Interaction frequency
A shy albatross fighting a black-browed albatross for food
Ship following seabirds often compete with each other for bait-taking opportunities. In this photo, a shy albatross (left) is fighting with a black-browed albatross (right). Photo credit: Ed Dunens CC BY 2.0

Interaction frequency of seabirds with longline fisheries: risk factors and implications for management by Can Zhou and Nigel Brothers


Background Fishery bycatch poses a serious threat to seabird populations globally.

Problem statement Traditional haul-only post-capture observations are inadequate and inefficient to document seabird bycatch due to the substantial bycatch loss known to occur.

Contribution Pre-capture observations offer an alternative by documenting seabird interactions leading up to captures. Based on the long-term large-scale dedicated field observations, this study revealed significant risk factors for the pre-capture stages of the seabird bycatch process in pelagic longline fisheries using Bayesian methods. Rough sea conditions were found to correlate with more seabirds following fishing vessels. Species identity, density effect, inter-species effect, and sea condition were found to significantly affect how frequently seabirds aggregated around a fishing vessel engage in bait-taking interactions. Intra-species competition was found to be the dominant type of density effect. Moreover, a web of inter-species interactions was identified to facilitate the bait-taking of superior competitors at the expense of inferior ones.

Recommendations The findings of this study are relevant to fishery managers in updating current data collection protocols to alleviate data issues caused by bycatch loss, to conservation biologists in quantifying bycatch risks for susceptible seabird populations, and in aiding the design and evaluation of bycatch mitigation measures.

This is the third publication in this series of studies to unravel the difficulties in documenting seabird bycatch in pelagic longline fisheries. The previous two studies have looked at the bycatch loss and bycatch vulnerability part of the bycatch process, respectively. In this paper, we tie these seperate parts together in a conceptual diagram (Figure 1, not shown here due to copyright).

Is it really nessesary to break the bycatch process into this many pieces 🤔 you may ask. Well, it all depends on the complexity of the biological process and data collection protocol, and in this case, it is indeed nessesary. In particular, the analytical tools used in this study were specifically developed for this study (interaction frequency), for example the web of inter-species interactions and density dependence. On the other hand, because bycatch loss and bycatch vulnerability are intricately connected, the previous two studies do shared a substantial proportion of computer code, but they differ substantially in their significance in conservation and management, which is further explained here. This divide and conquer approach has been recommended by Hobday et al. (2011), and it also makes sense.

But why 🤔, why bycatch is so hard to study? You may notice the resemblance between bycatch estimation and CPUE standardization, which is routinely done for many managed species. Hardly anybody would say CPUE standardization is difficult. Then how come bycatch estimation is hard? The key here is that the estimation of the coefficient of (by-)cachability (q) is hard, and CPUE standardization has been MADE easy because the standardization procedure nullifies the need to estimate q, which is just a constant scalar. On the other hand, at the core of a bycatch study is the estimation of bycatchability coefficient, and there is no way around it. It would be laughable and useless for a bycatch study to try to estimate an abundance index for the bycaught species.

I hope I have provided enough motivation for you to download the full paper and read it yourself. Here is the link:
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