diff --git a/docs/make.jl b/docs/make.jl
index c83399052..aeb6f60c3 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"
-                                       ]
+                                       ],
+                  "Examples" => [
+                                 "Integration with Mangal" => "examples/integration_mangal.md"
+                                 ]
                  ]
         )
 
diff --git a/docs/src/examples/integration_mangal.md b/docs/src/examples/integration_mangal.md
new file mode 100644
index 000000000..f0e215c35
--- /dev/null
+++ b/docs/src/examples/integration_mangal.md
@@ -0,0 +1,126 @@
+# Integration with Mangal.jl
+
+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 this analysis, we need to upload the following packages:
+
+```@example mangal
+using EcologicalNetworks
+using Mangal
+using DataFrames
+using Plots
+```
+
+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.
+
+```@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, 5),
+                 [:id, :S, :L, :pred, :herb],
+                 number_of_networks)
+
+global cursor = 1
+@progress "Paging networks" for page in 1:number_of_pages
+    global cursor
+    networks_in_page = Mangal.networks("count" => 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")
+        mangal_networks[cursor,:] .= (current_network.id, S, L, pred, herb)
+        cursor = cursor + 1
+    end
+end
+```
+
+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.
+
+```@example mangal
+mangal_foodwebs = mangal_networks[mangal_networks[!, :pred] .+ mangal_networks[!, :herb] ./ mangal_networks[!, :L] .> 0.5, :]
+```
+
+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 = []
+
+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` 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.
+
+```@example mangal
+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
+```
+
+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)
+
+plotA = scatter(foodweb_measures.connect, foodweb_measures.nested,
+    markersize=foodweb_measures.rich ./ 30,
+    alpha=0.6, color=pal,
+    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,
+    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,
+    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)
+```