Inshore and offshore marine migration pathways of Atlantic salmon post-smolts from multiple rivers in Scotland, England, Northern Ireland, and Ireland.

The migratory behavior of Atlantic salmon (Salmo salar) post-smolts in coastal waters is poorly understood. In this collaborative study, 1914 smolts, from 25 rivers, in four countries were tagged with acoustic transmitters during a single seasonal migration. In total, 1105 post-smolts entered the marine study areas and 438 (39.6%) were detected on a network of 414 marine acoustic receivers and an autonomous underwater vehicle. Migration pathways (defined as the shortest distance between two detections) of up to 575 km and over 100 days at sea were described for all 25 populations. Post-smolts from different rivers, as well as individuals from the same river, used different pathways in coastal waters. Although difficult to generalize to all rivers, at least during the year of this study, no tagged post-smolts from rivers draining into the Irish Sea were detected entering the areas of sea between the Hebrides and mainland Scotland, which is associated with a high density of finfish aquaculture. An important outcome of this study is that a high proportion of post-smolts crossed through multiple legislative jurisdictions and boundaries during their migration. This study provides the basis for spatially explicit assessment of the impact risk of coastal pressures on salmon during their first migration to sea.

Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea.

The freshwater phase of the first seaward migration of juvenile Atlantic salmon (Salmo salar) is relatively well understood when compared with our understanding of the marine phase of their migration. In 2021, 1008 wild and 60 ranched Atlantic salmon smolts were tagged with acoustic transmitters in 12 rivers in England, Scotland, Northern Ireland and Ireland. Large marine receiver arrays were deployed in the Irish Sea at two locations: at the transition of the Irish Sea into the North Atlantic between Ireland and Scotland, and between southern Scotland and Northern Ireland, to examine the early phase of the marine migration of Atlantic salmon smolts. After leaving their natal rivers' post-smolt migration through the Irish Sea was rapid with minimum speeds ranging from 14.03 to 38.56 km.day-1 for Atlantic salmon smolts that entered the Irish Sea directly from their natal river, to 9.69-39.94 km.day-1 for Atlantic salmon smolts that entered the Irish Sea directly from their natal estuary. Population minimum migration success through the study area was strongly correlated with the distance of travel, populations further away from the point of entry to the open North Atlantic exhibited lower migration success. Post-smolts from different populations experienced different water temperatures on entering the North Atlantic. This was largely driven by the timing of their migration and may have significant consequences for feeding and ultimately survivorship. The influence of water currents on post-smolt movement was investigated using data from previously constructed numerical hydrodynamic models. Modeled water current data in the northern Irish Sea showed that post-smolts had a strong preference for migrating when the current direction was at around 283° (west-north-west) but did not migrate when exposed to strong currents in other directions. This is the most favorable direction for onward passage from the Irish Sea to the continental shelf edge current, a known accumulation point for migrating post-smolts. These results strongly indicate that post-smolts migrating through the coastal marine environment are: (1) not simply migrating by current following (2) engage in active directional swimming (3) have an intrinsic sense of their migration direction and (4) can use cues other than water current direction to orientate during this part of their migration.

Representation, simulation, and hypothesis generation in graph and logical models of biological networks.

This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as background knowledge for hypothesis generation by Robot Scientists using yeast as a model eukaryote, where experimentation and machine learning are used to identify additional knowledge to improve the metabolic model. The logical modeling concept is being adapted to cell signaling and transduction biological networks.

Impact of naturally spawning captive-bred Atlantic salmon on wild populations: depressed recruitment and increased risk of climate-mediated extinction.

The assessment report of the 4th International Panel on Climate Change confirms that global warming is strongly affecting biological systems and that 20-30% of species risk extinction from projected future increases in temperature. It is essential that any measures taken to conserve individual species and their constituent populations against climate-mediated declines are appropriate. The release of captive bred animals to augment wild populations is a widespread management strategy for many species but has proven controversial. Using a regression model based on a 37-year study of wild and sea ranched Atlantic salmon (Salmo salar) spawning together in the wild, we show that the escape of captive bred animals into the wild can substantially depress recruitment and more specifically disrupt the capacity of natural populations to adapt to higher winter water temperatures associated with climate variability. We speculate the mechanisms underlying this seasonal response and suggest that an explanation based on bio-energetic processes with physiological responses synchronized by photoperiod is plausible. Furthermore, we predict, by running the model forward using projected future climate scenarios, that these cultured fish substantially increase the risk of extinction for the studied population within 20 generations. In contrast, we show that positive outcomes to climate change are possible if captive bred animals are prevented from breeding in the wild. Rather than imposing an additional genetic load on wild populations by releasing maladapted captive bred animals, we propose that conservation efforts should focus on optimizing conditions for adaptation to occur by reducing exploitation and protecting critical habitats. Our findings are likely to hold true for most poikilothermic species where captive breeding programmes are used in population management.

Intelligent software for laboratory automation.

The automation of laboratory techniques has greatly increased the number of experiments that can be carried out in the chemical and biological sciences. Until recently, this automation has focused primarily on improving hardware. Here we argue that future advances will concentrate on intelligent software to integrate physical experimentation and results analysis with hypothesis formulation and experiment planning. To illustrate our thesis, we describe the 'Robot Scientist' - the first physically implemented example of such a closed loop system. In the Robot Scientist, experimentation is performed by a laboratory robot, hypotheses concerning the results are generated by machine learning and experiments are allocated and selected by a combination of techniques derived from artificial intelligence research. The performance of the Robot Scientist has been evaluated by a rediscovery task based on yeast functional genomics. The Robot Scientist is proof that the integration of programmable laboratory hardware and intelligent software can be used to develop increasingly automated laboratories.