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3.3 FIAN Seagrass Community Relations with the Environmental and Physical |
Measurements |
3.3.1 Relationships between Seagrass Presence and the Environmental and Physical |
Conditions |
The baseline regression model (Table 12, Model 1A) investigating temporal effect |
(seasonal and annual) on seagrass presence was not statistically significant (p > 0.05). |
Year and season of collection were not significant predictors of the presence of seagrass. |
However, the baseline regression model (Table 12, Model 1B) investigating the effect of |
environmental and physical variables on the presence of seagrass was statistically |
significant (F = 8.025, p < 0.001) and explained 12.1% (R |
2 = 0.121) of the variability. |
Temperature, salinity and sediment depth were not significant predictors of the presence |
of seagrass (p > 0.05). Only water depth (β = -0.001, p < 0.001) and turbidity (β = - |
0.020, p = 0.009) were significant predictors of seagrass presence. |
When the baseline models were combined to include effects of both temporal |
(year and season) and the environmental and physical variables on seagrass presence, the |
model did not change, and still explained 12.1% of variability (R |
2 = 0.121, F = 6.213, p < |
0.001) (Table 12, Model 1C). Adding temporal variables did not influence the model, |
because they were not significant contributors (p > 0.05), as seen in the baseline temporal |
model (model 1A). Water depth (β = -0.001, p < 0.001) and turbidity (β = -0.020, p = |
0.020) remained the only effective predictors of seagrass presence. |
The final regression model (Table 12, Model 1D), which investigated the |
combined effects of temporal, the environmental and physical variables, and the presence |
of algae on seagrass presence, was the best model (F = 5.798, p < 0.001) but still only |
explained 12.5% (R |
2 = 0.125) of variability. However, the temporal variables and algae |
presence did not significantly affect the model (p > 0.05). Water depth (β = -0.001, p < |
0.001) and turbidity (β = -0.019, p = 0.025) were the only significant predictors of |
seagrass presence in the final model. As such, seagrasses are more likely to occur in |
shallower waters with greater water clarity. Overall, depth and water clarity in the POM |
basin were the only effective predictors of seagrass presence in all models involving |
environmental effects, see Table 12, Models 1B-D. |
59 |
Table 12. Models 1A-D. Multiple Regression models for Seagrass occurrence and |
environmental variables. Seagrass and algae measured as present (1) or absent (0). |
Water depth, and sediment depth are measured in cm, temperature is measured in oC, |
salinity is measured in ‰, and turbidity is measured in NTU. is an unstandardized |
coefficient of regression, reported with standard error and significance, p-value. |
Conventional variables include year of sample (2005-2011), season (1 = spring, 2 = fall). |
A. Models evaluating grass occurrence for conventional variables (year*season). B. |
Models evaluating grass occurrence for environmental variables (depth, temp, salinity, |
etc.). C. Models evaluating grass occurrence adjusted for conventional and |
environmental variables. D. Models evaluating grass occurrence adjusted for |
conventional, environmental and algae variables. *Significance = p < 0.05. |
Model Set 1A-D Model 1-A Model 1-B |
2005-2011 N SE p N SE p |
Seagrass Present |
Year 420 0.010 0.007 0.135 |
Season 0.005 0.027 0.860 |
Water Depth 418 -0.001 0.000 <0.001 * |
Sediment Depth 0.000 0.000 0.225 |
Turbidity -0.020 0.007 0.009 * |
Surface Temperature -0.014 0.023 0.542 |
Bottom Temperature 0.014 0.023 0.548 |
Surface Salinity -0.007 0.006 0.274 |
Bottom Salinity 0.005 0.009 0.565 |
Model 1-C Model 1-D |
Year 418 -0.001 0.008 0.944 Year 418 -0.001 0.008 0.887 |
Season -0.005 0.052 0.921 Season -0.002 0.052 0.977 |
Water Depth -0.001 0.000 <0.001 * Water Depth -0.001 0.000 <0.001 * |
Sediment Depth 0.000 0.000 0.235 Sediment Depth 0.000 0.000 0.224 |
Turbidity -0.020 0.009 0.020 * Turbidity -0.019 0.009 0.025 * |
Surface Temperature -0.015 0.024 0.538 Surface Temperature -0.015 0.024 0.513 |
Bottom Temperature 0.015 0.026 0.560 Bottom Temperature 0.016 0.026 0.538 |
Surface Salinity -0.007 0.006 0.277 Surface Salinity -0.007 0.006 0.281 |
Bottom Salinity 0.005 0.009 0.607 Bottom Salinity 0.004 0.009 0.636 |
Algae Present 0.061 0.044 0.166 |
60 |
3.3.2 Relationships between Seagrass Species Densities and Environmental and Physical |
Conditions |
The baseline regression models (Table 13, Models 2-4A) investigating |
temporal effects (seasonal and annual) on individual seagrass cover-densities were not |
statistically significant (p > 0.05). There were no temporal effects seen in the seagrass |
species cover-densities within the POM basin. Cover of individual seagrass species did |
not change dramatically between seasons or years during the collection period, but some |
environmental measurements within the basin were found to have an effect on the |
seagrasses. The baseline regression models (Table 13, Models 2-4B) investigating the |
effects of environmental variables on seagrass species cover were all statistically |
significant (p < 0.001). Temperature, salinity and turbidity were not significant |
predictors of individual seagrass coverage within the basin (p > 0.05). Water depth was |
a significant predictor for each species (p < 0.01), and was the only significant predictor |
in the models for Thalassia (β = -0.005, p < 0.001) and Halodule cover-densities (β = - |
0.003, p < 0.001). The environmental effect baseline models explained 20.2% (R |
2 = |
0.202, F = 3.320, p = 0.002) of variability in Thalassia cover-density (Table 13, Model |
3B), and 5.4% (R |
2 = 0.054, F = 14.789, p < 0.001) of variability in Halodule coverdensity (Model 4B). The baseline model for Syringodium explained 12.9% (R |
2 = 0.129, |
F = 8.663, p < 0.001) of variability in cover-density (Table 13, Model 2B), and in |
addition to water depth, sediment depth was also a significant predictor. Increased water |
depth predicted decreased cover-density for all seagrass species, and increased sediment |
depth predicted increased Syringodium cover-density (Table 13, Models 2-4B). |
However, when the baseline models were combined to include effects of both |
temporal and environmental variables on seagrass cover-density, all models improved |
(Table 13, Models 2-4C). Other variables were not effective predictors of seagrass |
cover-density, but the combined models showed a significant annual effect on |
Syringodium F = (7.398, p < 0.001) (Model 2C) and Thalassia (F = 12.323, p < 0.001) |
(Table 13, Model 3C) density. 14% (R |
2 = 0.140) of the variability in Syringodium and |
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