Widespread genetic introgression of escaped farmed Atlantic salmon in wild salmon populations

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

Humans have a penchant for unintentionally selecting against that which they desire most. In fishes, unprecedented reductions in abundance have been associated with unprecedented changes in harvesting and aquaculture technologies. Fishing, the predominant cause of fish-population collapses, is increasingly believed to generate evolutionary changes to characters of import to individual fitness, population persistence and levels of sustainable yield. Human-induced genetic change to wild populations can also result from interactions with their domesticated counterparts. Our examination of fisheries- and farming-induced evolution includes factors that may influence the magnitude, rate and reversibility of genetic responses, the potential for shifts in reaction norms and reduced plasticity, loss of genetic variability, outbreeding depression and their demographic consequences to wild fishes. We also suggest management initiatives to mitigate the effects of fisheries- and farming-induced evolution. Ultimately, the question of whether fishing or fish farming can cause evolutionary change is moot. The key issue is whether such change is likely to have negative conservation- or socio-economic consequences. Although the study of human-induced evolution on fishes should continue to include estimates of the magnitude and rate of selection, there is a critical need for research that addresses short- and long-term demographic consequences to population persistence, plasticity, recovery and productivity.