River Discharge as a Predictor of Lake Erie Yellow Perch Recruitment

Primary Investigator:

Doran Mason - NOAA/GLERL

Co-Investigators:

Stuart Ludsin* - Ohio State University
Tom Croley (Emeritus), Craig Stow, George Leshkevich, Hank Vanderploeg, Nathan Hawley - NOAA/GLERL
Tom Johengen - CILER, University of Michigan
Brian Fryer, Dan Heath - University of Windsor*
Tim Johnson - Ontario Ministy of Natural Resources (OMNR)*
Jeff Tyson - Ohio Department of Natural Resouces (ODNR)*
Bo Bunnell - U.S. Geological Survey (USGS)*
Tomas Höök* - Purdue University
Chris Mayer (U of Toledo)

Executive Summary of Rationale

During CY08, we will continue our exploration into mechanisms underlying the strong positive relationship found between Maumee River (Maumee River) discharge during spring (March-May) and yellow perch (YP) recruitment to the fishery at age-2 (i.e., high spring discharge leads to strong recruitment events), as well as continue to make forecasts of YP recruitment operational. First, we would use two recent General Circulation Models to forecast how expected changes in Maumee River discharge over the next 30 years might influence YP recruitment in Lake Erie during this time span (Objective 1). Second, we would continue development of an operational model to forecast YP recruitment to the fishery 3 to 6 months earlier than currently is possible (prior to the annual Lake Erie Committee, LEC, meeting)–using predictions from GLERL’s Advanced Hydrologic Prediction System (AHPS)–such that LEC agencies can potentially use our forecasts in the determination of harvest quotas for the upcoming year (see Objective 2). Third, we would continue to use historical remote sensing data from western Lake Erie to create an index that describes the areal size of the Maumee River plume in western Lake Erie during spring, which might also help explain why annual springtime Maumee River discharge is correlated with yellow perch recruitment. In so doing, we also would create a database of remotely sensed imagery data that could be used for harmful algal bloom (HAB) and other fisheries-related research (Objective 3). Fourth, we would continue to sample nutrients and other water chemistry data at fixed stations in both the Detroit and Maumee River plumes (Objective 4). Finally, we would continue with field sampling and laboratory analyses designed to identify the relative importance of zooplankton availability (bottom-up effects) versus larval predation mortality to understanding YP recruitment dynamics (Objective 5). Ultimately, through these efforts, we seek to provide Lake Erie agencies with the much-desired mechanisms underlying the Maumee River discharge-YP recruitment relationship, as well as provide them a means to predict how their YP fisheries might be expected to change with respect to Maumee River discharge on both short (2-3 years in advance) and long (decades in advance) time scales.

Proposed Work

Current/Ongoing

1. Determine climate change impacts on YP recruitment to the fishery

During CY07, we used the baseline period of 1961-1990 and a future 30-yr period (2040-2069) from recent General Circulation Model (GCM) simulations to estimate Maumee River discharge during March, April, and May. Specifically, we used GCM simulations from 1) the Canadian Climate Centre (warm-dry runs, CGCM2 A21, and less warm-dry runs, CGCM2 B23], and 2) Great Britain’s Hadley Centre (warm-wet runs, HadCM3 A1FI, and less warm-wet runs, HadCM3 B22). These four scenarios were selected in a recent IJC study of Lake Ontario-St. Lawrence River Regulation as encompassing the range of future GCM simulations under increasing greenhouse gases. We used differences between the two 30-yr periods to modify historical meteorology and simulate resulting hydrology changes in order to estimate outflow from the Maumee River into Lake Erie during March, April, and May.

During CY08, we will use these predictions of Maumee River outflow to predict future recruitment to the fishery at age-2 for both western and central Lake Erie. Further, we will place this predictive relationship between future spring Maumee River discharge and YP recruitment into a Bayesian modeling framework to allow incorporation of model uncertainty as well as uncertainty in the inputs. In this way, our forecasts of YP recruitment under future climate scenarios would be probabilistic. This model could be readily updated as new data are acquired. We will provide probability estimates for the four GCM scenarios by interviewing experts in the field, and using their opinions in a Bayesian framework to narrow down appropriate probability estimates.

We seek to report our revised model and forecasts of future YP recruitment in a peer-reviewed MS to be submitted during the second quarter of CY08.

