Consequences of changing water clarity on the fish and fisheries of the Laurentian Great Lakes

Although scientists have increasingly documented changing water clarity in freshwater ecosystems worldwide, we do not fully understand its effects on aquatic food webs and the fisheries that they support. Through the 1970s, increasing anthropogenic eutrophication (i.e., excessive sediment and nutrient loading) tended to reduce water clarity in affected waters (e.g., Likens 1972; Schindler 1974). Similarly, during the 2000s, water clarity declined in some lakes, owing to “browning” associated with increases in terrestrially derived dissolved organic matter (Monteith et al. 2007; Solomon et al. 2015; Williamson et al. 2016a). By contrast, water clarity in a number of lakes in North America and Europe has increased during the last several decades as a consequence of the implementation of nutrient abatement programs (Ludsin et al. 2001; Jeppesen et al. 2005) and (or) the proliferation of filter feeders, such as invasive dreissenid mussels (Dreissena polymorpha and Dreissena bugensis), which have at least doubled phytoplankton filtration rates compared with pre-invasion levels (e.g., Mayer et al. 2014; Geisler et al. 2016).

These changes in water clarity can strongly influence lake ecosystems and the services that they provide. For example, reduced water clarity has been shown to degrade property values (Clapper and Caudill 2014; Liao et al. 2016) and erode recreational activities, including tourism associated with snorkeling, swimming, and boating (Keeler et al. 2012; Lee 2016). Likewise, altered water clarity holds great potential to affect the behavior, distribution, and production of fish populations and fisheries that they might support (e.g., Lester et al. 2004; Lehtiniemi et al. 2005; Tucker et al. 2012), which in turn, could have major social, nutritional, and economic consequences (McIntyre et al. 2016; Lynch et al. 2016). While much independent research, both experimental and observational, has been devoted to identifying linkages between water clarity and fish population and community dynamics in lake ecosystems, especially in small, shallow ones (e.g., Scheffer 1998), a comprehensive, unified understanding of how changing water clarity can affect the dynamics and performance of large-lake fish populations and fisheries, such as those found in the North American Great Lakes basin, is lacking. This information gap is concerning because it limits the ability of agencies to manage their fisheries in response to present-day and future changes in water clarity.

While many of the world’s great lakes have been experiencing changes in water clarity, owing to human-driven environmental change (e.g., African Rift Lakes: Seehausen et al. 1997; Cohen et al. 1993; Lake Baikal: Izmest’eva et al. 2016), the Laurentian Great Lakes (hereinafter, Great Lakes) are illustrative of a suite of lakes that would greatly benefit from a conceptual framework that integrates existing knowledge to help agencies better understand how changing water clarity affects their diverse fish communities and valued fisheries. Not only do these lakes support fisheries that have been valued as high as US$2.2 billion annually (in terms of expenditures on recreational trips and equipment; US Department of Interior et al. 2018), some of these lakes have undergone major changes in water clarity during the last several decades (Barbiero et al. 2012; Binding et al. 2015; Yousef et al. 2017). For example, Secchi disk depth — a common measure of water clarity in lake ecosystems — has increased more than twofold since offshore monitoring began during the early 1980s in Lakes Huron, Michigan, and Ontario (Figs. 1a–1c; see online Supplementary material¹). By contrast, other lakes and habitats within the Great Lakes basin have not undergone marked long-term changes (Fig. 1d) but are prone to nonpoint nutrient and sediment runoff or wind-driven sediment resuspension. Western Lake Erie (Richards et al. 2008; Niu et al. 2018) often experiences temporary (days to weeks), but large-scale, reductions in water clarity due to the formation of turbid sediment plumes in the spring (Reichert et al. 2010; Fig. 1e) and dense harmful cyanobacteria blooms in the late summer and fall (Stumpf et al. 2012; Fig. 1f). Improved understanding of how changing water clarity, in either direction, affects fish and fisheries in the Great Lakes, and large lakes in general, is needed.

