diff --git a/src/lineagetree/lineage_tree_manager.py b/src/lineagetree/lineage_tree_manager.py
index 4673107..7b713df 100644
--- a/src/lineagetree/lineage_tree_manager.py
+++ b/src/lineagetree/lineage_tree_manager.py
@@ -515,6 +515,8 @@ def plot_tree_distance_graphs(
         size: float = 10,
         lw: float = 0.3,
         ax: np.ndarray | None = None,
+        vmin=None,
+        vmax=None,
     ) -> tuple[plt.figure, plt.Axes]:
         """
         Plots the subtrees compared and colors them according to the quality of the matching of their subtree.
@@ -552,6 +554,11 @@ def plot_tree_distance_graphs(
             The width of the edges, defaults to 0.3
         ax : np.ndarray, optional
             The axes used, if not provided another set of axes is produced, defaults to None
+        vmin, vmax: float, optional
+            Values within the range ``[vmin, vmax]`` from the input data will be
+            linearly mapped to ``[0, 1]``.
+            *vmin* defaults to the 0.05 quantile of the values of the unordered tree edist distances of the subtrees.
+            *vmax* defaults to the 0.95 quantile of the values of the unordered tree edist distances of the subtrees.
 
         Returns
         -------
@@ -699,7 +706,11 @@ def plot_tree_distance_graphs(
         if ax is None:
             fig, ax = plt.subplots(nrows=1, ncols=2)
         cmap = colormaps[colormap]
-        c_norm = mcolors.Normalize(0, 1)
+        if vmin is None:
+            vmin = np.percentile(list(colors1.values()), 5)
+        if vmax is None:
+            vmax = np.percentile(list(colors1.values()), 95)
+        c_norm = mcolors.Normalize(vmin, vmax)
         colors1 = {c: cmap(c_norm(v)) for c, v in colors1.items()}
         colors2 = {c: cmap(c_norm(v)) for c, v in colors2.items()}
         tree1.lT.plot_subtree(
diff --git a/src/lineagetree/measure/uted.py b/src/lineagetree/measure/uted.py
index 84a9104..719db07 100644
--- a/src/lineagetree/measure/uted.py
+++ b/src/lineagetree/measure/uted.py
@@ -6,6 +6,7 @@
 from itertools import combinations
 from typing import TYPE_CHECKING, Literal
 
+import numpy as np
 import matplotlib.colors as mcolors
 from edist import uted
 from matplotlib import colormaps
@@ -360,6 +361,8 @@ def plot_tree_distance_graphs(
     size: float = 10,
     lw: float = 0.3,
     ax: list[plt.Axes] | None = None,
+    vmin=None,
+    vmax=None,
 ) -> tuple[plt.figure, plt.Axes]:
     """
     Plots the subtrees compared and colors them according to the quality of the matching of their subtree.
@@ -392,6 +395,11 @@ def plot_tree_distance_graphs(
         The width of the edges, defaults to 0.3
     ax : np.ndarray, optional
         The axes used, if not provided another set of axes is produced, defaults to None
+    vmin, vmax: float, optional
+        Values within the range ``[vmin, vmax]`` from the input data will be
+        linearly mapped to ``[0, 1]``.
+        *vmin* defaults to the 0.05 quantile of the values of the unordered tree edist distances of the subtrees.
+        *vmax* defaults to the 0.95 quantile of the values of the unordered tree edist distances of the subtrees.
 
     Returns
     -------
@@ -509,7 +517,11 @@ def plot_tree_distance_graphs(
     if ax is None:
         fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True)
     cmap = colormaps[colormap]
-    c_norm = mcolors.Normalize(0, 1)
+    if vmin is None:
+        vmin = np.percentile(list(colors.values()), 5)
+    if vmax is None:
+        vmax = np.percentile(list(colors.values()), 95)
+    c_norm = mcolors.Normalize(vmin, vmax)
     colors = {c: cmap(c_norm(v)) for c, v in colors.items()}
     lT.plot_subtree(
         lT.get_ancestor_at_t(n1),