diff --git a/stdpopsim/catalog/AraTha/dfes.py b/stdpopsim/catalog/AraTha/dfes.py
index fbcbdd092..fad3117ad 100644
--- a/stdpopsim/catalog/AraTha/dfes.py
+++ b/stdpopsim/catalog/AraTha/dfes.py
@@ -10,19 +10,15 @@
 
 
 def _HuberDFE():
-    id = "Gamma_H18"
-    description = "Deleterious Gamma DFE"
+    id = "GammaAdditive_H18"
+    description = "Deleterious additive Gamma DFE"
     long_description = """
-    Return negative (no neutral, in this case) MutationType()s
-    representing an Arabidopsis DFE. From Huber et al. (2018).
-    Gamma parameters are based on Supplementary Table 4,
-    the genome-wide, additive-only model for A. LYRATA- due to
-    challenges with simulating with selfing for A. thaliana.
-    The Supplementary Table 4 DFEs are not noted to contain
-    any neutral mutations (in contrast, Supplementary Table 3
-    notes neutral proportions for two other DFEs), and in the
-    main text including neutral mutations seems like a
-    supplementary analysis rather than the main strategy.
+    Additive, deleterious DFE from Huber et al. (2018) for Arabidopsis,
+    estimated from the SFS of A. lyrata as a Gamma distribution
+    of deleterious effects. Parameters are from Supplementary Table 4,
+    the "genome-wide, additive-only model for A. lyrata". The DFE for
+    A. lyrata (rather than A. thaliana) is provided due to challenges
+    with simulating selfing.
     """
     citations = [
         stdpopsim.Citation(
@@ -34,12 +30,15 @@ def _HuberDFE():
     ]
     neutral = stdpopsim.MutationType()
     gamma_shape = 0.27  # shape
-    gamma_mean = -0.0004  # expected value
+    gamma_scale = -0.0004  # scale
+    gamma_mean = gamma_shape * gamma_scale
     h = 0.5  # dominance coefficient
     negative = stdpopsim.MutationType(
         dominance_coeff=h,
         distribution_type="g",  # gamma distribution
-        distribution_args=[gamma_mean, gamma_shape],
+        # extra factor of 2 is to convert dadi to SLiM
+        # (1+s for homozygote in SLiM versus 1+2s in dadi)
+        distribution_args=[-2 * gamma_mean, gamma_shape],
     )
 
     return stdpopsim.DFE(
diff --git a/stdpopsim/qc/AraTha.py b/stdpopsim/qc/AraTha.py
index 6fd5ec96f..8ef64d3a5 100644
--- a/stdpopsim/qc/AraTha.py
+++ b/stdpopsim/qc/AraTha.py
@@ -208,3 +208,29 @@ def HuberThreeEpoch():
 
 
 _species.get_demographic_model("African3Epoch_1H18").register_qc(HuberThreeEpoch())
+
+
+def HuberDFE():
+    id = "QC-GammaAdditive_H18"
+    # Huber et al. 2018 Supplementary table 4.
+    #  genome-wide A. lyrata additive fitness effects
+    neutral = stdpopsim.MutationType()
+    gamma_shape = 0.27
+    gamma_scale = 0.0004
+    gamma_mean = -(gamma_shape * gamma_scale)  # negative since deleterious mutations
+    h = 0.5
+    negative = stdpopsim.MutationType(
+        dominance_coeff=h,
+        distribution_type="g",
+        distribution_args=[-2 * gamma_mean, gamma_shape],
+    )
+    return stdpopsim.DFE(
+        id=id,
+        description=id,
+        long_description=id,
+        mutation_types=[neutral, negative],
+        proportions=[0.0, 1.0],
+    )
+
+
+_species.get_dfe("GammaAdditive_H18").register_qc(HuberDFE())
diff --git a/tests/test_slim_engine.py b/tests/test_slim_engine.py
index 722dec002..810285203 100644
--- a/tests/test_slim_engine.py
+++ b/tests/test_slim_engine.py
@@ -1213,7 +1213,7 @@ def test_not_simulated_outside_region(self):
 
         left, right = 100000, 900000
         contig = species.get_contig("1", left=left, right=right)
-        dfe = species.get_dfe("Gamma_H18")
+        dfe = species.get_dfe("GammaAdditive_H18")
         exons = species.get_annotations("araport_11_exons")
         exon_intervals = exons.get_chromosome_annotations("1")
         contig.add_dfe(intervals=exon_intervals, DFE=dfe)