diff --git a/predator_prey_generic.jl b/predator_prey_generic.jl
index 1b2b91c..95c6072 100644
--- a/predator_prey_generic.jl
+++ b/predator_prey_generic.jl
@@ -5,6 +5,7 @@ using Agents, Random, GLMakie
     Predation
 end
 mutable struct AnimalDefinition
+    n::Int32
     symbol::Char
     color::GLMakie.ColorTypes.RGBA{Float32}
     energy_threshold::Float64
@@ -19,10 +20,6 @@ reproduction_prop(a) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_
 Δenergy(a) = abmproperties(model)[Symbol(a.def.type*"_"*"Δenergy")]
 perception(a) = abmproperties(model)[Symbol(a.def.type*"_"*"perception")]
 energy_threshold(a) = abmproperties(model)[Symbol(a.def.type*"_"*"energy_threshold")]
-struct StartDefinition
-    n::Int32
-    def::AnimalDefinition
-end
 #might be better to use @multiagent and @subagent with predator prey as subtypes. Allows to dispatch different functions per kind and change execution order with schedulers.bykind
 @agent struct Animal(GridAgent{2})
     energy::Float64
@@ -178,19 +175,13 @@ end
 
 function initialize_model(;
         events = [],
-        start_defs = [
-            StartDefinition(100,AnimalDefinition('●',RGBAf(1.0, 1.0, 1.0, 0.8),20, 0.3, 6, 1, "Sheep", ["Wolf"], ["Grass"])),
-            StartDefinition(20,AnimalDefinition('▲',RGBAf(0.2, 0.2, 0.3, 0.8),20, 0.07, 20, 1, "Wolf", [], ["Sheep"]))
+        animal_defs = [
+            AnimalDefinition(100,'●',RGBAf(1.0, 1.0, 1.0, 0.8),20, 0.3, 6, 1, "Sheep", ["Wolf"], ["Grass"]),
+            AnimalDefinition(20,'▲',RGBAf(0.2, 0.2, 0.3, 0.8),20, 0.07, 20, 1, "Wolf", [], ["Sheep"])
         ],
         dims = (20, 20),
         regrowth_time = 30,
-        Δenergy_sheep = 4,
-        Δenergy_wolf = 20,
         Δenergy_grass = 5,
-        sheep_reproduce = 0.04,
-        wolf_reproduce = 0.05,
-        sheep_perception = 0,
-        wolf_perception = 0,
         seed = 23182,
     )
     rng = MersenneTwister(seed)
@@ -199,11 +190,6 @@ function initialize_model(;
     ## and the time to regrow. Also have static parameter `regrowth_time`.
     ## Notice how the properties are a `NamedTuple` to ensure type stability.
     ## define as dictionary(mutable) instead of tuples(immutable) as per https://github.com/JuliaDynamics/Agents.jl/issues/727
-    ## maybe instead of AnimalDefinition we build the properties dict dynamically and use model properties during the simulation
-    animal_defs = Vector{AnimalDefinition}()
-    for start_def in start_defs
-        push!(animal_defs,start_def.def)
-    end
     animal_properties = generate_animal_parameters(animal_defs)
     model_properties = Dict(
         :events => events,
@@ -217,10 +203,10 @@ function initialize_model(;
         agent_step! = animal_step!, model_step! = model_step!,
         properties, rng, scheduler = Schedulers.Randomly(), warn = false, agents_first = false
     )
-    for start_def in start_defs
-        for _ in 1:start_def.n
-            energy = rand(abmrng(model), 1:(start_def.def.Δenergy*2)) - 1
-            add_agent!(Animal, model, energy, start_def.def, nothing, [], [], [], [])
+    for def in animal_defs
+        for _ in 1:def.n
+            energy = rand(abmrng(model), 1:(def.Δenergy*2)) - 1
+            add_agent!(Animal, model, energy, def, nothing, [], [], [], [])
         end
     end
     ## Add grass with random initial growth
diff --git a/test_predator_prey_generic.ipynb b/test_predator_prey_generic.ipynb
index 54fe99d..ce7b85c 100644
--- a/test_predator_prey_generic.ipynb
+++ b/test_predator_prey_generic.ipynb
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,20 +71,18 @@
     "events = RecurringEvent[]\n",
     "#push!(events, RecurringEvent(\"Drought\", 80, 30, 30, 50, 120, 1))\n",
     "#push!(events, RecurringEvent(\"Flood\", 50, 30, 70, 80, 120, 1))\n",
-    "push!(events, RecurringEvent(\"PreyReproduceSeasonal\", 0.5, 0.1, 1, 4, 12, 1))\n",
-    "push!(events, RecurringEvent(\"PredatorReproduceSeasonal\", 0.1, 0.07, 4, 6, 12, 1))\n",
-    "sheep_def = AnimalDefinition('●',RGBAf(1.0, 1.0, 1.0, 0.8),20, 0.3, 20, 3, \"Sheep\", [\"Wolf\",\"Bear\"], [\"Grass\"])\n",
-    "wolf_def = AnimalDefinition('▲',RGBAf(0.2, 0.2, 0.3, 0.8),20, 0.07, 20, 1, \"Wolf\", [], [\"Sheep\"])\n",
-    "bear_def = AnimalDefinition('■',RGBAf(1.0, 0.8, 0.5, 0.8),20, 0.07, 20, 1, \"Bear\", [], [\"Sheep\"])\n",
-    "parameter_ranges = generate_animal_parameter_ranges([sheep_def,wolf_def,bear_def])\n",
+    "#push!(events, RecurringEvent(\"PreyReproduceSeasonal\", 0.5, 0.1, 1, 4, 12, 1))\n",
+    "#push!(events, RecurringEvent(\"PredatorReproduceSeasonal\", 0.1, 0.07, 4, 6, 12, 1))\n",
+    "animal_defs = [\n",
+    "AnimalDefinition(30,'●',RGBAf(1.0, 1.0, 1.0, 0.8),20, 0.3, 20, 3, \"Sheep\", [\"Wolf\",\"Bear\"], [\"Grass\"])\n",
+    "AnimalDefinition(3,'▲',RGBAf(0.2, 0.2, 0.3, 0.8),20, 0.07, 20, 1, \"Wolf\", [], [\"Sheep\"])\n",
+    "#AnimalDefinition('■',RGBAf(1.0, 0.8, 0.5, 0.8),20, 0.07, 20, 1, \"Bear\", [], [\"Sheep\"])\n",
+    "]\n",
+    "parameter_ranges = generate_animal_parameter_ranges(animal_defs)\n",
     "\n",
     "stable_params = (;\n",
     "    events = events,\n",
-    "    start_defs = [\n",
-    "        StartDefinition(30,sheep_def),\n",
-    "        StartDefinition(3,wolf_def),\n",
-    "        #StartDefinition(3,bear_def)\n",
-    "        ],\n",
+    "    animal_defs = animal_defs,\n",
     "    dims = (30, 30),\n",
     "    regrowth_time = 30,\n",
     "    Δenergy_grass = 6,\n",