From 6345d252682641c81c2dfbac7d7e1a494c5b528e Mon Sep 17 00:00:00 2001 From: Francis Banville Date: Tue, 10 Nov 2020 16:56:56 -0500 Subject: [PATCH 1/5] example use case - integration with mangal --- docs/make.jl | 5 +- docs/src/examples/fig/LS.png | Bin 0 -> 1586781 bytes docs/src/examples/fig/nestmod.png | Bin 0 -> 1015455 bytes docs/src/examples/integration_mangal.md | 178 ++++++++++++++++++++++++ 4 files changed, 182 insertions(+), 1 deletion(-) create mode 100644 docs/src/examples/fig/LS.png create mode 100644 docs/src/examples/fig/nestmod.png create mode 100644 docs/src/examples/integration_mangal.md diff --git a/docs/make.jl b/docs/make.jl index c83399052..52fcc83f9 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -29,7 +29,10 @@ makedocs( "Random networks" => [ "Null models" => "random/null.md", "Structural models" => "random/structure.md" - ] + ], + "Running examples" => [ + "Integration with Mangal" => "examples/integration_mangal.md" + ] ] ) diff --git a/docs/src/examples/fig/LS.png b/docs/src/examples/fig/LS.png new file mode 100644 index 0000000000000000000000000000000000000000..34321d8c884e9824e48acf58c8180c004642a914 GIT binary patch literal 1586781 zcmeFa2{e{_`!;;<-K5<>6T1{D(LhQvPbo#7K5^Opp`d7*EG^&gI@+Zo6ag)-ZLc1py2LDbeO>iT zW_ZTv-RESJ7ppp38J$lr!8H~f7iY?}1s1$A`xmaGrNISOg{$)W_E01hm{6?n}6#0u^BOMd@XNj{$`NxVsed8ZroHN8f z;qucV{z*DN4e?Lv{%MGRs>@G9{8OWU8sfhIBeulRkw`oGE3#&WdmYJ>ITKAy8oU82 z5tbGf3X$bqEq{{6f;Niw=M8d58e6X?k-lBYk6pc8NhrFaQ{1C=hMz=wW4_%vmL)of zSzIQ1vbODgju_enVI=V|JcO%hY!cY%TnjA^|`2M za(ZTXu(`Q8At9kAXa$LsY{VPjFfeHyS5;eG{qt=i6x|k+6V@J#wzjrOLX;$~XH^EW z`dM7ik5 zZEZe#Xlh~!{(&?klHjRD4W?7{IeBkwZS6Xvq>9cPR7M?!M=7w31AnL6PjX6@y1BVU zMn*n-_|V_RkwhBX^vep&j|Ue|j`m7RN!@uiR_K3J{${M~s;ATf73;m9~HF9QH^4BPC1yvZ9YB+;-#{`=pxb+BMsq?>`g`bV;YM z9)HR-81BI#UScB<$?V2q$mub(A@5PY)l^4Wc#?601#duIUET5QUb{a?V>|4Mmo6~x zx9|g7*Q#bY)b)-EStopQz3k^x`s`6l)@Z=dbs}fH5}wHx#450gdrpY>?dRxlb$dSJ znk;c<^;`$kdujiP6$OLZ+W&Ys=FWPLEt zbAnr0kJFpl!zLruOS4K^XxhxqZmO*~ST=mvQHTNcZPy}tLiq6jKe@>ll1H0aZHOZ=!|L>Bw))KAQesLE$U18AxI*R zQ9L#=HPxZFK0p8Dm8aubj;B2sBXe@fzkjdpP>4LcYWr%j;JCL{;=LODfm7ehPZzk9 zs&h#C(9l%7`kZ~(>`KG7{QBD;x$dPL)AmIxNu(^s#aA_`&#)Z@l%NtZUoY|zALmZF zJaWWVSNF=LE0^puij%K4)H8|4)*7)T)!6c-wPrVp_clGZ^k~g_D6ThtQRZ%F;lmd; 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zCq{o6a}mA4%(KjZ$N%}EN9~(Ihyp=1<{$&zH{Ow)3AMBJ>3@E9_snQq)Ijeg?U8_k z7!inFN_oad;KcuanZ`jZoiCe;sqVIZ6pGUS)86%kG!=#MZ(_}|XeA?B1taznNeVrb zgTX~^v7yMNAvXt$`4m3XLynCi<03uGLRcjQdq^u0#gKZihe@qJWb_ne4-yGcAe15W zFezHt@4Jq2*RQvp;+&@q&d$#L-S6-3eCM2-obNiCws{f+0E|Is`Z2`4@a=p&xey<6PXicA~GZ>`Q(sr;=Dc0E32-f|03>Z?0Yk_ElBSCS&5~mM zj9@DT{3`ZC&ctgdn_07s^Udew>*sGZl0We~#&^ur_<;iH?^wOc z!E?faEHxe4*0(D)+leihr;%e!TGiHDUHZrO1J`jA%)=6k4;(H?();kQ5G#c6EFl0J zYRlTn=cgmgvU$r|3Jgj{h{)A9GUb?@{nffFX&RB=FTFydYH7C6A&cv%);5nvl1ZW} zyR>ahe@&C5kEkZ|m#30MQB9t4Rmdx`VL4C=tE>&nf$RS_m8C*z@fbgAB3mfdr@VhM zfjS}V!Hx`F^jVEtg~*qvRTrXrA~iH4zN#reR)N7E6(9 count_per_page, "page" => page-1) + @progress "Counting items" for current_network in networks_in_page + S = count(MangalNode, current_network) + L = count(MangalInteraction, current_network) + pred = count(MangalInteraction, current_network, "type" => "predation") + herb = count(MangalInteraction, current_network, "type" => "herbivory") + mutu = count(MangalInteraction, current_network, "type" => "mutualism") + para = count(MangalInteraction, current_network, "type" => "parasitism") + mangal_networks[cursor,:] .= (current_network.id, S, L, pred, herb, mutu, para) + cursor = cursor + 1 + end +end +``` + +We can classify all networks according to their most frequent type of interactions. Note that trophic interactions in food webs comprise interactions of predation and of herbivory. Note also that we discard small networks having less than 5 interactions for reliability reasons. + +```julia +# number of trophic interactions +mangal_networks.foodweb = mangal_networks.pred .