QFC Post-Doctoral Research Associate, Dr. Sam Truesdell, and QFC Co-Director, Dr. Jim Bence, recently taught a workshop on effective sample size in catch-at-age and catch-at-size models. Effective sample size is the likelihood weighting value corresponding to age or length composition data that describes the relative abundance of age or size classes in the data. It is related to the size of the length or age sample but often not directly and so needs to be estimated.
There were nine attendees at the workshop, from organizations including Michigan Department of Natural Resources, US Fish and Wildlife Service, Ontario Ministry of Natural Resources and Forestry, and several Native American tribes that participate in the management of fishery resources in the 1836 Treaty-ceded waters of the Great Lakes. The theory of effective sample size in the context of stock assessment was discussed, with an emphasis on methods for estimating effective sample size using iterative approaches. Attendees also had the opportunity to try out these approaches on actual Great Lakes stock assessment models. The material covered during the workshop was based in part on a recent QFC publication on this topic.
Bryan Stevens (QFC PhD student), Jim Bence (QFC Co-Director), Bill Porter, and Chad Parent (both of MSU’s Quantitative Wildlife Center) recently published a paper in Journal of Wildlife Management testing the performance of published fall harvest guidelines for wild turkeys under a range of conditions representing uncertainties that are relevant to modern turkey management. Existing recommendations were largely developed without consideration of structural uncertainty in regional population dynamics, which is particularly relevant to current management in light of recent evidence for broad-scale recruitment declines. As part of their research, they used simulation modeling to test turkey either-sex fall harvest strategies across different scenarios of recruitment, harvest of males during spring, and female poaching rates during the male-only spring hunting season. Their findings have broad implications for how we should think about designing either-sex fall hunting strategies for turkeys in the face of uncertain regional demography, particularly in light of recent declines to recruitment and increases to harvest of males during spring.
The citation for the published article and the abstract is below.
Stevens, B.S., J.R. Bence, W.F. Porter, and C.J. Parent. In press. Structural uncertainty limits generality of fall harvest strategies for wild turkeys. Journal of Wildlife Management.
Abstract.- Wild turkey (Meleagris gallopavo) populations are broadly distributed, occupy a variety of habitats, and have demographic rates that are heterogeneous through space and time. Dynamics of turkey populations are sensitive to the magnitude of fall either-sex harvest, yet there have been few attempts to study performance of fall harvests systematically across a range of plausible demographic scenarios. Thus robustness of existing recommendations to structural uncertainty in population dynamics is marginally understood. We used a stochastic, sex-specific theta-logistic model to simulate performance of fall harvests (0–15%) across scenarios representing uncertainty about current rates of population productivity (3 levels), female losses during spring hunting (2 levels), and spring male harvest (3 levels), with uncertainty in the strength of density dependence as a common attribute. We demonstrated that performance of previously recommended fall harvests was not robust to changes in demographic parameters that occur within and among populations, and thus previous management recommendations may not be appropriate for all regions. Fall harvest rates capable of maintaining large populations with high probability varied from 0–6% with changes to population productivity, when median male and female spring harvests were 30% and 5%, respectively. In general, risks and management tradeoffs accompanying fall harvests were tightly linked to assumed values of population parameters, where changes to productivity and female loss had particularly strong effects on management outcomes. Specifically, reduced productivity or increased female loss decreased the ability to maintain large populations for a given fall harvest rate. Thus, fall harvest recommendations deduced from models that considered only a small portion of the demographic parameter space may not meet modern management objectives over a broader range of conditions. Moreover, our results suggest that future management could be improved by reducing structural uncertainty about turkey demography to allow for region-specific harvest strategies, or by using decision-theoretic approaches to identify harvest strategies that are robust to uncertainty about population parameters.
