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Helping Agencies Manage Natural Resources

In 2004, Michigan State University joined forces with the Michigan Department of Natural Resources (MDNR) and the Great Lakes Fishery Commission (GLFC) to establish the MSU Quantitative Fisheries Center (QFC) to provide research and training support to fisheries management agencies around the Great Lakes basin. One of the main objectives was to take advantage of the quantitative and statistical expertise of MSU faculty members. The techniques and technologies pioneered by QFC are very important to researchers such as Michael Jones, MSU professor of fisheries and wildlife, but they are even more crucial to management agencies, whose scientific needs do not always mesh with traditional academic research programs.

Expertise for a challenging task

Jones 100px“The agencies’ interest in starting our center was motivated by a recognition that, at the time, they lacked the expertise in quantitative fisheries that was becoming increasingly important for good fisheries management in the Great Lakes,” Jones said. “Quantitative fisheries science takes a high amount of expertise.”

Jones and his colleagues had been developing that expertise, as well as a relationship with the fisheries management agencies of the Great Lakes, for more than a decade through the Partnership for Ecosystem Research and Management (PERM). PERM is a collaborative effort between MSU and the MDNR established in 1993. Its focus has been on traditional research, however, the need for an entity that could assist with the short-term research and training needs of management agencies became apparent. That’s when QFC originated.

“PERM is something that QFC sort of grew out of,” said James Bence, QFC co-director along with Jones. “When we were first talking about the idea that would become the QFC, the discussion centered around the issue that a lot of times agencies need help on an array of projects that are not really suitable to being regularly sponsored research projects on their own.”

Having accurate population estimates is a critical tool in fisheries management because the estimates enable fisheries managers to determine the amount of fish that can be harvested by commercial and recreational fishers and to track the overall health of the ecosystems under their authority. Obtaining them is a challenging, complex task, one that the experts working for the QFC are especially qualified and equipped to perform.

A numbers game

One way that QFC helps management agencies is through stock assessments, in which all the data an agency has for a particular fishery is integrated to evaluate the status of an entire fish population. This means combining the numbers of fish with information on age, types of environment and harvest rate.

What sounds like simple addition is actually a very complicated mathematical process requiring an in-depth understanding of both fisheries and statistical methods. QFC works with agencies across the Great Lakes to conduct and improve stock assessments.

“We’ve played a very important role in providing education on stock assessment methods to our agency partners, and we’ve done important work on species assessments around the basin,” Bence said.

QFC scientists have helped management agencies assess populations of walleye in Lake Erie, lake trout in Lake Superior and Chinook salmon across three of the five Great Lakes, among others. By applying highly developed statistical models to the various species, researchers can also develop a description of the underlying characteristics that will affect growth.

This provides a window into the future as well as the present. QFC scientists are able to turn this data into forecasting models, which allow their management agency partners to plan for the long-term sustainability of the fisheries in their charge.

“What we do with stock assessments helps the agencies set reasonable harvest rates,” Bence said. “It helps them determine how best to use their resources, whether they should stock certain species, alter the fraction of a population that can be harvested or even how often they should conduct new stock assessments. How we go about figuring out an issue is often just as important as how we solve it.”

Developing better stock assessment methods is one of the key contributions made by QFC.

“Agencies need to know how many fish there are. That’s a simple question with a very complicated answer,” Jones said. “One of the main thrusts of our work is coming up with clever ways to count fish.”

Counting Fish

QFC researchers collaborated to assist the Ontario Ministry of Natural Resources measure changes in the biodiversity of Lake Huron’s fish species using data collected over the past 30 years. Using a technique called dynamic factor analysis, the researchers were able to take data from five locations around the lake, adjust it to ensure that it was comparable and draw conclusions that have helped shape Ontario’s fishery management policies.

“The bottom line was that we found a major decline in the abundance of fish across the community,” said Brian Maurer, director of the MSU Center for Statistical Training and Consulting (CSTAT). “We also found that the proportion of species remained relatively constant, which implied that all species had experienced this decline.”

CSTAT is dedicated to improving the quality of MSU research by providing expertise in various levels of statistical analysis, a mission that makes it a natural partner to the QFC.

“We’re still working on this project, but it’s already a very interesting story that we need to learn more about if we’re going to be wise about how we impact our natural resources,” said Maurer, a professor in the Department of Fisheries and Wildlife and MSU AgBioResearch community ecologist. “Using quantitative fisheries methods has revealed just how huge of an impact we’ve had on the Great Lakes ecosystem. You have these huge lakes that you’d think we couldn’t possible have an impact on, but biologically they’ve changed dramatically even in the past three decades.”

Full story at AgBioResearch

– from Futures, James Dau, AgBioResearch

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