Synthesis

INTRODUCTION

Eutrophication, water quality, and fisheries

Eutrophication from anthropogenic nutrient inputs is increasingly impacting many aquatic ecosystems (Glibert and Burford 2017, Maúre et al. 2021, Fang et al. 2022), with associated hypoxia and harmful algal blooms (HABs) compromising a variety of ecological and ecosystem services, such as biodiversity, water potability, and recreational opportunities (Smith and Schindler 2009, MacDonald et al. 2016, St-Gelais et al. 2020). However, eutrophication can also provide some benefits through enhanced primary production. This added ecosystem productivity can drive bottom-up increases in the abundance and growth of secondary consumers (Lindeman 1942), such as fishes (Ludsin et al. 2001, Nixon and Buckley 2002, Müller et al. 2007, Saarinen and Candolin 2020), which can benefit different sectors of society by increasing total fishery yields (Pauly and Christensen 1995, Ware and Thomson 2005, Capuzzo et al. 2018, Marshak and Link 2021). These differing effects of eutrophication create a problem for management. Specifically, reducing nutrient inputs to mitigate water quality impairments via better water treatment and/or agricultural conservation practices can result in a trade-off of reduced fishery yields. Such trade-offs are exemplified by ecosystems in which fish biomass or fishery yield declined following nutrient abatement efforts (Baer et al. 2017, Capuzzo et al. 2018, Hossain et al. 2019). Therefore, as suggested elsewhere (Ludsin et al. 2001, Nixon and Buckley 2002, Breitburg et al. 2009, O’Higgins and Gilbert 2014, MacDonald et al. 2016), a better understanding of how best to navigate these trade-offs is critical to the effective management of multi-service aquatic ecosystems.

Trade-offs between eutrophication’s water quality detriments versus fishery benefits can be represented using a unimodal “subsidy-stress” curve (sensu Odum et al. 1979; Fig. 1A). Eutrophication initially subsidizes fisheries yield through bottom-up effects, but as enrichment intensifies these subsidies become outweighed by the stresses of degraded water quality, driving declines in fishery yield via declines in fish survival, growth, and reproduction (Breitburg 2002, Ludsin et al. 2009, Taipale et al. 2018, Jeppesen et al. 2018). This unimodal concept has been thoroughly detailed elsewhere (Ryder 1982, Caddy 1993, Stockner et al. 2000) and has been observed in marine (e.g., Oczkowski and Nixon 2008, O’Higgins and Gilbert 2014), estuarine (e.g., Breitburg et al. 2009), and freshwater ecosystems (e.g., Oglesby et al. 1987, Jonsson et al. 2011). Based on this relationship, total fisheries yield and water quality can be hypothesized to be optimized at intermediate levels of ecosystem productivity where yield is maximized and water quality is not yet severely compromised. The exact range that constitutes “intermediate” productivity likely varies among ecosystems, but the overall concept provides a useful starting point for managers and researchers.

Here, we argue that although it is conceptually intuitive that some eutrophication can be good but too much is bad, applying this concept to real-world ecosystems is more complicated than simply aiming for an optimal range of intermediate productivity. This complexity stems from the fact that many productive aquatic ecosystems support multiple fish populations of recreational, commercial, and/or cultural importance, each of which can require different habitat conditions associated with different levels of ecosystem productivity (Oglesby et al. 1987, Ludsin et al. 2001, Hondorp et al. 2010, Jacobson et al. 2017, Sundblad et al. 2020). Thus, no single target level of nutrient inputs may exist that achieves the desired harvests of every fishery (Fig. 1B). Similarly, “water quality” also encompasses multiple services, including those related to ecology, human health, recreation, agriculture, and fisheries, and the most valued service can differ among the different agencies and end users involved (Anders and Ashley 2007, MacDonald et al. 2016, St-Gelais et al. 2020). Some may prioritize services achieved with the cleanest water possible, whereas others may prioritize services that necessitate less-clean water, such as total fishery yield or agricultural expansion. Thus, there may be no single target range for nutrient inputs that can address the water quality needs and interests of every group (Fig. 1C).

