Feasibility of implementing an integrated long-term database to advance ecosystem-based management in the Laurentian Great Lakes basin

Abstract

The North American Great Lakes have been experiencing dramatic change during the past half-century, highlighting the need for holistic, ecosystem-based approaches to management. To assess interest in ecosystem-based management (EBM), including the value of a comprehensive public database that could serve as a repository for the numerous physical, chemical, and biological monitoring Great Lakes datasets that exist, a two-day workshop was organized, which was attended by 40+ Great Lakes researchers, managers, and stakeholders. While we learned during the workshop that EBM is not an explicit mission of many of the participating research, monitoring, and management agencies, most have been conducting research or monitoring activities that can support EBM. These contributions have ranged from single-resource (-sector) management to considering the ecosystem holistically in a decision-making framework. Workshop participants also identified impediments to implementing EBM, including: 1) high anticipated costs; 2) a lack of EBM success stories to garner agency buy-in; and 3) difficulty in establishing common objectives among groups with different mandates (e.g., water quality vs. fisheries production). We discussed as a group solutions to overcome these impediments, including construction of a comprehensive, research-ready database, a prototype of which was presented at the workshop. We collectively felt that such a database would offer a cost-effective means to support EBM approaches by facilitating research that could help identify useful ecosystem indicators and management targets and allow for management strategy evaluations that account for risk and uncertainty when contemplating future decision-making.

Introduction

During the past century, Laurentian Great Lakes basin ecosystems have experienced enormous physical, biological, and chemical change (Allan et al., 2013; Danz et al. 2007; Smith et al., 2019), with many other changes likely remaining undetected. These changes have presented ongoing challenges to regulatory and management agencies seeking to maintain the services and resources provided by these ecosystems (e.g., potable water, swimmable beaches, fisheries; Allan et al., 2013; Jones et al., 2006; McCrimmon, 2002; Smith et al., 2019; Steinman et al., 2017; Taylor and Ferreri, 2002). As a result, ecosystem-based management (EBM) approaches that consider the complex suite of biological, physical, economic, and social factors associated with managing natural resources have long been touted as a means to assist agencies in their missions within the Great Lakes (Christie et al., 1986; Guthrie et al., 2019b; Lee et al., 1982; Vallentyne and Beeton, 1988). This call for EBM has resulted in much independent research and modeling that, in theory, could support EBM within and across lakes in the basin (e.g., Hobbs et al., 2002; Krantzberg and De Boer 2008; Rutherford et al., 2021; Sinclair et al., 2023b; Smith et al., 2019). Resource management in the Great Lakes, however, has continued to primarily focus on single sectors (e.g., a species, activity, or concern) without fully considering possible interactions with other components of the broader ecosystem (Gaden et al., 2013; Sinclair et al., 2023b). For example, declines in lake productivity and lower trophic levels caused by dreissenid mussels have been an ongoing concern for decades owing to their potential to affect the balance between predator demand and prey production (e.g., Barbiero et al., 2018; Hecky et al., 2004; Madenjian et al. 2002; Mills et al., 2003). While these impacts have been explored through modeling and seem of interest to agencies (e.g., Kao et al., 2014; Zhang et al., 2019), we are unaware of any Great Lakes fishery management plan that has explicitly incorporated ecosystem productivity or the lower food web into its management models. Thus, the need for holistic, ecosystem-based approaches to management remains.

The continued arguments for employing EBM in the Great Lakes have been strengthened by our growing recognition that human-induced rapid environmental change (HIREC; Sih et al., 2011) can have complex and far-reaching effects on an ecosystem. Climate change, species invasions, habitat modification, altered nutrient inputs, and other forms of HIREC can modify food-web structure and function, reduce habitat quantity and quality, and alter physicochemical processes, as well as adversely affect resources valued by society in unanticipated ways (Christensen et al., 2006; Knapp et al., 2001; Peer and Miller, 2014; Steinman et al., 2017; Williams and Jackson, 2007). Such outcomes have been documented in the Great Lakes (e.g., Sterner, 2021; Tillitt et al., 2009), especially with respect to fish populations and the fisheries that they support (e.g., DeVanna Fussell et al., 2016; Farmer et al., 2015;). Through such changes, interactions between different forms of HIREC and/or efforts to abate them hold the potential to also cause tradeoffs in fisheries resources (e.g., see Dippold et al., 2020; Fraker et al., 2020; Sinclair et al., 2021, 2023b for Lake Erie examples). At the same time, managing a fishery in response to rapid environmental change can sometimes negatively influence other valued aspects of an ecosystem (e.g., water quality, ecosystem function, other fish and wildlife resources; e.g., Carey et al., 2011; Knapp et al., 2001; Zaret and Paine, 1973), making a holistic understanding of management actions necessary.

For these reasons, EBM has become a pillar of the strategic vision of the Great Lakes Fishery Commission (GLFC), an international (USA-Canada) agency that coordinates fisheries research and facilitates cooperative fishery management among the state, provincial, Indigenous, and federal agencies within the basin (GLFC, 2021a). The GLFC values EBM because it offers an approach intended to keep fisheries sustainable in the face of continued HIREC while ensuring that other ecosystem services are also considered.

In response to such calls for managers to consider whole ecosystems, non-governmental, resource management, and regulatory agencies, as well as international governing organizations worldwide have encouraged some version of EBM (Arkema et al., 2006; Cohen-Shacham et al., 2016; Day et al., 2008; GLFC, 2021a; Koontz and Bodine, 2008; NOAA, 2023). Even so, many efforts to broaden management perspectives have not been successful, in part due to linguistic uncertainty regarding both what EBM means and how it can be implemented (Arkema et al., 2006; Link and Browman, 2014; Waylen et al., 2014; Yaffee, 1999). Acknowledgment that resource management strategies exist along a hierarchical continuum starting with single-sector approaches building towards more systemic and multi-sector perspectives (Dolan et al., 2016) offers a possible solution to these uncertainties. Within this continuum the more focused activities directed at single-resource management (e.g., stock assessments, invasive species control) and environmental monitoring (e.g., nutrient loading, water quality) should theoretically be able to inform and benefit from more holistic, integrative efforts involving multi-species management or EBM (Figure 1). Additionally, we suggest that any mechanism that can reveal linkages among these different approaches and facilitate sharing of information among their practitioners could facilitate EBM by helping managers and decision-makers overcome their apprehension and alleviate some of the logistical and technical impediments to implementation of EBM. For example, single-species management endeavors such as understanding a species’ foraging behavior, energetic needs, and population demographics could provide the basis to perform more complex (multi-sector) modeling such as Ecopath with Ecosim, which is a bioenergetic-based trophodynamic modeling approach that can help provide ecosystem understanding and guide resource management decision-making (Craig and Link, 2023; Heymans et al., 2016; for Great Lakes examples, see Kao et al. 2014, Rutherford et al. 2021; Zhang et al., 2019). Thus, we view data consolidation and sharing among the many entities working throughout the resource management continuum in the Great Lakes basin as being critical to achieving this integration and facilitation of EBM.

