Modeling Hydrology-Habitat-Fish Population ...

Modeling Hydrology-Habitat-Fish Population Linkages for Lake Erie RICHARD M. ANDERSON AND BENJAMIN F. HOBBS Department of Geography and Environmental Engineering 313 Ames Hall The Johns Hopkins University Baltimore, MD 21218 JOSEPH F. KOONCE AND ANA B. LOCCI Department of Biology Case Western Reserve University Cleveland, OH 44106 Abstract: Most large scale ecological models focus on population dynamics or nutrient and energy flows without explicitly considering habitat limitations. For example, in Lake Erie, tributary and near-shore habitat limit recruitment; yet these effects are not represented in existing models. Habitats will change with alterations of hydrology (e.g., due to climate change) or land use; moreover, such alterations can be deliberate tools for fishery management. Thus their omission limits the usefulness of those models. This paper will report on methods for linking the Lake Erie Ecological Model and a Great Lakes hydrologic model. The purpose of this linkage is to allow managers to analyze the implications for the Lake Erie ecosystem of habitat management measures, climate change scenarios, and other influences upon habitat. This linkage addresses the relationship of lake levels and tributary flows to habitat supply (quantified as habitat suitability indices), and in turn the relation of supply to mortality of various age classes of fish. Given the absence of detailed information on habitat supply and preferences, we will illustrate the use of the linked models to explore the implications of alternative assumptions.

INTRODUCTION Recovery of the Lake Erie ecosystem from the extremely degraded condition of the 1960s has been followed by a period of instability in the structure of the fish community (Koonce et al. 1996a). Degradation of the fish community reached its worst level in the 1960s (Hartman 1973). Beginning in the 1970s, management initiatives were successfully implemented to counteract pollution and overexploitation, discovered to be the dominant and controlling stresses on the fish community. As a result, accelerated eutrophication was halted. Walleye (Stizostedion vitreum vitreum), prized commercially as well as for sport, rebounded. Other indicators also evidenced the recovery of the ecosystem. Since then, however, the future of the Lake Erie fish community has again become uncertain. Invasion of exotics such as the zebra mussel has altered the trophic balance of the lake. After the dramatic peak of the mid-1980s, walleye are again on the decline, as are smelt. Only whitefish have recovered, but it is not clear why. Total harvests and virtual population reconstructions indicate that abundances of yellow perch are down, but this cannot be explained based on fishing effort (data reproduced in Koonce et al. 1996a). The hypothesis of habitat availability as a central factor in controlling the composition of the fish community holds promise in helping to explain the observed changes (Koonce et al.

1

1996a). Minns et al. (1996a) recently developed a model that explicitly relates the population of northern pike in Hamilton Harbor, Lake Ontario, to the supply of habitat suitable to each life stage. In this paper we focus on the dynamics of the Lake Erie fish population as represented by 14 key species. We develop a modeling framework that enables us to explore the implication of certain hypotheses regarding the impacts of habitat supply. Furthermore, because habitat supply itself is dynamic, we attempt to explicitly model factors such as lake hydrology and climatic change which might alter the supply of habitat over time. Specifically, we link a Great Lakes hydrologic model (Chao et al. 1996) to habitat to fish population as simulated by the Lake Erie Ecological Model (LEEM) (Koonce et al. 1996b) (See Figure 1). In Section 1 we report on progress in developing a biologically relevant habitat typology for the 14 Lake Erie species under consideration. In Section 2 we explore the effects of hydrology, climate change, and management on physical habitat. In Section 3 we discuss our modeling methodology as incorporated within LEEM. In Section 4 we present some preliminary findings, and Section 5 contains conclusions and a discussion of future work. 1. REPRESENTATION OF HABITAT SUPPLY Herdendorf et al. (1992) reviewed the physical and chemical components of the Great Lakes as they shape and control the use of habitat by fish. They proposed a classification system based on the horizontal (coastal, including tributaries; near-shore; intermediate; and offshore zones), the vertical (pelagic and benthic), and the substrate as the three major categories of physical habitat. Besides assessing the supply of physical habitat, Herdendorf et al. (1992) emphasized that a habitat classification system should “attempt to provide a description of habitat quality.” Temperature, contaminants, dissolved oxygen, nutrients, water quality (ammonia, CO2, and hydrogen sulfide concentration) are some suggested habitat quality measures. As will be made clear below, we focus on physical factors that affect the quality of habitat within Lake Erie. Otherwise, the habitat typology represented within this paper generally agrees with their development. Table 1 summarizes the habitat categories that are important to each species as they vary by life stage. In particular, it highlights the processes that impact fish utilization of each of these categories (data mainly from Lane et al. 1996a, b, c). The substrate habitat category includes the entire horizontal geographic extent of substrate available to Lake Erie fish, including near-shore and tributary. Tributary and near-shore/wetland categories refer to the water column above the substrate (pelagic habitat). Though the work of Minns et al. (1996a) presents some evidence to the contrary for northern pike, offshore and hypolimnion habitat are not expected to limit the fish population in Lake Erie since they exist in large quantity. These regions have been excluded from Table 1.

