For whatever your reason you have decided to study seabird bycatch quantitatively. This tutorial will get you started right away. I am not assuming any specific background knowledge about fishing or seaibrd biology. However, it is assumed that you have taken your undergraduate level biology and math courses. For analytical skills, calculus is your best friend, but when it is not available (I mean when the problem does not have a nice analytical form, not when you are rusty on calculus), you can always use logic.

I list the core literature below. They deserve to be read in full, not just the title and abstract. I have read them multiple times, and each time I get some fresh ideas on seabird research. Note that the order of the list is significant. It is recommended to start from the 1st item and go down the list one by one. Also the provided external link may break without notice. In that case, a simple google search can help you locate the article.

[Background]
1. The incidental catch of seabirds by longline fisheries: worldwide review and technical guidelines and mitigation. [FAO report]
2. The influence of environmental variables and mitigation measures on seabird catch rates in the Japanese tuna longline fishery within the Australian Fishing Zone, 1991–1995 [DOI link]
3. Seabird mortality in the Japanese tuna longline fishery around Australia, 1988–1995 [DOI link]
4. Principles and approaches to abate seabird by‐catch in longline fisheries [DOI link]
5. Seabird bycatch in pelagic longline fisheries is grossly underestimated when using only haul data [DOI link]
6. [Modelling]
7. Seabird bycatch loss rate variability in pelagic longline fisheries [DOI link and also here]
8. Seabird bycatch vulnerability in pelagic longline fisheries based on modelling of a long-term dataset [DOI link and also here]
9. Interaction frequency of seabirds with longline fisheries: risk factors and implications for management [DOI link and also here]
10. How much do we know about seabird bycatch in pelagic longline fisheries? A simulation study on the potential bias caused by the usually unobserved portion of seabird bycatch [DOI link]

Note that the modelling articles included above have all taken a Bayesian approach, which is not absolutely required. Bayesian computational methods are notoriously slow, but since the dataset for seabird bycatch is usually small, that slugginess doesn't hurt that much. In case you have a moderately large dataset, it is recommended to ditch the Bayesian way and the modelling stragegy should hold no matter which computational method you choose to take.

Also note that I have left out a vast body of literature on seabird bycatch that are purely observational. For example, the bycatch rate in region A is X bird/thousand hooks in year Y - Z. Such references are excellent for your introductory section or provide raw data for your meta-analysis. But for a modeller, you need to think much deeper than that.

Just a friendly reminder:

Each day, 80k acres of forests are disappearing ...

So think about that when you try to print something next time.
      
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