Figure 1.

Depiction of a management continuum where activities directed at single-resource management (e.g., stock assessments) and environmental monitoring (e.g., nutrient loading and water quality) inform and benefit more holistic, integrative efforts involving multi-species management or EBM. Figure adapted from NOAA (2023).

Within the Great Lakes Basin, ecosystem-based approaches have been implemented in various forms during the past 40 years (Table 1). These approaches have been conspicuously used to support fisheries management through consideration of environmental and ecological factors that affect population dynamics. For example, predator-prey analyses have been used to inform salmonine stocking levels in lakes Michigan and Ontario (Fitzpatrick et al., 2022; Stewart and Ibarra, 1991; Stewart et al., 1981; Tsehaye et al., 2014) and artificial reefs have been constructed to encourage lake trout (Salvelinus namaycush) reproduction in lakes Michigan, Huron, and Ontario where natural spawning habitats were degraded through human alteration (Fitzsimons, 1996; Foster and Kennedy, 1995; Gannon et al., 1985; Kevern et al., 1985). The GLFC has taken an approach closer to ecosystem-based fisheries management, where major ecosystem components and services (both structural and functional) are considered in managing multiple Great Lakes fish stocks (GLFC, 2021a). For example, the GLFC has mandated the control of sea lamprey (Petromyzon marinus), with GLFC lake committees beginning to consider broader habitat-based actions that provide ecosystem-service benefits to lake fisheries, including restoring aquatic connectivity within tributaries (GLFC, 2017; GLFC-CLC, 2016). Additionally, owing to a 1987 amendment to the Great Lakes Water Quality Agreement (GLWQA), which was jointly signed by Canada and the USA in 1972, agencies and governing organizations across the basin have also implemented EBM-like approaches (Remedial Action Plans, RAPs), wherein safe drinking water, access to recreation, habitat restoration, sediment remediation, and other considerations are included in management objectives (e.g., Hartig et al., 2020; Williams and Hoffman, 2020). In total, 43 RAPs have been implemented across the Great Lakes to mitigate water quality impairments in Areas of Concern (AOCs), with each plan being independent of all others and locally adapted (Kellogg, 1988; Mackenzie et al., 1993). Although effort to integrate fisheries management into RAPs has been encouraged (Hartig et al., 1996), and some progress has been made locally (e.g., 1 of the 14 beneficial use impairments monitored in AOCs focuses on fish; Blukacz-Richards and Koops, 2012), these RAPs are generally not tied to larger fishery management planning and are not fully integrative and holistic (i.e., not multi-sectorial), given their primary focus on abating water quality impairments (but see Koops et al., 2009 and Minns et al., 2022 for a counter-example in the Bay of Quinte, Ontario). Thus, while some progress has been made locally, multi-sector EBM is still not occurring widely or at the lake-level in the Great Lakes basin (Weinstein et al., 2021), helping to understand why the call for it continues (Guthrie et al, 2019b; Minns, 2014; Munawar and Hartig, 2020; Sinclair et al, 2023b).

Table 1.

Summary of long-term monitoring data included in the Great Lakes Recruitment and Ecosystem (GLaRE) database. Data source acronyms represent: Great Lakes Fishery Commission Lake Erie Committee (LEC), National Oceanic and Atmospheric Administration (NOAA), Environment and Climate Change Canada (ECCC), Heidelberg University’s National Water Quality Research Center (NWQRC), United States Geological Survey (USGS), United States Army Corps of Engineers (USACE), United States Environmental Protection Agency (EPA), State University of New York Buffalo State College (SUNYBSC), Fisheries and Oceans Canada (DFO), New York State Department of Environmental Conservation (NYSDEC), Pennsylvania Fish and Boat Commission (PFBC), Ontario Ministry of Natural Resources and Forestry (OMNRF), United States Department of Agriculture (USDA), and Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin departments of Natural Resources (ILDNR, INDNR, MIDNR, MNDNR, ODNR, and WDNR).

Category	Data Types (examples)	Spatial Resolution	Temporal Extent	Data Source(s)
Meteorology/climate	Temperature, precipitation, windstorms	Lake-wide and basin-specific	1950 – 2019	LEC, NOAA, ECCC
Watershed features	Tributary flows, nutrient and sediment runoff/loading	Lake-wide, basin- and river-specific	1898 – 2019	NWQRC, NOAA, USGS, USACE
Lake features	Water level, ice cover, retention time, water supply, evaporation	Lake-wide	1950 – 2019	NOAA, USACE
Water quality	Temperature, pH, water clarity, dissolved oxygen, nutrients, turbidity	Lake-wide and basin-specific	1958 – 2019	LEC, USEPA, ECCC, NOAA
Lower food web	Phytoplankton, zooplankton, benthic macroinvertebrates	Basin-specific	1983 – 2019	LEC, USEPA, NOAA, SUNYBSC
Fish and fisheries	Abundance, biomass, harvest, condition, growth, survival, mortality, species, diversity	Lake-wide and basin-specific	1969 – 2019	LEC, DFO, NYSDEC, PFBC, OMNRF, ILDNR, INDNR, MIDNR, MNDNR, ODNR, WDNR
Human dimensions	Agriculture	Basin-specific	1928 – 2021	USDA