+ mangal_networks.herb + +# number of other types of interactions +mangal_networks.other = mangal_networks.L .- mangal_networks.foodweb .- mangal_networks.mutu .- mangal_networks.para + +# discard networks with less than 5 links +mangal_networks2 = mangal_networks[mangal_networks.L .> 4, :] + +# classify networks +number_interactions_max_type = maximum.(eachrow(mangal_networks2[:,[:mutu, :para,:foodweb,:other]])) + +foodwebs = mangal_networks2[mangal_networks2[:foodweb] .== number_interactions_max_type ,:] +parasitism_webs = mangal_networks2[mangal_networks2[:para] .== number_interactions_max_type ,:] +mutualism_webs = mangal_networks2[mangal_networks2[:mutu] .== number_interactions_max_type ,:] +other_webs = mangal_networks2[mangal_networks2[:other] .== number_interactions_max_type ,:] +``` + +The association between the number of species and the number of links in different types of networks can then be plotted, using a given color palette: + +```julia + +# color palette +pal = ( + foodwebs=RGB(204/255,121/255,167/255), + parasitism=RGB(230/255,159/255,0/255), + mutualism=RGB(0/255,158/255,115/255), + other=RGB(86/255,190/255,233/255), + ) + +# plot +scatter(mutualism_webs.S, mutualism_webs.L, + alpha=0.2, color=pal.mutualism, markersize=5, markershape=:rect, + lab="mutualism", + legend=:topleft, foreground_color_legend=nothing, background_color_legend=:white, + dpi=1000, framestyle=:box) +scatter!(parasitism_webs.S, parasitism_webs.L, + alpha=0.2, color=pal.parasitism, markersize=5, markershape=:diamond, + lab="parasitism") +scatter!(foodwebs.S, foodwebs.L, + alpha=0.2, color=pal.foodwebs, markersize=5, + lab="food webs") +scatter!(other_webs.S, other_webs.L, + alpha=0.2, color=pal.other, markersize=5, markershape=:utriangle, + lab="other types") +xaxis!(:log, "Number of species") +yaxis!(:log, "Number of links") +``` + +![Association between the number of species (nodes) and the number of links (edges) in ecological networks archived on `mangal.io`. Networks with less than 5 interactions are discarded. Different types of interactions can be listed within the same network. We classify all networks according to their most frequent type of interactions. ](fig/LS.png) + +Further analysis of food-web structure are conducted using `EcologicalNetworks.jl`. We first read food webs from `mangal.io` using their ID numbers. We then need to convert the type of these MangalNetworks objects to UnipartiteNetworks, when possible. + +```julia + +mangal_foodwebs = network.(foodwebs.id) + +unipartite_foodwebs=[] + +for i in eachindex(mangal_foodwebs) + try + unipartite_foodweb = convert(UnipartiteNetwork, mangal_foodwebs[i]) + push!(unipartite_foodwebs, unipartite_foodweb) + catch + println("Cannot convert mangal food web $(i) to a unipartite network") + end +end +``` + +We can then compute any measure supported by `EcologicalNetworks.jl` on these UnipartiteNetworks. In this example, we compute species richness, connectance, nestedness, and modularity. + +```julia +foodweb_measures = DataFrame(fill(Float64, 4), + [:rich, :connect, :nested, :modul], + length(unipartite_foodwebs)) + +# species richness +foodweb_measures.rich = richness.(unipartite_foodwebs) + +# connectance +foodweb_measures.connect = connectance.(unipartite_foodwebs) + +# nestedness +foodweb_measures.nested = ρ.