Ecosystems with multiple optima for fishery yield and nutrient inputs could create a “wicked” (sensu DeFries and Nagendra 2017) management problem of inevitable trade-offs in which one or more services must be sacrificed in pursuit of others. This issue has been partly acknowledged elsewhere (Hondorp et al. 2010, Sundblad et al. 2020, Link and Marshak 2022), but a better understanding of how these trade-offs play out across both multiple fishery and multiple water quality objectives is still needed. This understanding is essential to making management decisions and selecting nutrient management targets that account for different agency and end-user valuations of ecosystem services (Levin et al. 2009, Granek et al. 2010). To explore such trade-offs and to support our argument, we use Lake Erie (US–Canada) as an empirical case study. We begin by describing the relevant social-ecological features of the lake, with emphasis on its history of anthropogenic eutrophication, current policy objectives for water quality, and changes in three fisheries: lake whitefish (Coregonus clupeaformis), walleye (Sander vitreus), and yellow perch (Perca flavescens). Based on the best available data, we then provide an analysis of how these fisheries, as well as their combined yield, may exhibit trade-offs across the different water quality objectives. This case study supports the notion that, for multi-service ecosystems, no single target range for ecosystem productivity may exist that satisfies all fisheries and water quality objectives, resulting in inevitable trade-offs. We further argue that these trade-offs are best resolved through an ecosystem-based management approach (Slocombe 1993, Berkes 2012, Link and Marshak 2022) that facilitates open discussion of different agency and end-user values before setting management targets, that supports interdisciplinary research and monitoring, and that employs cooperative management decision making.

LAKE ERIE: A HISTORY OF EUTROPHICATION

Lake Erie is the 11th largest freshwater lake globally and is divided into three basins—western, central, and eastern—with productivity tending to decline from the warmer, shallower western basin to the colder, deeper central and eastern basins (Sly 1976, Ludsin et al. 2001, Bunnell et al. 2014). During the early 1900s, Lake Erie was typically mesotrophic in the western basin and oligotrophic in the central and eastern basins (Sgro and Reavie 2018), with seasonal periods of more eutrophic conditions. Anthropogenic eutrophication likely first began during the late 1800s owing to expanded human settlement and deforestation in the lake’s watershed (Schelske et al. 1983). Eutrophication then intensified during the 1930s (Sgro and Reavie 2018), driven by industrial, urban, and agricultural development. Conditions consistent with hyper-eutrophy occurred during the late 1950s through 1960s, characterized by extensive and widely publicized HABs and bottom hypoxia (Makarewicz and Bertram 1991, Watson et al. 2016). These conditions abated somewhat during the 1970s through 1980s owing to better management of primarily point-source phosphorus inputs, resulting in a period of “oligotrophication” in which productivity declined (Makarewicz and Bertram 1991, Ludsin et al. 2001). However, nutrient-driven water quality impairments have consistently occurred throughout the last four decades (Scavia et al. 2014, Watson et al. 2016). The lake has also undergone a recent period of anthropogenic “re-eutrophication” during the 2000s and 2010s (Kane et al. 2014, Scavia et al. 2014, Watson et al. 2016) driven by increased non-point-source inputs of more bioavailable forms of phosphorus from agriculture (Scavia et al. 2016, Baker et al. 2019). This history of eutrophication in Lake Erie, including its recent re-eutrophication, prompted action to reduce nutrient inputs to improve water quality, which we discuss in the next subsection.

Current water quality objectives

Initially, to mitigate water quality impairments from anthropogenic eutrophication during the 1950s through 1970s, the U.S. and Canadian governments signed the Great Lakes Water Quality Agreement in 1972 (with an amendment in 1978; IJC 2015). Associated abatement programs targeted and successfully reduced point-source phosphorus inputs from > 20,000 metric tons per annum (MTA) to 11,000 MTA (Dolan and McGunagle 2005). During the recent period of re-eutrophication, non-point-source inputs (particularly from agricultural runoff) became the focus of water quality management efforts (Scavia et al. 2016, Baker et al. 2019, GLWQA NAS 2019). The principal water quality objectives of these contemporary abatement efforts are to: (1) reduce HAB severity; (2) minimize bottom hypoxia; and (3) return the overall productivity of the lake to oligotrophic/mesotrophic conditions (Dove and Chapra 2015, GLWQA NAS 2019). The level of nutrient reductions required to reduce HABs are expected to be the least severe, with more severe reductions required to reduce hypoxia (IJC 2015, Scavia et al. 2016). Additionally, given that oligotrophic/mesotrophic conditions have not consistently occurred since before the 1930s (Sgro and Reavie 2018), the trophic status target likely requires even further reductions compared to the HABs and hypoxia targets.

