are free or almost free of parasites, while a minority carry a large number of parasites; this is known as an aggregated distribution (Poulin, 2015).

: Clonorchis sinensis, the Chinese liver fluke, is trophically transmitted.

Trophically-transmitted parasites are transmitted by being eaten by a host. They include trematodes (all except schistosomes), cestodes, acanthocephalans, pentastomids, many round worms, and many protozoa such as Toxoplasma (Poulin, 2015). They have complex life-cycles involving hosts of two or more species. In their juvenile stages they infect and often encyst in the intermediate host. When the intermediate-host animal is eaten by a predator, the definitive host, the parasite survives the digestion process and matures into an adult; some live as intestinal parasites. Many trophically-transmitted parasites modify the behavior of their intermediate hosts, increasing their chances of being eaten by a predator. As with directly transmitted parasites, the distribution of trophically transmitted parasites among host individuals is aggregated (Poulin, 2015). Coinfection by multiple parasites is common (Cox, 2001). Autoinfection, where (by exception) the whole of the parasite's life-cycle takes place in a single primary host, can sometimes occur in helminths such as Strongyloides stercoralis (Australian Society of Parasitology, 2017).

: The vector-transmitted protozoan endoparasite Trypanosoma among human red blood cells.

Vector-transmitted parasites rely on a third party, an intermediate host, where the parasite does not reproduce sexually (Australian Society of Parasitology, 2010) to carry them from one definitive host to another (Poulin & Randhawa, 2015). These parasites are microorganisms, namely protozoa, bacteria, or viruses, often intracellular pathogens (disease-causers) (Poulin & Randhawa, 2015). Their vectors are mostly hematophagic arthropods such as fleas, lice, ticks, and mosquitoes (Poulin & Randhawa, 2015; PEOI, 2013). For example, the deer tick Ixodes scapularis acts as a vector for diseases including Lyme disease, babesiosis, and anaplasmosis (Steere, 2001). Protozoan endoparasites, such as the malarial parasites in the genus Plasmodium and sleepingsickness parasites in the genus Trypanosoma, have infective stages in the host's blood which are transported to new hosts by biting insects (Pollitt et al., 2011).

Parasitoids Parasitoids are insects which sooner or later kill their hosts, placing their relationship close to predation (Stevens, 2010). Most parasitoids are parasitoid wasps or other hymenopterans; others include dipterans such as phorid flies. They can be divided into two groups, idiobionts and koinobionts, differing in their treatment of their hosts (Gullan & Cranston, 2010). Idiobiont parasitoids sting their often large prey on capture, either killing them outright or paralyzing them immediately. The immobilized prey is then carried to a nest, sometimes alongside other prey if it is not large enough to support a parasitoid throughout its development. An egg is laid on top of the prey and the nest is then sealed. The parasitoid develops rapidly through its larval and pupal stages, feeding on the provisions left for it (Gullan & Cranston, 2010).

: Idiobiont parasitoid wasps immediately paralyze their hosts for their larvae (Pimplinae, pictured) to eat (Poulin & Randhawa, 2015).

Koinobiont parasitoids, which include flies as well as wasps, lay their eggs inside young hosts, usually larvae. These are allowed to go on growing, so the host and parasitoid develop together for an extended period, ending when the parasitoids emerge as adults, leaving the prey dead, eaten from inside. Some koinobionts regulate their host's development, for example preventing it from pupating or making it molt whenever the parasitoid is ready to molt. They may do this by producing hormones that mimic the host's molting hormones (ecdysteroids), or by regulating the host's endocrine system (Gullan & Cranston, 2010).

: Koinobiont parasitoid wasps like this braconid lay their eggs inside their hosts, which continue to grow and moult.

: Phorid fly (center left) is laying eggs in the abdomen of a worker honey-bee, altering its behavior.

: Mosquitoes are micropredators, and important vectors of disease.

A micropredator attacks more than one host, reducing each host's fitness by at least a small amount, and is only in contact with any one host intermittently. This behavior makes micropredators suitable as vectors, as they can pass smaller parasites from one host to another (Poulin & Randhawa, 2015; Lafferty & Kuris, 2002; Wilson et al., 2017). Most micropredators are hematophagic, feeding on blood. They include annelids such as leeches, crustaceans such as branchiurans and gnathiid isopods, various dipterans such as mosquitoes and tsetse flies, other arthropods such as fleas and ticks, vertebrates such as lampreys, and mammals such as vampire bats (Poulin & Randhawa, 2015).

Hyperparasitism Hyperparasites feed on another parasite, as exemplified by protozoa living in helminth parasites (Dissanaike, 1957), or facultative or obligate parasitoids whose hosts are either conventional parasites or parasitoids (Poulin & Randhawa, 2015; Gullan & Cranston,

2010). Levels of parasitism beyond secondary also occur, especially among facultative parasitoids. In oak gall systems, there can be up to five levels of parasitism (Askew, 1951). Hyperparasites can control their hosts' populations, and are used for this purpose in agriculture and to some extent in medicine. The controlling effects can be seen in the way that the CHV1 virus helps to control the damage that chestnut blight, Cryphonectria parasitica, does to American chestnut trees, and in the way that bacteriophages can limit bacterial infections. It is likely, though little researched, that most pathogenic microparasites have hyperparasites which may prove widely useful in both agriculture and medicine (Parratt & Laine, 2016).

: A hyperparasitoid pteromalid wasp on the cocoons of its host, itself a parasitoid braconid wasp.

Social parasitism Social parasites take advantage of interspecific interactions between members of eusocial animals such as ants, termites, and bumblebees. Examples include the large blue butterfly, Phengaris arion, its larvae employing ant mimicry to parasitise certain ants (Thomas et al., 2010), Bombus bohemicus, a bumblebee which invades the hives of other bees and takes over reproduction while their young are raised by host workers, and Melipona scutellaris, a eusocial bee whose virgin queens escape killer workers and invade another colony without a queen (Van Oystaeyen et al., 2013). An extreme example of interspecific social parasitism is found in the ant Tetramorium inquilinum, an obligate parasite which lives exclusively on the backs of other Tetramorium ants (Antkeepers, 2016).

: The large blue butterfly is an ant mimic and social parasite.

Brood parasitism In brood parasitism, the hosts act as parents as they raise the young as their own. Brood parasites include birds in different families such as cowbirds, whydahs, cuckoos, and black-headed ducks. These do not build nests of their own, but leave their eggs in nests of other species. The eggs of some brood parasites mimic those of their hosts, while some cowbird eggs have tough shells, making them hard for the hosts to kill by piercing, both mechanisms implying selection by the hosts against parasitic eggs (Payne, 1997; Rothstein, 1990; De Marsico et al., 2013). The adult female European cuckoo further mimics a predator, the European sparrowhawk, giving her time to lay her eggs in the host's nest unobserved (Welbergen & Davies, 2011).

: In brood parasitism, the host raises the young of another species, here a cowbird's egg, that has been laid in its nest.

Big Brother Eviction Cuckoo Style | N BBC Earth Watch on Kleptoparasitism In kleptoparasitism (from Greek (klepts), "thief"), parasites steal food gathered by the host. The parasitism is often on close relatives, whether within the same species or between species in the same genus or family. For instance, the many lineages of cuckoo bees lay their eggs in the nest cells of other bees in the same family (Slater et al., 2005). Kleptoparasitism is uncommon generally but conspicuous in birds; some such as skuas are specialized in pirating food from other seabirds, relentlessly chasing them down until they disgorge their catch (Furness, 1978).

: The great skua is a powerful kleptoparasite, relentlessly pursuing other seabirds until they disgorge their catches of food.

Sexual parasitism A unique approach is seen in some species of anglerfish, such as Ceratias holboelli, where the males are reduced to tiny sexual parasites, wholly dependent on females of their own species for survival, permanently attached below the female's body, and unable to fend for themselves. The female nourishes the male and protects him from predators, while the male gives nothing back except the sperm that the female needs to produce the next generation (Pietsch, 2005).

: The male anglerfish Ceratias holboelli lives as a tiny sexual parasite permanently attached below the female's body.

Adelphoparasitism Adelphoparasitism, (from Greek (adelphós), brother - Maggenti et al., 2005), also known as sibling-parasitism, occurs where the host species is closely related to the parasite, often in the same family or genus (Rochat & Gutierrez, 2001). In the citrus blackfly parasitoid, Encarsia perplexa, unmated females of which may lay haploid eggs in the fully developed larvae of their own species, producing male offspring (University of Florida, 2018), while the marine worm Bonellia viridis has a similar reproductive strategy, although the larvae are planktonic (Berec et al., 2005).

