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Record W2045464845 · doi:10.1016/j.icesjms.2004.08.003

Influence of social behaviour and behavioural interactions in understanding temporal and spatial dynamics and their effect on availability and catchability

2004· article· en· W2045464845 on OpenAlexaboutno aff
Chris Glass, John Gunn

Bibliographic record

VenueICES Journal of Marine Science · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsDynamics (music)Environmental scienceTemporal scalesFisheryEcologyBiologyPsychology

Abstract

fetched live from OpenAlex

The objectives of this session were to examine the influence of social behaviour and behavioural interactions (i) in understanding temporal and spatial dynamics of fishes, and (ii) their effect on availability and catchability in resource and monitoring surveys, fishing operations, and in the design of species-selective and ecosystem-friendly fishing gears. In her stimulating keynote address, Dr Julia Parrish challenged many traditional approaches to the study of fish behaviour within the realm of fisheries biology. She reviewed the extensive theory and literature covering the motivations, benefits, and costs of schooling and other group behaviour in fishes. She explored behavioural flexibility, that is, the ability to alter latency to, or even type of, response that would allow individuals to innovate behavioural pathways in the face of new situations. In exploited systems, behavioural response, behavioural flexibility, and, ultimately, individual fitness is dictated by the degree to which the school can receive, process, and respond, as a group, to fishing pressure. Dr Parrish explored the concepts of reactive and proactive approaches to behavioural change in shoaling species in exploited systems, with specific respect to learning, information transfer, group memory, individuality, behavioural flexibility as well as group size and architecture. She challenged fisheries researchers interested in behaviour to become better acquainted with the behavioural ecology literature, to interact with behavioural ecologists (who would also benefit from these interactions), and through these interactions develop strategic directions (agendas) for joint research. Dr Parrish also stressed the need for researchers to take risks and undertake what she termed “stretchy” science, as this is the approach most likely to deliver major advances. The session that followed addressed many issues raised by Dr Parrish. Papers and posters presented within the session covered the topics of schooling behaviour, factors affecting clustering and aggregations, aggression, territoriality, competition for food, reproductive behaviours, impact of site fidelity, homing, migrations (horizontal and vertical), and habitat selection behaviours. Each presentation was followed by a brief question-and-answer session, and a general outline of the discussion is presented here. The session included a number of papers and posters on electronic tagging studies, in particular the use of data storage tags (DSTs) and acoustic listening stations. These technologies are providing detailed descriptions of movements and behaviours of individuals in a wide range of species. Where sample sizes are large, high-order patterns across the tagged population are being seen, and these are feeding into advice for management. The large volumes of data being collected by DSTs present the challenge of how to tease out patterns from noise, and how to synthesize across observations on many individuals to population level. As more and more data become available through the widespread use of DSTs, a persistent feature evident across species/studies is the significant variability among individuals within tagged populations. How best to deal with this statistically, and the significance of population signals vs. individual variability (or “noise”), remains a significant challenge. Discussion following papers on patterns of schooling and clustering in a range of clupeoid and scombrid species focused on whether similarities existed across species. No agreement was reached; one strongly voiced opinion held that observations made on Norwegian herring – for many years seen as a “typical schooling fish” – should not be thought of as the basis for generality, as they may be atypical of clupeoids. One particularly passionate topic of discussion centred around the fact that stock assessment has received a disproportionate allocation of the resources available for fisheries research to date. In response, a number of Symposium attendees noted that significant resources have been directed towards behavioural research in fisheries applications since the last Fish Behaviour Symposium, and that the lack of integration of available data (catch, conventional tagging, DSTs, etc.) is a key factor limiting advances and development of future work. Putting resources into integration of existing behavioural data with catch and fisheries population biology data was recommended. With comprehensive integration complete, it was argued that researchers would be more likely to agree on strategic directions for future behavioural research rather than just follow their own interests. At the conclusion of verbal presentations, the meeting was opened for a general discussion. This was at times vigorous and highlighted frustrations as well as notable successes within the field. The following summarizes the tone and content of the ensuing open-format discussion. More emphasis is required on understanding the extent of variability among individuals within schools, clusters, populations, etc. For this to be achieved, new methods are required to characterize the extent of individual variation in behaviour, and to tease out “general” patterns. At the same time, the research community needs to address the challenge of how to deal with individuality in the context of including individuality in group, school, or population characterizations that would be useful in stock/ecosystem assessments. More emphasis is also required on the influence on fish behaviour in the ecosystem. Many of the fish stocks being studied are overexploited, yet there is little understanding of how drastic reductions in population may have changed behaviour. For example, cod populations in Newfoundland seem to have lost their “memory” of migration routes and now over-winter in water temperatures much lower than those in which cod were ever observed previously. These changes in behaviour likely introduce additional stress, limiting the probability of stock recovery. As noted in earlier discussion, greater integration of behavioural, environmental, and fishery data is seen as essential, with a priority being on the influence of behaviour on catchability. A number of participants emphasized that for behavioural studies to remain relevant, and provide results with implications for stocks and ecosystems, researchers must stop acting as individuals and integrate not just within the field, but up through stock assessment and into fisheries management. There is a need to improve the ways in which behavioural science (in fact all of fisheries science) “reach” fishers and managers. Better visualization of results was suggested as a useful first step. It was also claimed that advances since the last Behaviour Symposium in the field of fisheries acoustics mean that there is no longer uncertainty over whether the variability in observations being reported is “real” or is simply an artefact of the instrumentation. In his opening remarks, the session Chair (Dr Chris Glass) challenged delegates to take a deep look at what progress, if any, had been made in the 10 years since the previous symposium, to make an honest assessment of any failures, and to identify reasons for those failures. Throughout ensuing discussion, there appeared to be a general feeling that technology with which to “observe” behaviour has increased immensely, and we now have a greater understanding of how the behaviour of individuals contributes to variability of school structure, etc.; but that we have made little or no progress (does this qualify as collective failure?) in integration of our science with other related disciplines, and perhaps more importantly that we have failed (or perhaps never attempted?) to convince regulators, managers, stock assessors, or indeed the fishing industry, that the data we have collected, or our collective knowledge, are useful and important in the wider realm of fisheries management. It was also made abundantly clear that many feel behavioural biologists as a group have become scientifically risk averse – that is, afraid to speculate/hypothesize, and afraid to go out on a limb. There is a sense that this attitude may have hampered progress, and if we are truly to make the study of behaviour relevant and advance our science, our collective challenge for the future should be to take the group recommendations outlined above and to be more proactive, to strive for better integration of disciplines, and to become risk-aware – that is, to challenge our preconceived notions and to promote the science of behaviour as being not only important but vital to better management of our marine resources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.287
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2004
Admission routes1
Has abstractyes

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