2. Provide forecasts of YP recruitment earlier than is currently possible, which might benefit harvest quota decisions.

A second forecast that we propose to continue to generate is a daily probabilistic outlook of YP recruitment for up to two years into the future from the present day, running in real time. We would use GLERL’s Advanced Hydrologic Prediction System (AHPS) and extract probabilistic outlooks of Maumee River flow for spring months (March, April, and May) to estimate probabilistic YP recruitment two years ahead.

At present, agencies can only forecast yellow perch recruitment to the fishery two years in advance, based on a) age-0 abundance indices generated in August (i.e., August abundance indices and age-2 recruitment are positively correlated), or b) using observed river discharge (i.e., our model) from March through May. Importantly, both of these forecasts can only be made after the annual Lake Erie Committee (LEC) meeting in March, when harvest quota decisions for the upcoming year are made. Thus, when these harvest quota decisions are made in March of any given year (e.g., 2008), they only have an estimate of how many yellow perch would recruit to the fishery during 2008 (based on 2006 age-0 abundance indices) and 2009 (based on 2007 age-0 abundance indices). At present, the LEC has no way to estimate age-0 abundance during the upcoming year (2008), which precludes forecasting recruitment to the fishery two years into the future (2010).

Using GLERL’s AHPS, which can be used to forecast Maumee River discharge months in advance, we propose to forecast a) the strength of the upcoming yellow perch year-class (e.g., 2008 age-0 abundance) and b) recruitment of age-2 yellow perch (in 2010) prior to the annual LEC meeting in March. In essence then, these forecasts would provide the LEC with an extra year of forecasted recruitment so that in 2008 (for example), the LEC would have an indication of what recruitment would be like during 2008 (using age-0 abundance data from 2006), 2009 (using age-0 abundance data from 2007), and 2010 (using predicted age-0 abundance data from 2008, via predictions made by GLERL’s AHPS). These forecasts would be probabilistic so that agencies could know how confident they should be in that forecast. This information (i.e., a forecast of upcoming year-class strength, and in turn, a forecast of age-2 yellow perch recruitment an extra year down the road) could be valuable in making harvest quota decisions. These early probabilistic forecasts are of great interest to the LEC (Roger Knight, LEC Chair, personal communication).

Further, by running the models in hindcasting mode (i.e., after river discharge happens and the year-class if produced), the LEC could evaluate the success of the model. Thus, during CY08, we would extend this forecasting model by extracting actual and simulated flows for the immediate past year to estimate YP recruitment 0 and 1 years ahead, and then use these same outputs in “hindcast” mode to check the accuracy of our forecasts (i.e., compare observed YP recruitment to forecasted YP recruitment). In this way, agencies would have a way to evaluate the accuracy of our predictions. As part of this future hindcasting effort, we also would quantify the accuracy of predictions made 6, 3, 1, and 0.5 months in advance of the annual LEC meeting so as to determine the earliest point at which reliable predictions could be made.

Ultimately, by merging Ludsin’s empirical Maumee River discharge-YP recruitment modeled with real-time forecasts from GLERL’s Advanced Hydrologic Prediction System (in a Bayesian modeling framework), we seek to make forecasts of Lake Erie YP recruitment fully operational and available to managers. As far as we are aware, a forecasting model of this nature would be the first of its kind.

3. Create a database of remotely sensed data that can be used to help understand variation in YP recruitment, as well as variation in HAB distributions and fish catch rates.

Through an agreement with the University of Wisconsin, which operates a real-time receiving station for MODIS direct broadcast data, MODIS imagery is subset into four Great Lakes CoastWatch scenes. These scenes are converted into true color images, can be downloaded daily, and made available to the project via the Great Lakes CoastWatch website. Individual bands in the visible (red, green, and blue) and near infrared also are available.

During CY07, we began to use Moderate-Resolution Imaging Spectroradiometer (MODIS) 250-m resolution, true color, near real-time imagery from the Terra and Aqua satellites, in addition to visible band channels, to map and track tributary plumes in western Lake Erie. Specifically, we downloaded and processed daily images from mid-April through June 2006 and 2007, which corresponds to the larval yellow perch production period in western Lake Erie (S. Ludsin, unpub. data). Using, these data, we used ArcGIS to quantify the areal extent of the west basin that is affected by the Maumee and Detroit

Figure 1. Areal extent of Maumee and Detroit River plumes during mid-April through June 2006 and 2007 in western Lake Erie. Colors indicate the degree to which west basin water was occupied by each plume on a weekly basis. Areas in red indicate that that those particular areas were highly influenced by a plume (e.g., for the Maumee plume in 2006, red = 10, indicating that those particular areas were occupied by the Maumee River plume during 10 weeks of the mid-April through June sampling period).