: Encarsia perplexa (center), a parasitoid of citrus blackfly (lower left), is also an adelphoparasite, laying eggs in larvae of its own species.

Sources Berec, L., Schembri, P.J., & Boukal, D.S. (2005). Sex determination in Bonellia viridis (Echiura: Bonelliidae): Population dynamics and evolution. Oikos, 108(3), pp. 473-484. doi:10.1111/j.0030-1299.2005.13350.x. Cheng, T.C. (2012). General parasitology. Elsevier Science, pp. 13-15. ISBN 978-0-323-14010-2. Cox, F.E. (2001). Concomitant infections, parasites and immune responses. Parasitology. 122. Supplement: S23-38. doi:10.1017/s003118200001698x. PMID 11442193. S2CID 150432. De Marsico, M.C., Gloag, R., Ursino, C.A., & Reboreda, J.C. (2013). A novel method of rejection of brood parasitic eggs reduces parasitism intensity in a cowbird host. Biology Letters, 9(3): 20130076. doi:10.1098/rsbl.2013.0076. PMC 3645041. PMID 23485877. Dissanaike, A.S. (1957). On Protozoa hyperparasitic in Helminth, with some observations on Nosema helminthorum Moniez, 1887. Journal of Helminthology, 31(1-2), pp. 47-64. doi:10.1017/s0022149x00033290. PMID 13429025.

Elumalai, V., Viswanathan, C., Pravinkumar, M., & Raffi, S.M. (2013). Infestation of parasitic barnacle Sacculina spp. in commercial marine crabs. Journal of Parasitic Diseases, 38(3), pp. 337-339. doi:10.1007/s12639-013-0247-z. PMC 4087306. PMID 25035598. Featured creatures. Encarsia perplexa. University of Florida. Retrieved 6 January 2018. Furness, R.W. (1978). Kleptoparasitism by great skuas (Catharacta skua Brünn.) and Arctic skuas (Stercorarius parasiticus L.) at a Shetland seabird colony. Animal Behaviour, 26, pp. 1167-1177. doi:10.1016/0003-3472(78)90107-0. S2CID 53155057. Garcia, L.S. (1999). Classification of human parasites, vectors, and similar organisms. Clinical Infectious Diseases, 29(4), pp. 734- 746. doi:10.1086/520425. PMID 10589879. Getz, W.M. (2011). Biomass transformation webs provide a unified approach to consumer-resource modelling. Ecology Letters, 14(2), pp. 113-124. doi:10.1111/j.1461-0248.2010.01566.x. PMC 3032891. PMID 21199247. Gullan, P.J., & Cranston, P.S. (2010). The insects: An outline of entomology (4th ed.). Wiley, pp. 308, 365-367, 375, 440-441. ISBN 978-1-118-84615-5. Helminth Parasites. Australian Society of Parasitology. Retrieved 9 October 2017. Lafferty, K.D., & Kuris, A.M. (2002). Trophic strategies, animal diversity and body size. Trends in Ecology and Evolution, 17(11), pp. 507-513. doi:10.1016/s0169-5347(02)02615-0. Archived from the original (PDF) on 3 October 2019. Maggenti, A.R., Maggenti, M.A., & Gardner, S.L. (2005). Online dictionary of invertebrate zoology. University of Nebraska, p. 22. Archived from the original (PDF) on 18 April 2018. Overview of parasitology. Australian Society of Parasitology and Australian Research Council/National Health and Medical Research Council) Research Network for Parasitology. July 2010. ISBN 978-1-8649999-1-4. Parasitism | The encyclopedia of ecology and environmental management. Blackwell Science. Retrieved 8 April 2018. Parratt, S.R., & Laine, A.L. (2016). The role of hyperparasitism in microbial pathogen ecology and evolution. The ISME Journal, 10(8), pp. 1815-1822. doi:10.1038/ismej.2015.247. PMC 5029149. PMID 26784356. Pathogenic parasitic infections. PEOI. Retrieved 18 July 2013. Payne, R.B. (1997). Avian brood parasitism. In Clayton, D.H., & Moore, J. (Eds.), Host-parasite evolution: General principles and avian models. Oxford University Press. pp. 338-369. ISBN 978-0198548928. Pietsch, T.W. (2005). Dimorphism, parasitism, and sex revisited: Modes of reproduction among deep-sea ceratioid anglerfishes (Teleostei: Lophiiformes). Ichthyological Research, 52(3), pp. 207-236. doi:10.1007/s10228-005-0286-2. S2CID 24768783. Pollitt, L.C., MacGregor, P., Matthews, K., & Reece, S.E. (2011). Malaria and trypanosome transmission: Different parasites, same rules? Trends in Parasitology, 27(5), pp. 197-203. doi:10.1016/j.pt.2011.01.004. PMC 3087881. PMID 21345732. Poulin, R. (2011). The many roads to parasitism: A tale of convergence. In Rollinson, D., & Hay, S.I. (Eds.), Advances in parasitology, 74. Academic Press. pp. 27-28. doi:10.1016/B978-0-12-385897-9.00001-X. ISBN 978-0-12-385897-9. PMID 21295676. Poulin, R., & Randhawa, H.S. (2015). Evolution of parasitism along convergent lines: From ecology to genomics. Parasitology, 142(Suppl 1): S6-S15. doi:10.1017/S0031182013001674. PMC 4413784. PMID 24229807. Rochat, J., & Gutierrez, A.P. (2001). Weather-mediated regulation of olive scale by two parasitoids. Journal of Animal Ecology, 70(3), pp. 476-490. doi:10.1046/j.1365-2656.2001.00505.x. S2CID 73607283. Rothstein, S.I. (1990). A model system for coevolution: Avian brood parasitism. Annual Review of Ecology and Systematics, 21, pp. 481-508. doi:10.1146/annurev.ecolsys.21.1.481. Slater, P.J.B., Rosenblatt, J.S., Snowdon, C.T., Roper, T.J., Brockmann, H.J., & Naguib, M. (2005). Advances in the study of behavior. Academic Press, p. 365. ISBN 978-0-08-049015-1. Social parasites in the ant colony. Antkeepers. Retrieved 4 April 2016.

Steere, A.C. (2001). Lyme disease. New England Journal of Medicine, 345(2), pp. 115-125. doi:10.1056/NEJM200107123450207. PMID 11450660. Stevens, A.N.P. (2010). Predation, herbivory, and parasitism. Nature Education Knowledge, 3(10), pp. 36. Retrieved 12 February 2018. Thomas, J.A., Schönrogge, K., Bonelli, S., Barbero, F., & Balletto, E. (2010). Corruption of ant acoustical signals by mimetic social parasites: Maculinea butterflies achieve elevated status in host societies by mimicking the acoustics of queen ants. Commun Integr Biol., 3(2), pp. 169-171. doi:10.4161/cib.3.2.10603. PMC 2889977. PMID 20585513. Van Oystaeyen, A., Araujo Alves, D., Caliari Oliveira, R., Lima do Nascimento, D., Santos do Nascimento, F., Billen, J., & Wenseleers, T. (2013). Sneaky queens in Melipona bees selectively detect and infiltrate queenless colonies. Animal Behaviour, 86(3), pp. 603-609. CiteSeerX 10.1.1.309.6081. doi:10.1016/j.anbehav.2013.07.001. S2CID 12921696. Wilson, A.J., et al. (2017). What is a vector? Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1719): 20160085. doi:10.1098/rstb.2016.0085. PMC 5352812. PMID 28289253. 16.4: Parasitism is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.

16.5: Infection An infection is the invasion of an organism's body tissues by disease-causing agents, their multiplication, and the reaction of host tissues to the infectious agents and the toxins they produce. An infectious disease, also known as a transmissible disease or communicable disease, is an illness resulting from an infection. Infections can be caused by a wide range of pathogens, most prominently bacteria and viruses (Sehgal & Ladd, 2020). Hosts can fight infections using their immune system. Mammalian hosts react to infections with an innate response, often involving inflammation, followed by an adaptive response. 16.5.1 Pathophysiology

: Chain of infection; the chain of events that lead to infection.

There is a general chain of events that applies to infections, sometimes called the chain of infection. The chain of events involves several steps - which include the infectious agent, reservoir, entering a susceptible host, exit and transmission to new hosts. Each of the links must be present in a chronological order for an infection to develop. Understanding these steps helps health care workers target the infection and prevent it from occurring in the first place.