(unipartite_foodwebs) + +# modularity (BRIM algorithm) +number_of_modules = repeat(3:15, outer=100) +modules = Array{Dict}(undef, length(number_of_modules)) + +for i in eachindex(unipartite_foodwebs) + current_network = unipartite_foodwebs[i] + for j in eachindex(number_of_modules) + _, modules[j] = n_random_modules(number_of_modules[j])(current_network) |> x -> brim(x...) + end + partition_modularity = map(x -> Q(current_network,x), modules); + foodweb_measures.modul[i] = maximum(partition_modularity) +end +``` + +Finally, the association between these food-web measures can be plotted. Here marker size is proportional to species richness. + +```julia +plotA = scatter(foodweb_measures.connect, foodweb_measures.nested, + markersize=foodweb_measures.rich ./ 30, + alpha=0.6, color=pal.foodwebs, + lab="", + dpi=1000, framestyle=:box) +xaxis!("Connectance") +yaxis!((0.4,0.9), "Nestedness") + +plotB = scatter(foodweb_measures.connect, foodweb_measures.modul, + markersize=foodweb_measures.rich ./ 30, + alpha=0.6, color=pal.foodwebs, + lab="", + dpi=1000, framestyle=:box) +xaxis!("Connectance") +yaxis!("Modularity") + +plotC = scatter(foodweb_measures.modul, foodweb_measures.nested, + markersize=foodweb_measures.rich ./ 30, + alpha=0.6, color=pal.foodwebs, + lab="", + dpi=1000, framestyle=:box) +xaxis!("Modularity") +yaxis!((0.4,0.9), "Nestedness") + +plot(plotA, plotB, plotC, layout=(1,3), + title=["($i)" for j in 1:1, i in 1:3], titleloc=:right, titlefont = font(12), + size=(700,350), margin=5Plots.mm) +``` + +![Association between (1) connectance and nestedness, (2) connectance and modularity, and (3) modularity and nestedness in food webs archived on `mangal.io`. We compute nestedness as the spectral radius of a network (i.e. the largest absolute eigenvalue of its matrix of interactions). We optimize network modularity using the BRIM algorithm (best partition out of 100 random runs for 3 to 15 modules). The marker size is proportional to the number of species in a network, which varies between 5 and 714 species.](fig/nestmod.png) + +The script to reproduce these figures is available in the [GitHub repository](https://github.com/EcoJulia/EcologicalNetworks.jl/blob/joss-article/paper/main.jl). This example is taken from the manuscript describing `Mangal.jl` and `EcologicalNetworks.jl` submitted to the Journal of Open Source Software (JOSS). From 6f69c175b960c838a53fe0b2e598ddce2a2b8b34 Mon Sep 17 00:00:00 2001 From: Francis Banville Date: Tue, 10 Nov 2020 17:10:35 -0500 Subject: [PATCH 2/5] :memo: update name of the use case section in the doc --- docs/make.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/make.jl b/docs/make.jl index 52fcc83f9..60544b45f 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -30,7 +30,7 @@ makedocs( "Null models" => "random/null.md", "Structural models" => "random/structure.md" ], - "Running examples" => [ + "Use cases" => [ "Integration with Mangal" => "examples/integration_mangal.md" ] ] From 088cebf298044550ffcf1f1b4b4b8e9cd0eabd50 Mon Sep 17 00:00:00 2001 From: Francis Banville Date: Tue, 10 Nov 2020 17:14:01 -0500 Subject: [PATCH 3/5] :memo: fix brackets --- docs/make.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/make.jl b/docs/make.jl index 60544b45f..e190fd167 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -31,8 +31,8 @@ makedocs( "Structural models" => "random/structure.md" ], "Use cases" => [ - "Integration with Mangal" => "examples/integration_mangal.md" - ] + "Integration with Mangal" => "examples/integration_mangal.md" + ] ] ) From 433bb57803f1040f13be53b091b5c9e35bd12708 Mon Sep 17 00:00:00 2001 From: Francis Banville Date: Wed, 11 Nov 2020 10:03:45 -0500 Subject: [PATCH 4/5] :fire: remove figures --- docs/src/examples/fig/LS.png | Bin 1586781 -> 0 bytes docs/src/examples/fig/nestmod.png | Bin 1015455 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 docs/src/examples/fig/LS.png delete mode 100644 docs/src/examples/fig/nestmod.png diff --git a/docs/src/examples/fig/LS.png b/docs/src/examples/fig/LS.png deleted file mode 100644 index 34321d8c884e9824e48acf58c8180c004642a914..