Long-term monitoring and research programs throughout the Great Lakes basin have also been designed to support EBM and associated research. To this end, the United States Environmental Protection Agency-Great Lakes National Programs Office (USEPA-GLNPO), Environment and Climate Change Canada (ECCC), the International Association for Great Lakes Research (IAGLR), and/or the GLFC have coordinated periodic “State of the Great Lakes” (e.g., ECCC and USEPA, 2021b), “State of the Lake” (e.g., Riley and Ebener, 2020), “Lake Committee” (e.g., Forage Task Group, 2023), and “Cooperative Science and Monitoring Initiative” (Foley and Collingsworth, 2018) meetings and reports. These activities have offered a forum for researchers and agencies that have been monitoring ecosystem change to provide updates on the status of the Great Lakes (ECCC and EPA, 2021b; GLFC, 2021b; IAGLR, 2021; USEPA, 2021). They have also allowed for the accrual of highly informative data on physical, biological, environmental, and climate trends, which could facilitate EBM in the basin. While these reports are synthetic in the sense that they qualitatively assess many indicators within each lake (e.g., ECCC and USEPA, 2021b; Riley and Ebener, 2020) and are synthesized in an overall state of each lake, these qualitative assessments 1) are often based on quantitative data that may or may not be available in a public database and 2) use indicators that often require combining data from different databases (Barbiero et al., 2018, Kovalenko et al., 2018). Similarly, the Lakewide Action and Management Plans (LAMPs), which were mandated for each lake by the 1987 GLWQA and are jointly coordinated by the USEPA-GLNPO and ECCC (IJC, 1987), have provided a foundation for EBM by identifying lake-specific management objectives, qualitatively assessing ecosystem indicators, and providing status updates on these indicators (e.g., ECCC and USEPA, 2021a). Thus, many of the necessary components for EBM appear to be in place within the basin.

Despite these many excellent efforts to accumulate and summarize environmental monitoring data in each lake to theoretically support and guide EBM in the Great Lakes, quantitative multi-sectoral analyses designed to describe the current state of the ecosystem and understand their drivers (e.g., interactions among forms of HIREC) are not mandated. As such, no single entity has a coordinating role to bring data and technical experts together to implement EBM. Such analyses are only being conducted by independent researchers who take the time to compile the many disparate datasets (e.g., Barbiero et al., 2018, Fraker et al., 2022; Kao et al., 2012; Rutherford et al., 2021; Sinclair et al., 2021, 2023b; Zhang et al., 2019). We attribute the limited integration and synthesis to two primary causes: (1) the lack of a suitable governmental and societal infrastructure needed to support such a synthesis (Guthrie et al., 2019b); and (2) the absence of consolidated, comprehensive, user-friendly databases for the research community to use to access the vast array of Great Lakes monitoring and research data known to exist. Thus, the wealth of data contained in these synthetic reports and the analyses emanating from them has yet to be incorporated in an integrated ecosystem assessment (IEA) framework, which has been extensively used to inform management decision-making in other ecosystems, both marine (Levin et al., 2009; Tallis et al., 2009) and terrestrial (Graham et al., 2000; Sier and Monteith, 2016). Collectively then, while a solid foundation for EBM exists in the Great Lakes, barriers to EBM implementation remain.

Given the clear linkages that are known to exist among sectors that are managed independently, we should remove these barriers to EBM. For example, water quality decision-making for any lake has historically been made independent of fisheries management decision-making, with the converse also being true (Guthrie et al., 2019b; but see GLSAB and GLWQB, 2023). This practice has been occurring despite much research inside (e.g., Bunnell et al.., 2014; Dippold et al., 2020; Ludsin et al., 2001; Sinclair et al., 2021, 2023b) and outside (e.g., Caddy, 1993; Capuzzo et al., 2018; Jeppesen et al., 2000) of the Great Lakes basin demonstrating that altering nutrient inputs to mitigate water quality impairments can impact fisheries production. Owing to the hump-shaped relationship that often exists between nutrient availability (or ecosystem productivity) and fish production via impacts on habitat quality (Leach et al., 1977; Oglesby et al., 1987; Caddy, 1993; Sinclair et al., 2023b), the effects of nutrient management on fish production can be complex. To illustrate, Sinclair et al. (2023b) showed that impending efforts to reduce total phosphorus inputs into Lake Erie by 40% to reduce harmful algal blooms in west basin and hypoxia in the central basin (GLWQA NAS, 2019) might simultaneously reduce the yields of yellow perch (Perca flavescens) and walleye (Sander vitreus), two species of great commercial, recreational, and cultural value in the lake. By contrast, this same modeling study showed that other species that suffer from eutrophication, such as lake whitefish (Coregonus clupeaformis), are likely to benefit from reduced nutrient loading. Water quality-fisheries tradeoffs such as those in Lake Erie help to explain past calls for more holistic (i.e., multi-sector) EBM within the basin (e.g., Guthrie et al., 2019b; Minns, 2014), and highlight the need to identify ways to help advance this long-sought goal within the basin.

Given the progress that has been made in marine ecosystems, we are optimistic that the Great Lakes can continue to move toward more holistic EBM approaches. Non-synthetic overviews of aquatic ecosystems were once typical of marine ecosystem assessments, with physical and biological monitoring programs, stock assessments, and various other status reports (e.g., marine contaminants) conducted independently. However, this approach changed during the early 2000s, and EBM and fisheries-based IEAs have improved the understanding and management of marine resources worldwide (DFO, 2003; Harvey et al., 2020; Link and Marschak, 2019). Ecosystem-based management approaches have also been predicted to ameliorate climate change impacts on fisheries in the near-term (Holsman et al., 2020). Given the observed and predicted effectiveness of EBM in other ecosystems, which are facing similar, multifaceted problems as the Great Lakes, we view the lack of coordinated EBM and IEAs in the Great Lakes basin as an important problem that needs to be resolved if agencies are to avoid ecological surprises (Doak et al., 2008; Paine et al., 1998) that limit their ability to effectively rehabilitate, sustain, and/or expand their valued fisheries in the face of continued ecosystem change. Therefore, like our predecessors within the basin (Lee et al., 1982; Christie et al., 1986; Vallentyne and Beeton, 1988), we see continuous progress towards EBM as critical to successfully managing both fisheries and other valued services provided in the basin.