Drs. Kelly Robinson and Mike Jones of the QFC are collaborating with the Michigan Department of Natural Resources to engage biologists and managers around the Great Lakes in a structured decision making and adaptive management process for managing grass carp (Ctenopharyngodon idella) in Lake Erie. Grass carp are non-native to the region and could potentially cause harm to the wetland habitats of Lake Erie, which are important spawning and rearing habitat for native fishes and waterfowl. Although some states in the Great Lakes region allow the stocking of triploid (sterile) grass carp for vegetation control purposes, grass carp eggs were recently detected in the Sandusky River, a major tributary to Lake Erie, indicating that grass carp are reproducing in the system. As a result of concerns about the spread of grass carp throughout Lake Erie and the other Great Lakes, a series of workshops have been planned to work through the steps of structured decision making (SDM) to find the best management actions for controlling the potential spread of this fish.
The first workshop was held December 12 and 13, 2016, and was attended by representatives from the U.S. Fish and Wildlife Service, Department of Fisheries and Oceans Canada, U.S. Geological Survey, Michigan Department of Natural Resources, Ohio Department of Natural Resources, Ontario Ministry of Natural Resources, Illinois Department of Natural Resources, Great Lakes Fishery Commission, Michigan State University, Central Michigan University, University of Toledo, and University of Toronto. In this 2-day workshop, researchers presented the state of the science on grass carp in Lake Erie, and the group worked through the first few steps of the SDM process, including laying out the problem statement, identifying objectives that must be achieved to solve the problem, potential actions that could be taken, and key uncertainties that must be addressed. The next step will be to convene a sub-group to create a grass carp population model to make predictions about how management actions would affect objectives related to grass carp population growth. A new postdoctoral research associate, Mark DuFour, will be joining the QFC in January to help create this population model for predicting effects of management actions. He will also assist with the upcoming workshops, which have yet to be scheduled. Through this process, the group hopes to gain a better understanding of the management actions that could be taken to address the spread of grass carp in Lake Erie, as well as the key uncertainties that could affect management decisions.
Sam Truesdell (QFC Post-Doctoral Research Associate), Jim Bence (QFC Co-Director), John Syslo (QFC Post-Doctoral Research Associate), and Mark Ebener (Chippewa-Ottawa Resource Authority) recently had a paper accepted for publication in Fisheries Research for a special issue on data weighting in stock assessment models. These results have previously been shared with members of the Modeling Subcommittee for 1836 Treaty Waters, and the methodology is the basis for a planned workshop at Michigan State University in December 2016.
Integrated stock assessment models use data from multiple sources. For example, in catch-at-age models both the annual landed fish weight and the proportion of fish in each age class (measured via scientific sampling) are considered together. Catch-at-age models evaluate the trends in these data simultaneously, but some data sources are better indicators of population trajectories than others. Part of developing stock assessment models is assigning levels of certainty to each data source (i.e., a weight). This can be particularly difficult for the proportion-at-age samples (termed composition data). Common methods for assigning assessment model weights to fishery compositions typically do not account for processes often seen in the analysis of fishery data, such as that fish with similar characteristics (e.g., of similar ages) are often caught together, leading to correlations in the composition data. This means that composition data are often less informative then they appear based on the number of fish sampled and aged. This can lead to inflated composition weights that can produce biased estimates of population characteristics (such as stock size) and may lead to sub-optimal management strategies. Truesdell and co-authors survey methods for estimating the optimum weight for composition data (the effective sample size) in stock assessment models and apply them to two Great Lakes fish stocks – one in Lake Michigan and one in Lake Huron. They also introduces a new method that can account for correlations in composition data using a statistical approach.
The in-press article can be found here. The citation for the article and the abstract is below.