Colonization Infection begins when an organism successfully enters the body, grows and multiplies. This is referred to as colonization. Individuals with compromised or weakened immune systems have an increased susceptibility to chronic or persistent infections. Individuals who have a suppressed immune system are particularly susceptible to opportunistic infections. Entrance to the host at host-pathogen interface, generally occurs through the mucosa in orifices like the oral cavity, nose, eyes, genitalia, anus, or the microbe can enter through open wounds. While a few organisms can grow at the initial site of entry, many migrate and cause systemic infection in different organs. Some pathogens grow within the host cells (intracellular) whereas others grow freely in bodily fluids. Wound colonization refers to non-replicating microorganisms within the wound, while in infected wounds, replicating organisms exist and tissue is injured (Negut et al., 2020). All multicellular organisms are colonized to some degree by extrinsic organisms, and the vast majority of these exist in either a mutualistic or commensal relationship with the host. An example of the former is the anaerobic bacteria species, which colonizes the mammalian colon, and an example of the latter are the various species of staphylococcus that exist on human skin. Neither of these colonizations are considered infections. The difference between an infection and a colonization is often only a matter of circumstance. Non-pathogenic organisms can become pathogenic given specific conditions, and even the most virulent organism requires certain circumstances to cause a compromising infection. Some colonizing bacteria, such as Corynebacteria sp. and viridans streptococci, prevent the adhesion and colonization of pathogenic bacteria and thus have a symbiotic relationship with the host, preventing infection and speeding wound healing.

: This image depicts the steps of pathogenic infection.

The variables involved in the outcome of a host becoming inoculated by a pathogen and the ultimate outcome include: the route of entry of the pathogen and the access to host regions that it gains the intrinsic virulence of the particular organism the quantity or load of the initial inoculant the immune status of the host being colonized As an example, several staphylococcal species remain harmless on the skin, but, when present in a normally sterile space, such as in the capsule of a joint or the peritoneum, multiply without resistance and cause harm.

Disease Disease can arise if the host's protective immune mechanisms are compromised and the organism inflicts damage on the host. Microorganisms can cause tissue damage by releasing a variety of toxins or destructive enzymes. For example, Clostridium tetani releases a toxin that paralyzes muscles, and staphylococcus releases toxins that produce shock and sepsis. Not all infectious agents cause disease in all hosts. For example, less than 5% of individuals infected with polio develop disease. On the other hand, some infectious agents are highly virulent. The prion causing mad cow disease and Creutzfeldt-Jakob disease invariably kills all animals and people that are infected. Persistent infections occur because the body is unable to clear the organism after the initial infection. Persistent infections are characterized by the continual presence of the infectious organism, often as latent infection with occasional recurrent relapses of active infection. There are some viruses that can maintain a persistent infection by infecting different cells of the body. Some viruses once acquired never leave the body. A typical example is the herpes virus, which tends to hide in nerves and become reactivated when specific circumstances arise. Transmission

: A southern house mosquito (Culex quinquefasciatus) is a vector that transmits the pathogens that cause West Nile fever and avian malaria among others.

For infecting organisms to survive and repeat the infection cycle in other hosts, they (or their progeny) must leave an existing reservoir and cause infection elsewhere. The relationship between virulence versus transmissibility is complex; if a disease is rapidly fatal, the host may die before the microbe can be passed along to another host. Infection transmission can take place via many potential routes: Droplet contact, also known as the respiratory route, and the resultant infection can be termed airborne disease. If an infected individual coughs or sneezes on another individual the microorganisms, suspended in warm, moist droplets, may enter the body through the nose, mouth or eye surfaces. Fecal-oral transmission, wherein food or water become contaminated and individuals who eat and drink them become infected. Common fecal-oral transmitted pathogens include Vibrio cholerae, Giardia species, rotaviruses, Entameba histolytica, Escherichia coli, and tapeworms. Most of these pathogens cause gastroenteritis. Sexual transmission, with the resulting disease being called sexually transmitted disease. Oral transmission, diseases that are transmitted primarily by oral means may be caught through direct or indirect oral contact between individuals.

Transmission by direct contact, some diseases that are transmissible by direct contact include athlete's foot, impetigo and warts. Vehicle transmission, transmission by an inanimate reservoir (food, water, soil). Vertical transmission, directly from the mother to an embryo, fetus or offspring during pregnancy or birth. It can occur as a result of a pre-existing infection or one acquired during pregnancy. Latrogenic transmission, due to medical procedures such as injection or transplantation of infected material. Vector-borne transmission, transmitted by a vector, which is an organism that does not cause disease itself but that transmits infection by conveying pathogens from one host to another.

16.5.2 Modeling Infectious Disease Mathematical modeling of infectious diseases often assigns individuals within populations to specific compartments - for example, S, I, or R, (Susceptible, Infectious, or Recovered). Individuals may progress between these compartments over time. The SIR model is one of the simplest compartmental models, and many models are derivatives of this basic form (Harko et al., 2014). The flow of this model may be considered as follows: S -> I -> R Specifically, the three compartments represent: S: The number of susceptible individuals. When a susceptible and an infectious individual come into "infectious contact", the susceptible individual contracts the disease and transitions to the infectious compartment. I: The number of infectious individuals. These are individuals who have been infected and are capable of infecting susceptible individuals. R: The number of recovered (and immune) or deceased individuals. These are individuals who have been infected and have either recovered from the disease and entered the removed compartment, or died. It is assumed that the number of deaths is negligible with respect to the total population. This compartment may also be called "removed" or "resistant". These variables (S, I, and R) represent the number of people in each compartment at a particular time. To represent that the number of susceptible, infectious and recovered individuals may vary over time (even if the total population size remains constant), we make the precise numbers a function of t (time): S(t), I(t) and R(t). For a specific disease in a specific population, these functions may be worked out in order to predict possible outbreaks (Yang et al., 2020). As implied by the variable function of t, the model is dynamic in that the numbers in each compartment may fluctuate over time. The importance of this dynamic aspect is most obvious in an endemic disease with a short infectious period, such as measles in the UK prior to the introduction of a vaccine in 1968. Such diseases tend to occur in cycles of outbreaks due to the variation in number of susceptibles (S(t)) over time. During an epidemic, the number of susceptible individuals falls rapidly as more of them are infected and thus enter the infectious and removed compartments. The disease cannot break out again until the number of susceptibles has built back up, e.g. as a result of offspring being born into the susceptible compartment. This model is reasonably predictive for infectious diseases that are transmitted from human to human, and where recovery confers lasting resistance, such as measles, mumps and rubella. The dynamics of an epidemic, for example, the flu, are often much faster than the dynamics of birth and death, therefore, birth and death are often omitted in simple compartmental models. The SIR system without so-called vital dynamics (birth and death,

sometimes called demography) described above can be expressed by the following system of ordinary differential equations (Hethcote, 2000), which include transition rates (described below):

Transition rates For the full specification of the SIR model, the arrows should be labeled with the transition rates between compartments. Between S and I, the transition rate is assumed to be d(S/N)/dt = -SI/N2, where N is the total population, is the average number of contacts per person per time, multiplied by the probability of disease transmission in a contact between a susceptible and an infectious subject, and SI/N2 is the fraction of those contacts between an infectious and susceptible individual which result in the susceptible person becoming infected. Between I and R, the transition rate is assumed to be proportional to the number of infectious individuals which is I. This is equivalent to assuming that the probability of an infectious individual recovering in any time interval dt is simply dt. For the special case in which there is no recovery from the infectious compartment ( = 0), the SIR model reduces to a very simple SI model, which has a logistic solution, in which every individual eventually becomes infected. Each member of the population typically progresses from susceptible to infectious to recovered. This can be shown as a flow diagram in which the boxes represent the different compartments and the arrows the transition between compartments.

: States in an SIR epidemic model and the rates at which individuals transition between them.

Figure 5: Dynamics of SIR model occurring over time. Yellow = Susceptible, Maroon = Infectious, Teal = Recovered

Figure 6: Diagram of the SIR model with initial values S(0) = 997, I(0) = 3, R(0) = 0 and rates for infection = 0.4 and for recovery = 0.04.

