Numerical response

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The numerical response in ecology is the change in predator density as a function of change in prey density. The term numerical response was coined by M. E. Solomon in 1949. [1] It is associated with the functional response, which is the change in predator's rate of prey consumption with change in prey density. As Holling notes, total predation can be expressed as a combination of functional and numerical response. [2] The numerical response has two mechanisms: the demographic response and the aggregational response. The numerical response is not necessarily proportional to the change in prey density, usually resulting in a time lag between prey and predator populations. [3] For example, there is often a scarcity of predators when the prey population is increasing.

Contents

Demographic response

The demographic response consists of changes in the rates of predator reproduction or survival due to a changes in prey density. The increase in prey availability translates into higher energy intake and reduced energy output. This is different from an increase in energy intake due to increased foraging efficiency, which is considered a functional response. This concept can be articulated in the Lotka-Volterra Predator-Prey Model.

a = conversion efficiency: the fraction of prey energy assimilated by the predator and turned into new predators
P = predator density
V = prey density
m = predator mortality
c = capture rate

Demographic response consists of a change in dP/dt due to a change in V and/or m. For example, if V increases, then predator growth rate (dP/dt) will increase. Likewise if the energy intake increases (due to greater food availability) and a decrease in energy output (from foraging), then predator mortality (m) will decrease and predator growth rate (dP/dt) will increase. In contrast, the functional response consists of a change in conversion efficiency (a) or capture rate (c).

The relationship between available energy and reproductive efforts can be explained with the life history theory in the trade-off between fecundity and growth/survival. If an organism has more net energy, then the organism will sacrifice less energy dedicated to survival per reproductive effort and will therefore increase its reproduction rate.

In parasitism, functional response is measured by the rate of infection or laying of eggs in host, rather than the rate of prey consumption as it is measured in predation. Numerical response in parasitism is still measured by the change in number of adult parasites relative to change in host density. Parasites can demonstrate a more pronounced numerical response to changes in host density since there is often a more direct connection (less time lag) between food and reproduction in that both needs are immediately satisfied by its interaction with the host. [4]

Aggregational response

The aggregational response, as defined by Readshaw in 1973, is a change in predator population due to immigration into an area with increased prey population. [5] In an experiment conducted by Turnbull in 1964, he observed the consistent migration of spiders from boxes without prey to boxes with prey. He proved that hunger impacts predator movement. [6]

Riechert and Jaeger studied how predator competition interferes with the direct correlation between prey density and predator immigration. [7] [8] One way this can occur is through exploitation competition: the differential efficiency in use of available resources, for example, an increase in spiders' web size (functional response). The other possibility is interference competition where site owners actively prevent other foragers from coming in vicinity.

Ecological relevance

The concept of numerical response becomes practically important when trying to create a strategy for pest control. The study of spiders as a biological mechanism for pest control has driven much of the research on aggregational response. Antisocial predator populations that display territoriality, such as spiders defending their web area, may not display the expected aggregational response to increased prey density. [9]

A credible, simple alternative to the Lotka-Volterra predator-prey model and its common prey dependent generalizations is the ratio dependent or Arditi-Ginzburg model. [10] The two are the extremes of the spectrum of predator interference models. According to the authors of the alternative view, the data show that true interactions in nature are so far from the Lotka-Volterra extreme on the interference spectrum that the model can simply be discounted as wrong. They are much closer to the ratio dependent extreme, so if a simple model is needed one can use the Arditi-Ginzburg model as the first approximation. [11]

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Theoretical ecology

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Predation A biological interaction where a predator kills and eats a prey organism

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 behaviours that includes parasitism and micropredation and parasitoidism. It is distinct from scavenging on dead prey, though many predators also scavenge; it overlaps with herbivory, as seed predators and destructive frugivores are predators.

<i>Parasteatoda tepidariorum</i>

Parasteatoda tepidariorum, the common house spider, referred to internationally as the American house spider, is a spider species of the genus Parasteatoda that is mainly indigenous to the New World, with P. tepidariorum australis but has achieved a cosmopolitan distribution. American house spiders are synanthropic and build their tangled webs in or near human dwellings, greenhouses or similar, often in secluded areas such as between loose walls and behind open doors and attic windows. Statistically, they are the most often encountered spider by humans in North America, and least likely to adopt defensive behavior in their vicinity. Their prey mechanism is similar to that of the other cobweb spiders: the spider follows disturbances transmitted along the web to entangle and then paralyze its prey, which usually consists of household insects and other invertebrates.

Lotka–Volterra equations Pair of equations modelling predator-prey cycles in biology

The Lotka–Volterra equations, also known as the predator–prey equations, are a pair of first-order nonlinear differential equations, frequently used to describe the dynamics of biological systems in which two species interact, one as a predator and the other as prey. The populations change through time according to the pair of equations:

Population dynamics is the type of mathematics used to model and study the size and age composition of populations as dynamical systems.

Population ecology Study of the dynamics of species populations and how these populations interact with the environment

Population ecology is a sub-field of ecology that deals with the dynamics of species populations and how these populations interact with the environment, such as birth and death rates, and by immigration and emigration).

