View source: R/calcInteractionIntensity.r

calc_interaction_intensity | R Documentation |

The function uses the body mass in Kg of predator/consumer and prey/resources and the dimensionality of the interaction as source data,
then the interaction intensity is estimated with all the coefficients from Pawar (2012) as `alfa*xR*mR/mC`

, where `alpha`

is the search rate `xR`

the resource density, `mR`

the resource body mass and `mC`

the consumer body mass. This value of the interaction strength quantifies
the effect of the predator on the prey by biomass unit of the predator. Assuming a Lotka-Volterra model is equivalent to the entry A(i,j) of the community matrix, where i is the
prey and j the predator.

```
calc_interaction_intensity(da, res_mm, res_den, con_mm, int_dim, nsims = 1)
```

`da` |
data.frame with the interactions body mass and type of interaction dimensionality |

`res_mm` |
name of the column with the resource body mass mean |

`res_den` |
name of the column with the resource density in Individuals/m^2 in 2D or m^3 in 3D. If lower than 0 it uses the previously mentioned estimation. |

`con_mm` |
name of the column with the consumer body mass mean |

`int_dim` |
name of the column with the interaction dimensionality |

If the resource density is unknown (parameter `res_den`

) you could set the column to a less than 0 value; and it
will be estimated according to the equation S18 and supplementary figures 2i & j (individuals/m2 - m3)

If the mean mass of the resource for detritus or sediment (parameter `res_mm`

) is unknown, it can be designated as
negative. This will result in the calculation of the resource body mass (in kilograms) using allometric formulas given
in the Equation S9 and Supplementary Figures 2c & d from the paper. This is only valid when the size ratios tend
to be optimal.

If the Biomass of the resource is known you should use it as `res_mm`

and set `res_den`

to 1. This is the best choice to
avoid the previous allometric calculations of `res_den`

and `res_mm`

when they are unknown.

If resource size `res_mm`

and resource density `res_den`

are decoupled from consumer size you could assign 1 to both see
pag 487 **Dimensionality and trophic interaction strengths** in Pawar's paper.

If the parameter 'nsims > 1 ' the function will estimate the variability on each interaction strength. It takes random values from a normal distribution with mean and standard deviation given by the Pawar's regressions for the slopes of allometric exponents.

A data.frame based on `da`

with the following fields added

mR: if

`res_mm<0`

is the resource mass calculated with the equations from ref 1, if`res_mm>0`

duplicates the value of`res_mm`

xR: calculated resource density or the same value as in the input data.frame.

alfa: calculated search rate.

qRC: calculated trophic interaction strength as

`alfa*xR*mR/mC`

where`mC`

is the consumer body mass.

Pawar, S., Dell, A. I., & Van M. Savage. (2012). Dimensionality of consumer search space drives trophic interaction strengths. Nature, 486, 485. https://doi.org/10.1038/nature11131

```
## Not run:
g <- netData[[1]]
require(dplyr)
# build the data.frame with random values
set.seed(7815)
da <- as_long_data_frame(g) %>% dplyr::select(from:to) %>% mutate(con_mm=rlnorm(n(),5,2),res_mm=con_mm - 30 ,int_dim=sample(c("2D","3D"),n(),replace=TRUE), res_den = -999)
calc_interaction_intensity(da,res_mm,res_den,con_mm,int_dim, nsims=1)
## End(Not run)
```

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