This post could be useful to anybody interested in fish advocacy, wild animal welfare, working in the civil service/public policy, or building skills in biology/ecology or mathematics/statistics. This post is in response to Aaron Gertler’s and Lizka's suggestions. I’m writing about a previous job of mine. This job was a stepping stone on my pathway to effective animal advocacy, and I thought that my journey could hold some useful lessons for others.

Summary

• The job I’m writing about was as a fisheries scientist in the government’s department of natural resource management.
• Fisheries science could directly help wild fish and other wild animals, or it could provide career capital.
• This job is great for maths/statistics geeks, and you generally need an advanced degree (e.g. Masters or PhD) in statistics, biological modelling, or fisheries science. It pays pretty well.

What is fisheries science all about?

Fisheries science is the application of science to understanding fisheries, usually to help a government manage the fishing industry and optimise the industry for economic, biological, or social objectives. Fisheries science is usually done by trained fisheries scientists in government departments or universities. One part of fisheries science is fisheries modelling, which involves applying quantitative/statistical methods to advise on fisheries management. Fisheries modelling is a large focus of fisheries science, and it’s the area where my job focused.

Fisheries modelling can be relevant for EA either directly or indirectly. Directly, you could use fisheries modelling to engage in policy debates to benefit wild fish. You can also do this for wild animals other than fish, as the tools used to model fish populations are readily adapted to other wild animals. Indirectly, you can use fisheries modelling to build career capital. Fisheries modelling is a cross-disciplinary field, and it can help you build knowledge and skills in fish biology, population biology, ecology, policy, natural resource management, statistics, and mathematics. You can also dip into related fields like economics and social science. The value of fisheries modelling for career capital is the main reason I chose fisheries science as a stepping stone on my journey to animal advocacy.

My choice of fisheries science as a stepping stone

A few years back, as my undergraduate college degree was wrapping up, I was trying to decide on a career path. I found myself increasingly compelled by arguments for using my career to try to do the most good, and I found myself inspired by the work being done in the EA community.

Given my experience, interests, personal fit, etc, I decided that a smart plan was to chart a course towards working in effective animal advocacy. I took stock of the skills that I had and I reflected on where I wanted to end up (influencing policy to do the most good for animals, and specifically fish). The big question mark was the middle: to get where I wanted to go, what was an appropriate stepping stone? I figured that I could look for a way to apply my ecological and quantitative skills to something related to fish and/or government policy. This led me to choose fisheries science.

Of course, fisheries science takes some training (see below). Fortunately, I was already considering beginning a PhD. I spoke with the professor who’d advised my Bachelor's thesis, and he happened to know of a couple of fisheries scientists in the country. Some emails were sent out, and eventually a fisheries scientist in my regional government agreed to collaborate with me on a PhD project.

This government fisheries scientist joined my panel of PhD advisors, and we developed a PhD research project together. This research project aimed to take an existing fisheries model for a local fish population and update it to better match some recent discoveries relating to that particular fish species. During the project, I learned how to write fisheries models in R; I reviewed the publications detailing the equations and use of this specific model; and I then replicated the model in R. From here, I basically made a few updates to the model, ran the model to see how it affected our understanding of the fish population, and wrote up the results as part of my thesis. This was entirely desk research; since I didn’t want to witness any fish suffering up close and wasn't comfortable travelling, this suited me well.

My experience working as a fisheries scientist in government

Now, onto the job itself. After my PhD, the regional government (where that fisheries scientist who helped advise my PhD worked) advertised a job opening for a fisheries modeller. I applied and was successful, largely because I’d recently completed that PhD project on modelling a local fish population. (I’m sure the personal connection to a member of staff didn’t hurt.)

Day-to-day tasks

The biggest part of this job was contributing to modelling and data analysis relating to local fish populations. This work involved obtaining the latest catch and monitoring data (from a different government department) and inputting that data into the fisheries models appropriate for a particular species. These models had generally been written years or decades beforehand and were well-understood, so most of my work involved cleaning and pre-processing data, updating model code (year = n+1 basically), running the model, and interpreting and writing about the results. The “running the model” step was particularly complicated, and the experience of the senior members of the team was essential in understanding what was actually going on. 

