Flow Analysis: Simple

The enaR library can be used to perform the Flow Analysis of Ecological Network Analysis. This includes finding the node throughflows, calculating the Input and Output analyses, and determining a set of whole-network metrics.

Prepare the workspace

rm(list = ls())
library(enaR)
library(network)

Load the data

# load a model
data(enaModels)
m <- enaModels[[9]] # select the oyster reef model

Perform the analysis

f <- enaFlow(m)  # peform the ENA flow analysis
attributes(f)
$names
[1] "T"   "G"   "GP"  "N"   "NP"  "TCC" "TDC" "ns" 

show(f$T)  # throughflow vector
    Filter Feeders         Microbiota          Meiofauna    Deposit Feeders 
           41.4700             8.1721             8.4805             2.5100 
         Predators Deposited Detritus 
            0.6856            22.2651 

show(f$N)  # integral output-oriented flow intensity
                   Filter Feeders Microbiota Meiofauna Deposit Feeders
Filter Feeders                  1  0.1970605 0.2044972      0.06052568
Microbiota                      0  1.1018630 0.2532824      0.19036255
Meiofauna                       0  0.2862988 1.2971032      0.16586629
Deposit Feeders                 0  0.4039454 0.4191895      1.12406883
Predators                       0  0.2424763 0.2516269      0.07447480
Deposited Detritus              0  0.5096313 0.5288639      0.15652949
                    Predators Deposited Detritus
Filter Feeders     0.01653243          0.5368966
Microbiota         0.01305235          0.2775284
Meiofauna          0.01137274          0.7800287
Deposit Feeders    0.07707261          1.1005597
Predators          1.00510642          0.6606330
Deposited Detritus 0.01073256          1.3885039

show(f$ns) # vector of flow-based network statisics
     Boundary     TST     TSTp      APL       FCI       BFI       DFI       IFI
[1,]    41.47 83.5833 125.0533 2.015512 0.1101686 0.4961517 0.1950689 0.3087794
         ID.F   ID.F.I   ID.F.O    HMG.I    HMG.O AMP.I AMP.O mode0.F  mode1.F
[1,] 1.582925 1.716607 1.534181 2.051826 1.891638     3     1   41.47 32.90504
      mode2.F  mode3.F mode4.F
[1,] 9.208256 32.90504   41.47

Ascendency Metrics

The ascendancy metrics proposed by Dr. Ulanowicz are also most often applied to the network flow distributions. In enaR this is done as follows.

a <- enaAscendency(m)  # calculate the Ascendnecy metrics
show(a)
            H      AMI       Hr      CAP      ASC       OH   ASC.CAP    OH.CAP
[1,] 3.018275 1.330211 1.688063 377.4452 166.3473 211.0979 0.4407191 0.5592809
 robustness     ELD       TD  A.input A.internal A.export A.respiration
[1,]  0.3611021 1.79506 2.514395 66.03696   72.62476        0      27.68558
 OH.input OH.internal OH.export OH.respiration CAP.input CAP.internal
[1,]        0    103.2914         0       107.8065  66.03696     175.9162
     CAP.export CAP.respiration
[1,]          0         135.492