One fundamental need of any EBM program is a shared environmental monitoring database that would ideally include physical, chemical, biological, and socioeconomic information over long timescales (Carollo et al., 2009; Jensen et al., 1994), including traditional ecological and Indigenous knowledge (Edwards and Heinrich, 2006; Pert et al., 2015). Such a database could be used to understand the state, functioning, and dynamics of the Great Lakes, which in turn could provide missing insights that could help inform decision-making. For example, an environmental monitoring database could be used by agencies and researchers alike to 1) identify key indicators of ecosystem state and potential environmental drivers, 2) reveal novel changes in ecosystem structure, function, and dynamics, and 3) allow hypotheses to be generated and predictions tested concerning the factors driving these changes and their influence on valued ecosystem components/services (Bunnell et al., 2014; Cowles et al., 2021; Dobiesz et al., 2010; Fraker et al., 2022; Sinclair et al., 2021). Analysis of a comprehensive database could also help identify early warning indicators of ecosystem change that are crucial to preventing catastrophic ecological surprises (e.g., fishery collapses; Lindenmayer et al., 2010), facilitate simulations or management strategy evaluations to predict fishery responses to projected ecosystem state change (e.g., Barange et al., 2014; Blanchard et al., 2012; Brown et al., 2010), and understand how fisheries or water quality management decision-making impacts other aspects of the ecosystem (e.g., water quality, other fisheries, non-game species; Levin et al., 2009; Travis et al., 2014). Additionally, development of data or even metadata repositories could help foster collaborations and data-sharing that can promote the use of single-sector management outcomes in more complex, ecosystem-based endeavors (Carollo et al., 2009; Grüss et al., 2018; McKenney et al., 1996). In turn, such repositories could offer an additional mechanism to garner the support of managers or decision-makers that are skeptical of EBM.

Development of a comprehensive, shared database and increased inter-agency communication and cooperation are not newly identified needs within the basin (see Christie et al., 1986; GLFC, 2011; Sullivan and Gurdak, 2022; Vallentyne and Beeton, 1988). Many comprehensive databases already exist within the basin, including the Great Lakes Observing System (GLOS; Read et al., 2010), Great Lakes Environmental Database (GLENDA; USEPA, 2023), the Great Lakes Acoustic Telemetry Observation System (GLATOS, Krueger et al., 2018), and Great Lakes Aquatic Habitat Framework (GLAHF, Wang, 2015), among many others. Each of these databases offers public access to unique kinds of data. For example, GLOS provides oceanographic time-series buoy data (mostly physical) at specific locations and high temporal (sub-daily) resolutions, whereas GLENDA provides limnological time-series data (physicochemical and lower food web) at less-fine temporal (seasonal) scales. By contrast, GLATOS provides acoustic telemetry observations on fish horizontal locations within the water column at sub-basin and sub-daily levels, with GLAHF providing a wide array of physical, chemical, and biological data at varying degrees of spatial resolution (e.g., primarily synoptic snapshots of information that can be used in GIS modeling). Despite these many excellent resources, we feel that a comprehensive, user-friendly public database with a wide array of data types (physical, chemical, and biological) that is research-ready and can be immediately used for historical modeling, whether quantitative and/or qualitative, in an IEA framework (e.g., Levin et al., 2009; Tallis et al., 2010) is still lacking. Such a repository would ideally hold time-series data at broad spatial (sub-basin to lake-basin to lake-wide) and temporal (annual) resolutions (e.g., data housed within the synthetic State of Great Lakes, Lake Committee, and State of the Lake reports). Doing so would provide ready access to these datasets so that they can be used in multivariate and ecosystem modeling frameworks to identify how the ecosystem has changed through time and the likely causal mechanisms (e.g., Bunnell et al., 2014; Fraker et al., 2022; Kao et al., 2014; Koops et al., 2009; Ludsin et al., 2001; Möllmann et al., 2014; Rutherford et al., 2021; Zhang et al., 2019). Such information could also help identify key indicators of change, identify thresholds for these indicators, and threats to the ecosystem (Levin et al., 2009; Tallis et al., 2010), which are critical to IEA endeavors being conducted to support EBM.

To evaluate the level of interest in implementing coordinated EBM strategies in the Great Lakes basin, the GLFC sponsored a multi-day workshop that discussed the pros and cons of EBM and the development of a comprehensive, integrated database that would help provide improved access to biological, chemical, and physical monitoring data to support IEA activities. While a large part of this workshop focused on the database itself (i.e., structure, datasets, platform, allocation of credit to data contributors), this workshop also sought to: (1) determine how EBM has been and is currently being used within the Great Lakes Basin; (2) identify practical and philosophical impediments to adopting EBM in the Great Lakes basin; and (3) discuss ways to overcome impediments to adopting EBM approaches, including the development and use of a comprehensive, integrated database (see Electronic Supplementary Material (ESM) Appendix S1 for detailed workshop agenda). Given that the database presented at the workshop is still being developed, we only briefly describe it herein, and instead focus on some of the key insights generated during this workshop, which we hope will enhance movement toward EBM in the Great Lakes, including setting the foundation for fishery-focused IEAs.

Great Lakes fish recruitment and ecosystem database

Several of us (R. Budnik, L. Collis, K. Frank, S. Ludsin, and L. Mason) constructed the preliminary (alpha) version of an integrative, user-friendly relational database that consisted of biological and physicochemical monitoring data collected by numerous federal, state, and provincial agencies throughout the Great Lakes basin (Table 1). The justification for the database seemed like a simple matter. While a large variety of spatially referenced data exist for the Great Lakes, much of it resides in many separate locations and is saved in different formats (electronic, non-electronic). In turn, those wishing to conduct synthetic analyses at the ecosystem-level cannot do so without substantial effort to find, organize, and develop a system to use the data. Thus, we sought to create a relational database that ties together two types of data, one focused on fish and fisheries and the other focused on non-fish ecosystem attributes.

This “Great Lakes Recruitment and Ecosystem” (GLaRE) database is being modeled after the RAM Legacy Database (Ricard et al., 2012), which is a compilation of stock assessment results for commercially exploited marine populations from around the world. The GLaRE database consists of fisheries-independent and fisheries-dependent assessment data collected or overseen by fishery management agencies (e.g., trawl and gillnetting catch-per-unit-effort, size, biomass, and age information; total catch/harvest and associated effort data; estimated stock and population sizes developed from these assessment data), which includes additional information presented in annual agency committee (task group; e.g., Forage Task Group, 2023) reports and less-frequent State of the Lake reports spearheaded by the GLFC (see examples at GLFC, 2023a). All data were summarized into annual indices at the level of stock (i.e., local population or subpopulation), lake basin (e.g., western, central, and eastern Lake Erie; southern and northern Lake Michigan), and management unit (e.g., Management Units 1, 2, 3, 4, and 5 in Lake Erie; see Wagner et al., 2009), or other meaningful geographic region (e.g., specific bay, river, or spawning location). In general, the data were averaged within an area to create an annual index, although many of the included indices represent sub-annual information (e.g., monthly or seasonal averages).