Truesdell, S.B., J.R. Bence, J.M. Syslo, and M.P. Ebener. In press. Estimating multinomial effective sample size in catch-at-age and catch-at-size models. Fisheries Research
Abstract.-Catch-at-age or catch-at-size stock assessment models require specification of an effective sample size (ESS) as a weighting component for multinomial composition data. ESS weights these data relative to other data that are fit, and is not an estimable parameter within a model that uses a multinomial likelihood. The ESS is typically less than the actual sample size (the number of fish) because of factors such as sampling groups of fish (clusters) that are caught together. A common approach for specifying ESS is to iteratively re-fit the model, estimating ESS after each fit so that the standardized residual variance is “correct,” until ESS converges. We survey iterative methods for determining ESS for a multinomial likelihood and apply them to two Great Lakes whitefish stocks. We also propose an extension of such methods: (the Generalized Mean Approach – GMA) for the case where ESS is based on mean age (or length) to account for correlation structures among proportions. Our extension allows for greater flexibility in the relationship between ESS and sampling intensity. Our results show that the choice of ESS estimation method can impact assessment model results. Simulations (in the absence of correlation structures) showed that all the approaches to calculating effective sample size could provide reasonable results on average, however methods that estimated annual ESS independently across years were highly imprecise. In our simulations and application, methods that did account for correlation structure in annual proportions produced lower ESS than those that did not and suggested that these methods are adjusting for a deviation from the multinomial correlation structure. We recommend using methods that adjust for correlation structures in the proportions, and either assuming a constant ESS or, when there is substantial inter-annual variation in sampling levels, assuming ESS is related to sampling intensity and using the GMA or a similar approach to estimate that relationship.
Yang Li (QFC PhD student), Jim Bence (QFC Co-Director), and Travis Brenden (QFC Associate Director) recently published a paper in North American Journal of Fisheries Management exploring the consequences of moving from annual to less-frequent assessment of fish stocks. Annually assessing fish stocks can be difficult and time consuming; consequently, agencies are often interested in moving away from annual assessments. As part of their research, they evaluated how assessment frequency affected fishery management outcomes and how outcomes were affected by factors such as data quality, variability in population recruitment, and harvest level. They additionally evaluated methods for setting target harvest levels in years between assessments when assessments were not conducted annually.
The published article can be found here. The full citation for the published article and the abstract is below.
Li, Y., J.R. Bence, and T.O. Brenden. 2016. The influence of stock assessment frequency on achievement of fishery management objectives. North American Journal of Fisheries Management 36:793-812.
Abstract.-Because of resource limitations with respect to both funding and staff expertise, there is growing interest among fishery management agencies in moving from annual to less-frequent assessments of fish stocks. We conducted simulations based on Lake Whitefish populations in the Laurentian Great Lakes to evaluate (1) how statistical catch-at-age assessment frequency, the time lag between data collection and assessment, and approaches to setting target harvests in the years between assessments affected the achievement of management objectives; and (2) how the outcomes were influenced by the quality of assessment data, features of the populations, and characteristics of the fisheries exploiting the populations. We found that as assessments became less frequent, relative yields were reduced and the risk of stock depletion and interannual variation in yield increased. The effects of less-frequent assessments were ameliorated in populations with greater levels of productivity and when target mortality was lower. Conversely, the effects of assessment frequency were largely insensitive to changes in recruitment variation or the quality of assessment data.
As part of the Great Lakes Fishery Commission’s Science Transfer Program, members of the QFC will hold two, two-day workshops during November 14-17, 2016. The first workshop will take place in Detroit, MI, and the second in Toronto, ON. Intended for Great Lakes fisheries managers, each workshop will consist of a mock Structured Decision Making (SDM) process for barrier removal decisions, presentations by experts on the state of science in predicting native and invasive fish production above barriers, and guided discussion on research and decision-making. Workshop participants will learn the methods of SDM and gain an increased understanding of the scientific knowledge and uncertainty in predicting fish production above existing barriers.
If interested in attending, you can register for one of the workshops by providing your name, email address, and preferred workshop location to Alex Jensen at email@example.com, or inquire further about the workshops at the same address. Initial registration will be used to determine final workshop enrollment on a first-come, first-serve basis, as attendance is capped at 30 participants per workshop. There is no registration fee for these workshops
The International Association for Great Lakes Research (IAGLR) holds its annual conference in Guelph Ontario, June 6-10 and the QFC will be attending in force. Co-Directors Bence and Jones, both former Presidents of IAGLR, will be attending, and Bence will be at the IAGLR board meeting and reporting for the Publication Committee. Dr. Dobiesz, QFC research scientist/scientific programmer, will be attending in her role as Technical Editor for the Journal of Great Lakes Research, and will be presenting the Editor’s Workshop entitled “Reviews: How to Give and Take”, providing guidance and background on the review process for authors and reviewers. Yang Li (PhD candidate) will be presenting a poster co-authored by Drs. Bence and Brenden in the “Biology and Ecology of Great Lakes Fish” session entitled “Bayesian variable selection for the determination of factors related to fish movement distance.” Alex Maguffee (MS student) will be presenting a talk titled “Differences in otolith chemistry of Lake Michigan Chinook Salmon to identify natal origin” co-authored by Drs. Jones, Clark, and Reilly in that same session. Lisa Peterson (PhD student) will be presenting a talk co-authored with Dr. Jones titled “Evaluating methods for estimating mortality of Great Lakes Walleye using acoustic telemetry in the “Using acoustics as a tool for ecosystem-based aquatic research and monitoring” session.