River plumes on a weekly basis (Figure 1) and created indices to describe this coverage (e.g., average surface area influenced by the Maumee River plume monthly and during mid-April-June). We also calculated average red, green, and blue values for these plumes. Further, using CTD and other limnological data collected as part of Objectives 4 and 5 (see below), we quantified average chlorophyll a, turbidity, transimissometry, Secchi disk, and light extinction coefficients for each plume.

Ultimately, we are seeking to use these processed images and indices of plume size explore the hypothesis that enhanced Maumee River discharge promotes YP survival by providing a greater areal extent of favorable habitat (e.g., high copepod zooplankton; per Table 1) for larvae. During CY08, we propose to generate index values for all remaining years in which we have historical remote sensing and larval yellow perch data (2000-2005, 2008). In turn, this index, in combination with average P and sediment loading into the system from the Maumee River during the same years, could help us determine whether it is the actual amount of P (or sediment) loading into the system that is important, or whether it is how those loads (regardless of absolute size) are distributed as plumes, via wind-driven circulation, that is important to YP recruitment. These data would be compared against observed estimates of zooplankton availability and yellow perch recruitment from 2000-present (data from ODNR, OMNR), and our own collections.

Although we have only processed two years of data, our expectations of plume size in relation to yellow perch year-class strength were supported; 2007 had both a larger plume and yellow perch year-class than during 2006. Once we have all years of data processed, we hope to better explore relationships between river discharge, plume size, and yellow perch recruitment.

Importantly, we propose to also make the remotely sensed data (e.g., images and red, blue, green values, as well as limnological data below) available to other researchers, through NOAA GLERL’s website. Specifically, we will create a user-friendly database, building upon what we started during CY07. We envision that these data would be of interest to other researchers that are trying to help understand distributions of their organism(s) of interest (e.g., harmful algal blooms, fish catch rates and distributions).

4. Continue water quality monitoring in the Detroit and Maumee River plumes

During 2006-2007, some pilot water quality work was conducted to help us verify that our assumptions (see Table 1) about higher phosphorus, turbidity, and chlorophyll levels in the Maumee River plume versus Detroit River plume were correct. Specifically analyzed was, total suspended matter (TSM), total phosphorus (P), total dissolved P, and chlorophyll at four stations in each of the plumes (Figure 2), finding that levels of each of these variables was indeed higher in the Maumee River plume than in the Detroit River plume (Table 2).

Figure 2. Sampling stations for water quality during May through July 2006. Stations F2 through F5 are in the Maumee River plume, whereas stations F8-F12 are in the Detroit River plume.

During CY08, we seek to quantify these same attributes. This information would be valuable in three ways. First, the data could be used to calibrate CTD information (e.g., transmissometer, fluorometer data) collected at these and other stations. Secondly, these data would benefit future manuscripts that compare zooplankton abundance and larval yellow perch growth, condition, and survival between plumes by removing the need to make any assumptions about turbidity and P levels. Finally, these collections could lead to another large-scale proposal concerning how river plumes influence the flow of energy through the food web. This work would likely involve GLERL and non-GLERL researchers who were involved in an earlier NOAA COP proposal back in 2003 regarding the same topic as this proposal.

Table 1. Water quality measurements during May through June 2006 in the Maumee and Detroit River plumes. TSM-total suspended matter; TP-total phosphorus; TDP-total dissolved phosphorus; Chla-chlorophyll a

5. Continue conducting field and laboratory work to determine the mechanisms underlying the relationship between Maumee River discharge and yellow perch recruitment

To identify mechanisms underlying the river discharge-YP recruitment relationship, we would use rigorous field sampling coupled with innovative laboratory and modeling techniques. We would use field collections to quantify how Maumee River discharge influences west basin physical habitat and food (zooplankton) for larval YP, and how spatial differences in habitat (e.g., conditions in the Maumee vs. Detroit River plume and their corresponding spatial extent) influence larval YP spatial abundance patterns, feeding, growth, and condition (see Hypothesis 1). As part of this effort, we would use otolith microchemistry to quantify how growth disparities arising from differential habitat availability and use (e.g., Maumee River plume vs. Detroit River plume) as larvae, influence survival to the juvenile stage (when recruitment is set). We also would combine field sampling with bioenergetics modeling and quantitative genetics to explore the role of predation mortality in explaining the river discharge relationship (see Hypothesis 2).