Toward this end, we developed a framework (see Fig. 2) that seeks to synthesize and organize our collective understanding of how changing water clarity influences fish populations, the broader communities that they comprise, the fisheries that they support, and how these affect key aspects of fishery performance. This framework consists of several tenets, which are briefly outlined below and described in more detail through the remainder of this paper. First, we argue that changes in water clarity hold the capacity to drive change in fish population abundance, fish community composition, and fisheries production through a variety of ecological mechanisms, including potentially lethal exposure to ultraviolet radiation, foraging (energy intake), predation risk (survival), reproduction, and other behaviors (e.g., reproduction, schooling, territoriality), all of which can affect fitness. Second, we argue that altered water clarity can directly influence fisheries through nonecological mechanisms, such as altered catchability by fishing gears, which can affect both the success (e.g., catch per unit effort, CPUE) of fishers and the ability of agencies to assess fish populations, and altered fisher behavior (e.g., reduced fishing due to turbid algal blooms; Kotwicki et al. 2015; Nieman et al. 2020). Finally, we assert that the effects of water clarity on fish populations and their associated fisheries can ultimately influence fishery performance, defined as how well a fishery delivers community (e.g., social, cultural) and economic benefits in a biologically sustainable manner (see Anderson et al. 2015). Changes to water clarity in lake ecosystems, including the Great Lakes, can be expected to continue owing to climate change (e.g., Hayhoe et al. 2010), land-use change (e.g., LaBeau et al. 2014), and invasive species (e.g., Pagnucco et al. 2015). The need to continue to improve our understanding of how water clarity influences fish populations and fisheries is paramount given these continued expected changes.

We originally sought to develop our framework using examples only from large, deep lakes, such as the Great Lakes, where our expertise and experience is concentrated and where stakeholders, scientists, and managers have observed dramatic changes in water clarity. We realized, however, that less research existed in these systems than expected and that research from shallower systems, marine systems, and even controlled experiments would be required to understand and make predictions about the responses of fish in large, deep lakes. During this synthesis, we identified priority research needs for the Great Lakes and described them in Box 1. Given the broad swath of studies that we considered and that we are unaware of other frameworks or syntheses that have been developed to unpack the linkages among changing water clarity, fish, and fisheries, we anticipate that our framework will have application well beyond the Great Lakes, in both large and small ecosystems.

To help explain our framework, we organized this paper into several sections. First, we define water clarity and describe the key factors that attenuate sunlight in large, deep lakes. Second, we discuss how fish perceive light, which is critical to understanding the ecology of fish in relation to water transparency. Third, we compile existing knowledge on how changing water clarity can directly affect processes influencing fish, the fisheries they support, and ultimately fishery performance. Finally, we describe general predictions gleaned from the synthesis and discuss their implications for priority research in the Great Lakes. Collectively, our perspective offers a novel synthesis on how changes in water clarity affect the fish populations and fisheries of large, deep lakes, while simultaneously offering advice to management that will help keep these fisheries sustainable in the face of continued ecosystem change.

In this section, we provide a high-level overview of the optical principles of light penetration, absorption, and scattering within the water column to inform subsequent discussion on how changing water clarity can influence fish and fisheries. There is no commonly accepted definition of “water clarity” nor is there a single metric that has been consistently used to measure the clarity of water. However, because understanding the factors affecting transmission of light though the water (the fundamental physical basis of “clarity”) is so important for many studies of biotic and abiotic aquatic processes, the literature is rich with works that describe different measures related to water clarity, and more detailed information on light in lakes can be found in Kirk (2011), Johnsen (2012), Neale et al. (2021), and Williamson and Neale (2021). In the discussion below, it is important to understand the electromagnetic spectral composition of sunlight, which includes not only the most commonly measured “light” visible to humans and used by phytoplankton and plants for photosynthesis (400–700 nm), but also shorter wavelength ultraviolet radiation (290–400 nm, hereinafter “UV”) and infrared radiation (700–3000 nm). The wavelength-specific attenuation of sunlight determines the depths to which prey, predators, or mates can be seen, damaging UV can penetrate, as well as the heat distribution and thermal structure of the lake, all of which, in turn, regulate the distribution and abundance of all life stages of fish.