In addition to fisheries information, the database contains within-lake environmental data collected from numerous agencies, including the EPA-GLNPO, ECCC, the United States Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory, the USDA National Agricultural Statistics Service, and state and provincial agencies. These data include non-fish biota (e.g., phytoplankton, zooplankton, benthic invertebrates) and physicochemical attributes (e.g., pH, nutrients, pollutants, dissolved oxygen, turbidity/water clarity, air/water temperature, ice extent/duration, stratification, water levels, etc.).

At the time of the workshop, the alpha version of our database contained > 100 time-series of fisheries and environmental data, with appropriate metadata recorded to help users understand what data exist, how datasets are related (so queries of all needed data tables are done), and details about them (e.g., original source, data restrictions, duration of collection, gear type, units). All of these data will be able to be queried using SQL or SQLiteStudio (GitHub, 2024), and the data can be easily transferred into Microsoft Access for use. Thus, database users will have the capability to pull out small bits of information (e.g., data on a particular species from a particular lake in a subset of years) or information in bulk (e.g., all fish community data across all lakes across all years). Completion and release of version 1.0 of the database is expected during the first half of 2024.

Workshop approach

As a first step towards evaluating the level of interest in an integrated long-term database to advance EBM in the Laurentian Great Lakes basin, the GLFC sponsored a two-day workshop on 3 and 7 May, 2021, entitled “The Great Lakes Fish Recruitment and Ecosystem Database Workshop.” Before the workshop, participants were required to complete a survey to determine general demographic information (i.e., affiliation, role with their organization, expertise, lake(s) of focus, experience with data management and databases) and opinions on topics related to the theme of the workshop (i.e., EBM, best practices for data and database management; see ESM Appendix S2 for full survey). Participants (N = 41) were affiliated with state, provincial, and federal agencies, universities, Indigenous and USA inter-Tribal groups, and non-profit organizations (Figure 2A). Participants described their roles within these organizations as resource managers, program administrators, researchers, data managers, or technical staff (Figure 2B). Workshop participants included individuals with self-identified expertise in fish recruitment and population dynamics, fisheries management, lower trophic levels, nutrient and carbon cycling, remote sensing, physical modeling, water quality, and data management (Figure 2C). With that said, other known areas of expertise were not mentioned, as they fell under umbrella of the self-identified category (e.g., many of the participants with expertise in fish, the lower food web, and nutrient carbon cycling also had expertise in population, food web, and/or ecosystem modeling). Most participants indicated working within the Great Lakes, with over half (N = 21 of 41) of the attendees reporting working in more than one lake and nearly a quarter (N = 9 participants) reporting working in all five Great Lakes. Thus, a diverse array of perspectives was likely to emerge at the workshop, capable of providing useful feedback on the database and EBM initiatives within the Great Lakes basin as a whole.

Figure 2.

Affiliations (A), roles within organizations (B), and area(s) of expertise (C) for participants of a Great Lakes Fish Recruitment and Ecosystems Database workshop held virtually during May 2021. Total percentages in Figure 2C exceed 100 as many attendees had expertise in multiple areas.

During the workshop, its organizers (R. Budnik, K. Frank, and S. Ludsin) introduced the alpha version of the database to the Great Lakes community. This database served to organize workshop discussions, as well as offer a tangible product to which all attendees could contribute and utilize for future research. While the database itself was under construction at the time of the workshop, the workshop offered a forum to readily identify improvements so that the database might be widely adopted and supported. Brief presentations were also given by the workshop organizers on 1) the rationale for designing an integrated Great Lakes fisheries and ecosystems database, 2) the use of databases in support of EBM in marine ecosystems, and 3) the extent to which EBM has been used in the Great Lakes region. This latter element included a case study focusing on the application of the database for improving understanding of ecosystem dynamics and fisheries management in Lake Erie. Following these introductory presentations, three breakout group discussions occurred in which workshop participants were split into groups of 5–10 and asked to discuss workshop-related topics in detail. These topics included: 1) “Ecosystem-based management: How do we define it and why is it valuable?”; 2) “Impediments to EBM approaches”; and 3) “Integrated Great Lakes Database: Next steps to ensure success” (ESM Appendix S3).

Following each presentation and breakout-group discussion, all participants reconvened to share findings (from individual breakout groups), to solicit views on the information presented and discussed, and to identify if consensus existed on the discussion topic. In addition to the breakout groups, participants were asked to identify potential datasets for inclusion in the database and to complete a writing exercise. During the writing exercise, participants were asked to identify important agency goals or research questions that could, and could not, be addressed with an integrated Great Lakes database (ESM Appendix S4). The workshop outcomes that we present below are an amalgamation of the ideas and opinions expressed in the pre-workshop survey, presentations, writing assignments, breakout groups, and full-group discussions.

Workshop outcomes

Ecosystem-based management: How do we define it, and why is it valuable?

Numerous definitions of EBM exist, with most including objectives associated with resource sustainability or the maintenance of ecological health, as well as the need to consider societal (stakeholder) perspectives (Arkema et al., 2006; Grumbine, 1994; Wasson et al., 2015). When presented with NOAA’s definition of EBM during the pre-survey— “An integrated approach that incorporates the entire ecosystem, including humans, into resource management decisions, and is guided by an adaptive management approach.” (NOAA, 2023)— most participants (95%) agreed it was an appropriate definition that resonated with their agency’s mission or personal viewpoint. Based on the near unanimous consensus that emerged in pre-survey responses, we initially expected that formalizing a definition of EBM during the workshop would be straightforward and simple. However, no consensus was reached at the workshop following a prolonged discussion of the topic, a result that in hindsight should have been expected (Alexander and Haward, 2019).

Instead, the group agreed to adopt NOAA’s definition in lieu of attempting to derive a definition specific to the Great Lakes. The broadness of the existing NOAA definition offered agencies the needed flexibility to potentially contribute to EBM, while also remaining within the scope of their agency’s specific mandates and objectives (e.g., agencies focused on water quality could also consider fisheries). Additionally, the inclusion of human dimensions in NOAA’s EBM definition was deemed appropriate and necessary, particularly to those affiliated with Indigenous and USA inter-Tribal groups. Even so, specifying exactly which societal aspects should be considered was not easy, as some participants liked the idea of including all societal considerations (i.e., “true” EBM that considers all stakeholders and the services that they value, such as fishery performance, water quality, agricultural production, biodiversity, economic activity, etc.) whereas others preferred a more narrow definition that focused on a particular component of the ecosystem (e.g., fisheries performance in particular), thus simplifying management. While most attendants recognized the benefits of broader EBM, which seeks to identify and balance potentially competing interests of different stakeholder groups, many expressed a preference for fisheries-focused EBM approaches (i.e., ecosystem-based fisheries management) that do not necessarily consider other ecosystem components. Regardless of preference, all participants agreed that if true EBM is going to be implemented in the basin, this divide will need to be overcome, likely involving compromise.