: Animation of the SIR model with initial values S(0) = 997, I(0) = 3, R(0) = 0, and rate of recovery = 0.04. The

animation shows the effect of reducing the rate of infection from = 0.5 to = 0.12. If there is no medicine or vaccination

available, it is only possible to reduce the infection rate (often referred to as "flattening the curve") by appropriate measures such as

Note that the dynamics of the infectious class depends on the following ratio:

The so-called basic reproduction number or basic reproduction ratio (denoted as R0 - pronounced R nought or R zero) (Milligan & Barrett, 2015) is derived as the expected number of new infections (these new infections are sometimes called secondary infections) from a single infection in a population where all subjects are susceptible (Bailey, 1975). The most important uses of R0 are determining if an emerging infectious disease can spread in a population and determining what proportion of the population should be immunized through vaccination to eradicate a disease. In commonly used infection models, when R0 > 1 the infection will be able to start spreading in a population, but not if R0 < 1. Generally, the larger the value of R0, the harder it is to control the epidemic. For simple models, the proportion of the population that needs to be effectively immunized (meaning not susceptible to infection) to prevent sustained spread of the infection has to be larger than 1 - 1/R0 (Kröger, 2020). Conversely, the proportion of the population that remains susceptible to infection in the endemic equilibrium is 1/R0.

The basic reproduction number is affected by several factors, including the duration of infectivity of affected people, the infectiousness of the microorganism, and the number of susceptible people in the population that the infected people contact. Sources Bailey, N.T.J. (1975). The mathematical theory of infectious diseases and its applications (2nd ed.). London: Griffin. ISBN 085264-231-8. Harko, T., Lobo, F.S., & Mak, M.K. (2014). Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates. Applied Mathematics and Computation, 236, pp. 184-194. doi:10.1016/j.amc.2014.03.030 Hethcote, H. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), pp. 599-653. doi:10.1137/s0036144500371907 Milligan, G.N., & Barrett, A.D. (2015). Vaccinology: An essential guide. Chichester, West Sussex: Wiley Blackwell, p. 310. ISBN 978-1-118-63652-7. Negut, I., Grumezescu, V., & Grumezescu, A.M. (2018). Treatment strategies for infected wounds. Molecules, 23(9), pp. 2392. doi:10.3390/molecules23092392 Sehgal, M., Ladd, H.J., & Totapally, B. (2020). Trends in epidemiology and microbiology of severe sepsis and septic shock in children. Hospital Pediatrics, 10(12), pp. 1021-1030. doi:10.1542/hpeds.2020-0174 Yang, W., Zhang, D., Peng, L., Zhuge, C., & Hong, L. (2020). Rational evaluation of various epidemic models based on the COVID-19 data of China. arXiv:2003.05666v1 16.5: Infection is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.

16.6: Sources and Attributions Full Chapter Sources Cougar. Hinterland Who's Who. Canadian Wildlife Service and Canadian Wildlife Federation. Archived from the original on 18 May 2007. Retrieved 22 May 2007. Featured creatures. Encarsia perplexa. University of Florida. Retrieved 6 January 2018. Helminth Parasites. Australian Society of Parasitology. Retrieved 9 October 2017. Parasitism | The encyclopedia of ecology and environmental management. Blackwell Science. Retrieved 8 April 2018. Pathogenic parasitic infections. PEOI. Retrieved 18 July 2013. Pikes (Esocidae). Indiana Division of Fish and Wildlife. Retrieved 3 September 2018. Predator & prey: Adaptations. (2012). Royal Saskatchewan Museum. Archived from the original (PDF) on 3 April 2018. Retrieved 19 April 2018. Predators and their prey. BBC. Retrieved 7 October 2015. Social parasites in the ant colony. Antkeepers. Retrieved 4 April 2016. Trapdoor spider. (2014). Arizona-Sonora Desert Museum. Retrieved 12 December 2014. Trapdoor spiders. BBC. Retrieved 12 December 2014. Abraham, M.A.A. (2006). Sustainability science and engineering. Elsevier, 1, pp. 123. ISBN 978-0444517128 Abrams, P.A. (1986). Adaptive responses of predators to prey and prey to predators: The failure of the arms-race analogy. Evolution, 40(6), pp. 1229-1247. doi:10.1111/j.1558-5646.1986.tb05747.x. PMID 28563514. S2CID 27317468. Arcese, P., Schuster, R., Campbell, L., Barber, A., & Martin, T.G. (2014). Deer density and plant palatability predict shrub cover, richness, diversity and aboriginal food value in a North American archipelago. Diversity and Distributions, 20(12), pp. 1368-1378. doi:10.1111/ddi.12241. ISSN 1366-9516. Bailey, N.T.J. (1975). The mathematical theory of infectious diseases and its applications (2nd ed.). London: Griffin. ISBN 085264-231-8. Barbosa, P., & Castellanos, I. (2005). Ecology of predator-prey interactions. Oxford University Press, p. 78. ISBN 9780199874545. Barceloux, D.G. (2008). Medical toxicology of natural substances: Foods, fungi, medicinal herbs, plants, and venomous animals. Wiley, p. 1028. ISBN 978-0-470-33557-4. Bar-Yam. Predator-prey relationships. New England Complex Systems Institute. Retrieved 7 September 2018. Belovsky, G.E. (1997). Optimal foraging and community structure: The allometry of herbivore food selection and competition. Evolutionary Ecology, 11(6), pp. 641-672. doi:10.1023/A:1018430201230. S2CID 23873922. Bengtson, S. (2002). Origins and early evolution of predation. In Kowalewski, M., & Kelley, P.H. (Eds.), The fossil record of predation. The Paleontological Society, pp. 289-317. Berec, L., Schembri, P.J., & Boukal, D.S. (2005). Sex determination in Bonellia viridis (Echiura: Bonelliidae): Population dynamics and evolution. Oikos, 108(3), pp. 473-484. doi:10.1111/j.0030-1299.2005.13350.x. Berenbaum, M.R. (1995). The chemistry of defense: Theory and practice. Proceedings of the National Academy of Sciences of the United States of America, 92(1), pp. 2-8. Bibcode:1995PNAS...92....2B. doi:10.1073/pnas.92.1.2. PMC 42807. PMID 7816816. Bergstrom, C.T., & Lachmann, M. (2001). Alarm calls as costly signals of antipredator vigilance: The watchful babbler game. Animal Behaviour, 61(3), 535-543. CiteSeerX 10.1.1.28.773. doi:10.1006/anbe.2000.1636. S2CID 2295026.

Berryman, A.A., & Hawkins, Bradford A. (2006). The refuge as an integrating concept in ecology and evolution. Oikos, 115(1), 192-196. doi:10.1111/j.0030-1299.2006.15188.x. Bester, C. (2017). Antennarius striatus. University of Florida. Retrieved 31 January 2018. Bond, W.J. (2012). Keystone species. In Schulze, E.D., & Mooney, H.A. (Eds.), Biodiversity and Ecosystem Function. Springer. p. 237. ISBN 978-3642580017. Boudreau, S.A., & Worm, B. (2012). Ecological role of large benthic decapods in marine ecosystems: A review. Mar. Ecol. Prog. Ser., 469, pp. 195-213 Bowers, M.D., Brown, I.L., & Wheye, D. (1985). Bird predation as a selective agent in a butterfly population. Evolution, 39(1), pp. 93-103. doi:10.1111/j.1558-5646.1985.tb04082.x. PMID 28563638. S2CID 12031679. Bray, D. Eastern frogfish, Batrachomoeus dubius. Fishes of Australia. Archived from the original on 14 September 2014. Retrieved 14 September 2014. Briggs, C.J., & Hoopes, M.F. (2004). Stabilizing effects in spatial parasitoid-host and predator-prey models: A review. Theor. Popul. Biol., 65, pp. 299-315 Brodie, E.D. (2009). Toxins and venoms. Current Biology, 19(20), R931-R935. doi:10.1016/j.cub.2009.08.011. PMID 19889364. S2CID 9744565. Brodie, E.D. (1999). Predator-prey arms races. BioScience, 49(7), pp. 557-568. doi:10.2307/1313476. JSTOR 1313476. Brousseau, D.J. (1978). Population dynamics of the soft-shell clam Mya arenaria. Mar. Biol. 50, pp. 63-71 Call, A., & St. Clair, S.B. (2018). In Ryan, M. (Eds.), Timing and mode of simulated ungulate herbivory alter aspen defense strategies. Tree Physiology, 38(10), pp. 1476-1485. doi:10.1093/treephys/tpy071. ISSN 1758-4469. PMID 29982736. Castello, M.E., Rodriguez-Cattaneo, A., Aguilera, P.A., Iribarne, L., Pereira, A.C., & Caputi, A.A. (2009). Waveform generation in the weakly electric fish Gymnotus coropinae (Hoedeman): The electric organ and the electric organ discharge. Journal of Experimental Biology, 212(9), pp. 1351-1364. doi:10.1242/jeb.022566. PMID 19376956. Catania, K.C. (2015). Electric eels use high-voltage to track fast-moving prey. Nature Communications, 6(1), pp. 8638. Bibcode:2015NatCo...6.8638C. doi:10.1038/ncomms9638. ISSN 2041-1723. PMC 4667699. PMID 26485580. Cetaruk, E.W. (2005). Rattlesnakes and other Crotalids. In Brent, J. (Eds.), Critical care toxicology: Diagnosis and management of the critically poisoned patient. Elsevier Health Sciences. p. 1075. ISBN 978-0-8151-4387-1. Cheng, T.C. (2012). General parasitology. Elsevier Science, pp. 13-15. ISBN 978-0-323-14010-2. Collins, S.L. (1998). Modulation of diversity by grazing and mowing in native tallgrass prairie. Science, 280(5364), pp. 745-747. Bibcode:1998Sci...280..745C. doi:10.1126/science.280.5364.745. ISSN 0036-8075. PMID 9563952. Combes, S.A., Salcedo, M.K., Pandit, M.M., & Iwasaki, J.M. (2013). Capture success and efficiency of dragonflies pursuing different types of prey. Integrative and Comparative Biology, 53(5), pp. 787-798. doi:10.1093/icb/ict072. PMID 23784698. Cory, R.L., & Redding, M.J. (1976). Mortalities caused by Tropical Storm Agnes to clams and oysters in the Rhode River area of Chesapeake Bay. In Anderson, A.M. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, p 478-487. Cott. (1940). pp. 368-389 Cox, F.E. (2001). Concomitant infections, parasites and immune responses. Parasitology. 122. Supplement: S23-38. doi:10.1017/s003118200001698x. PMID 11442193. S2CID 150432. Cressman, R., & Garay, J. (2009). A predator-prey refuge system: Evolutionary stability in ecological systems. Theoretical Population Biology, 76(4), pp. 248-57. doi:10.1016/j.tpb.2009.08.005. PMID 19751753. Cushing, J.M. (2005). Book reviews | Mathematics in population biology, by Horst R. Thiene. Bulletin of the American Mathematical Society, 42(4), pp. 501-505. doi:10.1090/S0273-0979-05-01055-4.