Water clarity and water transparency are two terms often used interchangeably to describe how deep light penetrates into a lake. The greater the water clarity or transparency, the less light is attenuated with depth. Light is attenuated with depth by two fundamental processes: absorption and scattering. Both processes are regulated by dissolved and particulate compounds in the water, as well as by absorption by water itself (Kirk 2011). Absorption involves the conversion of light (photons) into primarily heat energy, which influences vertical temperature gradients in lakes. By contrast, scattering is the redirection of photons from their original paths by molecular collisions without loss of photons and without any heat gain by the water. Although often used mistakenly to refer to water clarity, the term turbidity is a measure of only the scattering by the particulate components in the water, including both inorganic (e.g., sediments, glacial flour) and organic (e.g., phytoplankton, bacteria, detritus).

Major regulators of water clarity in large lakes include sediment and coloured dissolved organic matter (CDOM) plumes from terrestrial runoff, rivers, phytoplankton blooms, and wind-driven resuspension of sediments in shallower waters. The UV range is particularly sensitive to attenuation by CDOM, whereas absorption and scattering by sediments and phytoplankton blooms attenuate light more uniformly across all wavelengths. Water clarity in large lakes is often greater in offshore than in nearshore waters due to the influence of terrestrially derived CDOM and particulates in nearshore environments. Heavy precipitation events can cause CDOM, as well as inorganic silt and clay particles, to flow in from tributaries (Reichert et al. 2010; Howell et al. 2012). Similarly, riverine inflow of nutrients can contribute to nearshore phytoplankton blooms. Both sediment and nutrient-induced algal blooms can be advected substantial distances, reducing water clarity offshore as well. High wind events can also cause sediment resuspension in nearshore regions, as well as in shallow lakes (Mortimer 1987; Lick et al. 1994; Niu et al. 2018). Remote sensing techniques are now permitting large-scale assessment of not only water clarity and sediment plumes, but the relative contributions of attenuating substances such as CDOM versus harmful algal blooms (Kutser et al. 2015; Urquhart et al. 2017). In temperate lakes, ice cover can greatly reduce light levels and primary productivity, although type of ice, whether suspended sediments were included during freezing, and whether ice is covered with snow are key factors (Bolsenga and Vanderploeg 1992; Vanderploeg et al. 1992; Twiss et al. 2014). One other abiotic factor that can affect light attenuation is the precipitation of oversaturated minerals, such as calcium carbonate (CaCO₃), commonly referred to as “whiting events”. These events have been shown to dramatically reduce water clarity for days to weeks in lakes that have high CaCO₃ concentrations, such as Lakes Michigan, Ontario, and Erie (Strong and Eadie 1978; Hodell et al. 1998; Watkins et al. 2013). The effects of these whiting events, especially on fishes, however, remain largely unstudied.

Biological processes have also been shown to attenuate light in lakes. Grazing of primary producers by herbivorous zooplankton, for example, can cause a period of abrupt and sometimes prolonged high water clarity referred to as a clear-water phase during the spring and early summer (Lampert et al. 1986; Williamson et al. 2007; Sommer et al. 2012). Filter-feeding species also can modify light penetration and water clarity. Most prominent have been the effects of dreissenid mussels on small and large lakes throughout North America, wherein their filtration of phytoplankton has caused long-term increases in water clarity (e.g., Karatayev et al. 2002; Mayer et al. 2014; Rowe et al. 2015). The impacts of these mussels are stronger during isothermal periods, where they have access to plankton throughout the water column (Rowe et al. 2015). Mussels have also been hypothesized to reduce the intensity of whiting events, owing to their uptake of calcium (Barbiero et al. 2006; Binding et al. 2007; but see Watkins et al. 2013). However, the ability of mussels to regulate water clarity is low in shallow regions that receive high sediment inputs from rivers or are prone to sediment resuspension, such as western Lake Erie (Barbiero and Tuchman 2004; Niu et al. 2018) or certain shallow areas of Lake Ontario (Howell et al. 2012). This interaction has complicated our ability to model water clarity in dynamic ecosystems such as the Great Lakes, including its effects on other organisms.

