PHYSICOCHEMICAL CONTROLS ON FORAMINIFERAL SIZE


reflects the amount of food available to benthic life. McClain et al. (2012b) used the model of Lutz et al. (2007) to estimate the export flux of POC to the seafloor (g of C m−2 year−1)in order to study the influence of food availability on patterns of marine mollusk body-size distributions. The model of Lutz et al. (2007) predicts the annual flux of POC to the seafloor using an algorithm that incorporates POC flux sediment trap measurements and remotely sensed sea surface temperature and net primary production data. This POC flux model yields an average grid spacing of ~12.5km and provides the best estimate of energy resource availability on the seafloor for both shallow and deep-water sites. This model also confirms that sites with the greatest seasonal variability in surface-water primary productivity receive higher POC fluxes because new production outpaces decompositional activity, thereby allowing a greater proportion of POC to be exported. Following the methods of McClain et al. (2012b), we applied the model of Lutz et al. (2007) to test the constraint food availability places on benthic foraminiferal test morphology.


localities to the nearest grid points for these data. Using the R package seacarb, Version 3.0.11 (Gattuso et al., 2015), we estimated the calcite saturation state of seawater for each locality. Alkalinity, DIC, salinity, temperature, and pressure (estimated in bars by dividing meters below sea level by 10) are required input parameters for seacarb to determine the calcite saturation state of the seawater. The mean annual flux of POC to the seafloor


Statistical Analysis and Model Selection


We applied a predictor–corrector method to compute the regularization path of generalized linear models to identify the best environmen- tal predictors of North American benthic foraminiferal test size and volume–to–surface area ratio. We used the R package glmpath, Version 0.97 (Park and Hastie 2013) to compute a series of multiple linear regression solutions, in which each new solution introduces an additional environmental parameter, estimat- ing the coefficients with less regularization (i.e., a larger sum of the absolute values of the coefficients) based on the previous solution.


599


We included the following environmental parameters in the model: mean annual tem- perature (°C), dissolved oxygen concentration (ml/liter), POC flux to the seafloor (g of Cm−2), and calcite saturation state. Only sites that had environmental data for all four predictor variables were included in the analyses. The mean values of the oceano- graphic variables at each site represent the environmental conditions that foraminiferal species might experience over their life span; for example, the maximum and minimum temperature at a site are strongly correlated with the mean annual temperature (p < 0.0001, adjusted R2 = 0.89 and p < 0.0001, adjusted R2 = 0.90, respectively). Prior to analyses, each environmental parameter was rescaled to mean zero and unit variance to enable direct comparison of regression coefficients and, thus, the L1-regularized regression approach. Test volumes and volume–to–surface area ratios were log10-transformed prior to analysis. We analyzed the foraminiferal data set pre- sented here for the North American continental margin as a whole, and then compared the Pacific and Atlantic open-marine continental shelf environments. We used the Akaike information criterion (AIC) and Bayesian information criterion (BIC) to determine the best model in each analysis. In cases in which the best model differed between AIC and BIC metrics, we selected the simpler model (i.e., the model with the fewest environmental predic- tors) (Payne et al. 2012b).


Results Foraminiferal test size and volume–to–surface


area ratio from the North American continental margin are most strongly correlated with seawater temperature and dissolved oxygen concentrations (Fig. 2). Temperature is inversely correlated with (log10-transformed) test volume and volume–to–surface area ratio, whereas, oxygen is positively correlated with both measures of morphology (Fig. 2). In both cases, the direction of association is consistent with expectations from physiological first principles (Chapelle and Peck 1999; Gillooly et al. 2001). Theabsolutevalue of


thecoefficient for temperature is larger than that for oxygen,


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