To help determine whether Maumee River discharge might be influencing YP via effects on food availability, larval YP would be collected weekly in the west basin in spring, 2006-09. Remote sensing would guide sampling to contrast areas influenced by the Maumee River versus other areas. Larval diet analyses would determine consumption of zooplankton (prey) and size- and taxonomic-selectivity among areas. Otolith analyses (daily aging/growth, microchemical) of larvae and juveniles would be used to quantify growth differences among areas (as larvae), and detect potential differential selection for larval habitat use and growth rate. RNA:DNA analyses would be used to contrast larval fish condition.

To assess the relative importance of predation, potential predators would be captured using 3-hr gillnet sets and bottom trawling. Larvae in predator diets would be identified to species morphologically and using genetic methods (quantitative real-time PCR of mtDNA fragments with sequencing of sub-clones to estimate relative abundance). Validation of relative abundance would be performed using lab studies.

Finally, the suite of findings would be incorporated into a spatially-explicit, individual-based model to quantify habitat suitability for larvae across our study areas. Ideally, this analysis, combined with our empirical research, would provide LEC agencies with knowledge of the relative roles of oligotrophication-driven ZP availability and predation mortality in regulating YP recruitment. In so doing, we seek to substantiate use of MR discharge as a means to provide early forecasts of YP recruitment to the fishery.

Scientific Rationale

Ability to forecast recruitment is the ‘holy grail’ of fisheries management. Attaining this predictive capability, however, has proven difficult in most systems, owing in large part to an inability to understand mechanisms that drive recruitment variation. This lack of understanding is attributable to a complexity of physical, chemical, and biological factors that can simultaneously influence fisheries dynamics (Houde 1987, 1987, 1994; Miller et al. 1988; Leggett and DeBlois 1994; Bradford and Cabana 1997). Fortunately, we are now making headway in this arena, due to the growing awareness and acceptance that forcing factors external to the aquatic system can play a dominant role in regulating fish recruitment (e.g., Jain and DePinto 1996; Boynton and Kemp 2000; Ludsin et al. 2001; Luo et al. 2001; Sirabella et al. 2001; Koslow et al. 2002; Friedland et al. 2003; MacKenzie and Koster 2004). In turn, the call for ecosystem (watershed) approaches to fisheries management has been strong, especially in U.S. coastal systems, including Hudson River (NYSDEC 1996), Chesapeake Bay (FAC 1998), and the Pacific Northwest (USDA 1996). Similarly, the Great Lakes Fishery Commission (GLFC), with a primary goal of promoting coordinated research and management programs among federal, state, and provincial Laurentian Great Lakes agencies to maximize sustained fisheries productivity, has advocated development of ecosystem approaches to management and research (GLFC 2001). These approaches should seek to define and understand linkages among all ecosystem components whether in land, air, or water (GLFC 2001). A similar approach to resource management in the Great Lakes was advocated in the 1987 amendment to the Great Lakes Water Quality Agreement (GLWQA). Consistent with this recent emphasis on ecosystem approaches to fisheries management, we propose to explore how watersheds, through river inputs and corresponding food web response, regulate fish recruitment in Lake Erie.

The need for ecosystem management strategies in many large coastal systems, including the Laurentian Great Lakes, emanates from extensive tributary networks that intimately link aquatic systems and their watersheds. Watershed-derived inputs from these tributary networks can be a major source of both inorganic and organic materials to the system. For example, ~90% of the total nitrogen and ~60% of the total phosphorus (P) that enters Chesapeake Bay, does so from non-point sources (Boynton et al. 1995). Likewise, precipitation-driven river discharge events now are the major source (60%-75%) of P into Lake Erie (Dolan 1993; Richards et al. 2001; Baker and Richards 2002). Because external inputs (e.g., freshwater, sediments, nutrients, organic matter) can drive spatiotemporal variability in both physical (e.g., temperature, oxygen, nutrients) and biological (e.g., microbial, phytoplankton, and zooplankton production) attributes of coastal systems (Cloern 2001; Malone et al. 1988; Boynton and Kemp 2000; Kremer et al. 2000; Rabalais et al. 2000; Nixon et al. 2001; Rabalais et al. 2002; Weise et al. 2002; Biddanda and Cotner 2003), it is quite reasonable to expect that fisheries production also would be influenced by allochthonous inputs from the watershed. Indeed, numerous investigations in coastal systems have suggested linkages between external watershed inputs and fish community and recruitment dynamics (e.g., Nixon 1988; Kremer et al. 2000; Ludsin et al. 2001; Hobbs et al. 2002; Nixon 2003; Ludsin et al. in press). In fact, for the west basin of Lake Erie, we have developed a simple empirical relationship (Figure 2) that predicts (2 yr in advance) the number of yellow perch (YP; Perca flavescens) that would recruit into the fishery as a function of spring (March through May) discharge from the Maumee River (OH), a west-basin tributary that drains a largely agricultural watershed (Richards et al. 2002). Similar strong, positive correlations between river inflows and recruitment to the fishery, which could be of potential use to management as predictive tools, also have been identified in other coastal systems, including Chesapeake Bay (Stevens 1977), Gulf of St. Lawrence (Sutcliffe 1972), and the Pacific Northwest (Scarnecchia 1981).