Impediments to implementing ecosystem-based management approaches

Nearly all agencies represented were conscious of EBM and had an interest in contributing data to EBM efforts. This notion was supported by their participation in the workshop itself and by the fact that 95% of the participants in our pre-workshop survey indicated that they or their organization are already working to contribute to EBM (see ESM Appendix S2). Thus, movement to true EBM might be possible if existing impediments could be overcome.

While most workshop participants mentioned that they or their organizations contributed in some way to EBM, they also indicated that it was often in an indirect manner. When asked if their organizations implement EBM, 39% of participants replied affirmatively, 46% indicated no, and the remainder were uncertain (12%). Numerous reasons for the disconnect between contributing and implementing EBM were identified. The most common reason was that most agencies and organizations in the Great Lakes region do not have an ecosystem-based mission or mandate (22%), largely because such an approach is beyond the scope or authority of a single agency or group. The pre-workshop survey also identified working for a non-management entity (e.g., science organization), a lack of accessible data or analytical capacity (e.g., staff shortages, data analysis expertise), and a desire to maintain status-quo approaches due to fear of change and/or increased workloads (i.e., institutional inertia) as impediments to implementing EBM. Workshop discussions also revealed other barriers to implementing EBM in the Great Lakes, including 1) the high perceived cost of EBM, 2) a lack of known “success stories” to indicate that EBM has led to positive ecosystem effects and management outcomes, and 3) the challenge of establishing common objectives among organizations and agencies that may have conflicting interests and/or support from differing stakeholder groups. We elaborate on these three impediments below.

Impediment: Cost—

Implementing EBM can come at a high cost in terms of both resources and time. The first phase of regional EBM, in which large-scale management strategies are developed, assessed, and selected, can cost millions of dollars and thousands of person-hours (Wasson et al., 2015). The cost of acquiring data for ecosystem models can also be significant given that monitoring all aspects of an ecosystem can easily cost tens to hundreds of times more than biological sampling of the target population alone (Hilborn, 2011). Likewise, EBM strategy implementation can be a long, time-consuming process, when done correctly, and requires sustained funding that can support robust stakeholder engagement and syntheses of relevant science (Wasson et al., 2015). For these reasons, one can appreciate why many agencies and stakeholders may view EBM as a daunting, perhaps unattainable, task.

Fortunately, Great Lakes agencies are not starting from ground zero and are well-positioned to avoid some of the expected high costs of developing an EBM approach. Many management strategies have already been developed, assessed, and selected (Alsip et al., 2021; Guthrie, et al., 2019a). Additionally, a large amount of long-term biological and physicochemical data exist within the basin, owing to monitoring and assessment activities initiated by the various federal, provincial, state, Indigenous, and non-governmental organizations in the region (see Table 1). Owing to these previous investments, the need for time and money to design and implement new long-term monitoring programs has been lessened, which we view as potentially the most conspicuous barrier to the short-term development of EBM strategies. The primary cost-impediments for EBM in the Great Lakes Basin then become data consolidation and sharing (e.g., a comprehensive, shared database) and the funding of ecosystem-based research and management.

Impediment: Lack of success stories—

While researchers (Lee et al., 1982; Christie et al., 1986; Vallentyne and Beeton, 1988; Minns, 2014; Guthtrie et al., 2019b) and organizations (GLFC, 2011, 2001, 1991) have been advocating for EBM in the Great Lakes basin for decades, opposition remains. In the workshop, for example, some participants questioned its utility, given a seeming scarcity of success stories. They argued that movement towards EBM in the Great Lakes would only occur when the benefits of this type of approach are clearly shown. While examples of EBM success stories exist in the marine literature (e.g., Harvey et al., 2020; Holsman et al., 2020; Townsend et al., 2020), we are unaware of successful outcomes of this magnitude in the Great Lakes. The success of RAPs within the Great Lakes offers the closest approximation of successful EBM (Hartig et al., 2020); these plans, however, operate at a much smaller spatial scale than a Great Lake. For this reason, proponents of EBM in the Great Lakes are facing a “catch-22”: EBM will only be implemented when success stories exist, but success stories will only occur if EBM is implemented.

While we recognize that overcoming this problem will require understanding the governance structure (including its strengths and limitations) and mission of numerous agencies, as well as the culture that underlies communication and decision-making (Link and Marshak, 2019; Stephenson et al., 2021; Wondolleck and Yaffee, 2022), we see development of a comprehensive, user-friendly, and easily accessible database as an integral component of the solution. Development of such a database would provide access to long-term data, which we view as the fundamental resource needed to help identify and create the much-desired success stories. Long-term data are integral to 1) understanding ecosystem processes, including those that occur over long time periods, 2) identifying, describing, and forecasting ecosystem change, 3) developing, parameterizing, and validating models, 4) promoting collaboration and multidisciplinary research, and 5) providing information at the appropriate scale to allow evidence-based management (Lindenmayer et al., 2012; Möllmann et al., 2014; Fraker et al., 2022). In our view, achieving all of these ends is critical to identifying and developing the much-desired success stories that can lead to EBM.

An integrated database would also offer researchers, management agencies, and legislators the means to examine change in the components of the ecosystem important to them. While information gaps still exist in the Great Lakes region (e.g., winter ecology; Marsden et al., 2021; Ozersky et al., 2021), and filling these gaps will certainly require expanded research and monitoring (GLSAB, 2022), we must also recognize that the Great Lakes are rich with long-term environmental datasets and monitoring programs. In turn, the high costs and prolonged time needed to develop these resources has removed, unlike other large ecosystems (e.g., African Rift Lakes). Thus, we feel that we are long overdue for providing a comprehensive, user-friendly environmental monitoring database, not just promoting it (sensu Christie et al., 1986). The development and analysis of long-term monitoring data lie at the heart of all good EBM plans (Arkema et al., 2006; Harvey et al., 2020; Möllman et al., 2014), and without this critical resource, we will continue to struggle in our fight to identify and develop the success stories necessary to achieve stakeholder buy-in for more holistic ecosystem-based approaches to management. Additionally, compiling such a database could help identify where data gaps exist, including identifying gaps where we think we are data-rich but perhaps are not.

