Rune Vabø

TL;DR: A proposed standard protocol for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology, and considered as a first step for establishing a more detailed common format of the description of IBm and ABM.

TL;DR: An object-orientated, two-dimensional, cellular automata (CA) model is developed to describe and predict the schooling behaviour of fish in general, with Norwegian spring-spawning herring being used as a case study.

TL;DR: The degree of motivational synchronisation between individuals in a school will determine whether or to what degree a school splits into different components or remains integrated, and it is found that with increasing population size there are new system behaviours emerging, not present with lower population size.

TL;DR: A range of possible search strategies that fish might apply whensearching for prey are discussed in order to improve the understanding of flsh movement dynamicstowards baited gear.

Abstract: The understanding of distribution and aggregation in herring (Clupea harengus) can be enhanced by integrated multi-scale studies in small ecosystems. Hydro-acoustics, underwater cameras, herring and predator gillnet samples and oceanographical measurements were used to quantify herring schooling dynamics. During autumn (September) after an active feeding period, the herring was distributed in small and dense schools, mostly close to land and in relatively shallow water (< 30 m depth). During the late overwintering period in February altogether 5-7 rather dense and geographically stationary herring schools were found midpelagic in deeper waters (> 40 m depth). All recorded herring schools were then vertically extended in the water column within the most variable temperature and oxygen profiles, presumably enabling individuals to adjust maturation rate to prevailing environmental conditions and synchronize spawning of individuals within the school. From late February prior to spawning, only one major pelagic school was observed, extraordinarily stationary at the only inlet to the inner basin. The pre-spawning herring aggregation was fairly easy to detect acoustically for more than one month. Just prior to spawning and during spawning, herring spread out and became extremely difficult to detect acoustically. Only underwater cameras and bottom set gillnets could then be used to identify herring and selected spawning areas. We argue that the dramatic seasonal changes in acoustic detectability and catchability we observe is best understood and predicted based on detailed knowledge of how herring react to a changing environment according to their physiological state and motivation. Such factors should also be taken more systematically into account when performing acoustic surveys in large marine ecosystems. We need to study in more detail the vital underlying processes behind the substantial variability observed in acoustic detectability and catchability of pelagic planktivorous fish species during their annual life cycle in order to better understand and quantify variability in acoustic surveys, and thereby improve our acoustic abundance estimation.