9 research outputs found

    Relationships between <i>S. apama</i> size and breeding durations using data in Payne et al. [27].

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    <p>Breeding time is the number of days each individual was present at the Point Lowly breeding aggregation, and breeding period is the number of days between the first and last day that each individual was present. Least-squares regression identified a significant effect of size for both metrics (<i>n</i>  =  19; <i>P</i> < 0.05, <i>r</i><sup>2</sup>  =  0.24 and 0.22 for breeding time and duration, respectively).</p

    Estimated gross cost of transport in cephalopods, including the minimum cost (open circles) and actual tracked speeds (filled circles).

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    <p>Data for <i>S. apama</i> are derived from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058694#pone.0058694.e001" target="_blank">Equation 1</a> and tracked speeds in the field <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058694#pone.0058694-Payne1" target="_blank">[21]</a>. Data for other species are from O’Dor & Webber <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058694#pone.0058694-ODor5" target="_blank">[17]</a> and O’Dor <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058694#pone.0058694-ODor8" target="_blank">[39]</a>.</p

    Time taken to fatigue (mean ± SE) for <i>S. apama.</i>

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    <p>Five individuals were swum at each speed, and the percentage of individuals that fatigued within 180 min of swimming is indicated.</p

    Sequential model runs using a first-order auto-correlation structure, and corresponding difference in AIC values.

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    <p>The P-value of the term removed from each model is given, and the best model is highlighted in <b>bold</b>.</p

    Optimisation of the autocorrelation function, showing the auto-regressive (AR, <i>φ</i><sub>n</sub>) and moving-average (MA, <i>θ</i><sub>n</sub>) correlation parameters for models of increasing order.

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    <p>The AIC for each model was used to select the model which best described the error structure (shown in <b>bold</b>). As increasing complexity failed to produce models with a lower AIC, models with more than 2 auto-regressive and 1 moving average parameters were not run <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080962#pone.0080962-Zuur1" target="_blank">[39]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080962#pone.0080962-Schabenberger1" target="_blank">[44]</a>.</p

    Tagging information for yellowfin bream (<i>Acanthopagrus australis</i>) tracked in the Georges River, showing the numbers of days on which data were recorded during the study period, the temporal window within which these data were recorded (e.g. for Fish 1, data were recorded on 89 days within a window of 100 d), and the number of activity and depth measurements from each tag recorded by the receivers shown in Figure 1.

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    <p>Tagging information for yellowfin bream (<i>Acanthopagrus australis</i>) tracked in the Georges River, showing the numbers of days on which data were recorded during the study period, the temporal window within which these data were recorded (e.g. for Fish 1, data were recorded on 89 days within a window of 100 d), and the number of activity and depth measurements from each tag recorded by the receivers shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080962#pone-0080962-g001" target="_blank">Figure 1</a>.</p
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