Impediment: Establishing common objectives—

In addition to a lack of success stories, the diversity of interests among researchers, managers, stakeholders, and policymakers in the Great Lakes region presents another major impediment to EBM. In turn, having members of the Great Lakes community agree on common EBM goals and objectives could be problematic if conflicting interests cannot be overcome. For example, managing for fisheries productivity could come at odds with managing for water quality, given that many species of fish benefit from bottom-up effects associated with enhanced nutrient inputs (i.e., increased primary production, reduced water clarity), which in excess can cause water quality degradation resulting in bottom hypoxia, turbid river plumes, and harmful cyanobacterial blooms (Caddy, 1993; Diaz and Rosenberg, 1995; Ludsin et al., 2001; Sinclair et al., 2023b). Even within the realm of fisheries management, conflicts can arise. Management agencies on lakes Huron, Michigan, and Ontario have had to decide whether to manage for economic returns by balancing the demand for Pacific salmon fisheries with declining prey (alewife, Alosa pseudoharengus) production or to manage for rehabilitation of native fishes previously suppressed by alewife (Dettmers et al., 2012). Therefore, the desired missions or visions of stakeholders need to be considered and common ground achieved through reasonable compromise when developing EBM plans. The social and ecological states and services that each stakeholder is most interested in maintaining or restoring should also be identified (Leslie and McLeod, 2007; Tallis et al., 2010). When conflicting goals become evident, a framework needs to exist to work towards a compromise.

Because single-sector management decisions generally fall on non-federal government agencies within the Great Lakes basin, ecosystem-based decision-making becomes difficult as no single institution has the authority to manage the multiple sectors and complex set of factors that would be required to implement true EBM. Ecosystem-based management, therefore, requires numerous collaborators and governance arrangements (Wondolleck and Yaffee, 2022). Notable examples of different parties agreeing on an EBM vision exist, however, although most have occurred outside the Great Lakes Basin. The concerted effort by stakeholders in the multijurisdictional Chesapeake Bay watershed (USA) to reduce nonpoint source nutrient and sediment runoff in an effort to improve water quality and living resources (Lefcheck et al., 2018; Leslie, 2018) offers a contemporary example with much relevance to the current problems that many areas of the Great Lakes basin now face (e.g., Muskegon Lake: Carter et al., 2006; upper Great Lakes: GLWQA, 1978, 2019; Lake Erie: IJC, 2013). Similar efforts to develop EBM plans to the benefit of fisheries and non-fisheries stakeholders have also been developed across the globe (e.g., Eastern Scotian Shelf: DFO, 2003; California Current ecosystem: Harvey et al., 2020; Puget Sound, Washington, USA and Raja Ampat, Indonesia: Tallis et al., 2010). Thus, despite the many known challenges to EBM identified during the workshop and elsewhere (e.g., Mackenzie, 1993; Jones and Taylor, 1999; Koontz and Bodine, 2008; Link and Marshak, 2019; Weinstein et al., 2021; Wondolleck and Yaffee, 2023), we are optimistic that EBM can become a reality in the Great Lakes basin. The recent consideration of fish habitat in planning phosphorus loading targets in Lake Erie to reduce eutrophication-induced bottom hypoxia and harmful cyanobacteria blooms (GLWQA NAS, 2019) offers a clear example of Great Lakes agencies moving in the right direction towards EBM.

Many additional examples exist of stakeholder groups organizing to discuss the ecosystem as a whole, with the goal of increasing ecosystem understanding and the ability to manage effectively.

• The GLFC has facilitated cooperative management among fishery management agencies through the creation of multi-agency task groups within each lake (e.g., Coldwater, Forage, Habitat, Walleye, and Yellow Perch task groups exist within Lake Erie) to monitor and understand the status of different ecosystem components, with lake committees consisting of representatives from the different state and provincial agencies and Indigenous groups tasked with making collective management decisions. Meetings are held annually at which status updates are provided by task groups to both lake management committees and public stakeholders (e.g., commercial and recreational fishing groups), with discussion following. At these meetings, the GLFC also has occasionally brought in experts with knowledge of other components of the ecosystem (e.g., water quality) to help the fisheries sector understand ecosystem changes and emerging issues happening in other sectors of the ecosystem. Furthermore, the GLFC has continued supporting and implementing EBM, stating in its strategic vision (GLFC, 2021a, Pillar 1): “The commission will encourage the conservation and rehabilitation of healthy Great Lakes ecosystems that sustain fisheries and benefit society.”

• Formalization of RAPs and the LAMP process in the 1987 amendment to the GLWQA also offers an example of government and non-government partners working to improve Great Lakes ecosystems (IJC, 1987). LAMPs—developed and implemented in each lake by the federal governments of both the USA and Canada via cooperation and consultation with provincial and state governments, Tribal Governments, First Nations, Métis, municipal governments, watershed management agencies, other local public agencies—are designed to assess, restore, protect, and monitor the health of each Great Lake and its surrounding watershed (IJC, 1987). In a more recent amendment to the GLWQA, the governments of Canada and the USA have begun to establish a vision for EBM in the Great Lakes by committing to restore and maintain the physical, biological, and chemical integrity of the waters of the Great Lakes (GLWQA, 2012). Further, in the Lakewide Management Annex of the 2012 GLWQA (p. 24), Canada and the USA have committed to: “… contribute to the achievement of the General and Specific Objective of this Agreement by assessing the status of each Great Lake, and by addressing environmental stressors that adversely affect the Waters of the Great Lakes which are best addressed on a lake-wide scale through an ecosystem approach.”

• The Great Lakes Science Advisory Board (GLSAB) of the International Joint Commission has further advocated for EBM, recommending the formation of a multiagency Cooperative Ecosystem Monitoring and Modeling Advisory Committee, the focus of which includes: “Identifying key data requirements for the effective use of ecosystem modeling and forecasting science by managers in the Great Lakes and fostering the exchange of such data, especially among fishery and water quality programs to support integrative decision support” and “Identifying and implementing strategies to enhance collaborative decision making and adaptive management of the Great Lakes ecosystems among water quality and fisheries managers through existing administrative structures or, if necessary, new collaborative structures.” (GLSAB, 2020).

We are optimistic that continued efforts to establish a shared vision for EBM in the Great Lakes will encourage the science required to inform collaborative decision-making and help build trust among the many stakeholders that need to be involved in the process. However, additional coordination of cross-sector decision-making is needed to enhance the ability of resource managers to evaluate the cumulative impacts of HIREC and achieve multiple ecosystem objectives simultaneously.

