diff --git a/q2_ritme/model_space/_static_trainables.py b/q2_ritme/model_space/_static_trainables.py
index 61db63e..934de93 100644
--- a/q2_ritme/model_space/_static_trainables.py
+++ b/q2_ritme/model_space/_static_trainables.py
@@ -106,8 +106,8 @@ def train_linreg(
     host_id: str,
     seed_data: int,
     seed_model: int,
-    tax: pd.DataFrame,
-    tree_phylo: skbio.TreeNode,
+    tax: pd.DataFrame = pd.DataFrame(),
+    tree_phylo: skbio.TreeNode = skbio.TreeNode(),
 ) -> None:
     """
     Train a linear regression model and report the results to Ray Tune.
@@ -249,8 +249,8 @@ def train_rf(
     host_id: str,
     seed_data: int,
     seed_model: int,
-    tax: pd.DataFrame,
-    tree_phylo: skbio.TreeNode,
+    tax: pd.DataFrame = pd.DataFrame(),
+    tree_phylo: skbio.TreeNode = skbio.TreeNode(),
 ) -> None:
     """
     Train a random forest model and report the results to Ray Tune.
@@ -521,8 +521,8 @@ def train_xgb(
     host_id: str,
     seed_data: int,
     seed_model: int,
-    tax: pd.DataFrame,
-    tree_phylo: skbio.TreeNode,
+    tax: pd.DataFrame = pd.DataFrame(),
+    tree_phylo: skbio.TreeNode = skbio.TreeNode(),
 ) -> None:
     """
     Train an XGBoost model and report the results to Ray Tune.
diff --git a/q2_ritme/process_data.py b/q2_ritme/process_data.py
index d7571df..fa1a89a 100644
--- a/q2_ritme/process_data.py
+++ b/q2_ritme/process_data.py
@@ -127,9 +127,10 @@ def load_tax_phylo(
         num_leaves = tree_phylo_f.count(tips=True)
         assert num_leaves == ft.shape[1]
     else:
-        raise ValueError(
-            "Simulation of taxonomy and phylogeny data not implemented yet."
-        )
+        # load empty variables
+        df_tax_f = pd.DataFrame()
+        tree_phylo_f = skbio.TreeNode()
+
     return df_tax_f, tree_phylo_f
 
 
@@ -215,10 +216,10 @@ def load_n_split_data(
         is used.
         path2ft (str, optional): Path to features file. If None, simulated data
         is used.
-        path2tax (str, optional): Path to taxonomy file. If None, simulated data
-        is used.
-        path2phylo (str, optional): Path to phylogeny file. If None, simulated data
-        is used.
+        path2tax (str, optional): Path to taxonomy file. If None, model options
+        requiring taxonomy can't be run.
+        path2phylo (str, optional): Path to phylogeny file. If None, model
+        options requiring taxonomy can't be run.
         host_id (str, optional): ID of the host. Default is HOST_ID from config.
         target (str, optional): Name of target variable. Default is TARGET from
         config.
diff --git a/q2_ritme/tests/test_process_data.py b/q2_ritme/tests/test_process_data.py
index a61e858..c0b0a4e 100644
--- a/q2_ritme/tests/test_process_data.py
+++ b/q2_ritme/tests/test_process_data.py
@@ -169,9 +169,11 @@ def test_split_data_by_host_error_one_host(self):
 
     def test_load_n_split_data(self):
         # Call the function with the test paths
-        train_val, test = load_n_split_data(
+        train_val, test, tax, tree_phylo = load_n_split_data(
             self.tmp_md_path,
             self.tmp_ft_rel_path,
+            None,
+            None,
             host_id="host_id",
             target="supertarget",
             train_size=0.8,
@@ -179,10 +181,14 @@ def test_load_n_split_data(self):
             filter_md_cols=["host_id", "supertarget"],
         )
 
-        # Check that the dataframes are not empty
+        # Check that the train + test dataframes are not empty
         self.assertFalse(train_val.empty)
         self.assertFalse(test.empty)
 
         # Check that the dataframes do not overlap
         overlap = pd.merge(train_val, test, how="inner")
         self.assertEqual(len(overlap), 0)
+
+        # tax and phylo should be empty in this case
+        self.assertTrue(tax.empty)
+        self.assertTrue(tree_phylo.children == [])
diff --git a/q2_ritme/tune_models.py b/q2_ritme/tune_models.py
index 4447b5e..4037a0b 100644
--- a/q2_ritme/tune_models.py
+++ b/q2_ritme/tune_models.py
@@ -171,6 +171,15 @@ def run_all_trials(
 ) -> dict:
     results_all = {}
     model_search_space = ss.get_search_space(train_val)
+
+    # if tax + phylogeny empty we can't run trac
+    if tax.empty or tree_phylo.children == []:
+        model_types.remove("trac")
+        print(
+            "Removing trac from model_types since no taxonomy and phylogeny were "
+            "provided."
+        )
+
     for model in model_types:
         # todo: parallelize this for loop
         if not os.path.exists(path_exp):