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1586781 zcmeFa2{e{_`!;;<-K5<>6T1{D(LhQvPbo#7K5^Opp`d7*EG^&gI@+Zo6ag)-ZLc1py2LDbeO>iT zW_ZTv-RESJ7ppp38J$lr!8H~f7iY?}1s1$A`xmaGrNISOg{$)W_E01hm{6?n}6#0u^BOMd@XNj{$`NxVsed8ZroHN8f z;qucV{z*DN4e?Lv{%MGRs>@G9{8OWU8sfhIBeulRkw`oGE3#&WdmYJ>ITKAy8oU82 z5tbGf3X$bqEq{{6f;Niw=M8d58e6X?k-lBYk6pc8NhrFaQ{1C=hMz=wW4_%vmL)of zSzIQ1vbODgju_enVI=V|JcO%hY!cY%TnjA^|`2M za(ZTXu(`Q8At9kAXa$LsY{VPjFfeHyS5;eG{qt=i6x|k+6V@J#wzjrOLX;$~XH^EW z`dM7ik5 zZEZe#Xlh~!{(&?klHjRD4W?7{IeBkwZS6Xvq>9cPR7M?!M=7w31AnL6PjX6@y1BVU zMn*n-_|V_RkwhBX^vep&j|Ue|j`m7RN!@uiR_K3J{${M~s;ATf73;m9~HF9QH^4BPC1yvZ9YB+;-#{`=pxb+BMsq?>`g`bV;YM z9)HR-81BI#UScB<$?V2q$mub(A@5PY)l^4Wc#?601#duIUET5QUb{a?V>|4Mmo6~x zx9|g7*Q#bY)b)-EStopQz3k^x`s`6l)@Z=dbs}fH5}wHx#450gdrpY>?dRxlb$dSJ znk;c<^;`$kdujiP6$OLZ+W&Ys=FWPLEt zbAnr0kJFpl!zLruOS4K^XxhxqZmO*~ST=mvQHTNcZPy}tLiq6jKe@>ll1H0aZHOZ=!|L>Bw))KAQesLE$U18AxI*R zQ9L#=HPxZFK0p8Dm8aubj;B2sBXe@fzkjdpP>4LcYWr%j;JCL{;=LODfm7ehPZzk9 zs&h#C(9l%7`kZ~(>`KG7{QBD;x$dPL)AmIxNu(^s#aA_`&#)Z@l%NtZUoY|zALmZF zJaWWVSNF=LE0^puij%K4)H8|4)*7)T)!6c-wPrVp_clGZ^k~g_D6ThtQRZ%F;lmd; 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models" => "random/null.md", "Structural models" => "random/structure.md" ], - "Use cases" => [ + "Examples" => [ "Integration with Mangal" => "examples/integration_mangal.md" ] ] diff --git a/docs/src/examples/integration_mangal.md b/docs/src/examples/integration_mangal.md index 793d4763d..f0e215c35 100644 --- a/docs/src/examples/integration_mangal.md +++ b/docs/src/examples/integration_mangal.md @@ -1,25 +1,25 @@ # Integration with Mangal.jl -Here we provide an example use case to get you started with `EcologicalNetworks.jl`. We show how to analyse meaningful network properties using all networks archived on the [`mangal.io`](https://mangal.io/#/) online database. We will create two plots: (1) the association between the number of species and the number of links in different types of networks, and (2) the association between species richness, connectance, nestedness, and modularity in food webs. +In this example, we will show how `EcologicalNetworks.jl` can be integrated with [`Mangal.jl`](https://github.com/EcoJulia/Mangal.jl) to analyse many ecological networks. Specifically, we will show how to analyse the association between meaningful network properties (i.e. species richness, connectance, nestedness, and modularity) using all food webs archived on the [`mangal.io`](https://mangal.io/#/) online database. -To conduct these analyses, we need to upload the following packages: +To conduct this analysis, we need to upload the following packages: -```julia +```@example mangal using EcologicalNetworks using Mangal using DataFrames using Plots ``` -We first retrieve relevant metadata for all networks archived on `mangal.io`. We count the number of species $S$ and the total number of interactions $L$ in each network, as well as their numbers of interactions of predation, of herbivory, of mutualism, and of parasitism. We store these information in a data frame along with the networks' ID numbers. +We first retrieve relevant metadata for all networks archived on `mangal.io` using the `Mangal.jl` package. We count the number of species $S$ and the total number of interactions $L$ in each network, as well as their number of trophic interactions (predation and herbivory). We store these information in a data frame along with the networks' ID numbers. -```julia +```@example mangal number_of_networks = count(MangalNetwork) count_per_page = 100 