Methods for measuring water clarity have advanced tremendously through the years. Despite these advances, the classic, but still most widely used, method of measuring water clarity is the black and white Secchi disc because of its low cost, simplicity of use, and availability of long-term data sets (see Fig. 1). For improved understanding of spectral composition, a submersible radiometer that measures changes in light at a given wavelength (λ) or a range of wavelengths is required. The commonly used photosynthetically active radiation (PAR) radiometer measures light in the 400–700 nm λ range, which is useful for studies of primary production and has also been used to quantify light availability to foraging plankton or fish. One commonly estimated parameter is the diffuse attenuation coefficient (K_dλ, m⁻¹) for a given λ, which can be estimated from the slope of the log-linear relationship between light intensity and depth and is used to estimate the penetration of a given λ to a given depth. For example, 1% light penetration depth for a given λ (Z_1%λ, m) is often a depth of interest; Z_1%PAR is the depth to which 1% of PAR penetrates and has often been used to estimate the photic compensation depth, the depth below which community respiration exceeds photosynthesis. One justification for collection of radiometer profiles is the common poor correlation between Secchi disk depth and K_dPAR in lakes (Kirk 2011).

The portion of the electromagnetic spectrum perceived by a fish depends on characteristics of its eyes. Similar to humans, fish have both rods adapted to low light conditions and cones adapted to detect photons within a specific range of wavelengths. Cones allow fish to detect colour. Fish can be dichromatic (two types of cones), trichromatic (like humans), or even tetrachromatic with the ability to detect UV (Johnsen 2012). Eyes that are sensitive to UV are mainly found in young fish, including yellow perch (Perca flavescens; Loew and Wahl 1991), and the ability to detect UV can improve foraging (Loew 1999; Leech et al. 2009). Some species retain the ability to detect UV as adults (Harosi and Hashimoto 1983), perhaps as a means to help choose mates (e.g., Smith et al. 2002). Regardless, the properties of the eye interact with the spectral quality of available light to affect the brightness perceived by a fish.

Measuring the brightness perceived by fish or other aquatic animals is not as straightforward as using a radiometer to measure PAR. For humans, perceived brightness is measured in lux by photometric sensors that weigh the incoming light by a λ-specific luminosity function (defined for light- (photopic) or dark-adapted (scotopic)), which mimics the λ-specific sensitivity of the human eye. Thus, how appropriate lux is as a unit of brightness to a fish depends on how similar the fish eye is to the human eye. Ideally, one would measure the complete spectrum of available sunlight using a radiometer and apply a luminosity function specific for each species of interest (Johnsen 2012). Such functions have been derived for some species, including some in the Great Lakes, with new units of brightness being defined as a result (e.g., the “alelux” for alewife (Alosa pseudoharengus) (Boscarino et al. 2010); the “mylux” for the invertebrate Mysis diluviana (Gal et al. 1999)). Using these species-specific units has allowed for improved predictions of feeding rates (for alewife; Boscarino et al. 2010) and distributions (for mysids; Gal et al. 1999) in the Great Lakes, yet remain surprisingly rare in the freshwater literature.

The ability of aquatic animals to detect an image depends on the ambient brightness, how light scatters, and the size of the image (Johnsen 2012). In general, visual fish foragers can detect larger prey from a further distance than they can detect smaller prey (Wright and O’Brien 1984). In turbid water, however, the scattering of light by particulates can reduce the sharpness of an image and make it more difficult to detect a prey even though the amount of light may be sufficient. Because image deterioration associated with scattering increases with distance, planktivorous fish that rely on detecting small, nearby prey are less affected by turbidity than predatory fish, which typically detect larger prey at a longer distance (Breck 1993; De Robertis et al. 2003). Hence, with reduced water clarity owing to turbidity, foraging by a piscivore is more negatively affected than foraging by a planktivore (De Robertis et al. 2003). As we discuss more fully below, this interaction among fish size, prey size, and turbidity-induced scattering can influence both habitat use and foraging behavior and success.

Changes in water clarity can affect processes relevant to individual fishes and aspects of the fisheries that they support, as illustrated in Fig. 3. For example, direct effects of changing water clarity on fish include alteration of foraging success, predation risk, and other behaviors such as schooling, movement (both vertical and horizontal), and territoriality. Furthermore, reduced attenuation of UV or visible light can affect reproductive behavior or the distribution and survival of fish eggs and larvae. With respect to effects on fisheries, changing water clarity can also directly affect catchability in assessment gear used to inform fishery management decision-making, as well as the behavior of fishers, which can influence both fishing effort and harvest. Finally, Fig. 3 illustrates an example of an “indirect” effect of changing water clarity on fish — the growth of benthic macrophytes. Unless one is studying a fish that consumes macrophytes (e.g., Wiley et al. 1984), we consider changes in macrophyte abundance to have more indirect effects on fishes in the context of spawning habitat (e.g., Lane et al. 1996) or refuge from predation (e.g., Savino and Stein 1982). Another similar example of a more indirect effect is how changing water clarity can influence phytoplankton production and distribution. Even though most fish species do not directly feed on phytoplankton, spatiotemporal distribution of phytoplankton can affect other aspects of the food web (e.g., zooplankton, benthic invertebrates) that more typically can influence fish foraging. Although these and other indirect effects are potentially important for fish growth, reproduction, and survival, our synthesis only focused on the more direct effects of changing water clarity, given their greater relevancy to our objectives and the need to limit the length and scope of this paper.

