On October 24, 2017, QFC co-Director, Dr. Mike Jones, testified before the Senate Commerce, Science, and Transportation Committee Oceans, Atmosphere, Fisheries, and Coast Guard Subcommitte on a hearing related to the re-authorization of the Magnuson-Stevens Fishery Conservation and Management Act, which is commonly referred to as the Magnuson-Stevens Act. This act is the primary law governing marine fisheries management in US federal waters and was enacted to promote optimal exploitation of coastal fisheries. The focus of the hearing was on the state of U.S. fisheries and the science that supports sustainable management. As part of his testimony, Mike discussed the importance of accounting for uncertainty in management decisions and how management strategy evaluation methods, which use computer simulation methods to evaluate how alternative fishery management strategies are likely to perform relative to pre-defined sets of management goal, account for uncertainty. Mike testified on two MSE-applications that he and other QFC staff have worked on in recent years, Great Lakes sea lamprey control and Lake Erie walleye and yellow perch management. A recording of Mike’s testimony can be viewed here.
Several QFC staff and student recently taught a revised version of the R graphing short course entitled “Unleashing the Power of R as a Graphing Tool.” The developers (Lisa Peterson, Yang Li, Sam Truesdell, Matt Vincent, and Charlie Belinsky) refined and restructured the short course into its current version using feedback from the participants of the original course taught in 2016. The short course provided an introduction to creating advanced and publishable graphics using the R base package. The instructors covered a variety of topics, including adding components to existing graphs (e.g., error bars), the use of color (e.g., creating a color ramp), multipanel plots, and creating graphics using spatial data, as well as tips on how to fulfill journal requirements. Information was conveyed through both lecture and “hands-on” sections where participants, using an R environment on their own computer, went through the code with the instructor. Participants also worked on exercises that gave them an opportunity to graph their own data. Twelve graduate students from the Michigan State University Department of Fisheries and Wildlife participated in the day-long course. The feedback on the course was very positive, with participants reporting an increase in their practical knowledge of R and the belief they would be able to apply the concepts that were taught to their own work in the future.
The Quantitative Fisheries Center (QFC) recently hosted a two day workshop (June 22-23) as part of a larger structured decision-making (SDM) exercise to address management of Grass Carp in Lake Erie. Grass Carp is an invasive herbivorous fish that at high densities can pose a threat to coastal wetlands and aquatic communities. Some Great Lakes states allow inland stocking of sterile Grass Carp (triploid), which have been sporadically captured in Lake Erie; however, increasing captures of reproductively viable Grass Carp (diploid), and documented reproduction and recruitment over the past several years have raised concerns about potential effects on Lake Erie aquatic and wildlife communities. The SDM exercise is being led by Drs. Kelly Robinson, Mike Jones, and Mark DuFour and is designed to bring fisheries managers and researchers together to cohesively develop a management strategy for Lake Erie Grass Carp. Twenty-nine individuals participated in the June workshop from ten different institutions and agencies: Michigan Department of Natural Resources, Ohio Department of Natural Resources, Ontario Ministry of Natural Resources and Forestry, U. S. Geological Survey, U. S. Fish and Wildlife Service, Department of Fisheries and Oceans Canada, University of Toledo, Michigan State University, University of Toronto, and the Great Lakes Fishery Commission. Because the Grass Carp threat to Lake Erie is new, there are many uncertainties about their potential effects on the Lake Erie ecosystem as well as the likelihood of success of different management options. During this workshop, the research and management group discussed these uncertainties and the QFC presented a Grass Carp population model to help evaluate our current knowledge and inform future management actions. A final workshop is tentatively scheduled for September 2017, where the QFC will present the results of our uncertainty analysis. These initial workshops, and SDM exercise as a whole, represent the first step in an adaptive management process, where we can refine our management strategies as we learn more about the Lake Erie Grass Carp population and the effectiveness of management options.
In February, Alex Jensen, QFC Master’s student advised by QFC Co-Director Dr. Mike Jones, successfully defended his MS thesis, entitled “Modeling the Impacts of Barrier Removal on Great Lakes Sea Lamprey”. Alex’s thesis research addressed two key uncertainties regarding barrier removals in the Great Lakes region: how much suitable sea lamprey habitat is available above barriers and what is the expected sea lamprey population response to barrier removal. To address these uncertainties, Alex developed and evaluated competing models to predict larval sea lamprey habitat availability upstream of Lake Michigan barriers using reach-scale landscape predictors. Alex then modified and used a sea lamprey management strategy evaluation (SLaMSE) model to evaluate the impacts of barrier removals on the response of the Lake Michigan sea lamprey population. Alex’s simulation modeling revealed a disproportionate, exponential response to increasing barrier removals, due in part to decreasing control effectiveness with a fixed lampricide treatment budget. The same model was used to inform the potential effects of different management actions associated with the possible removal of the Grand River’s Sixth Street Dam.