Lake whitefish, walleye, and yellow perch

Lake Erie supports multi-billion dollar commercial, recreational, and sustenance freshwater fisheries. Two percids, walleye and yellow perch, currently supply much of these fisheries, but less-abundant salmonids like lake whitefish are also focal targets for fishing, management, and re-introduction. Salmonids used to be harvested extensively but these fisheries collapsed during the late 1800s to mid-1900s owing to a variety of anthropogenic pressures (Applegate and Van Meter 1970). In the case of the lake whitefish fishery, which collapsed during the mid-1900s, eutrophication was a principal driver in addition to overexploitation and invasive species (Regier et al. 1969, Hartman 1972). As salmonids and other species declined, yields of walleye and yellow perch increased (Applegate and Van Meter 1970). This shift was partly driven by exploitation as people adjusted to which species were available, but bottom-up effects from eutrophication also contributed (Regier et al. 1969, Parsons 1970) as did the higher tolerance of walleye and yellow perch to nutrient pollution (Ludsin et al. 2001, Jacobson et al. 2017). Eventually the walleye fishery also collapsed during the late 1960s, coinciding with both a sharp rise in exploitation and severe eutrophication, the latter of which negatively affects walleye’s ability to forage (Nieman and Gray 2019) and the survival of its invertebrate prey that require oxygenated sediments (Parsons 1970, Bridgeman et al. 2006). The walleye fishery has since recovered owing to a combination of better fishery and nutrient management (Vandergoot et al. 2019). Conversely, the yellow perch fishery never collapsed because this species can persist and even proliferate under extremely eutrophic conditions (Reichert et al. 2010, Carreon-Martinez et al. 2014, Marcek et al. 2021). These historical patterns in the lake whitefish, walleye, and yellow perch fisheries indicate three example species with differing tolerances to eutrophication. Lake whitefish is the least tolerant of the three followed by walleye and then yellow perch.

Quantifying productivity, water quality objectives, and fisheries

This brief history of Lake Erie illustrates why it is an excellent case study for examining water-quality-fisheries trade-offs. Lake Erie’s productivity has varied widely (across oligotrophic, mesotrophic, eutrophic, and hyper-eutrophic conditions), it is being managed for multiple water quality objectives that are achieved at different levels of ecosystem productivity, and it supports multiple fisheries that differ in their tolerance to eutrophication. This combination of factors allows us to examine potential productivity-driven trade-offs across multiple water quality and fishery services. Evaluating these trade-offs requires quantifying how productivity has changed, where the different water quality objectives fall along this gradient, and what level of productivity has historically maximized the yield of the different fisheries. No available metrics do any of this perfectly given that changes have been occurring since the early 1900s and most long-term monitoring programs only began (at best) around the 1970s (Fraker et al. 2022). However, we can develop proxy measures and we can verify the veracity of these proxies by comparing them to the known historical changes in the lake detailed above.

To quantify productivity, we developed a multi-metric index of annual lake-wide productivity during 1915–2011 (see Appendix 1 for methods). In brief, we collected and estimated annual data on five metrics representing non-point-source phosphorus inputs, sediment phosphorus and carbon concentrations, aqueous phosphorus, and the phytoplankton community across the different basins of the lake. We scaled these five metrics relative to their respective means and standard deviations and then averaged these values, with this average hereafter referred to as the “productivity index” (Fig. 2). The index therefore measures how the overall productivity of Lake Erie has varied through time in units of standard deviations from the long-term mean. Other metrics could be incorporated, but the current index acceptably represents nutrient-driven changes in Lake Erie’s productivity given that it closely matches the known patterns and timings of anthropogenic eutrophication (Fig. 2).

To link each water quality objective to our productivity index, we calculated target ranges for non-point-source phosphorus inputs that would potentially achieve the three water quality objectives for HABs, bottom hypoxia, and trophic status in Lake Erie (see Appendix 2 for methods). We used non-point phosphorus inputs as a common currency because they are a known driver of recent eutrophication and are thus the focal target for recent management action aimed at achieving each water quality objective (Scavia et al. 2016, Baker et al. 2019, GLWQA NAS 2019). In addition, non-point-source inputs can be directly related to our productivity index because they are incorporated in its calculation. The results also match our expectations for these targets, specifically that more severe reductions are required to reduce hypoxia compared to HAB severity, with the most severe reductions required to achieve consistently oligotrophic/mesotrophic conditions.

