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Thus, the small-selected fish only experienced four rather than five generations of intensive size-selective harvest.
These results collectively showed that contemporary harvest-induced evolution is conceivable in response to intensive size-selective exploitation.
Recent empirical work examining size and weight distributions of exploited sea cucumber populations in Turkey finds evidence for the loss of larger size classes, as predicted from intensive size-dependent harvesting (González-Wangüemert et al. 2015).
We nevertheless documented effects of positively size-selective harvesting on a range of traits and also presented molecular evidence of directional selection altering the genotypes, reinforcing the previously expressed notion that rapid evolutionary change is possible over very short time periods of intensive size-selective harvesting (Conover and Munch 2002; van Wijk et al. 2013).
In some cases, the magnitude of change appears to be positively correlated with the intensity of fishing, such that life-history modification has been hypothesized to reflect evolutionary responses to intensive, size-selective fishing (Devine et al. 2012; Audzijonyte et al. 2013).
Thus, our results provided unambiguous evidence for rapid, harvest-induced phenotypic and evolutionary change when harvesting is intensive and size selective.
A common expectation and empirically reported effect of intensive and size-selective harvesting is the downsizing of body size (Conover and Munch 2002; Jørgensen et al. 2007; Swain et al. 2007; Alós et al. 2014).
Because life-history, morphological, and behavioral traits are at least moderately heritable (Mousseau and Roff 1987), intensive and size-selective fishing over multiple generations is expected to cause genetic (i.e. evolutionary) changes in a range of traits (e.g. Law 2007; Dunlop et al. 2009; Laugen et al. 2014; Marty et al. 2015).
Because this analysis becomes computationally intensive as the size of the simulated genomic region increases, we investigated the relation between the threshold value and the size of the simulated region.
Pre-emptive decolonisation of all patients remained the most cost effective option in sensitivity analyses of varying intensive care unit size, prevalence of MRSA on admission, and proportion of high risk patients admitted (see web extra appendix 3).
However, SRD has an intrinsic limitation regarding the number of objects: namely the calculation of the Gaussian random probability distribution curves becomes computationally intensive above sample sizes of 100 200 objects (the largest dataset processed in a reasonable amount of time so far is 1400 objects).
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