Reneé Reilly, a QFC Post-Doctoral Research Associate since 2013, braved 3 Michigan winters but recently elected to return to Virginia to pursue a marketing position in the private sector. While at the QFC, Reneé worked on a variety of projects, including management strategy evaluation for Lake Erie percids, Minnesota Mille Lacs walleye assessment and management review, movement modeling of Chinook salmon between Lakes Michigan and Huron, and determining natal origins of Chinook salmon in Lake Michigan through otolith microchemistry. Reneé also served as the technical lead for the QFC’s Introduction to R online course and recently co-led the development of an R graphing short course. Reneé also enlivened the QFC’s social element and was always encouraging of a common lunch time, coffee break, or St. Patrick’s Day party. QFC staff and students wish Reneé all their best and hope she enjoys the warmer weather and her new career opportunity!
Dr. Kelly Robinson has joined the QFC and the MSU Department of Fisheries and Wildlife as an Assistant Professor. Kelly is originally from Chesapeake, VA, and attended the University of Virginia as an undergraduate, majoring in Biology and Spanish. She then received her M.S. in Marine Biology from the College of Charleston, where she studied age, growth and reproduction of barrelfish. Kelly received her Ph.D. in Fisheries Science from the University of Georgia. Her doctoral research focused on fish assemblages in managed wetlands in coastal South Carolina. She studied the productivity of fish guilds within these structures, compared resident fish assemblages in managed and unmanaged wetlands, and used structured decision making (SDM) to evaluate impoundment management strategies. This interest in SDM and decision analysis led Kelly to a Post-Doctoral appointment at Cornell University, where she worked with the New York State Department of Environmental Conservation to use SDM to evaluate harvest management strategies for white-tailed deer and wild turkey. While at Cornell, Kelly had the opportunity to work with other groups to implement SDM, including evaluating the effects of nest exclosures on piping plover populations with managers and biologists in the northeastern U.S. and two projects designed to aid managers and biologists in making the best decisions for mitigating the effects of climate change on the ecosystems of the San Francisco Bay Estuary. Kelly is excited to return to the aquatic environment to study the fisheries of Michigan and the Great Lakes and to use SDM to help the Great Lakes Fishery Commission and other stakeholders to make sound management decisions for fish populations in the Great Lakes and throughout Michigan.
On February 26, the Department of Fisheries and Wildlife Graduate Student Organization (GSO) hosted its 11th Annual GSO Research Symposium. This year’s symposium, which consisted of oral presentations by Fisheries and Wildlife graduate students and poster presentations by undergraduate researchers, was financially supported in part by the QFC. At last year’s symposium, Matt Vincent, a QFC Ph.D. student, won the Best Fisheries Presentation Award. At this year’s symposium, 3 M.S. students (Nick Fisch, Alex Jensen, and Alex Maguffee) and 1 Ph.D. student (Lisa Peterson) from the QFC gave oral presentations. The titles of their presentations were the following:
Nick Fisch: “Quantitative Tools for Assessing and Managing Cisco Populations”
Alex Jensen: “Hierarchical Modeling of Larval Sea Lamprey Habitat”
Alex Maguffee: “Quantifying Differences in Otolith Chemistry of Chinook Salmon in Lake Michigan to Determine Natal Origins”
Lisa Peterson: “Evaluating Methods for Estimating Mortality of Great Lakes Walleye Using Acoustic Telemetry”
The Keynote Speaker for the symposium was Dr. Hillary Young, a community ecologist from the University of California-Santa Barbara.