Unfortunately, the mechanisms responsible for positive correlations between river discharge and fisheries production remain largely enigmatic, which has limited their value for fisheries management purposes. For example, the Ohio Department of Natural Resources (ODNR) and the Ontario Ministry of Natural Resources (OMNR), which are responsible for managing the YP commercial and recreational fisheries in Lake Erie, would not consider using our empirical model (Figure 2) to forecast future recruitment until its mechanistic underpinnings are more fully identified. Such misgivings for applying correlative models stem from the discomfort associated with blindly accepting the implicit assumptions of the approach; that is: 1) conditions do not change, and 2) the phenomena modeled adequately reflect the causal pattern of interest (Koehl 1989). Because conditions in Lake Erie are continuously changing, owing to natural and anthropogenic influences (e.g., invasive species, habitat alteration, altered nutrient loading regimes, fishery harvest, climate; see chapters in Munawar et al. 1999), we currently can only speculate as to how the Maumee River plume might be causing annual fluctuations in YP recruitment. Clearly, identification of the ecological mechanisms underlying our model would allow management agencies to begin to anticipate when the model might fail (e.g., during 2003 and 2004; i.e., outliers in Figure 3), and help guide decisions regarding annual harvest regulations.

Beyond ability to understand and forecast variation in fishery resources, Great Lakes management agencies desire the opportunity to positively influence fishery production (i.e., enhance yields, reduce inter-annual variation). Because inter-agency strategies to manage exploitation rates on major recreational and commercial species (e.g., YP, walleye Sander vitreus) are already in place, interest in learning whether fishery production also could be positively influenced by watershed management approaches (e.g., regulations on land-use, river flows) is strong (T. Johnson, OMNR, and J. Tyson, ODNR, pers. comm.). In order to evaluate if this alternative approach to fisheries management actually might exist for Lake Erie, we must begin to evaluate mechanisms underlying the relationship between river discharge and YP recruitment to the fishery in Lake Erie (i.e., Figure 3).

Herein, we propose a hypothesis-driven, field-, laboratory-, experimental-, and modeling-based research approach to evaluate two hypotheses regarding the linkage between river discharge and YP recruitment in western Lake Erie. Hypothesis 1 suggests that springtime Maumee River discharge regulates YP recruitment via bottom-up control of food (zooplankton, zooplankton) production for pelagic larvae, whereas Hypothesis 2 argues that Maumee River discharge benefits YP recruitment via enhanced turbidity that reduces predation mortality on YP larvae. Different watershed management plans may need to be established, if P inputs from the Maumee River were found to regulate YP recruitment more than sediment inputs. Thus, beyond providing Lake Erie managers with a better understanding of the limitations of our forecasting model (see Figure 1), our research should provide them with an indication of whether watershed approaches to fisheries management (e.g., regulations on P and/or sediment inputs) can protect and sustain the economically, ecologically, and culturally important YP fishery. In addition, the forecasting models of yellow perch dynamics in both the short (2 years in advance) and long-term (decades in advance) are of considerable interest, especially considering that we propose to eventually use hindcasts to evaluate the predictions that we made.

Governmental/Societal Relevance

In accordance with a 1987 amendment to the GLWQA, the GLFC has advocated development of ecosystem approaches to management and research (GLFC 2001). To date, however, no Great Lakes fishery is currently managed with consideration of forces external to the aquatic realm, such as climate and nutrient inputs. Instead, those fisheries managed in the Great Lakes are done so primarily through regulations placed on harvest of fish biomass. Although we cannot say for certain, we believe that a lack of mechanistic understanding concerning linkages between the broader ecosystem (e.g., watershed) and fish production is a primary reason why ecosystem management strategies have not been adopted as of yet. Clearly, until the mechanistic research is conducted to quantify these linkages, development of sound ecosystem approaches to fisheries management would remain unattainable.