Dawkins, R., & Krebs, J.R. (1979). Arms races between and within species. Proceedings of the Royal Society B: Biological Sciences, 205(1161), pp. 489-511. Bibcode:1979RSPSB.205..489D. doi:10.1098/rspb.1979.0081. PMID 42057. S2CID 9695900. De Marsico, M.C., Gloag, R., Ursino, C.A., & Reboreda, J.C. (2013). A novel method of rejection of brood parasitic eggs reduces parasitism intensity in a cowbird host. Biology Letters, 9(3): 20130076. doi:10.1098/rsbl.2013.0076. PMC 3645041. PMID 23485877. de Mazancourt, C., Loreau, M., & Dieckmann, U. (2001). Can the evolution of plant defense lead to plant-herbivore mutualism? The American Naturalist, 158(2), pp. 109-123. doi:10.1086/321306. ISSN 0003-0147. PMID 18707340. S2CID 15722747. Dearing, M.D., Mangione, A.M., & Karasov, W.H. (2000). Diet breadth of mammalian herbivores: Nutrient versus detoxification constraints. Oecologia, 123(3), pp. 397-405. Bibcode:2000Oecol.123..397D. doi:10.1007/s004420051027. PMID 28308595. S2CID 914899. Descombes, P., Marchon, J., Pradervand, J.N., Bilat, J., Guisan, A., Rasmann, S., & Pellissier, L. (2016). Community-level plant palatability increases with elevation as insect herbivore abundance declines. Journal of Ecology, 105(1), pp. 142-151. doi:10.1111/1365-2745.12664. ISSN 0022-0477. deVries, M.S., Murphy, E.A.K., & Patek S.N. (2012). Strike mechanics of an ambush predator: The spearing mantis shrimp. Journal of Experimental Biology, 215(Pt 24), pp. 4374-4384. doi:10.1242/jeb.075317. PMID 23175528. Dissanaike, A.S. (1957). On Protozoa hyperparasitic in Helminth, with some observations on Nosema helminthorum Moniez, 1887. Journal of Helminthology, 31(1-2), pp. 47-64. doi:10.1017/s0022149x00033290. PMID 13429025. Dominey, W.J. (1983). Mobbing in colonially nesting fishes, especially the bluegill, Lepomis macrochirus. Copeia, (4), pp. 1086- 1088. doi:10.2307/1445113. JSTOR 1445113. Douglas, A.E. (1998). Nutritional interactions in insect-microbial symbioses: Aphids and their symbiotic bacteria buchnera. Annual Review of Entomology, 43, pp. 17-37. doi:10.1146/annurev.ento.43.1.17. PMID 15012383. Drake, J.M., & Griffen, B.D. (2010). Early warning signals of extinction in deteriorating environments. Nature, 467, pp. 456-459 Eastman, L.B., & Thiel, M. (2015). Foraging behavior of crustacean predators and scavengers. In Thiel, M., & Watling, L. (Eds.), Lifestyles and feeding biology. Oxford University Press. pp. 535-556. ISBN 9780199797066. Edmunds, M. (2012). Deimatic Behavior. Springer. Retrieved 31 December 2012. Edwards, P.J., & Wratten, S.D. (1985). Induced plant defenses against insect grazing: Fact or artefact?. Oikos, 44(1), pp. 70-74. doi:10.2307/3544045. JSTOR 3544045. Elumalai, V., Viswanathan, C., Pravinkumar, M., & Raffi, S.M. (2013). Infestation of parasitic barnacle Sacculina spp. in commercial marine crabs. Journal of Parasitic Diseases, 38(3), pp. 337-339. doi:10.1007/s12639-013-0247-z. PMC 4087306. PMID 25035598. Estes, J.A., Doak, D.F., Springer, A.M., & Williams, T.M. (2009). Causes and consequences of marine mammal population declines in southwest Alaska: A food-web perspective. Philos. Trans. R. Soc. B., 364, pp. 1647-1658 Feulner, P. G., Plath, M., Engelmann, J., Kirschbaum, F., & Tiedemann, R. (2009). Electrifying love: Electric fish use speciesspecific discharge for mate recognition. Biology Letters, 5(2), pp. 225-228. doi:10.1098/rsbl.2008.0566. PMC 2665802. PMID 19033131. Forbes, L.S. (1989). Prey defenses and predator handling behaviour: The dangerous prey hypothesis. Oikos, 55(2), pp. 155-158. doi:10.2307/3565418. JSTOR 3565418. Forbes, P. (2009). Dazzled and deceived: Mimicry and camouflage. Yale University Press, p. 134. ISBN 978-0-300-17896-8. Furness, R.W. (1978). Kleptoparasitism by great skuas (Catharacta skua Brünn.) and Arctic skuas (Stercorarius parasiticus L.) at a Shetland seabird colony. Animal Behaviour, 26, pp. 1167-1177. doi:10.1016/0003-3472(78)90107-0. S2CID 53155057. Futuyma, D.J., & Gould, F. (1979). Associations of plants and insects in deciduous forest. Ecological Monographs, 49(1), pp. 33- 50. doi:10.2307/1942571. ISSN 0012-9615. JSTOR 1942571.

