Appendix A. Univariate fits of latitude and latitude squared on oyster- and reef-level responses.
In each analysis the polynomial term is centered on the mean. In all cases there is a significant
non-linear effect of latitude.
A) Oyster density.
Term
Intercept
Latitude
(Latitude-32.65)2
Estimate
7268.0
-191.6
-148.4
SE
1775.3
52.9
34.4
t
4.09
-3.62
-4.32
p
0.0002
0.0007
<0.0001
SE
11.45
0.34
0.22
t
4.45
-3.68
-2.12
p
<0.0001
0.0006
0.039
B) Oyster cluster biomass.
Term
Intercept
Latitude
(Latitude-32.65)2
Estimate
50.96
-1.26
-0.47
C) Oyster spat recruitment [ln(x+1) transformed].
Term
Intercept
Latitude
(Latitude-32.65)2
Estimate
49.62
-1.30
-0.87
SE
4.28
0.13
0.083
t
11.58
-10.21
-10.45
p
<0.0001
<0.0001
<0.0001
SE
8.0
0.24
0.16
t
10.69
-9.67
-9.73
p
<0.0001
<0.0001
<0.0001
SE
0.89
0.027
0.017
t
4.15
-3.40
-3.98
p
0.004
0.011
0.005
D) Slope of oyster reefs.
Term
Intercept
Latitude
(Latitude-32.65)2
Estimate
85.50
-2.31
-1.51
E) Depth of water inundation.
Term
Intercept
Latitude
(Latitude-32.65)2
Estimate
3.69
-0.090
-0.069
Appendix B. Vertical relief of oyster reefs [ln (x+1) transformed] for the A) high zone, B) mid
zone, and C) low zone as a function of latitude and latitude squared. In the High Zone analysis
the polynomial term is centered on the mean. In the Mid and Low zones the polynomial term was
not significant, and thus removed and reanalyzed as a linear relationship with vertical relief.
A) High Zone
Term
Intercept
Latitude
(Latitude-32.65)2
Estimate
1.67
-0.0030
-0.12
SE
1.28
0.038
0.025
t
1.31
-0.08
-4.72
Estimate
0.056
0.038
SE
1.079
0.033
t
0.05
1.15
Estimate
-1.098
0.070
SE
1.056
0.032
t
-1.04
2.15
p
0.19
0.94
<0.0001
B) Mid Zone
Term
Intercept
Latitude
p
0.96
0.25
C) Low Zone
Term
Intercept
Latitude
p
0.30
0.033
Appendix C. Model selection results for regression analyses on the biomass of oysters at each
site. The best model (shown in bold) as selected by the lowest AICc is with depth of inundation
alone: biomass = 11.8 (depth of inundation) + 2.0].
No.
Model
R2
AICc
ΔAICc
wi
predictors
1
depth of inundation
0.61
50.69
0
0.36
2
depth of inundation, inundation duration
0.78
51.00
0.32
0.31
2
depth of inundation, fall water
temperature
0.74
52.62
1.94
0.14
1
inundation duration
0.43
54.49
3.80
0.05
1
fall water temperature
0.36
55.69
5.00
0.03
0
Null model [Intercept only]
—
55.88
5.20
0.03
1
salinity
0.35
55.90
5.22
0.03
3
inundation duration, depth of
inundation, fall water temperature
0.85
55.96
5.28
0.03
salinity, inundation duration
0.63
56.17
5.49
0.02
2
Appendix D. Model selection results for regression analyses on the recruitment of oysters [(ln
(x+1) transformed] at each site. The best model (shown in bold) as selected by the lowest AICc
is with depth of inundation alone: [ln (recruitment +1) = 9.6 (depth of inundation) – 0.70].
ΔAICc is the difference between the lowest AICc score and the AICc score of each model. Akaike
weight (wi) is calculated as the model likelihood, exp(−Δi/2), normalized by the sum of all model
likelihoods; values close to 1 indicate greater confidence in the selection of a model
No.
Model
R2
AICc
ΔAICc
wi
0.60
46.9
0
0.63
—
51.9
5.4
0.05
predictors
1
depth of inundation
0
Null model [Intercept only]
2
depth of inundation, fall water temperature
0.72
49.6
5.6
0.17
2
inundation duration, depth of inundation
0.67
51.2
11.9
0.07
1
fall water temperature
0.33
52.2
13.9
0.05
Appendix E. Model selection results for regression analyses on the slope of oyster reefs at each
site. The best model (shown in bold) as selected by the lowest AICc is with depth of inundation
alone: slope = 16.9 [depth of inundation] – 3.44.
No.
Model
R2
AICc
ΔAICc
wi
predictors
1
depth of inundation
0.63
57.35
0.00
0.48
2
depth of inundation, inundation duration
0.76
58.74
1.39
0.24
3
depth of inundation, inundation
duration, fall water temperature
0.89
59.92
2.57
0.13
depth of inundation, fall water
temperature
0.69
61.37
4.02
0.06
2
salinity, depth of inundation
0.65
62.70
5.35
0.03
0
Null model [Intercept only]
—
62.92
5.57
0.03
1
fall water temperature
0.26
64.21
6.86
0.02
1
salinity
0.23
64.64
7.29
0.01
2
Appendix F. Model selection results for regression analyses on the vertical relief of oyster reefs
[(ln (x+1) transformed] at each site. The best model is shown in bold as selected by the lowest
AICc. A) For the high reef zone, depth of inundation alone was the best predictor: [ln (vertical
relief +1) = 6.3 (depth of inundation) + 0.3]. B) For the low reef zone the null model fit best,
followed by a model with inundation duration. C) For the mid reef zone the null model fit best,
followed by a model with salinity.
A) High zone
No.
Model
R2
AICc
ΔAICc
wi
predictors
1
depth of inundation
0.60
38.85
0
0.75
0
Null model [Intercept only]
—
43.63
4.78
0.07
1
salinity
0.33
43.95
5.10
0.06
2
depth of inundation, fall water
temperature
0.61
44.43
5.58
0.05
2
inundation duration, depth of inundation
0.60
44.65
5.80
0.04
2
salinity, depth of inundation
0.60
44.85
6.00
0.04
B) Low zone
No.
Model
R2
AICc
ΔAICc
wi
predictors
0
Null model [Intercept only]
—
27.98
0.00
0.38
1
inundation duration
0.28
28.92
0.94
0.24
2
inundation duration, fall water
temperature
0.57
29.78
1.80
0.15
1
fall water temperature
0.11
31.09
3.11
0.08
1
salinity
0.11
31.15
3.17
0.08
1
depth of inundation
0.02
32.02
4.04
0.05
2
salinity, inundation duration
0.33
34.32
6.34
0.02
R2
AICc
ΔAICc
C) Mid zone
No.
Model
wi
predictors
0
Null model [Intercept only]
—
32.12
0.00
0.42
1
salinity
0.31
32.63
0.51
0.33
2
depth of inundation
0.10
35.30
3.18
0.09
1
inundation duration
0.05
35.93
3.81
0.06
1
fall water temperature
0.00
36.40
4.28
0.05
2
salinity, depth of inundation
0.33
38.38
6.26
0.02
2
salinity, fall water temperature
0.32
38.52
6.40
0.02
2
salinity, inundation duration
0.32
38.53
6.41
0.02