Comparison of the allometric model (value of <i>b</i> regressed by model fitting) to the isometric model (<i>b</i> = 1).

<p>As the allometric model we used the predictive regression equation ‘<i>m</i>_{<i>I</i> + <i>II</i> + <i>III</i>}’. As size at the event of interest, we used average values at hatching SCL = 4.5 cm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143747#pone.0143747.ref033" target="_blank">33</a>], recruitment SCL = 48 cm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143747#pone.0143747.ref009" target="_blank">9</a>], and nesting SCL = 93 cm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143747#pone.0143747.ref028" target="_blank">28</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143747#pone.0143747.ref031" target="_blank">31</a>] for the relationships of carapace width and body depth to carapace length. For the relationship of body depth to carapace width we calculated SCW values that would correspond to average carapace lengths at hatching, recruitment, and nesting, using ‘<i>m</i>_{<i>I</i> + <i>II</i> + <i>III</i>}’. Error was calculated for log_<i>e</i> transformed data as [100(value predicted by isometric model—value predicted by allometric model)/ value predicted by allometric model].</p

Fit of suggested subset-specific (‘<i>m</i>_<i>I</i>’, ‘<i>m</i>_{<i>II</i> + <i>III</i>}’, panels (a), (c), (e)), and non-specific (‘<i>m</i>_{<i>I</i> + <i>II</i> + <i>III</i>}’, panels (b), (d), (f)) linear scaling models to data.

<p>The relationship of log(<i>SCW</i>) to log(<i>SCL</i>) is shown in panels (a) and (b), the relationship of log(<i>BD</i>) to log(<i>SCL</i>) in panels (c) and (d), and the relationship of log(<i>BD</i>) to log(SCW) in panels (e) and (f). The recommended regression equations are displayed in the plot, while parameters for remaining equations are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143747#pone.0143747.t005" target="_blank">Table 5</a>. Dashed lines mark the 95% confidence intervals of the predictions. Black arrows in panels (b), (d), and (f) point to the size range in which predictions are underestimated.</p

Predictions of SCW and BD by two types (M1—linear, and M2—saturating) of models ‘<i>m</i>_<i>I</i>’, ‘<i>m</i>_{<i>II</i> + <i>III</i>}’, and ‘<i>m</i>_{<i>I</i> + <i>II</i> + <i>III</i>}’.

<p>Predictions are given for average sizes at specific events (hatching, recruitment, nesting). Symbols are coded based on the model (each symbol corresponds to one model), and type (full or empty symbol).</p

Descriptive statistics.

<p>Number of data points (<i>N</i>), median, interquartile range (IQR), minimum, and maximum of ratios, for life stage subsets ‘I’, ‘II’, and ‘III’.</p

Predictions of log(<i>SCW</i>) from log(<i>SCL</i>) by regression equations ‘<i>m</i>_<i>north</i>’, ‘<i>m</i>_<i>south</i>’, and ‘<i>m</i>_<i>both</i>’ specific for regional subsets ‘north’, ‘south’, and ‘both’.

<p>Panels (a) and (b): data from the posthacthling group. Panels (c) and (d): data for the adult group. The recommended regression equations are displayed in the plot, while the parameters for remaining equations are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143747#pone.0143747.t003" target="_blank">Table 3</a>. Dashed lines mark the 95% confidence intervals of the predictions.</p

Overview of population dynamics.

<p>Top panels show dependence of maximum growth rate (left) and time to maximum growth rate (right) calculated from the model (solid line) and measured by Priester <i>et al.</i> (2009 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026955#pone.0026955-Priester1" target="_blank">[20]</a>) (dotted line). Bottom left panel shows time to maximum energy density as a function of exposure concentration. Bottom right panel shows growth rates for all treatments during the first 30 hours of the experiment.</p

Outline of the model.

<p>Bacteria assimilate substrate into energy reserves, which are utilized to fuel growth (linked to increase in cell concentration), maintenance and acclimation. Products related to respiration degrade the environment, reducing the ability of bacteria to utilize energy reserves. Both toxicants and degradation of the supernatant inhibit assimilation of the substrate, and absorbed toxicants bioaccumulate in bacterial cells. Toxicants in the cell, as well as the cell's own metabolism, increase aging acceleration (by creating damage-inducing compounds), thus increasing the hazard rate, and mortality.</p

Predicting high exposures.

<p>Exposures of 37.5, 75, 115, and 150 mg/L predicted using fits only of data on control and low exposures (10 and 20 mg/L). Data points marked with ‘x’: data used in fitting; ‘o’: data used for comparison only. Dashed line: fitted treatments (, , , and ). Solid line: predicted treatments.</p

Simulating the control.

<p>Cell concentration and all state variables of the model except acclimation and bioaccumulation (not applicable for control). Upper left corner: data (circles), best fit of the standard model (dotted line) and best fit of the model extended by including environmental degradation (solid line). See text for discussion.</p

Summary of state variables, units, and dynamic equations.

<p>Bacterial production rate and scaled functional response () are not state variables, but have been defined separately for brevity. Non-dimensional variables have been labeled ‘n.d.’. Subscript ‘+’ signifies that only positive values of the expression are considered, with the expression set to zero if its value turns out to be negative.</p