Lastly, to relate fish yields to the productivity index, we obtained data on the total U.S. and Canadian commercial harvest (kg) of lake whitefish, walleye, and yellow perch in Lake Erie during 1915–2011 from the Great Lakes Fishery Commission (http://www.glfc.org). This dataset is the only one that goes back far enough to address our questions, but it is imperfect because the harvest of each species is not necessarily related to its population size. However, some extenuating factors allow us to broadly make this connection in Lake Erie across the last century. First, lake whitefish was only harvested in sufficient numbers before its decline during the mid-1900s, then harvest declined as the population declined (Hartman 1972). Thus, harvest broadly tracked the historical highs then lows of this population during the past century (note that we corrected lake whitefish harvests by 1.17 times up to 1977 to account for a switch in Lake Erie from primarily dressed to round weights in 1978). Second, like lake whitefish, yellow perch is a species of medium economic value that has been consistently harvested since the early 1900s. Thus, yellow perch harvest has tended to track broad population trends in contrast to lower-valued species in which harvest is more driven by fluctuating market prices (Regier et al. 1969, Hartman 1972). Finally, the history of walleye harvest is more complex but can still be linked to population size. The U.S. commercial harvest of walleye broadly followed population trends up to the late 1960s, whereas the Canadian harvest did not because it was more influenced by changes in gear and effort (Regier et al. 1969, Parsons 1970). Both the U.S. and Canadian harvests then declined when the walleye population collapsed during late 1960s (Regier et al. 1969, Parsons 1970, Hartman 1972). The U.S. fishery permanently closed in 1970 owing to human health concerns over mercury bioaccumulation and so the U.S. harvest does not track the subsequent walleye recovery during this period. However, Canadian harvests do track this recovery because the Canadian fishery never closed and became more sustainably managed. Therefore, the best solution was to use U.S. harvests up to 1969 to reflect the increase and then collapse of the walleye population, then Canadian harvests thereafter to reflect its subsequent recovery. To summarize, we feel confident that our fisheries data sufficiently track the known historical responses of each species to eutrophication. We also conducted some follow-up analyses that confirmed that our results using the harvest data were robust to variability in the productivity index and variability in the relationship between harvest and the population size of each species (see Appendix 3).

To examine potential nutrient-driven trade-offs across multiple water quality and fishery services in Lake Erie, we related each species’ commercial harvest to our productivity index using quantile regression (via the “quantreg” package in R; Koenker 2021, R Core Team 2022). These models also included a quadratic term for the index to allow for non-linear relationships. We chose this approach to model harvest as a range of highs and lows, which more closely follow the historical highs and lows of each species’ population. Modeling harvest as a range also allows for inter-annual fluctuations within this range owing to other factors, such as changes in fishing effort or demographic and environmental variation. After modeling harvest in relation to productivity, we then plotted these relationships alongside the different target ranges for nutrient inputs to examine which fisheries might be optimized for each water quality objective.

FISHERY AND WATER QUALITY TRADE-OFFS IN LAKE ERIE

Each fishery’s maximum harvest occurred at a different level of ecosystem productivity (Fig. 3) and the highest total maximum harvests of our focal species have historically occurred at the highest productivities (Fig. 4), rather than at intermediate levels. The maximum harvests of lake whitefish occurred at low levels of ecosystem productivity (about -0.5 standard deviations below the long-term mean; Fig. 3A), in contrast to harvests of walleye and yellow perch which were highest at, respectively, intermediate (+0.15 standard deviations; Fig. 3B) and high productivities (+1.7 standard deviations; Fig. 3C). These relationships matched the known historical responses of these species to eutrophication in Lake Erie and agreed with the known lower tolerances of salmonids to the impacts of nutrient pollution versus the higher tolerances of percids.

The unique productivity relationships for lake whitefish, walleye, and yellow perch suggested that achieving each of the different water quality objectives (i.e., HABs, hypoxia, and trophic status) could create trade-offs in fishery yields. If non-point inputs are successfully reduced into the range of the HAB target, then walleye harvests may remain high as yellow fish harvests decline and lake whitefish harvests increase. If nutrient inputs are further reduced to meet the hypoxia and then lake trophic-status targets, then walleye harvests are likely to decline with further reductions in yellow perch, and even lake whitefish harvests may decline at the lowest extremes of ecosystem productivity (as has occurred in other North American Great Lakes; Rook et al. 2022).