This paper was motivated to improve limited understanding of how (i) dramatic long-term increases in water clarity in Lakes Michigan, Huron, and Ontario during the past several decades and (ii) seasonal reductions in water clarity owing to the increasing frequency of sediment plumes and cyanobacteria blooms in Lake Erie affect fish and the fisheries that they support. As we compiled existing knowledge, we summarized some of the general predictions that emerged (see Fig. 4) and recognized how these predictions could inform priority research needs for the Great Lakes that could be useful for funding agencies (see Box 1).

The first general prediction arising from this synthesis is that the reactive distance to enhance fish foraging will increase with increasing water clarity (Fig. 4a). Because larger prey can always be detected over a greater distance and tend to be less transparent than smaller planktonic prey, piscivorous fish may experience increased foraging success relative to planktivorous fish with increasing transparency (Breck 1993; De Robertis et al. 2003). In turn, this prediction affords a greater predation risk to prey fishes than to zooplankton as lakes become clearer. Applied to prey fishes in Lakes Michigan, Huron, and Ontario, we would predict that top-down regulation (i.e., predation) to be even more important in the 2000s than during the 1980s and 1990s when the water was less clear. As a result, the already strong evidence for top-down control (e.g., Murry et al. 2010; Tsehaye et al. 2014; He et al. 2015) could be bolstered by enhanced foraging efficiency in systems with increasing water clarity. Planktivorous fishes, in turn, could seek to mitigate increased predation risk via several potential behavioral responses (Fig. 4b), including more time spent migrating to refugia (e.g., dark bottom waters, vegetation patches) or shoaling (hence potentially increasing competition for prey), both of which could reduce foraging time and energy intake.

Another general prediction is that different fish species, even within a trophic level, will experience optimal foraging success under different levels of water clarity (Fig. 4c). Species-specific differences are a consequence of varied anatomical (e.g., eye structure) and behavioral (e.g., feeding preferences, DVM behavior) adaptations, yet relatively few Great Lakes fish species have been studied to identify optimal water clarity conditions. Percid species are one exception, given that previous research has documented walleye to optimally feed in intermediate levels of water clarity (Ryder 1977; Lester et al. 2004) and that the foraging of walleye and yellow perch is more impeded by organic components of turbidity than inorganic ones (Wellington et al. 2010; Nieman and Gray 2019, 2020). Scientists should consider developing feeding experiments for other key Great Lakes species that likely have experienced dramatic changes in water clarity. For example, while Chinook salmon (Oncorhynchus tshawytscha) and lake trout (Salvelinus namaycush) are generally the most abundant piscivores in Lakes Michigan, Huron, and Ontario, we are unaware of any experiments with these species that can help agencies understand how each component of foraging (search, prey encounter, pursuit, and capture) might have changed with the dramatic increases in water clarity (see Fig. 1) or whether one species has benefitted more than the other. Likewise, while extensive foraging experiments that simulate organic turbidity have been conducted for some key Lake Erie species, similar investigations have not been conducted for other ecologically and economically important species (e.g., white perch, white bass (Morone chrysops), rainbow smelt, but see Nieman and Gray (2019, 2020)) that may be exposed to cyanobacteria blooms in Lake Erie or other productive embayments in the Great Lakes.