Our proposed research offers an outstanding opportunity to begin to understand the extent of the linkage between Lake Erie’s watershed and its fisheries. Despite the strong correlation between Maumee River discharge and YP recruitment, it is possible that Lake Erie YP are not influenced by allochthonous inputs, and that zooplankton availability and predation mortality are not the mechanisms ie. However, this knowledge in-and-of-itself would be valuable by allowing us to redirect our efforts to alternative hypotheses. More likely, we would substantiate one or both of our hypotheses, which then could help guide Lake Erie management agencies in their search for watershed management plans (e.g., regulations on nutrient or sediment inputs from rivers) that eventually could be used to reduce inter-annual recruitment variation, or perhaps even enhance YP production in Lake Erie. Such a management strategy would be of great value to these agencies, complimenting their existing set of management tools.

Lake Erie management agencies (ODNR, OMNR), and the Lake Erie Committee itself, have expressed a strong interest in the results of this study for facilitating their ability to understand and forecast YP recruitment. This is best evidenced by their willingness to write letters of support (see 4 attached letters) and provide in-kind support to 1) collect fish during May through August (including providing gill nets, OMNR), 2) provide access to boats for sampling (OMNR, ODNR), 3) provide historical data (OMNR, ODNR), 4) support a student (OMNR), and 4) play a role in developing our research plan (OMNR, ODNR). Further, both Roger Knight (Chair of the Lake Erie Committee) and Jeff Tyson (ODNR Lake Erie Fisheries Research Station Supervisor) have expressed a lot interest for our goal of providing earlier annual forecasts of YP recruitment, using GLERL’s Advanced Hydrologic Forecasting System.

Clearly, these agencies recognize that our project would make an important contribution to the general understanding of YP recruitment dynamics in Lake Erie, with potential application to other “coastal” Great Lakes YP populations (e.g., Saginaw Bay, Green Bay). Our research also would help to identify the importance of nearshore and open-lake habitat, and their associated attributes (e.g., temperature, zooplankton, turbidity), to larval YP feeding, growth, and survival, which has relevance to several of the “Fisheries Research Priorities for the Great Lakes” (www.glfc.org/research.asp). In turn, this ecological understanding and the improved ability to predict variation in fishery production would provide agencies with a means to manage user-group (e.g., recreational and commercial fishers) expectations, as well as provide credibility in times when unpopular management decisions must be made (R. Knight, Lake Erie Committee, pers. comm.).

Relevance to Ecosystem Forecasting

Our previous research has demonstrated a positive relationship between river discharge and yellow perch recruitment to the fishery; however, the underlying mechanism(s) remain largely enigmatic, which has limited its value for fisheries management purposes. Because conditions in Lake Erie are continuously changing, owing to natural and human influences (e.g., invasive species, habitat alteration, altered nutrient regimes, fishery harvest, climate; see chapters in Munawar et al. 1999), we currently can only speculate as to how the Maumee River plume might be causing annual fluctuations in YP recruitment. Clearly, identification of the ecological mechanisms underlying our model would allow management agencies to begin to anticipate when the model might fail (e.g., during 2003 and 2004; i.e., outliers in Figure 3), and help guide decisions regarding annual harvest regulations.

Beyond increasing our basic ecological understanding of the importance of Maumee River discharge to YP recruitment, we propose to provide forecasts of meaningful to management. During FY08, we would forecast how YP recruitment to the fishery would be expected to vary during the next 30 yr, using the most recent GCM models as a basis to forecast springtime Maumee River discharge into Lake Erie. By using a broad spectrum of GCM climate scenarios in a Bayesian modeling framework, we could provide probabilistic predictions of future recruitment in the face of climate change.

We also propose to provide agencies with the ability to forecast YP recruitment earlier than is currently possible, which would ultimately provide them with an extra year of forecasting capability at the time that harvest quote decisions are made. By merging Ludsin’s empirical river discharge-recruitment model with GLERL’s Advanced Hydrologic Prediction System to make probabilistic predictions of river discharge and yellow perch recruitment, we seek to provide Lake Erie agencies with extra information that could benefit their ability to manage their fisheries. These early forecasts are of great interest to the LEC (Roger Knight, LEC Chair, personal communication).

