View source: R/calcFunctionalIndices.r

calc_QSS | R Documentation |

The QSS measure is the proportion of matrices that are locally stable, these matrices are created by sampling the values of the community matrix
(the Jacobian) from a uniform distribution, preserving the sign structure 1. If the 'ig' parameter is
an `mgraph`

network it needs to have been built with the order `c("Competitive", "Mutualistic", "Trophic")`

It also calculates the mean of the real part of the maximum eingenvalue, which is also a measure of stability 2.
It uses a uniform distribution between 0 and maximum values given by the parameters `negative`

, `positive`

and `selfDamping`

,
corresponding to the sign of interactions and self-limitation effect 3,4.
If the edges of the networks have a weigth attribute and `istrength`

parameter is true, weigth will be used as interaction strength,
then the limits of the uniform distribution will be `negative*-x`

, `positive*x`

, `selfDamping*x`

, where x is the value of the weigth for the edge.
If the values of these parameters are 0 then there is no interaction of that kind. The default values for `negative`

, `positive`

and `selfDumping`

assume a maximum ecological transfer efficience of 10%.

```
calc_QSS(
ig,
nsim = 1000,
ncores = 0,
negative = -10,
positive = 1,
selfDamping = -1,
istrength = FALSE,
returnRaw = FALSE
)
```

`ig` |
igraph or a list of igraph networks or mgraph network |

`nsim` |
number of simulations to calculate QSS, if the number of simulations is 1 then it calculates the maximum eingenvalue for the mean of interaction
strength, if |

`ncores` |
number of cores to use in parallel comutation if 0 it uses sequential processing |

`negative` |
the maximum magnitude of the negative interaction (the effect of the predator on the prey) must be <= 0 |

`positive` |
the maximum magnitude of the positive interaction (the effect of the prey on the predator) must be >= 0 |

`selfDamping` |
the maximum magnitude of the self-limitation (the effect of the species on itself) must be <= 0, only for species with links to itself. |

`istrength` |
If TRUE takes the weigth attribute of the network as interaction strength. |

`returnRaw` |
if TRUE returns all the values of the maximum eingenvalues |

if parameter `returnRaw`

is `FALSE`

returns a data.frame with the QSS, and MEing, the mean of the real part of the maximum eingenvalue.
If `returnRaw`

is `TRUE`

it returns the values of randomized real part of maximum eingenvalue (maxre)

Allesina, S. & Pascual, M. (2008). Network structure, predator - Prey modules, and stability in large food webs. Theor. Ecol., 1, 55–64.

Grilli, J., Rogers, T. & Allesina, S. (2016). Modularity and stability in ecological communities. Nat. Commun., 7, 12031

Monteiro, A.B. & Del Bianco Faria, L. (2017). Causal relationships between population stability and food-web topology. Functional Ecology, 31, 1294–1300.

Borrelli, J. J. 2015. Selection against instability: stable subgraphs are most frequent in empirical food webs. - Oikos 124: 1583–1588.

```
## Not run:
g <- netData[[2]]
tp <- calc_QSS(g)
# Read Multiplex network and calculate QSS
fileName <- c(system.file("extdata", package = "multiweb"))
dn <- list.files(fileName,pattern = "^Kefi2015.*\\.txt$")
gt <- readMultiplex(dn,types,"inst/extdata", skipColumn = 2)
calc_QSS(gt)
## End(Not run)
```

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