Improved knowledge of whether changing water clarity has affected the foraging efficiency of key species could help fishery management agencies determine the appropriate stocking rates of top predators. For example, a series of Lake Michigan stock assessment models that use a type II functional response to estimate the consumption of alewife by salmonines are used to inform salmonine stocking rates (Tsehaye et al. 2014). This functional response curve, however, does not account for varied water clarity, which is predicted to affect the foraging efficiency and energy intake per unit time (Gardner 1981; Sweka and Hartman 2001; Ljunggren and Sandström 2007). Given that water transparency in Lake Michigan has doubled since 2004 (see Fig. 1) and likely increased the foraging efficiency of piscivores especially (De Robertis et al. 2003; Fig. 4a), incorporating water clarity is a logical step in a revised model, which could reduce the risk of overstocking.

Another emerging general prediction is that as water clarity increases, visible light and even UV will concomitantly penetrate deeper in the water column, which could influence spawning behaviors, the distribution and abundance of larval fishes, and even their zooplankton prey (Fig. 4d). Unfortunately, because UV is not commonly measured in water profiles in Great Lakes monitoring, we do not know whether increased water transparency in Lakes Huron, Michigan, and Ontario has led to deeper penetration of UV. If we assume that UV now penetrates deeper in these lakes, species that have evolved in a high UV environment may have an advantage over other species because they can (i) use UV to improve foraging success or (ii) mitigate potential damage from UV through behavioral responses (e.g., migrating to deeper waters), morphological adaptations (e.g., protecting pigments), or DNA repair mechanisms (Zagarese and Williamson 2001). One hypothesis for future research is that the pelagic larvae of native Great Lakes species (e.g., Coregonus spp., deepwater sculpin) evolved in relatively high UV exposure environments before human activities increased nutrient inputs and reduced water clarity during the 20th century. By contrast, we would expect the early life stages of nonindigenous, naturalized species, such as alewife (dominant in Lakes Michigan and Ontario), rainbow smelt (abundant in all five lakes), and white perch (dominant in western and central Lake Erie), which evolved to spawn in tributaries or embayments of the Atlantic Ocean — where UV exposure was likely more limited — to have a lower tolerance to UV. Could low tolerance and increased UV exposure be contributing to the declines of alewives and rainbow smelt in Lakes Michigan, Huron, and Ontario over the past several decades?

As water clarity increases, a review of previous studies predicts that fish will defend territories more aggressively and discriminate mates more successfully (Fig. 4b). Because we found no research on these behaviors for common Great Lakes species, whether these behaviors have become more prevalent in Lakes Huron, Michigan, and Ontario during recent decades remains unknown. Such knowledge is important as it could help explain recruitment variation (via the ability to defend territories beneficial to reproduction and shelter; sensu Berg and Northcote 1985), highlighting the need for species-specific research. By contrast, in lakes or areas of lakes where water clarity becomes reduced for days or a few weeks (e.g., cyanobacteria blooms, wind-driven resuspension events, precipitation-driven sediment plumes out of river mouths) that happen to coincide with spawning periods, research from the African Great Lakes suggests fish may be more vulnerable to selecting suboptimal mates or even to hybridization. This latter question seems most relevant to nest-guarding Great Lakes species such as round goby, sculpins, and centrarchids, but also could be important to broadcast spawners such as yellow perch, which appear to use spawning areas that are defined by water clarity (Reichert et al. 2010; Carreon-Martinez et al. 2015) or that might rely on visual cues to maintain fine-scale population structure (Kocovsky et al. 2013).

A final general prediction is that as water clarity increases, catchability from gill nets and daytime trawls will decline, although the responses likely vary by species (Stapanian et al. 2007, 2009). Future research in Lakes Huron, Michigan, and Ontario could evaluate whether increased water clarity has changed the distribution of key planktivorous fishes that have traditionally undergone DVMs (e.g., alewife, rainbow smelt). Have they responded by increasing time spent in dark, bottom refuges during daytime or by intensifying daytime shoaling behavior? Their response may be mediated by the decline in energy-rich Diporeia spp. on the bottom of these lakes that would reduce daytime foraging opportunities in the benthos. With potential changes in fish distribution, future research could investigate whether increased water clarity has reduced the sampling efficiency of gears, especially if fish are able to detect and avoid the oncoming trawl earlier. Similar investigations into water clarity effects on catchability are relevant to western Lake Erie and other shallow areas of the Great Lakes that have been experiencing increased sediment plumes or cyanobacteria blooms (see Fig. 1). Beyond assessment, how are anglers and commercial fishers changing behaviors when these plumes or blooms occur? Catchability in these areas could increase due to fish aggregating in these areas to find prey or seek refuge (e.g., Briland et al. 2020) or by reducing the ability of fish to detect and avoid sampling gears. Finding that catchability has changed in either set of examples (increased or reduced water clarity) would have consequences for management agencies, which use long-term assessment data to inform fish stocking decisions or to estimate population sizes of fish and set harvest levels.