Finally, Great Lakes management agencies desire the opportunity to positively influence fishery production (i.e., enhance yields, reduce inter-annual variation). Because inter-agency strategies to manage exploitation rates on major recreational and commercial species (e.g., YP, walleye Sander vitreus) are already in place, interest in learning whether fishery production also could be positively influenced by watershed management approaches (e.g., regulations on land-use, river flows) is strong (T. Johnson, OMNR, and J. Tyson, ODNR, pers. comm.). In order to evaluate if this alternative approach to fisheries management actually might exist for Lake Erie, we must begin to evaluate mechanisms underlying the relationship between river discharge and YP recruitment to the fishery in Lake Erie.

Cited References

Baker, D.B., and R.P. Richards. 2002. P budgets and riverine P export in northwestern Ohio watersheds. Journal of Environmental Quality 31:96-108.

Biddanda, B.A., and J.B. Cotner. 2003. Enhancement of dissolved organic matter bioavailability by sunlight and its role in the carbon cycle of lakes Superior and Michigan. Journal of Great Lakes Research 29:228-241.

Boynton, W.R., and W.M. Kemp. 2000. Influence of river flow and nutrient loads on selected ecosystem processes: a synthesis of Chesapeake Bay data. Pages 269-298 in J.E. Hobbie, editor. Estuarine Science: A Synthetic Approach to Research and Practice. Island Press, Washington, D.C.

Boynton, W.R., J.H. Garber, R. Summers, and W.M. Kemp. 1995. Inputs, transformations, and transport of nitrogen and P in Chesapeake Bay and selected tributaries. Estuaries 18(1B):285-314.

Bradford, M.J., and G. Cabana. 1997. Interannual variability in stage-specific survival rates and the causes of recruitment variation. Pages 469-493 in R.C. Chambers and E.A. Trippel, editors. Chapman and Hall, London.

Cloern, J.E. 2001. Our evolving conceptual model of the coastal eutrophication problem. Marine Ecology Progress Series 210:223-253.

Dolan, D.M. 1993. Point source loadings of P to Lake Erie: 1986-1990. Journal of Great Lakes Research 19:212-223.

FAC (Federal Agencies Committee Chesapeake Bay Program). 1998. Federal agencies' Chesapeake ecosystem unified plan (FACEUP), Annapolis, MD.

Friedland, K.D., D.G. Reddin, J.R. McMenemy, and K.F. Drinkwater. 2003. Multidecadal trends in North American Atlantic salmon (Salmo salar) stocks and climate trends relevant to juvenile survival. Canadian Journal of Fisheries and Aquatic Sciences 59:563-583.

GLFC (Great Lakes Fishery Commission). 2001. A joint strategic plan for management of Great Lakes fisheries. GLFC, Ann Arbor, MI.

Hobbs, B.F., and coauthors. 2002. Fuzzy cognitive mapping as a tool to define management objectives for complex ecosystems. Ecological Applications 12:1548-1565.

Houde, E.D. 1987. Fish early life dynamics and recruitment variability. American Fisheries Society Symposium 2:17-29.

Houde, E.D. 1989. Subtleties and episodes in the early life of fishes. Journal of fish biology 35(suppl. A):29-38.

Houde, E.D. 1994. Differences between marine and freshwater fish larvae: implications for recruitment. International Council for the Exploration of the Sea 51:91-97.

Jain, R., and J.V. DePinto. 1996. Modeling as a tool to manage ecosystems under multiple stresses: an application to Lake Ontario. Journal of Aquatic Ecosystem Health 5(1):23-40.

Koehl, M.A.R. 1989. Discussion: from individuals to populations. Pages 39-53 in J. Roughgarden, R.M. May, and S.A. Levin, editors. Perspectives in Ecological Theory. Princeton University Press, Princeton, NJ.

Koslow, J.A., A.J. Hobday, and G.W. Boehlert. 2002. Climate variability and marine survival of coho salmon (Oncorhynchus kisutch) in the Oregon production area. Fisheries Oceanography 11:65-77.

Kremer, J.N., and coauthors. 2000. Linking biogeographical processes to higher trophic levels. Pages 299-341 in J.E. Hobbie, editor. Estuarine science: A synthetic approach to research and practice. Island Press, Washington, D.C.

Leggett, W.C., and E. DeBlois. 1994. Recruitment in marine fishes: Is it regulated by starvation and predation in the egg and larval stages? Netherland Journal of Sea Research 32:119-134.