Metapopulation Group of separated yet interacting ecological populations

A metapopulation consists of a group of spatially separated populations of the same species which interact at some level. The term metapopulation was coined by Richard Levins in 1969 to describe a model of population dynamics of insect pests in agricultural fields, but the idea has been most broadly applied to species in naturally or artificially fragmented habitats. In Levins' own words, it consists of "a population of populations".

Optimal foraging theory

Optimal foraging theory (OFT) is a behavioral ecology model that helps predict how an animal behaves when searching for food. Although obtaining food provides the animal with energy, searching for and capturing the food require both energy and time. To maximize fitness, an animal adopts a foraging strategy that provides the most benefit (energy) for the lowest cost, maximizing the net energy gained. OFT helps predict the best strategy that an animal can use to achieve this goal.

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The paradox of enrichment is a term from population ecology coined by Michael Rosenzweig in 1971. He described an effect in six predator–prey models where increasing the food available to the prey caused the predator's population to destabilize. A common example is that if the food supply of a prey such as a rabbit is overabundant, its population will grow unbounded and cause the predator population to grow unsustainably large. That may result in a crash in the population of the predators and possibly lead to local eradication or even species extinction.

Functional response

A functional response in ecology is the intake rate of a consumer as a function of food density. It is associated with the numerical response, which is the reproduction rate of a consumer as a function of food density. Following C. S. Holling, functional responses are generally classified into three types, which are called Holling's type I, II, and III.

Ecosystem model A typically mathematical representation of an ecological system

An ecosystem model is an abstract, usually mathematical, representation of an ecological system, which is studied to better understand the real system.

A population model is a type of mathematical model that is applied to the study of population dynamics.

The Nicholson–Bailey model was developed in the 1930s to describe the population dynamics of a coupled host-parasitoid system. It is named after Alexander John Nicholson and Victor Albert Bailey. Host-parasite and prey-predator systems can also be represented with the Nicholson-Bailey model. The model is closely related to the Lotka–Volterra model, which describes the dynamics of antagonistic populations using differential equations.

Population dynamics of fisheries

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The generalized Lotka–Volterra equations are a set of equations which are more general than either the competitive or predator–prey examples of Lotka–Volterra types. They can be used to model direct competition and trophic relationships between an arbitrary number of species. Their dynamics can be analysed analytically to some extent. This makes them useful as a theoretical tool for modeling food webs. However, they lack features of other ecological models such as predator preference and nonlinear functional responses, and they cannot be used to model mutualism without allowing indefinite population growth.

Lev R. Ginzburg

Lev R. Ginzburg is a mathematical ecologist and the President of the firm Applied Biomathematics.

The Arditi–Ginzburg equations describes ratio dependent predator–prey dynamics. Where N is the population of a prey species and P that of a predator, the population dynamics are described by the following two equations:

Pursuit predation

Pursuit predation is a form of predation in which predators give chase to fleeing prey. The chase can be initiated either by the predator or by the prey, should the prey be alerted to a predator's presence and attempt to flee before the predator gives chase. The chase ends when either the predator captures and consumes the prey, or the prey escapes. Pursuit predation is typically observed in carnivorous species within the kingdom Animalia, with some iconic examples being cheetahs, lions, and wolves.

References

  1. Solomon, M. E. "The Natural Control of Animal Populations." Journal of Animal Ecology. 19.1 (1949). 1-35
  2. Holling, C. S. "The components of predation as revealed by a study of small-mammal predation of the European pine sawfly." Canadian Entomologist 91: 293-320. (1959)
  3. Ricklefs, R. E. The Economy of Nature. 6th Edition. New York: Freeman and Company. 2010. p. 319.
  4. Holling, C. S. "The components of predation as revealed by a study of small-mammal predation of the European pine sawfly." Canadian Entomologist 91: 293-320.(1959)
  5. Readshaw, J.L. The numerical response of predators to prey density. In: Hughes, Ed., Quantitative Evaluation of Natural Enemy Effectiveness. J. Applied Biol. 10:342-351. 1973.
  6. Turnbull, A. L. The search for prey by a web-building spider Achaearanea tepidariorum (C. L. Koch) (Araneae, Theridiidae). Canadian Entomologist 96: 568-579. 1964.
  7. Riechert, Susan E. Thoughts on Ecological Significance of Spiders. BioScience. 24(6): 352-356. 1974.
  8. Jaeger, R.G. Competitive Exclusion: Comments on survival and extinction of species. BioScience. 24: 33-39. 1974
  9. Turnbull, A. L. The search for prey by a web-building spider Achaearanea tepidariorum (C. L. Koch) (Araneae, Theridiidae). Canadian Entomologist 96: 568-579. 1964.
  10. Arditi, R. and Ginzburg, L.R. 1989. Coupling in predator-prey dynamics: ratio dependence. Journal of Theoretical Biology 139: 311-326.
  11. Arditi, R. and Ginzburg, L.R. 2012. How Species Interact: Altering the Standard View on Trophic Ecology. Oxford University Press, New York, NY.