Garcia, L.S. (1999). Classification of human parasites, vectors, and similar organisms. Clinical Infectious Diseases, 29(4), pp. 734- 746. doi:10.1086/520425. PMID 10589879. Gazda, S.K., Connor, R.C., Edgar, R.K., & Cox, F. (2005). A division of labour with role specialization in group-hunting bottlenose dolphins (Tursiops truncatus) off Cedar Key, Florida. Proceedings of the Royal Society, 272(1559), pp. 135-140. doi:10.1098/rspb.2004.2937. PMC 1634948. PMID 15695203. Genovart, M., Negre, N., Tavecchia, G., Bistuer, A., Parpal, L., & Oro, D. (2010). The young, the weak and the sick: Evidence of natural selection by predation. PLOS ONE, 5(3): e9774. Bibcode:2010PLoSO...5.9774G. doi:10.1371/journal.pone.0009774. PMC 2841644. PMID 20333305. Getty, T. (2002). The discriminating babbler meets the optimal diet hawk. Anim. Behav., 63(2), pp. 397-402. doi:10.1006/anbe.2001.1890. S2CID 53164940. Getz, W.M. (2011). Biomass transformation webs provide a unified approach to consumer-resource modelling. Ecology Letters, 14(2), pp. 113-124. doi:10.1111/j.1461-0248.2010.01566.x. PMC 3032891. PMID 21199247. Glaspie, C.N. (2019). Blue crab Callinectes sapidus and softshell clam Mya arenaria index of abundance time series 1958-1992. Knowledge Network for Biocomplexity. doi: 10.5063/F19S1PB9 Glaspie, C.N., & Seitz, R.D. (2018). Habitat complexity and benthic predator-prey interactions in Chesapeake Bay. PLOS ONE, 13: e0205162 Glaspie, C.N., Seitz, R.D., Ogburn, M.B., Dungan, C.F., & Hines, A.H. (2018). Impacts of habitat, predators, recruitment, and disease on soft-shell clams Mya arenaria and stout razor clams Tagelus plebeius in Chesapeake Bay. Mar. Ecol. Prog. Ser., 603, pp. 117-133 Gleeson, S.K., & Wilson, D.S. (1986). Equilibrium diet: Optimal foraging and prey coexistence. Oikos, 46(2), pp. 139. doi:10.2307/3565460. ISSN 0030-1299. JSTOR 3565460. Goel, N.S., Maitra, S.C., & Montroll, E.W. (1971). On the Volterra and other non-linear models of interacting populations. Academic Press. ISBN 978-0122874505. Goldbogen, J.A., Calambokidis, J., Shadwick, R.E., Oleson, E.M., McDonald, M.A., & Hildebrand, J.A. (2006). Kinematics of foraging dives and lunge-feeding in fin whales. Journal of Experimental Biology, 209(7), pp. 1231-1244. doi:10.1242/jeb.02135. PMID 16547295. S2CID 17923052. Archived from the original (PDF) on 13 August 2011. Retrieved 4 September 2018. Gremillet, D., Wilson, R.P., Wanless, S., & Chater, T. (2000). Black-browed albatrosses, international fisheries and the Patagonian Shelf. Marine Ecology Progress Series, 195, pp. 69-280. Bibcode:2000MEPS..195..269G. doi:10.3354/meps195269. Griffiths, D. (1980). Foraging costs and relative prey size. The American Naturalist, 116(5), pp. 743-752. doi:10.1086/283666. JSTOR 2460632. S2CID 85094710. Gullan, P.J., & Cranston, P.S. (2010). The insects: An outline of entomology (4th ed.). Wiley, pp. 308, 365-367, 375, 440-441. ISBN 978-1-118-84615-5. Gurr, G.M., Wratten, S.D., & Snyder, W.E. (2012). Biodiversity and insect pests: Key issues for sustainable management. John Wiley & Sons, pp. 105. ISBN 978-1-118-23185-2. Harko, T., Lobo, F.S., & Mak, M.K. (2014). Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates. Applied Mathematics and Computation, 236, pp. 184-194. doi:10.1016/j.amc.2014.03.030 Haven, D.S. (1970). A study of the hard and soft clam resources of Virginia: Annual contract report for the period 1 July 1969 through 30 June 1970. Virginia Institute of Marine Science, Gloucester Point, VA. Haven, D.S., Hargis, W.J., Loesch, J.G., & Whitcomb, J.P. (1976). The effect of Tropical Storm Agnes on oysters, hard clams, soft clams, and oyster drills in Virginia. In Anderson, A.M. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, pp. 488-508.

Hawkes, C.V., & Sullivan, J.J. (2001). The impact of herbivory on plants in different resource conditions: A meta-analysis. 82, pp. 2045-2058 - via Wiley. Heil, M., Koch, T., Hilpert, A., Fiala, B., Boland, W., & Linsenmair, K.E. (2001). Extrafloral nectar production of the antassociated plant, Macaranga tanarius, is an induced, indirect, defensive response elicited by jasmonic acid. Proceedings of the National Academy of Sciences, 98(3), pp. 1083-1088. Bibcode:2001PNAS...98.1083H. doi:10.1073/pnas.031563398. PMC 14712. PMID 11158598. Hethcote, H. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), pp. 599-653. doi:10.1137/s0036144500371907 Hidding, B., Nolet, B.A., de Boer, T., de Vries, P.P., & Klaassen, M. (2009). Above- and below-ground vertebrate herbivory may each favour a different subordinate species in an aquatic plant community. Oecologia, 162(1), pp. 199-208. doi:10.1007/s00442009-1450-6. ISSN 0029-8549. PMC 2776151. PMID 19756762. Holling, C. (1959). The components of predation as revealed by a study of small mammal predation of the European pine sawfly. Can. Entomol., 91, pp. 293-320 Hubel, T.Y., Myatt, J.P., Jordan, N.R., Dewhirst, O.P., McNutt, J.W., & Wilson, A.M. (2016). Energy cost and return for hunting in African wild dogs. Nature Communications, 7: 11034. doi:10.1038/ncomms11034. PMC 4820543. PMID 27023457. Janis, C. (1990). Correlation of cranial and dental variables with body size in ungulates and macropodoids. In Damuth, J., & MacFadden, B.J. (Eds.), Body size in mammalian paleobiology: Estimation and biological implications. Cambridge University Press, pp. 255-299. Janis, C.M., & Wilhelm, P.B. (1993). Were there mammalian pursuit predators in the Tertiary? Dances with wolf avatars. Journal of Mammalian Evolution, 1(2), pp. 103-125. doi:10.1007/bf01041590. S2CID 22739360. Johnston, M.W., Purkis, S.J., & Dodge, R.E. (2015). Measuring Baha - mian lionfish impacts to marine ecological services using habitat equivalency analysis. Mar. Biol., 162, pp. 2501-2512 Jurkevitch, E., & Davidov, Y. (2006). Phylogenetic diversity and evolution of predatory prokaryotes. Predatory Prokaryotes. Springer. pp. 11-56. doi:10.1007/7171_052. ISBN 978-3-540-38577-6. Kane, A., Healy, K., Guillerme, T., Ruxton, G.D., & Jackson, A.L. (2017). A recipe for scavenging in vertebrates - the natural history of a behaviour. Ecography, 40(2), pp. 324-334. doi:10.1111/ecog.02817 Karasov, W.H., & Diamond, J.M. (1988). Interplay between physiology and ecology in digestion. BioScience, 38(9), pp. 602-611. doi:10.2307/1310825. JSTOR 1310825. Karban, R., & Agrawal, A.A. (2002). Herbivore offense. Annual Review of Ecology and Systematics, 33, pp. 641-664. doi:10.1146/annurev.ecolsys.33.010802.150443. King, A.A., & Schaffer, W.M. (2001. The geometry of a population cycle: A mechanistic model of snowshoe hare demography. Ecology, 82, pp. 814-830. Kozlov, V., & Vakulenko, S. (2013). On chaos in Lotka-Volterra systems: An analytical approach. Nonlinearity, 26(8), pp. 2299- 2314. Bibcode:2013Nonli..26.2299K. doi:10.1088/0951-7715/26/8/2299. Kramer, B. (1996). Electroreception and communication in fishes. Progress in Zoology, 42. Kramer, D.L. (2001). Foraging behavior. In Fox, C.W., Roff, D.A., & Fairbairn, D.J. (Eds.), Evolutionary ecology: Concepts and case studies. Oxford University Press, pp. 232-238. ISBN 9780198030133. Archived from the original (PDF) on 12 July 2018. Retrieved 20 September 2018. Krause, J., & Ruxton, G.D. (2002). Living in groups. Oxford University Press, pp. 13-15. ISBN 9780198508182. Krebs, C.J., Boonstra, R., Boutin, S., & Sinclair, A.R.E. (2001). What drives the 10-year cycle of snowshoe hares?. BioScience, 51(1), pp. 25. doi:10.1641/0006-3568(2001)051[0025:WDTYCO]2.0.CO;2. Krebs, C., & Myers, J. (2014). The snowshoe hare 10-year cycle - a cautionary tale. Ecological rants. University of British Columbia. Retrieved 2 October 2018.