Ludsin, S.A., and coauthors. in press. Identification of future ecosystem management objectives for Lake Erie: a fuzzy-cognitive modeling approach. J.J.H Ciborowski, M.N. Charlton, R.G. Kreis, Jr., and J.M. Reutter, editors. Lake Erie at the Millennium: Changes, Trends, and Trajectories. Canadian Scholars Press, Toronto.

Ludsin, S.A., M.W. Kershner, K.A. Blocksom, R.L. Knight, and R.A. Stein. 2001. Life after death in Lake Erie: nutrient controls drive fish species richness, rehabilitation. Ecological Applications 11:731-746.

Luo, J.G., K.J. Hartman, S.B. Brandt, C.F. Cerco, and T.H. Rippetoe. 2001. A spatially-explicit approach for estimating carrying capacity: an application for the Atlantic menhaden (Brevoortia tyrannus) in Chesapeake Bay. Estuaries 24:545-556.

MacKenzie, B.R., and F.W. Koster. 2004. Fish production and climate: Sprat in the Baltic Sea. Ecology 85:784-794.

Malone, T.C., L.H. Crocker, S.E. Pile, and B.W. Wendler. 1988. Influences of river flow on the dynamics of phytoplankton production in a partially stratified estuary. Marine Ecology Progress Series 48:235-249.

Miller, T.J., L.B. Crowder, J.A. Rice, and E.A. Marschall. 1988. Larval size and recruitment mechanisms in fishes: toward a conceptual framework. Canadian Journal of Fisheries and Aquatic Sciences 45:1657-1670.

Munawar, M., T. Edsall, and I.F. Munawar, editors. 1999. State of Lake Erie: Past, Present and Future. Backhuys Publishers, Leiden, Netherlands.

Nixon, S., B. Buckley, S. Granger, and J. Bintz. 2001. Responses of very shallow marine ecosystems to nutrient enrichment. Human and Ecological Risk Assessment 7:1457-1481.

Nixon, S.W. 1988. Physical energy inputs and the comparative ecology of lake and marine ecosystems. Limnology and Oceanography 33:1005-1025.

Nixon, S.W. 2003. Replacing the Nile: Are anthropogenic nutrients providing the fertility once brought to the Mediterranean by a great river? Ambio 32:30-39.

NYSDEC (New York State Department of Environmental Conservation). 1996. The Hudson River estuary management action plan. Hudson River Estuary Management Program, New Paltz, NY.

Rabalais, N.N., R.E. Turner, and W.J. Wiseman. 2002. Gulf of Mexico hypoxia, aka “the dead zone”. Annual Review of Ecology and Systematics 33:235-263.

Rabalais, N.N., R.E. Turner, D. Justic, Q. Dortch, W.J. Wiseman Jr., and B.K.S. Gupta. 2000. Gulf of Mexico biological system responses to nutrient changes in the Mississippi River. Pages 241-268 in J.E. Hobbie, editor. Estuarine science: A synthetic approach to research and practice. Island Press, Washington, D.C.

Richards, R.P., and coauthors. 2001. Storm discharge, loads, and average concentrations in northwest Ohio Rivers, 1975-1995. Journal of the American Water Resources Association 37(2):423-438.

Scarnecchia, D.L. 1981. Effects of streamflow and upwelling on yield of coho salmon (Oncorhynchus kisutch) in Oregon. Canadian Journal of Fisheries and Aquatic Sciences 38:471-475.

Sirabella, P., A. Giuliani, A. Colosimo, and J.W. Dippner. 2001. Breaking down the climate effects on cod recruitment by principal component analysis and canonical correlation. Marine Ecology Progress Series 216:213-222.

Stevens, D.E. 1977. Striped bass (Morone saxatilis) year class strength in relation to river flow in the Sacramento-San Joaquin estuary, California. Transactions of the American Fisheries Society 106:34-42.

Sutcliffe Jr., W.H. 1972. Some relations of land drainage, nutrients, particulate material and fish catch in two eastern Canadian bays. Journal of the Fisheries Research Board of Canada 29:357-362.

USDA (U.S. Department of Agriculture, Forest Service). 1996. Integrated scientific assessment for ecosystem management in the interior Columbia Basin and portions of the Klamath and Great basins. Pacific Northwest Research Station, Portland, OR.

Weise, A.M., and coauthors. 2002. The link between precipitation, river runoff, and blooms of the toxic dinoflagellate Alexandrium tamarense in the St. Lawrence. Canadian Journal of Fisheries and Aquatic Sciences 59:464-473.

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