number_of_pages = convert(Int, ceil(number_of_networks/count_per_page)) -mangal_networks = DataFrame(fill(Int64, 7), - [:id, :S, :L, :pred, :herb, :mutu, :para], +mangal_networks = DataFrame(fill(Int64, 5), + [:id, :S, :L, :pred, :herb], number_of_networks) global cursor = 1 @@ -31,75 +31,24 @@ global cursor = 1 L = count(MangalInteraction, current_network) pred = count(MangalInteraction, current_network, "type" => "predation") herb = count(MangalInteraction, current_network, "type" => "herbivory") - mutu = count(MangalInteraction, current_network, "type" => "mutualism") - para = count(MangalInteraction, current_network, "type" => "parasitism") - mangal_networks[cursor,:] .= (current_network.id, S, L, pred, herb, mutu, para) + mangal_networks[cursor,:] .= (current_network.id, S, L, pred, herb) cursor = cursor + 1 end end ``` -We can classify all networks according to their most frequent type of interactions. Note that trophic interactions in food webs comprise interactions of predation and of herbivory. Note also that we discard small networks having less than 5 interactions for reliability reasons. +We now have all the information we need to identify all food webs archived on `mangal.io`. Here we consider as food webs any ecological networks mainly composed of trophic interactions. -```julia -# number of trophic interactions -mangal_networks.foodweb = mangal_networks.pred .+ mangal_networks.herb - -# number of other types of interactions -mangal_networks.other = mangal_networks.L .- mangal_networks.foodweb .- mangal_networks.mutu .- mangal_networks.para - -# discard networks with less than 5 links -mangal_networks2 = mangal_networks[mangal_networks.L .> 4, :] - -# classify networks -number_interactions_max_type = maximum.(eachrow(mangal_networks2[:,[:mutu, :para,:foodweb,:other]])) - -foodwebs = mangal_networks2[mangal_networks2[:foodweb] .== number_interactions_max_type ,:] -parasitism_webs = mangal_networks2[mangal_networks2[:para] .== number_interactions_max_type ,:] -mutualism_webs = mangal_networks2[mangal_networks2[:mutu] .== number_interactions_max_type ,:] -other_webs = mangal_networks2[mangal_networks2[:other] .== number_interactions_max_type ,:] -``` - -The association between the number of species and the number of links in different types of networks can then be plotted, using a given color palette: - -```julia - -# color palette -pal = ( - foodwebs=RGB(204/255,121/255,167/255), - parasitism=RGB(230/255,159/255,0/255), - mutualism=RGB(0/255,158/255,115/255), - other=RGB(86/255,190/255,233/255), - ) - -# plot -scatter(mutualism_webs.S, mutualism_webs.L, - alpha=0.2, color=pal.mutualism, markersize=5, markershape=:rect, - lab="mutualism", - legend=:topleft, foreground_color_legend=nothing, background_color_legend=:white, - dpi=1000, framestyle=:box) -scatter!(parasitism_webs.S, parasitism_webs.L, - alpha=0.2, color=pal.parasitism, markersize=5, markershape=:diamond, - lab="parasitism") -scatter!(foodwebs.S, foodwebs.L, - alpha=0.2, color=pal.foodwebs, markersize=5, - lab="food webs") -scatter!(other_webs.S, other_webs.L, - alpha=0.2, color=pal.other, markersize=5, markershape=:utriangle, - lab="other types") -xaxis!(:log, "Number of species") -yaxis!(:log, "Number of links") +```@example mangal +mangal_foodwebs = mangal_networks[mangal_networks[!, :pred] .+ mangal_networks[!, :herb] ./ mangal_networks[!, :L] .