Kröger, M., & Schlickeiser, R. (2020). Analytical solution of the SIR-model for the temporal evolution of epidemics. Part A: Timeindependent reproduction factor. Journal of Physics A, 53(50): 505601. doi:10.1088/1751-8121/abc65d Kruuk, H. (1972). The spotted hyena: A study of predation and social behaviour. University of California Press, pp. 107-108. ISBN 978-0226455082. Lafferty, K.D., & Kuris, A.M. (2002). Trophic strategies, animal diversity and body size. Trends in Ecology and Evolution, 17(11), pp. 507-513. doi:10.1016/s0169-5347(02)02615-0. Archived from the original (PDF) on 3 October 2019. Langenheim, J.H. (1994). Higher plant terpenoids: A phytocentric overview of their ecological roles. Journal of Chemical Ecology, 20,(6), pp. 1223-1280. doi:10.1007/BF02059809. PMID 24242340. S2CID 25360410. Levin, S.A., Carpenter, S.R., Godfray, H.C.J., Kinzig, A.P., Loreau, M., Losos, J.B., Walker, B., & Wilcove, D.S. (2009). The Princeton guide to ecology. Princeton University Press, pp. 204-209. ISBN 9781400833023. Li, M., Fry, B.G., & Kini, R.M. (2005). Eggs-only diet: Its implications for the toxin profile changes and ecology of the marbled sea snake (Aipysurus eydouxii). Journal of Molecular Evolution, 60(1), pp. 81-89. Bibcode:2005JMolE..60...81L. doi:10.1007/s00239-004-0138-0. PMID 15696370. S2CID 17572816. Lipcius, R.N., & van Engel, W.A. (1990). Blue crab population dynamics in Chesapeake Bay: Variation in abundance (York River 1972-1988) and stock-recruit functions. Bull Mar. Sci., 46, pp. 180-194 Lloyd J.E. (1965). Aggressive mimicry in Photuris: Firefly femmes fatales. Science, 149(3684), pp. 653-654. Bibcode:1965Sci...149..653L. doi:10.1126/science.149.3684.653. PMID 17747574. S2CID 39386614. Pellissier, L., Ndiribe, C., Dubuis, A., Pradervand, J.N., Salamin, N., Guisan, A., & Rasmann, S. (2013). Turnover of plant lineages shapes herbivore phylogenetic beta diversity along ecological gradients. Ecology Letters, 16(5), pp. 600-608. doi:10.1111/ele.12083. PMID 23448096. Lubchenco, J., & Gaines, S.D. (1981). A unified approach to marine plant-herbivore interactions. I. Populations and communities. Annu. Rev. Ecol. Syst., 12, pp. 405-437 Lubchenco, J. (1978). Plant species diversity in a marine intertidal community: Importance of herbivore food preference and algal competitive abilities. The American Naturalist, 112(983), pp. 23-39. doi:10.1086/283250. ISSN 0003-0147. S2CID 84801707. MacArthur, R.H. (1984). The economics of consumer choice. Geographical ecology: Patterns in the distribution of species. Princeton University Press, pp. 59-76. ISBN 9780691023823. Maggenti, A.R., Maggenti, M.A., & Gardner, S.L. (2005). Online dictionary of invertebrate zoology. University of Nebraska, p. 22. Archived from the original (PDF) on 18 April 2018. McNair, J.N. (1986). The effects of refuges on predator-prey interactions: A reconsideration. Theoretical Population Biology, 29, (1), pp. 38-63. doi:10.1016/0040-5809(86)90004-3. PMID 3961711. Merilaita, S., Scott-Samuel, N.E., & Cuthill, I.C. (2017). How camouflage works. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1724): 20160341. doi:10.1098/rstb.2016.0341. PMC 5444062. PMID 28533458. Merilaita, S., Vallin, A., Kodandaramaiah, U., Dimitrova, M., Ruuskanen, S., & Laaksonen, T. (2011). Number of eyespots and their intimidating effect on naïve predators in the peacock butterfly. Behavioral Ecology, 22(6), pp. 1326-1331. doi:10.1093/beheco/arr135. Milchunas, D.G., & Noy-Meir, I. (2002). Grazing refuges, external avoidance of herbivory and plant diversity. Oikos, 99(1), pp. 113-130. doi:10.1034/j.1600-0706.2002.990112.x. Milligan, G.N., & Barrett, A.D. (2015). Vaccinology: An essential guide. Chichester, West Sussex: Wiley Blackwell, p. 310. ISBN 978-1-118-63652-7. Mitter, C., Farrell, B., & Futuyma, D.J. (1991). Phylogenetic studies of insect-plant interactions: Insights into the genesis of diversity. Trends in Ecology & Evolution, 6(9), pp. 290-293. doi:10.1016/0169-5347(91)90007-k. ISSN 0169-5347. PMID 21232484.

Moore, T.Y., & Biewener, A.A. (2015). Outrun or outmaneuver: Predator-prey interactions as a model system for integrating biomechanical studies in a broader ecological and evolutionary context. Integrative and Comparative Biology, 55(6), pp. 1188-97. doi:10.1093/icb/icv074. PMID 26117833. Murdoch, W.W., Briggs, C.J., & Nisbet, R.M. (2013). Consumer-resource dynamics. Princeton University Press, p. 39. ISBN 9781400847259. Neal, D. (2004). Introduction to population biology. Cambridge University Press, pp. 68-69. ISBN 9780521532235. Negut, I., Grumezescu, V., & Grumezescu, A.M. (2018). Treatment strategies for infected wounds. Molecules, 23(9), pp. 2392. doi:10.3390/molecules23092392 Nelson, E.H., Matthews, C.E., & Rosenheim, J.A. (2004). Predators reduce prey population growth by inducing changes in prey behavior. Ecology, 85(7), pp. 1853-1858. doi:10.1890/03-3109. JSTOR 3450359. Newell, C.R., & Hidu, H. (1986). Species profiles: Life histories and environmental requirements of coastal fishes and invertebrates (North Atlantic)--softshell clam. US Fish Wildl. Serv. Biol. Rep., 82(11.53). US Army Corps of Engineers, TR EL-824. https: //apps.dtic.mil/dtic/tr/fulltext/ u2/ a174837. Pdf Nishida, R. (2002). Sequestration of defensive substances from plants by Lepidoptera. Annual Review of Entomology, 47, pp. 57- 92. doi:10.1146/annurev.ento.47.091201.145121. PMID 11729069. Nowak, M., & May, R.M. (2000). Virus dynamics: Mathematical principles of immunology and virology. Oxford University Press, p. 8. ISBN 9780191588518. Olff, H., & Ritchie, M.E. (1998). Effects of herbivores on grassland plant diversity. Trends in Ecology & Evolution, 13(7), pp. 261- 265. doi:10.1016/s0169-5347(98)01364-0. hdl:11370/3e3ec5d4-fa03-4490-94e3-66534b3fe62f. ISSN 0169-5347. PMID 21238294. Orth, R.J., & Moore, K.A. (1983). Chesapeake Bay: An unprecedented decline in submerged aquatic vegetation. Science, 222, pp. 51-53 Overview of parasitology. Australian Society of Parasitology and Australian Research Council/National Health and Medical Research Council) Research Network for Parasitology. July 2010. ISBN 978-1-8649999-1-4. Parratt, S.R., & Laine, A.L. (2016). The role of hyperparasitism in microbial pathogen ecology and evolution. The ISME Journal, 10(8), pp. 1815-1822. doi:10.1038/ismej.2015.247. PMC 5029149. PMID 26784356. Payne, R.B. (1997). Avian brood parasitism. In Clayton, D.H., & Moore, J. (Eds.), Host-parasite evolution: General principles and avian models. Oxford University Press. pp. 338-369. ISBN 978-0198548928. Peckarsky, B.L., Abrams, P.A., Bolnick, D.I., Dill, L.M., Grabowski, J.H., Luttbeg, B., Orrock, J.L., Peacor, S.D., Preisser, E.L., Schmitz, O.J., & Trussell, G.C. (2008). Revisiting the classics: Considering nonconsumptive effects in textbook examples of predator-prey interactions. Ecology, 89(9), pp. 2416-2425. doi:10.1890/07-1131.1. PMID 18831163. Perry, G. (1999). The evolution of search modes: Ecological versus phylogenetic perspectives. The American Naturalist, 153(1), pp. 98-109. doi:10.1086/303145. PMID 29578765. S2CID 4334462. Pianka, E.R. (2011). Evolutionary ecology (7th (eBook) ed.). pp. 78-83. Pietsch, T.W. (2005). Dimorphism, parasitism, and sex revisited: Modes of reproduction among deep-sea ceratioid anglerfishes (Teleostei: Lophiiformes). Ichthyological Research, 52(3), pp. 207-236. doi:10.1007/s10228-005-0286-2. S2CID 24768783. Pillemer, E.A., & Tingey, W.M. (1976). Hooked trichomes: A physical plant barrier to a major agricultural pest. Science, 193(4252), pp. 482-484. Bibcode:1976Sci...193..482P. doi:10.1126/science.193.4252.482. PMID 17841820. S2CID 26751736. Epstein, E. (1994). A review. PNAS, 91. Pollitt, L.C., MacGregor, P., Matthews, K., & Reece, S.E. (2011). Malaria and trypanosome transmission: Different parasites, same rules? Trends in Parasitology, 27(5), pp. 197-203. doi:10.1016/j.pt.2011.01.004. PMC 3087881. PMID 21345732.

