GAMMSGuidelines for Assessing Marine Mammal Stocks
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Metabolic demand in ectotherms is regulated by environmental temperatures, as well as by body size (Hartman and Brandt, 1995; Brown, 2004; Sousa et al., 2010), and our GAMMs indicated that residence times of the 3 predators in the Navesink River were a function of the interaction between body size and water temperature.
Ranges of optimal temperatures for various performance measures are also generally broader for smaller, juvenile ectotherms (Freitas et al., 2010), and our GAMMs indicated that smaller fishes were more likely than larger fish to remain in the Navesink River over a broader range of temperatures.
A two-part generalized additive mixed model (GAMM) is presented for these data.
The predictor, [[eta].sub.ij], in a GAMM is then specified as
We applied a generalised additive mixed effect model (GAMM) with cyclic regression splines and binomial family with log-link (mgcv package in R; Wood 2006) for each habitat type.
Third, we created generalized additive mixed models (GAMMs) to investigate the possibility that the relationships between oxidative stress biomarkers and urinary phthalate metabolites were nonlinear.
GAMM models were created to examine deviation of the oxidative stress-phthalate metabolite relationships from linearity.
To explore potential nonlinear associations, we fitted generalized additive mixed effects models (GAMM) using the R mgcv package.
Results from our analysis in which we evaluated nonlinear associations using penalized splines for urinary biomarkers in GAMM models are presented in Figure 1.
Generalized Additive Mixed Models (GAMM, e.g., Wood 2006) were fit to the step data by species, deployment, and the presence/absence of food to evaluate the effects of time of day, the presence/absence of food, and local crowding on sea urchin displacement (i.e., steps).
Diel movement patterns during periods when food was present were analyzed using GAMM of displacement (as described above).