Publikationer

The timing with which salmonid larvae emerge from their gravel nests is thought to be correlated with a particular suite of behavioural and physiological traits that correspond to the stress coping style of the individual. Among these traits, aggressiveness, dominance and resilience to stress, are potentially interesting to exploit in aquaculture production. In the present study a series of experiments were performed, with the purpose of characterising behavioural, metabolic and production related traits in rainbow trout juveniles from different emergence fractions. Newly hatched rainbow trout were sorted according to their emergence time from an artificial redd. The early, middle, and late fractions were retained and assessed for their physiological response to stress, growth performance, metabolism, fasting tolerance, and potential for compensatory growth. The early emerging fraction showed proactive behavioural traits; they were faster to reappear following startling, showed a reduced cortisol response following stress, and a reduced metabolic cost of recovery. Emergence time was not correlated with any differences in standard or maximum metabolic rates, but was however, correlated with higher routine metabolic rates, as demonstrated by significantly bigger weight losses during fasting in the early emerging group. Growth rates and feed conversion efficiencies were not significantly different when fish were co-habitated under a restrictive feeding regime, suggesting that early emerging fish are not able to monopolise food resources. The intermediate emerging group, which makes up the bulk of a population and is often ignored, appears to possess the best growth performance traits, possibly because they do not expend excessive energy on dominance behaviour such as the early emerging group, while they are also not overly timid or stress prone such as the late emerging group.

Siden 1990’erne er der investeret et trecifret millionbeløb i vandløbene, der munder ud i Vadehavet. Der er fjernet spærringer, udrettede strækninger er genslynget, og der er udlagt gydegrus og sten. Det har foreløbig givet gode resultater i form af flere vilde laks og skabt adgang til egnede gyde- og opvækstområder for flere andre fiskearter herunder snæbel, havørred og stalling. Der er stadig behov for forbedringer, hvis åerne skal have store fiskebestande.

A contribution in this issue, Greer et al. (2019), models carbon dioxide emissions from fuel combustion in global fisheries. This is done based on a method using data on fishing effort, presenting results for two sectors: small-scale and industrial fisheries. The selection of these sectors is not motivated in relation to studying fuel use, and it is well-documented that other factors more accurately predict fuel use of fisheries and would constitute a more useful basis for defining sub-sectors, when the goal of the study is to investigate fuel use. Weakly grounded assumptions made in the translation of fishing effort into carbon dioxide emissions (e.g. the engine run time per fishing day for each sector) systematically bias results towards overestimating fuel use of “industrial” vessels, underestimating that of “small-scale”. A sensitivity analysis should have been a minimum requirement for publication. To illustrate how the approach used by Greer et al. (2019) systematically misrepresents the fuel use and emissions of the two sectors, the model is applied to Australian and New Zealand rock lobster trap fisheries and compared to observed fuel use. It is demonstrated how the approach underestimates emissions of small-scale fisheries, while overestimating emissions of industrial fisheries. As global fisheries are dominated by industrial fisheries, the aggregate emission estimate is likely considerably overestimated. Effort-based approaches can be valuable to model fuel use of fisheries in data-poor situations, but should be seen as complementary to estimates based on direct data, which they can also help to validate. Whenever used, they should be based on transparent, science-based data and assumptions.