> 0.5, :] ``` -![Association between the number of species (nodes) and the number of links (edges) in ecological networks archived on `mangal.io`. Networks with less than 5 interactions are discarded. Different types of interactions can be listed within the same network. We classify all networks according to their most frequent type of interactions. ](fig/LS.png) - -Further analysis of food-web structure are conducted using `EcologicalNetworks.jl`. We first read food webs from `mangal.io` using their ID numbers. We then need to convert the type of these MangalNetworks objects to UnipartiteNetworks, when possible. - -```julia +To analyse their properties, we first need to read all of these food webs using the `Mangal.jl` package, and then convert them to UnipartiteNetworks, when possible, using the `EcologicalNetworks.jl` package. +```@example mangal mangal_foodwebs = network.(foodwebs.id) -unipartite_foodwebs=[] +unipartite_foodwebs = [] for i in eachindex(mangal_foodwebs) try @@ -111,9 +60,9 @@ for i in eachindex(mangal_foodwebs) end ``` -We can then compute any measure supported by `EcologicalNetworks.jl` on these UnipartiteNetworks. In this example, we compute species richness, connectance, nestedness, and modularity. +We can then compute any measure supported by `EcologicalNetworks.jl` for UnipartiteNetworks. In this example, we compute species richness, connectance, nestedness, and modularity. To compute network modularity, we use 100 random species assignments in 3 to 15 groups as our starters, the BRIM algorithm to optimize the modularity for each of these random partitions, and retain the maximum value for each food web. -```julia +```@example mangal foodweb_measures = DataFrame(fill(Float64, 4), [:rich, :connect, :nested, :modul], length(unipartite_foodwebs)) @@ -141,12 +90,15 @@ for i in eachindex(unipartite_foodwebs) end ``` -Finally, the association between these food-web measures can be plotted. Here marker size is proportional to species richness. +The association between these food-web measures can then be plotted. In each subplot, marker size is proportional to species richness. + +```@example mangal +# color palette +pal=RGB(204/255,121/255,167/255) -```julia plotA = scatter(foodweb_measures.connect, foodweb_measures.nested, markersize=foodweb_measures.rich ./ 30, - alpha=0.6, color=pal.foodwebs, + alpha=0.6, color=pal, lab="", dpi=1000, framestyle=:box) xaxis!("Connectance") @@ -154,7 +106,7 @@ yaxis!((0.4,0.9), "Nestedness") plotB = scatter(foodweb_measures.connect, foodweb_measures.modul, markersize=foodweb_measures.rich ./ 30, - alpha=0.6, color=pal.foodwebs, + alpha=0.6, color=pal, lab="", dpi=1000, framestyle=:box) xaxis!("Connectance") @@ -162,7 +114,7 @@ yaxis!("Modularity") plotC = scatter(foodweb_measures.modul, foodweb_measures.nested, markersize=foodweb_measures.rich ./ 30, - alpha=0.6, color=pal.foodwebs, + alpha=0.6, color=pal, lab="", dpi=1000, framestyle=:box) xaxis!("Modularity") @@ -172,7 +124,3 @@ plot(plotA, plotB, plotC, layout=(1,3), title=["($i)" for j in 1:1, i in 1:3], titleloc=:right, titlefont = font(12), size=(700,350), margin=5Plots.mm) ``` - -![Association between (1) connectance and nestedness, (2) connectance and modularity, and (3) modularity and nestedness in food webs archived on `mangal.io`. We compute nestedness as the spectral radius of a network (i.e. the largest absolute eigenvalue of its matrix of interactions). We optimize network modularity using the BRIM algorithm (best partition out of 100 random runs for 3 to 15 modules). The marker size is proportional to the number of species in a network, which varies between 5 and 714 species.](fig/nestmod.png) - -The script to reproduce these figures is available in the [GitHub repository](https://github.com/EcoJulia/EcologicalNetworks.jl/blob/joss-article/paper/main.jl). This example is taken from the manuscript describing `Mangal.jl` and `EcologicalNetworks.jl` submitted to the Journal of Open Source Software (JOSS).

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