There were occasionally more unique projects that involved applying quantitative methods to answer specific questions about the region’s fish stocks (e.g. the agency head wants to test out a new regulation) or to evaluate new policy ideas (e.g. the industry wants to try some new fishing method).

Roughly 60% of my time was spent at my computer either writing and running code, reading papers or government reports, or writing government reports. About 35% of my time was spent in meetings with the other fisheries modellers on the team to discuss the projects at hand. And about 5% was spent actually speaking to members of the fishing industry. This mostly involved consulting industry on particular projects or reporting the outcomes of projects. The more senior members of the team would have had a greater proportion of their time dedicated to meetings and speaking with industry, but I was a happy desk lackey. There were opportunities to travel to other cities in my country for conferences and collaborative research projects.

Pros and cons

Pros: I found this job mostly engaging, as I enjoy working with numbers and statistics. My staff members were all basically fellow biology and maths geeks, so the meetings were usually enjoyable. The meetings with industry members generally went well (although my manager shared horror stories of meetings that turned aggressive, if not violent). As with many government jobs, it was comfortable with loads of perks (e.g. no expectation to work overtime, flexible working hours, lots of time off). The job was well-paid even compared to government jobs in other departments because of the cross-disciplinary skills that it required. My initial salary was around $65,000 USD, and this would have probably increased quite quickly had I chosen to stay and seek promotion. Other government jobs in my area typically pay closer to $50,000 - $55,000 USD.

Cons: There was limited room for creativity, as the purpose of the job is to answer specific, predetermined policy questions. There was also a fair amount of bureaucracy, although less than in other government departments. All of the work was focused on the region’s economic goals (i.e. growth, although with an acceptance of economic sustainability). This entirely anthropocentric focus was obviously something I knew about when I took the job, but as someone who cares deeply about animals, this did cause me some intense sadness at times.

Overall, I found this job to be a reasonably enjoyable stepping stone, but I would not have remained engaged in the work long-term. I do intuitively feel that this job did indeed give me the skills I was after (knowledge about fish and experience in policy/government). After this job, I ended up securing my current job in animal advocacy. I don’t know how much these skills impressed my employers, but I draw on my fish knowledge and policy experience most days in my animal advocacy work.

Epilogue: How to learn fisheries modelling

If you’d like to learn more about fisheries modelling, how might you go about it? Fisheries modelling, as a scientific discipline, is relatively small but well-developed. There are well-established principles, norms, and assumptions that can appear confusing or esoteric when you first encounter them. I made many mistakes due to not understanding these norms at first. For this reason, I think it would be of great help to find a mentor who is experienced in fisheries modelling. I achieved this by asking a fisheries modeller in my regional government to be an advisor on my PhD thesis. This is the connection that helped me to land a job as a fisheries scientist in that regional government.

Fisheries modellers usually work in regional or national governments or in universities. Governments and universities are more likely to host strong fisheries modellers if the country has economically important fisheries. Some arbitrary examples of fisheries modelling laboratories that I happen to be familiar with are:

• USA: University of Washington’s School of Aquatic and Fisheries Science
• Canada: University of British Columbia’s Institute for the Oceans and Fisheries
• Australia: CSIRO Oceans and Atmosphere (multiple cities)

Most capital cities with economically important fishing industries will have a fisheries science department in the government or a university, so you may not need to look far - try searching “(your city) fisheries science”. There are loads in North America, Europe, and Oceania at least. As long as there are staff who publish quantitative papers in fisheries science journals, you’re on the right track.

There is also a lot of progress you can make on your own (although I’d advise that this is best seen as preparation for, or further development during, a relationship with a more experienced mentor). You can get started with a good textbook. I highly recommend Malcolm Haddon’s book. This book assumes some knowledge of statistics, and it will walk you through the bread-and-butter techniques, like maximum-likelihood parameter estimation, as well as some more advanced applications. You can read some of the classic texts that established many of the methods still in use today, all of which would be mentioned in Haddon's book. In terms of software, you definitely need to know R, and you should know one of Stock Synthesis, AD Model Builder, and/or Template Model Builder. And if you’re comfortable with all of that, you could try finding recent papers published in fisheries modelling journals and trying to replicate their results, given the equations they use. (Make sure to pick a paper that has also published its raw input data.)

(Cover photo by Kevin Canlas on Unsplash)