Alex will remain at the QFC through the spring finalizing edits to his thesis and preparing the chapters of his thesis for potential journal publication. In early summer, Alex will be moving to Oregon to pursue a PhD with Dr. Jim Peterson at Oregon State University. Congratulation Alex!
Travis Brenden (QFC Associate Director) recently co-authored 4 papers with colleagues from the Michigan State University Aquatic Animal Health Laboratory on viral hemorrhagic septicemia virus genotype IVb (VHSV-IVb) disease dynamics in the Great Lakes region and the potential of using vaccination to protect fish against outbreaks of the virus. VHSV-IVb is an emerging sublineage of VHSV with a wide host range that quickly spread throughout the Great Lakes watershed and caused multiple large fish die offs. In the first paper (Throckmorton et al. 2017), potential reservoirs of VHSV-IVb were examined in Budd Lake, a Michigan inland lake that is considered enzootic for the virus. VHSV-IVb was detected in multiple samples of amphipods both in 2011 and 2012, but was not detected in Notropis spp., Lepomis spp., mussels, leeches, or water samples, suggesting that amphipods might serve as a reservoir or vector for the virus. In the second paper (Millard et al. 2017), a DNA vaccine containing the glycoprotein gene of VHSV-IVb was developed and evaluated in its ability to protect muskellunge, which is a species that is highly susceptible to the virus, against infection. The relative percent survival of immunized muskellunge when challenged with the virus was 45%. In a follow-up paper (Standish et al. 2016a), relative percent survival of immunized muskellunge when challenged with VHSV-IVb increased to 100% when vaccinated individuals were given a booster dose and allowed a longer incubation period prior to the challenge. In the fourth paper (Standish et al. 2016b), a laboratory study was conducted to determine whether a herd immunity response, which is a necessary precursor for a vaccination program to be successful, could be elicited in fish. The study used a novel flow-through tank design in which different combinations of naïve and immunized muskellunge housed in tanks were exposed to VHSV-IVb via infected muskellunge housed in a tank supplying water to the other tanks. The mortality of naïve muskellunge on average was lower when co-occurring with immunized muskellunge than when naïve muskellunge were housed alone (36.5% when co-occurring with vaccinated muskellunge versus 80.2% when housed alone), indicating a possible protective effect based on cohabitation with vaccinated individuals. Travis and QFC Post-Doctoral Research Associate, Lori Ivan, have recently completed a modeling project using the information learned from the Standish et al. (2016b) study to determine the feasibility of vaccinating and stocking hatchery-propagated fish as a means to protect a wild fish population again viral outbreak. They hope to publish this research soon. Travis and Lori will be giving presentations on their modeling results at two upcoming meetings: American Fisheries Society Fish Health Section meeting and the International Association of Great Lakes Research annual conference.
Millard, E.V., S.E. LaPatra, T.O. Brenden, A.M. Bourke, S.D. Fitzgerald, and M. Faisal. 2017. DNA vaccination partially protects muskellunge against viral hemorrhagic septicemia virus (VHSV-IVb). Journal of Aquatic Animal Health 29:50-56. (see here)
Standish, I.F., E.V Millard, T.O. Brenden, and M. Faisal. 2016a. A DNA vaccine encoding the viral hemorrhagic septicemia virus genotype IVb glycoprotein confers protection in muskellunge (Esox masquinongy), rainbow trout (Oncorhynchus mykiss), brown trout (Salmo trutta), and lake trout (Salvelinus namaycush). Virology Journal 13:203; doi:10.1186/s12985-016-0662-8. (see here)
Standish, I.F., T.O. Brenden, and M. Faisal. 2016b. Does herd immunity exist in aquatic animals? International Journal of Molecular Sciences 17:1898; doi:10.3390/ijms17111898. (see here)
Throckmorton, E., T. Brenden, A. Peters, T. Newcomb, G. Whelan, and M. Faisal. 2017. Potential reservoirs and risk factors for VHSV IVb in an enzootic system: Budd Lake, Michigan, United States. Journal of Aquatic Animal Health 29:31-42. (see here)
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.