Poore, A.G.B., Graba-Landry, A., Favret, M., Sheppard Brennand, H., Byrne, M., & Dworjanyn, S.A. (2013). Direct and indirect effects of ocean acidification and warming on a marine plant-herbivore interaction. Oecologia, 173(3), pp. 1113-1124. Bibcode:2013Oecol.173.1113P. doi:10.1007/s00442-013-2683-y. ISSN 0029-8549. PMID 23673470. S2CID 10990079. Poulin, R. (2011). The many roads to parasitism: A tale of convergence. In Rollinson, D., & Hay, S.I. (Eds.), Advances in parasitology, 74. Academic Press. pp. 27-28. doi:10.1016/B978-0-12-385897-9.00001-X. ISBN 978-0-12-385897-9. PMID 21295676. Poulin, R., & Randhawa, H.S. (2015). Evolution of parasitism along convergent lines: From ecology to genomics. Parasitology, 142(Suppl 1): S6-S15. doi:10.1017/S0031182013001674. PMC 4413784. PMID 24229807. Preisser, E.L., Bolnick, D.I., & Benard, M.F. (2005). Scared to death? The effects of intimidation and consumption in predator- prey interactions. Ecology, 86(2), pp. 501-509. doi:10.1890/04-0719. ISSN 0012-9658. Ripple, W.J., & Beschta, R.L. (2004). Wolves and the ecology of fear: Can predation risk structure ecosystems? BioScience, 54(8), pp. 755. doi:10.1641/0006-3568(2004)054[0755:WATEOF]2.0.CO;2. Rochat, J., & Gutierrez, A.P. (2001). Weather-mediated regulation of olive scale by two parasitoids. Journal of Animal Ecology, 70(3), pp. 476-490. doi:10.1046/j.1365-2656.2001.00505.x. S2CID 73607283. Rothstein, S.I. (1990). A model system for coevolution: Avian brood parasitism. Annual Review of Ecology and Systematics, 21, pp. 481-508. doi:10.1146/annurev.ecolsys.21.1.481. Rosenzweig, M.L., & MacArthur, R.H. (1963). Graphical representation and stability conditions of predator-prey interactions. The American Naturalist, 97, pp. 209-223. Ruppert, E.E., Fox, R.S., & Barnes, R.D. (2004). Invertebrate zoology, 7th edition. Cengage Learning. pp. 153-154. ISBN 978-81315-0104-7. Ruxton, Sherratt & Speed 2004, pp. vii-xii Sagers, C.L. (1992). Manipulation of host plant quality: Herbivores keep leaves in the dark. Functional Ecology, 6(6), pp. 741-743. doi:10.2307/2389971. JSTOR 2389971. Sanders, J.G., Beichman, A.C., Roman, J., Scott, J.J., Emerson, D., McCarthy, J.J., & Girguis, P.R. (2015). Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nature Communications, 6, pp. 8285. Bibcode:2015NatCo...6.8285S. doi:10.1038/ncomms9285. PMC 4595633. PMID 26393325. Sandsten, H., & Klaassen, M. (2008). Swan foraging shapes spatial distribution of two submerged plants, favouring the preferred prey species. Oecologia, 156(3), pp. 569-576. Bibcode:2008Oecol.156..569S. doi:10.1007/s00442-008-1010-5. ISSN 0029-8549. PMC 2373415. PMID 18335250. Schmidt, J.O. (2009). Wasps. ScienceDirect. Encyclopedia of Insects (Second ed.). pp. 1049-1052. doi:10.1016/B978-0-12374144-8.00275-7 Schubel, J.R. (1976). Effects of Agnes on the suspended sediment on the Chesapeake Bay and contiguous shelf waters. In Schubel, J.R. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, p 179-200 Schubel, J.R., Carter, H.H., & Cronin, W.B. (1976). Effects of Agnes on the distribution of salinity along the main axis of the Bay and in contiguous shelf waters. In Ruzecki, E.P. (Eds.), The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system. The Johns Hopkins University Press, Baltimore, MD, p 33-65 Sehgal, M., Ladd, H.J., & Totapally, B. (2020). Trends in epidemiology and microbiology of severe sepsis and septic shock in children. Hospital Pediatrics, 10(12), pp. 1021-1030. doi:10.1542/hpeds.2020-0174 Sheriff, M.J., Peacor, S.D., Hawlena, D., Thaker, M., & Gaillard, J.M. (2020). Non-consumptive predator effects on prey population size: A dearth of evidence. Journal of Animal Ecology, 89(6), pp. 1302-1316. doi:10.1111/1365-2656.13213. PMID 32215909.

Sih, A. (1987). Prey refuges and predator-prey stability. Theoretical Population Biology, 31, pp. 1-12. doi:10.1016/00405809(87)90019-0. Slater, P.J.B., Rosenblatt, J.S., Snowdon, C.T., Roper, T.J., Brockmann, H.J., & Naguib, M. (2005). Advances in the study of behavior. Academic Press, p. 365. ISBN 978-0-08-049015-1. Steere, A.C. (2001). Lyme disease. New England Journal of Medicine, 345(2), pp. 115-125. doi:10.1056/NEJM200107123450207. PMID 11450660. Stevens, A.N.P. (2010). Predation, herbivory, and parasitism. Nature Education Knowledge, 3(10), pp. 36. Retrieved 12 February 2018. Thieme, H.R. (2003). Mathematics in population biology. Princeton University Press. ISBN 978-0-691-09291-1. Thomas, J.A., Schönrogge, K., Bonelli, S., Barbero, F., & Balletto, E. (2010). Corruption of ant acoustical signals by mimetic social parasites: Maculinea butterflies achieve elevated status in host societies by mimicking the acoustics of queen ants. Commun Integr Biol., 3(2), pp. 169-171. doi:10.4161/cib.3.2.10603. PMC 2889977. PMID 20585513. Tyus, H.M. (2011). Ecology and conservation of fishes. CRC Press, p. 233. ISBN 978-1-4398-9759-1. Van Oystaeyen, A., Araujo Alves, D., Caliari Oliveira, R., Lima do Nascimento, D., Santos do Nascimento, F., Billen, J., & Wenseleers, T. (2013). Sneaky queens in Melipona bees selectively detect and infiltrate queenless colonies. Animal Behaviour, 86(3), pp. 603-609. CiteSeerX 10.1.1.309.6081. doi:10.1016/j.anbehav.2013.07.001. S2CID 12921696. Vermeij, G.J. (1993). Evolution and escalation: An ecological history of life. Princeton University Press, pp. 11 and passim. ISBN 978-0-691-00080-0. Wetzel, R.G., & Likens, G.E. (2000). Predator-prey interactions. Limnological Analyses. Springer. pp. 257-262. doi:10.1007/9781-4757-3250-4_17. ISBN 978-1-4419-3186-3. Williams, A.C., & Flaxman, S.M. (2012). Can predators assess the quality of their prey's resource? Animal Behaviour, 83(4), pp, 883-890. doi:10.1016/j.anbehav.2012.01.008. S2CID 53172079. Wilson, A.J., et al. (2017). What is a vector? Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1719): 20160085. doi:10.1098/rstb.2016.0085. PMC 5352812. PMID 28289253. Wilson, E.O. (2014). The meaning of human existence. W. W. Norton & Company, p. 112. ISBN 978-0-87140-480-0. Yang, W., Zhang, D., Peng, L., Zhuge, C., & Hong, L. (2020). Rational evaluation of various epidemic models based on the COVID-19 data of China. arXiv:2003.05666v1 Contributors and Attributions This chapter was written by N. Gownaris and T. Zallek, with text taken from the following CC-BY resources: Predation by Wikipedia, the free Encyclopedia Herbivore by Wikipedia, the free Encyclopedia Parasitism by Wikipedia, the free Encylopedia Infection by Wikipedia, the free Encyclopedia Donovan, T. M. and C. Welden. 2002. Spreadsheet exercises in ecology and evolution. Sinauer Associates, Inc. Sunderland, MA, USA. 16.6: Sources and Attributions is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by LibreTexts.