Integrated Great Lakes Database: Next steps to ensure success

The first step to ensure the success of an integrated Great Lakes ecosystem database is promotion. The Great Lakes Fish Recruitment and Ecosystem Database Workshop and this accompanying manuscript have attempted to act as a starting point for introducing a new database to the Great Lakes community. Additional manuscripts that provide an overview of the nearly completed database and present analyses using data from the database should also bring awareness to this project. Further, presentations at conferences and promotion of the database on university and agency websites will increase awareness within the Great Lakes community and beyond.

Even once the GLaRE database is established, additional steps will be required to ensure that it is used. We will facilitate use by maintaining the database on the GLFC (2023b) webpage, where it can be readily accessed and queried. Even better might be the eventual integration of this database with other extant databases (e.g., GLAHFS or GLOS) such is done in the Baltic Sea region (HELCOM, 2023). The feasibility of the GLaRE database for ecosystem-based research has been demonstrated over the past two years in Lake Erie, as data from it have been used to identify long-term fish community change (Sinclair et al., 2021), ecosystem state change (Fraker et al., 2023), and the impact of ecosystem state change on fish demographic processes (Sinclair et al., 2023a). We anticipate that the GLaRE database will continue to be of utility for fisheries scientists, ecologists, and resource managers, and that its eventual public release will enable and foster further analyses of biological communities and ecosystems.

To ensure the longer-term success of our integrated Great Lakes database, long-term support for continued upkeep of the metadata, data addition, and hosting of the database will be necessary. The addition of future data may also be contingent upon strategies that allow new monitoring data to be efficiently shared with database curators, without creating excessive additional work for agencies or researchers. These requirements highlight the fact that additional resources and funding will be necessary to ensure that the database will live on past its initial release.

Summary and conclusions

The Great Lakes have changed dramatically during the past century, owing to many human-driven stressors, including heavy metal contamination, nutrient pollution, exploitation, invasive species, habitat modification, and climate change (Smith et al., 2015). These stressors have challenged regulatory and resource management agencies alike at all bureaucratic levels. Of particular interest to us has been the challenge presented to those charged with managing the economically, ecologically, and culturally important fisheries of the Great Lakes. Owing to the far-reaching influences of many forms of environmental stressors, and their simultaneous occurrence, our understanding of the independent and interactive effects of these stressors on our fisheries remains largely uncertain. This uncertainty is problematic, as it limits the ability of agencies to develop management strategies in the face of ongoing environmental change.

As we have argued herein, making a firm commitment to EBM approaches would offer a viable means to help fisheries management agencies evaluate the impacts of HIREC on the Great Lakes. A further commitment to EBM would also provide opportunities to manage other aspects of the ecosystem (e.g., water quality, terrestrial wildlife, food production) while also identifying potential tradeoffs. The success that the EBM approach has afforded other ecosystems (DFO, 2003; Harvey et al., 2020; Holsman et al., 2020; Tallis et al., 2010) offers the clearest proof that working towards this vision for the Great Lakes ecosystems is worthwhile. Given that the Great Lakes are among the most dynamic and stressed aquatic ecosystems in the world due to multiple, simultaneous forms of human-driven environmental change, we view the need for EBM as paramount in the basin. For this reason, we are encouraged by actions taken within the Great Lakes Basin, including the creation of the RAP and LAMP process as part of the GLWQA (IJC, 1987), the more recent recognition by the GLWQA that unpolluted water is vital to the success of living resources such as fisheries, and the development of environmental principles for sustainable fisheries (GLFC-CLC, 2016). The formal commitment by binational agencies to ecosystem-based approaches to management (GLFC, 2011, 2001, 1991; GLWQA 2019, 2012, 1987, 1972) and the strong interest in the goals of our “Great Lakes Fish Recruitment and Ecosystem Database Workshop” provide further encouragement that progress towards EBM has been and will continue to be made in the Great Lakes Basin.

The core organizers of the workshop (R. Budnik, K. Frank, and S. Ludsin) are also excited by the overwhelming support that the workshop participants had for the development of our GLaRE database. The initial call for databases like this one began more than 35 years ago (Christie et al., 1986) and our database will hopefully help to finally answer these calls. Thus, while numerous excellent databases (e.g., GLAHF, GLENDA, GLATOS, GLOS) or other compilations of physical, chemical, and/or biological attributes have been created within the basin (see Table 1) and a few different research groups (Baldwin et al., 2018; Illinois-Indiana Sea Grant and University of Illinois, 2021), we are optimistic that the impending GLaRE database will act as a resource to allow Great Lakes researchers and agencies to identify long-term environmental change, understand its impacts on valued ecosystem components, and help these groups effectively connect science to management and policy decision-making in an IEA framework.

We learned in the workshop that the development of such a database can be complicated for many reasons (e.g., allocation of credit to those contributing data; ensuring data are used appropriately; database upkeep); however, we view these hurdles as minor compared to the larger one that already has been largely overcome—developing monitoring programs that have amassed thousands of standardized, long-term monitoring datasets. The Great Lakes are ripe with valuable data that required significant effort to collect, check for quality, and compile. In our view, the time has come to finally realize the call for a comprehensive, accessible database (or set of integrated databases) by visionaries of the past (e.g., Christie et al., 1986; Vallentyne and Beeton, 1988). We have great confidence that such deployment would facilitate efforts to implement EBM approaches within the Great Lakes, providing a means for regulatory agencies and resource managers to 1) gain new and different perspectives on the processes that govern their valued ecosystem components, 2) identify potential tradeoffs that might emerge with management, and 3) test the degree to which management actions are effective. Additionally, understanding the limitations of existing data will allow resource managers to identify gaps and further refine future research programs, such as the Cooperative Science and Monitoring Initiative (USEPA, 2021), by prioritizing critical data needs.

Such a comprehensive, user-friendly, and accessible database will not fully overcome the many practical and philosophical impediments to EBM that workshop participants identified (e.g., high cost; the need for success stories; and the need to establish common objectives); however, we argue, that it would serve to lower the barrier for success, allowing management goals and targets to be defined, indicators to be developed, risk to be assessed, and management strategies to be evaluated (Tallis et al., 2010). Thus, we are optimistic that the call by Christie et al. (1986) will finally be heeded, as databases offer a foundational element to the development of ecosystem-based approaches to management in the Great Lakes that can help protect our valued ecosystem services and improve integrity and health in the Great Lakes, both now and in the face of future environmental change.