In developing our framework to improve understanding of how changing water clarity could affect fish and the fisheries they support in the Great Lakes, we derived general predictions (see Fig. 4) that can guide future research in the Great Lakes (Box 1) and beyond. Given that the responses of fish to altered water clarity will be species-specific (and perhaps even population-specific), we should expect that some species or populations will benefit more than others. Our ability to forecast these “winners” or “losers”, or at a minimum understand the role of water clarity in driving fisheries performance, will hinge on our future success in estimating how changing water clarity affects these vital rates (e.g., foraging, predation risk), as well as our ability to accurately assess whether altered catchability could be affecting our ability to document real changes in population demographics. With respect to fisheries performance (i.e., Anderson et al. 2015), our framework development illuminated the need for additional Great Lakes research in the nascent field. Most obvious is the need for fisheries biologists and researchers to partner with social scientists and economists to evaluate nonbiological responses (e.g., cultural value, economic value) of fish and fisheries to changing water clarity. Doing so could help agencies better inform stakeholders about shifts in valued fish populations, as well as ensure that the values of these stakeholders are considered when forming future policies (e.g., harvest quotas, stocking rates). Furthermore, our research highlighted numerous ways in which improved understanding of the linkages among water clarity, fish, and fisheries could benefit efforts to conserve, rehabilitate, and sustain valued populations, or perhaps control nuisance populations (e.g., invasive species such as sea lamprey (Petromyzon marinus) and white perch). This understanding seems especially important given that recent changes in water clarity, both increases (as observed in Lakes Michigan, Huron, and Ontario) and decreases (as observed in Lake Erie), are not expected to be reversed and can be expected to interact with other anthropogenic stressors (e.g., climate change, invasive species, habitat modification; Smith et al. 2019). Such understanding can potentially offset ecological surprises (sensu Paine et al. 1998) that would make fisheries management even more difficult than it currently is. Finally, by offering (i) a new framework with which to view the effects of water clarity on fish populations, communities, and fisheries, (ii) a set of priority research questions that highlight important information gaps in the Great Lakes, and (iii) general (testable) predictions on how water clarity can be expected to influence fish, we are hopeful that more attention is paid to water clarity change, which is common to aquatic ecosystems worldwide, both freshwater and marine. Continued research into the impacts of water clarity change on lake fishes and fisheries, combined with a future synthesis of this research (like what we provided herein), would advance our ability to form general predictions that can be applied to ecosystems where data and (or) ecological understanding is lacking. In addition, findings from this research could improve the ability of management agencies to guide the expectations of their stakeholders such that they are well-matched with biological reality, as well as help managers keep their valued fish populations and fisheries sustainable in the face of continued ecosystem change.

This paper was developed based on a workshop funded by the Great Lakes Fishery Commission entitled “Changes in nutrient status and energy flow through lower trophic levels: Implications for Great Lakes fisheries managers” held at the Cornell Biological Field Station in August of 2017. We especially acknowledge the leadership of Tom Stewart for the funding and for organizing the workshop and Brian Weidel and Christine Mayer for their contributions to workshop discussions on how changing water clarity can affect fish and fisheries. We thank the following people for compiling and comparing Secchi disk data from across the Great Lakes: Andrew Briggs (Saginaw Bay, Lake Huron) and Vic Santucci and Dan Makauskas (nearshore Lake Michigan. We are grateful to Jean Adams for assisting with the analysis of Secchi disk trends and Darren Kirkendall for formatting help on the manuscript. Cory Brant and Robert Arlinghaus provided helpful reviews to improve the manuscript. Cory Brant also made substantial improvements to Figs. 2 and 3. CEW acknowledges support from National Science Foundation (NSF) DEB-1754276 and NSF DEB-1950170. This paper is NOAA Great Lakes Environmental Research Laboratory contribution No. 1976.