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Table 1 Literature overview

From: How long do Red Queen dynamics survive under genetic drift? A comparative analysis of evolutionary and eco-evolutionary models

Ref.

Authors (year)

focus

deterministic/ stochastic

equations/ method

population size

[31]

Schaffer and Rosenzweig (1978)

HP, CSS

deterministic

ODE

constrained4

[32]

Seger (1988)

HP, many genotypes, chaos

deterministic

RE

constant

[33]

Nee (1989)

HP, co-evolution, recombination

deterministic

RE

constant

[34]

Dybdahl and Lively (1998)

time lag, experiment

deterministic

RE

constant

[35]

Boots and Sasaki (1999)

infection on lattice

both

ODE, IBM, AD

variable

[36]

Peters and Lively (1999)

fluctuating epistasis

deterministic

RE

constant

[37]

Sasaki (2000)

multilocus GfG

deterministic

ODE

constant

[38]

Agrawal and Lively (2001)

HP, selfing vs outcrossing

deterministic

RE

constant

[39]

Agrawal and Lively (2002)

HP, GfG vs MA

deterministic

RE

constant

[40]

Gandon (2002)

HP, local adaptation (spatial)

deterministic

RE

constant

[41]

Gandon (2004)

SI, multihost parasites

deterministic

ODE, AD

variable

[20]

Kouyos et al. (2007)

HP, oscillations in stochastic model

both7

ODE

constant5

[42]

Alizon and van Baalen (2008)

multiple infections, within-host and SI

deterministic

ODE, AD

variable

[43]

Agrawal (2009)

HP, sex vs recombination

deterministic

RE

constant

[44]

Best et al. (2009)

SI, transmission, susceptibility

deterministic

ODE, AD

constant

[21]

Engelstädter and Bonhoeffer (2009)

HP, RQ oscillations

deterministic

RE

constant

[45]

Lively (2010)

sex (long term persistence)

both6

RE

variable

[46]

Greischar and Lively (2011)

HP, extinction risk

deterministic

RE

constrained

[47]

Gilman et al. (2012)

HP, multiple host traits, resistance

stochastic

IBM

constant, constrained4

[48]

Mostowy and Engelstädter (2012)

interaction matrices, sex, LD

deterministic

RE

constant

[28]

Gokhale et al. (2013)

HP, population size

stochastic

IBM

variable, constrained

[49]

Luijckx et al. (2013)

MA, Daphnia

deterministic

RE

constant

[50]

Abou Chakra et al. (2014)

HP, plastic behaviour

both

ODE, IBM

constant

[51]

Taylor et al. (2014)

HP, virus of virus

deterministic

ODE

constrained

[23]

Ashby and Gupta (2014)

SI, state-dependent sex, MA

deterministic

ODE

variable

[8]

Ashby and King (2015)

SI, diversity, transmission, sex

stochastic

IBM

constant

[52]

Engelstädter (2015)

HP, infection matrices

deterministic

RE

constant

[53]

Rabajante et al. (2015)

HP, many types

deterministic

ODE

constrained

[25]

Song et al. (2015)

HP, population size, GfG MA

deterministic

ODE

constant, variable

[54]

Hesse et al. (2015)

environment, specialisation

deterministic

ODE, AD

constrained4

[24]

Gómez et al. (2015)

oscillation vs. arms race

stochastic

IBM

variable

[55]

Rabajante et al. (2016)

HP, rare types

deterministic, noise1

ODE, SDE

constrained

[56]

Nordbotten and Stenseth (2016)

HP, RQ vs stasis

deterministic

PDE

constrained4

[57]

Best et al. (2017)

SI, no specificity, FSD

deterministic3

ODE, AD

constrained4

[58]

Bonachela et al. (2017)

crossfeeding

deterministic2

ODE

constrained

[59]

Greenspoon and Mideo (2017)

relatedness, transmission

deterministic

RE

constant

[60]

Lively (2017)

allopatric, sympatric parasites

deterministic2

RE

constrained

[61]

Nuismer (2017)

local, global adaptation

deterministic2

RE

constant

[62]

Veller et al. (2017)

HP, speed of evolution (RQ, RK)

stochastic

IBM

constant

[63]

Ashby and Boots (2017)

HP, SI, GfG MA

deterministic

ODE

constrained4

[27]

MacPherson and Otto (2018)

SI, HP, MA, RQ oscillations

deterministic

ODE

constant, constrained4

[18]

Ashby et al. (2019)

HP, population size change

deterministic

ODE, AD

constrained

[]

Current paper

(HP, MA, RQ) population size, extinction time

stochastic

IBM

constant, constrained, variable

  1. Mathematical models and properties discussed in this paper sorted by publication year. Many models deal with relative allele or genotype abundances without considering ecological dynamics – these have been categorised as constant population size models. Those models that do include a changing population size and stochastic effects mostly do not analyse the stability of long term oscillations which is the focus of this paper. (See the notes on this literature survey in the Additional file 1).
  2. ODE/PDE/SDE: ordinary/partial/stochastic differential equation, IBM: individual based model (stochastic simulations), RE: recursion equation, SI: susceptible-infected (epidemiological) model, HP: explicit host-parasite model, AD: adaptive dynamics (most often ODE with added mutants), MA: matching alleles, GfG: gene for gene, RQ: Red Queen (oscillations in genotype abundances or in trait space), RK: Red King (slow evolution favoured), CSS: coevolutionary stable strategy.
  3. 1not intrinsic stochasticity
  4. 2stochastic mutants added
  5. 3adaptive dynamics simulations (no intrinsic stochasticity)
  6. 4via carrying capacity (density dependent death or competition term)
  7. 5but discussed
  8. 6some randomness in infection (±1 in next generation)
  9. 7when time discrete, only host stochastic