Collectively, our case study portrays an ecosystem in which no single range of productivity likely exists that optimizes all fishery and water quality management objectives, creating a wicked management problem of inevitable trade-offs. For example, aiming to maximize total yields may allow for higher nutrient inputs, but this goal would compromise water quality and create a fish community dominated by only eutrophication-tolerant species (also see Ludsin et al. 2001). Alternatively, reducing nutrient inputs to reduce HAB severity would benefit water quality, and somewhat balance the harvests of all three fisheries, but would leave other water quality objectives unaddressed, such as minimizing hypoxia (Scavia et al. 2016). Further abatements of nutrient inputs to reach the hypoxia and then trophic-status targets would further benefit water quality and potentially lake whitefish, but may substantially reduce percid harvests. Managing anthropogenic nutrient inputs into Lake Erie may therefore be expected to induce trade-offs across its multiple fishery and water quality services regardless of which target range for nutrient inputs is selected.

Furthermore, the socioeconomic reality of the above trade-offs is undoubtedly more complex than what we have shown and will depend upon agency and end-user valuation of numerous other ecosystem services, including human health concerns for safe drinking water and safe beaches, and suitable habitat for different fish species other than those we examined. For example, we only focused on three key species with available data and known linkages to eutrophication, however Lake Erie supports many fish species valued for a range of commercial, recreational, cultural, and/or sustenance purposes (e.g., white bass, Morone chrysops; lake trout, Salvelinus namaycush). Further research is therefore needed to quantify trade-offs across a wide variety of fish species, other types of water quality services, and varying economic, social, and cultural values, all of which may be optimized at different levels of productivity.

Do these trade-offs apply beyond Lake Erie?

The extent to which other ecosystems follow the example we have shown depends on several factors. First, productivity-driven trade-offs across the yields of different fisheries can only occur when the taxa supporting these fisheries have differing tolerances to eutrophication and its discontents (e.g., water clarity, dissolved oxygen availability, prey availability, etc.). This condition applies to many large and small freshwater ecosystems (Jeppesen et al. 2000, Maceina and Bayne 2001, Hossain et al. 2019, Sinclair et al. 2021), as well as marine ecosystems (Townsend 2014). However, it would not apply to ecosystems in which a single species is the focus of management (e.g., marine cod fisheries; Nguyen et al. 2016) nor fisheries supported by different species with similar tolerances to eutrophication (e.g., Lake Victoria; Taabu-Munyaho et al. 2016). In such instances, a single optimal range for productivity may exist that achieves the desired objectives for multiple ecosystem services.

Second, the impacts of increasing or decreasing productivity depend on the environmental context. Lake Erie has exhibited comparatively little change in total harvests at the extreme ends of its productivity gradient across the three focal species (see Fig. 4), but other systems may exhibit more substantial declines and so may not exhibit multiple productivity optima. For example, Lake Erie is large and heterogeneous, which offers spatial refuges when conditions are unfavorable (Roberts et al. 2009, Stone et al. 2020). Ecosystems with no similar refuges, such as smaller lakes or marshes, may experience more severe declines in fishery yield in response to nutrient pollution. Furthermore, although our analysis and previous research in Lake Erie (e.g., Ludsin et al. 2001) suggest that total harvests are resilient to changes in productivity, the effects of declining productivity may be more pronounced in less-productive ecosystems. Lake Erie is naturally more productive relative to the other North American Great Lakes because it is warmer, shallower, and has a naturally nutrient-rich watershed (Dove and Chapra 2015). Consequently, even its lowest levels of productivity can support high fishery yields. By contrast, in more oligotrophic ecosystems, reductions in nutrient inputs can have much stronger negative impacts on total fishery yields (e.g., Rellstab et al. 2007, Bunnell et al. 2014, Baer et al. 2017, Rook et al. 2021). Nutrient abatement efforts in these lower productivity ecosystems may therefore have a greater potential to harm fisheries than what we have shown here for Lake Erie.

