Minor quickfixes to avoid another review round :)

src/inputs/foodwebs.jl

@@ -6,9 +6,6 @@ Generating FoodWeb objects
 const AdjacencyMatrix = SparseMatrixCSC{Bool,Int64}
 const Label = Union{String,Symbol}
 
-const disconnected_warning = "'A' contains disconnected species \
-    w.r.t. trophic interactions."
-
 #### Type definition and FoodWeb functions ####
 abstract type EcologicalNetwork end
 mutable struct FoodWeb <: EcologicalNetwork
@@ -121,7 +118,7 @@ These behaviours can respectively be changed by setting the keyword arguments
 `check_cycle` and  `check_disconnected` to `true` or `false`.
 
 By default, we set a tolerance corresponding to 10% of the number of links
-given in argument (`L`), or 10% of the connectance if you give a connectane (`C`).
+given in argument (`L`), or 10% of the connectance if you give a connectance (`C`).
 For instance, if you create a `FoodWeb` with 20 links,
 by default we ensure that `FoodWeb` in output will contain between 18 and 22 links.
 
@@ -146,7 +143,7 @@ true
 `FoodWeb`s are generated randomly and can be rejected if they do not satisfy
 one of the properties defined above (number of links not in the tolerance range,
 cycles, etc.).
-Thus we repeat the generation of the `FoodWeb` until of all criterion are satisfied
+Thus we repeat the generation of the `FoodWeb` until all criteria are satisfied
 or until the number of maximum iteraction `iter_max` is reached.
 If the maximum number of iterations is reached an error is thrown.
 That number can be controlled by the `iter_max` keyword argument,
@@ -269,6 +266,9 @@ function FoodWeb(
     FoodWeb(A, species, M, metabolic_class, method)
 end
 
+const disconnected_warning = "'A' contains disconnected species \
+    w.r.t. trophic interactions."
+
 function structural_foodweb_data(uni_net, M, Z, model)
     A = uni_net.edges
     species = uni_net.S
@@ -470,8 +470,8 @@ function model_foodweb_from_C(
     iter <= iter_max ||
         throw(ErrorException("Could not generate adequate network with C=$C \
         and tol_C=$ΔC before the maximum number of iterations ($iter_max) was reached. \
-        Consider either increasing the `tol`erance on the connectance \
-        or, if `check_cycle = true` to lower the connectance."))
+        Consider either increasing the tolerance on the connectance \
+        or, if `check_cycle = true`, to lower the connectance."))
     net
 end
 
@@ -501,7 +501,7 @@ function model_foodweb_from_L(
     iter <= iter_max ||
         throw(ErrorException("Could not generate adequate network with L=$L \
         and tol_L=$ΔL before the maximum number of iterations ($iter_max) was reached. \
-        Consider either increasing the `tol`erance on the number of links \
+        Consider either increasing the tolerance on the number of links \
         or, if `check_cycle = true` to lower the connectance."))
     net
 end

test/inputs/test-foodwebs.jl

@@ -79,17 +79,17 @@ end
         end
     end
 
-    # Can not provide both number of links (L) and connectance.
+    # Cannot provide both number of links (L) and connectance.
     @test_throws ArgumentError FoodWeb(nichemodel, 10, C = 0.1, L = 10)
 
-    # Can not provide both tolerance on number of links (tol_L) and connectance (tol_C).
+    # Cannot provide both tolerance on number of links (tol_L) and connectance (tol_C).
     @test_throws ArgumentError FoodWeb(nichemodel, 10, C = 0.1, tol_L = 1, tol_C = 0.01)
     @test_throws ArgumentError FoodWeb(nichemodel, 10, L = 10, tol_L = 1, tol_C = 0.01)
 
-    # Can not provide a number of links and a tolerance on the connectance.
+    # Cannot provide a number of links and a tolerance on the connectance.
     @test_throws ArgumentError FoodWeb(nichemodel, 10, L = 10, tol_C = 0.01)
 
-    # Can not provide a connectance and a tolerance on the number of links.
+    # Cannot provide a connectance and a tolerance on the number of links.
     @test_throws ArgumentError FoodWeb(nichemodel, 10, C = 0.1, tol_L = 1)
 end