This page illustrates how to use the enaStructure()
function to find selected structural properties of a network model.
Load the library of models and select one to use for this illustration.
load data
data(enaModels) # load library of Ecosystem Networks
names(enaModels) # view model names
#> [1] "Marine Coprophagy (oyster)"
#> [2] "Lake Findley "
#> [3] "Mirror Lake"
#> [4] "Lake Wingra"
#> [5] "Marion Lake"
#> [6] "Cone Springs"
#> [7] "Silver Springs"
#> [8] "English Channel"
#> [9] "Oyster Reef "
#> [10] "Baie de Somme"
#> [11] "Bothnian Bay"
#> [12] "Bothnian Sea"
#> [13] "Ythan Estuary"
#> [14] "Sundarban Mangrove (virgin)"
#> [15] "Sundarban Mangrove (reclaimed)"
#> [16] "Baltic Sea"
#> [17] "Ems Estuary"
#> [18] "Swartkops Estuary 15"
#> [19] "Southern Benguela Upwelling"
#> [20] "Peruvian Upwelling"
#> [21] "Crystal River (control)"
#> [22] "Crystal River (thermal)"
#> [23] "Charca de Maspalomas Lagoon"
#> [24] "Northern Benguela Upwelling"
#> [25] "Swartkops Estuary"
#> [26] "Sunday Estuary"
#> [27] "Kromme Estuary"
#> [28] "Okefenokee Swamp"
#> [29] "Neuse Estuary (early summer 1997)"
#> [30] "Neuse Estuary (late summer 1997) "
#> [31] "Neuse Estuary (early summer 1998)"
#> [32] "Neuse Estuary (late summer 1998)"
#> [33] "Gulf of Maine"
#> [34] "Georges Bank"
#> [35] "Middle Atlantic Bight"
#> [36] "Narragansett Bay"
#> [37] "Southern New England Bight"
#> [38] "Chesapeake Bay"
#> [39] "Mondego Estuary (Zostera sp. Meadows)"
#> [40] "Mdloti Estuary (C, March 2002)"
#> [41] "St. Marks Seagrass, site 1 (Jan.)"
#> [42] "St. Marks Seagrass, site 1 (Feb.)"
#> [43] "St. Marks Seagrass, site 2 (Jan.)"
#> [44] "St. Marks Seagrass, site 2 (Feb.)"
#> [45] "St. Marks Seagrass, site 3 (Jan.)"
#> [46] "St. Marks Seagrass, site 4 (Feb.)"
#> [47] "Sylt-Romo Bight (C)"
#> [48] "Graminoids (wet)"
#> [49] "Graminoids (dry)"
#> [50] "Cypress (wet)"
#> [51] "Cypress (dry)"
#> [52] "Lake Oneida (pre-ZM)"
#> [53] "Lake Oneida (post-ZM)"
#> [54] "Bay of Quinte (pre-ZM)"
#> [55] "Bay of Quinte (post-ZM)"
#> [56] "Mangroves (wet)"
#> [57] "Mangroves (dry)"
#> [58] "Florida Bay (wet)"
#> [59] "Florida Bay (dry)"
#> [60] "Hubbard Brook (Ca)(Waide)"
#> [61] "Hardwood Forest, NH (Ca)"
#> [62] "Duglas Fir Forest, WA (Ca)"
#> [63] "Duglas Fir Forest, WA (K)"
#> [64] "Puerto Rican Rain Forest (Ca)"
#> [65] "Puerto Rican Rain Forest (K)"
#> [66] "Puerto Rican Rain Forest (Mg)"
#> [67] "Puerto Rican Rain Forest (Cu)"
#> [68] "Puerto Rican Rain Forest (Fe)"
#> [69] "Puerto Rican Rain Forest (Mn)"
#> [70] "Puerto Rican Rain Forest (Na)"
#> [71] "Puerto Rican Rain Forest (Sr)"
#> [72] "Tropical Rain Forest (N)"
#> [73] "Neuse River Estuary (N, AVG)"
#> [74] "Neuse River Estuary (N, Spring 1985)"
#> [75] "Neuse River Estuary (N, Summer 1985)"
#> [76] "Neuse River Estuary (N, Fall 1985)"
#> [77] "Neuse River Estuary (N, Winter 1986)"
#> [78] "Neuse River Estuary (N, Spring 1986)"
#> [79] "Neuse River Estuary (N, Summer 1986)"
#> [80] "Neuse River Estuary (N, Fall 1986)"
#> [81] "Neuse River Estuary (N, Winter 1987)"
#> [82] "Neuse River Estuary (N, Spring 1987)"
#> [83] "Neuse River Estuary (N, Summer 1987)"
#> [84] "Neuse River Estuary (N, Fall 1987)"
#> [85] "Neuse River Estuary (N, Winter 1988)"
#> [86] "Neuse River Estuary (N, Spring 1988)"
#> [87] "Neuse River Estuary (N, Summer 1988)"
#> [88] "Neuse River Estuary (N, Fall 1988)"
#> [89] "Neuse River Estuary (N, Winter 1989)"
#> [90] "Cape Fear River Estuary (N, oligohaline)"
#> [91] "Cape Fear River Estuary (N, polyhaline)"
#> [92] "Lake Lanier (P) Averaged"
#> [93] "Great Lakes (N)"
#> [94] "Baltic Sea (N)"
#> [95] "Chesapeake Bay (N)"
#> [96] "Chesapeake Bay (P)"
#> [97] "Chesapeake Bay (P, Winter)"
#> [98] "Chesapeake Bay (P, Spring)"
#> [99] "Chesapeake Bay (P, Summer)"
#> [100] "Chesapeake Bay (P, Fall)"
#> [101] "Sylt-Romo Bight (N)"
#> [102] "Sylt-Romo Bight (P)"
#> [103] "Beijing Urban Metabolism (C)"
#> [104] "Vienna Urban Metabolism (C)"
NET <- enaModels[[9]] # select the oyster NETNext, we apply the structural network analysis.
s <- enaStructure(NET)
attributes(s)
#> $names
#> [1] "A" "ns"The s data object contains two objects: the adjacency matrix A and a vector of network statistics called ns.
# Adjacency matrix
show(s$A)
#> Filter Feeders Microbiota Meiofauna Deposit Feeders
#> Filter Feeders 0 0 0 0
#> Microbiota 0 0 1 1
#> Meiofauna 0 0 0 1
#> Deposit Feeders 0 0 0 0
#> Predators 0 0 0 0
#> Deposited Detritus 0 1 1 1
#> Predators Deposited Detritus
#> Filter Feeders 1 1
#> Microbiota 0 0
#> Meiofauna 0 1
#> Deposit Feeders 1 1
#> Predators 0 1
#> Deposited Detritus 0 0
# network stats
show(s$ns)
#> n L C LD ppr lam1A mlam1A rho R d
#> [1,] 6 12 0.3333333 2 2.147899 2.147899 1 2.147899 0.4655712 0.147899
#> no.scc no.scc.big pscc
#> [1,] 2 1 0.8333333