Additionally, the presence of other anthropogenic stressors could alter expected yield-productivity relationships. For example, lake whitefish and other similar salmonids require both well-oxygenated and cold-water habitat (Hartman 1972, Ludsin et al. 2001, Müller and Stadelmann 2004), thus their survival depends on optimal productivity and temperature. Progressing climate change may therefore inhibit the recovery of these cold-water taxa even if nutrient inputs are reduced (Jeppesen et al. 2012, Collingsworth et al. 2017). Invasive species could also disrupt expected responses by suppressing yields through negative inter-specific interactions (e.g., invasive parasites; Brant 2019) or by disrupting the transfer of primary productivity to higher trophic levels (e.g., invasive omnivores; Sinclair and Arnott 2015, Kao et al. 2018). These complexities highlight the need to consider the unique context of each ecosystem to forecast whether planned nutrient abatement efforts will induce trade-offs against different fisheries.

NEED FOR ECOSYSTEM-BASED MANAGEMENT

Managing anthropogenic nutrient inputs in multi-service aquatic ecosystems requires continued movement toward ecosystem-based management. This approach seeks to identify linkages among the important components of an ecosystem, quantify trade-offs across different management objectives, and facilitate transparent discussions among agencies and end users to balance multiple ecosystem services (Berkes 2012, Link and Marshak 2022). Adopting such a holistic management approach is complicated, however, because the various natural- and human-related sectors of aquatic ecosystems (e.g., water quality, fisheries, tourism, agriculture, industry, etc.) are often managed by different agencies. The mandates of these agencies do not necessarily overlap, such as those mandated to maintain water potability and beach safety (e.g., U.S. Environmental Protection Agency) versus those mandated to sustain the commercial or recreational yields of individual fish species (e.g., fishery management agencies). This more traditional, sectorial approach to aquatic ecosystem management can prevent agencies from cooperating, communicating, and sharing information, which is necessary to identify and resolve trade-offs. Sectorial approaches can even create conflicts and inter-agency competition that is detrimental to effective management (Granek et al. 2010, Alexander and Haward 2019). Such issues can only be overcome by creating governance structures that require agencies with different mandates to communicate and collaborate to identify solutions to their shared problems (Rosenberg and McLeod 2005, Alexander and Haward 2019). If implemented well, these collaborations would consider the needs of multiple end users from the outset, including water and fishery managers, lake users, anglers, commercial fishers, and Indigenous peoples (Lapointe et al. 2014). Ideally these collaborations would also support research identifying linkages between nutrient abatement efforts and fishery yields (e.g., via food web modeling; Heymans et al. 2016) and would establish cooperative long-term monitoring programs to collect the data necessary to inform these linkages (e.g., by directly measuring productivity; Hecky and DePinto 2020). Additionally, cooperative approaches to ecosystem management help to ensure that unanticipated outcomes or “ecological surprises” (Paine et al. 1998) that can disenfranchise end-user groups are kept to a minimum or avoided completely.

An additional complication to navigating trade-offs, which are always value-based, is that these values are changing through time owing to changing environmental contexts (e.g., climate change; Glibert 2020, Meerhoff et al. 2022) and societal expectations (e.g., “shifting” or “sliding” baselines; Pauly 1995, Dayton et al. 1998). Ecosystem-based management efforts should therefore also periodically re-assess the value of different ecosystem services. Such value shifts have already occurred in many anthropogenically impacted aquatic ecosystems. For instance, much of Lake Erie’s regional culture, livelihoods, and economies now depend on eutrophication-tolerant walleye and yellow perch. However, these species only became important after other fisheries were decimated by multiple anthropogenic stressors, including eutrophication, overexploitation, and invasive species. This modern social-ecological system, combined with our case study of potential fishery-water quality trade-offs, raises critical questions: (1) Does an optimal level of productivity that achieves all management objectives exist? If not, (2) do we prioritize currently valuable eutrophication-tolerant taxa over other species, while deprioritizing water quality objectives that may harm these fisheries? Or, (3) do we manage for the cleanest water possible, rehabilitate eutrophication-intolerant taxa (e.g., stocking salmonids like Coregonus spp.; Schmitt et al. 2020), and accept potential reductions in currently valued fisheries? These questions are difficult to answer and, combined with historical and ongoing temporal shifts in the value of different aquatic ecosystem services, further highlight the wickedness of trying to simultaneously balance multiple fisheries and water quality objectives. Continued movement toward ecosystem-based management that considers different end-user values, that better aligns different agency mandates and governance structures, and that helps base management decision making on a scientific understanding of ecosystem trade-offs will be key to navigating through such wicked management problems.

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