2

TABLE 1: Selected Lake Erie fish species (by common name) with impacts on habitat by life stage. Life stages: S = spawning. N = nursery. A = adult. H = Hydrologic: H1 = Tributary flow rate. H2 = Water temperature. H3 = Level fluctuation. H4 = winds. M = Management: M1 = Impoundment. M2 = Channelization. M3 = Shoreline development. M4 = Habitat augmentation. C = Climate Change: C1 = Lower tributary flow. C2 = Lower lake level. C3 = Higher Life stage

Substrate

Tributary pelagic

Near-shore/Wetland pelagic

Walleye

Species

S N A

H1,H4; M4 H1

H1,H2; M1,M2,M4

H3; C1,C2 H1,H2,H3; C1,C2 H3

Rainbow Smelt

S N A

H1; M4 H1

H1,H2,H3; M1,M2

H3; C1,C2 H1,H2,H3; C1,C2

White Perch

S N A

H1, H2; M1, M2

H3; C1,C2 H3,H4; C1,C2

Yellow Perch

S N A

Lake Whitefish

S N A

Lake Trout

S N A

Rainbow Trout

S N A

H1

H3; C2 H3; C2 C3 H1,H2; M1,M2

H3,H4; C2 H3,H4; C2 C3 H2,H3; C2 H3; C2

H1; M4 H1

H1,H2; M1,M2 H1,H2; M1,M2 H3; C1,C2

Gizzard Shad

S N A

H2,H3,H4; M3,M4; C2 H2,H3,H4; M3,M4; C2 H3,H4; C2; M4

Shiner

S N A

H2,H3,H4; M3,M4; C2 H2,H3,H4; M3; C2 H3,H4; C2

Freshwater Drum

S N A

H2,H3,H4; C2 H3,H4; C2

Burbot

S N A

H2,H3,H4: C2 H3,H4: C2

Smallmouth Bass

S N A

Alewife

S N A

White Bass

S N A

H4; M4

H2,H3; C2; M4 H3,H4; C2; M4 H3; C2,C3 H1,H2; M1,M2

H2,H3,H4; C2 H3,H4; C2 C3 H2,H3,H4; C2 H3,H4; C2 H3,H4; C2

3

We consider four hydrologic impacts: tributary flow rate (H1), water temperature (H2), lake level fluctuations (H3), and winds (H4). Climate change impacts discussed in this paper include lower tributary flows (C1), lower lake levels (C2), and higher temperatures (C3). Management measures under consideration are three negative effects (impoundment (M1), channelization (M2), and shoreline development (M3)), and habitat augmentation (M4). Regarding habitat augmentation (M4), we note that it could in reality be included in any fishes’ habitat category, since humans can in general either help or hurt in any of these life stage/habitat categories. Occurrences of M4 within Table 1 reflect specific knowledge of habitat improvement measures that currently have been proposed or are in progress (taken mainly from Kelso and Hartig 1995). For example, projects to augment desirable walleye spawning substrate are taking place on Current River in Lake Superior and Fox River in Lake Michigan’s Green Bay. Thus impact M4 is included under the spawning substrate category in Table 1. As a current example in Lake Erie, artificial reefs created in the near-shore region of the central basin from 1984 to 1989 have been very successful in attracting walleye, as attested by anglers who enthusiastically utilized these sites. Other management impacts such as impoundment and channelization (see M1 and M2 for walleye, smelt, whitefish, white perch, and alewife) indicate that these measures would restrict fish usage of the relevant habitats. The shoreline development impact (M3) for shad and shiner indicates their almost total dependence on the nearshore/wetland region during the spawning and nursery life stage. We hope that this approach, while less detailed than the characterization of Herdendorf et al. (1992), provides a basis for quantitative exploration of some of the linkages which may exist between habitat supply and Lake Erie fish populations. For example, Table 1 highlights the fact that the wetlands are especially vital habitat for Lake Erie (see e.g., Jude and Pappas 1992). All the species listed in Table 1 depend on the near-shore/wetland region at some point in their life span. Whitefish and lake trout utilize it during the spawning and nursery life stage.1 Rainbow trout use it as adults. Due to their high preference for the cover provided by aquatic vegetation, gizzard shad and shiners use it throughout all life stages. Quantification of the dynamics of wetland habitat will be an important goal of this paper. Lane et al. (1996a, b, c) (in great detail, based largely on exhaustive literature review) and Herdendorf et al. (1992) (more generally) summarize many of the particulars regarding fish preferences for spawning, nursery, and adult habitat. 2. EFFECTS OF HYDROLOGY, CLIMATE AND MANAGEMENT ON HABITAT SUPPLY In Section 2.1 we discuss how various hydrologic processes may impact habitat supply. In Section 2.2 we focus on the impacts of climate change. In Section 2.3 we consider the potential of various management measures to create or improve habitat. 2.1 Hydrology Hydrology affects habitat in several ways. We focus on physical effects such as the impact of flow rate on tributary substrate; winds and lake levels on near-shore/wetland; and temperature on habitat in general. 2.1.1 Tributary flows Tributary flows may significantly alter both the availability and the suitability of spawning areas for certain species. The tributaries along the south shore of Lake Erie drain the fertile farmlands of Ohio and Pennsylvania. In particular, the Maumee River, which empties into 1

A characterization of lake trout habitat has been included though they do not currently reproduce naturally in Lake Erie.

4

the lake’s southwest corner near Toledo, carries a suspended solids load larger than that of the Detroit River (2.3 million tons to 1.6 million tons annually), the major inlet to Lake Erie (with 90% of the inflow) (Herdendorf and Krieger 1988). Different species have different preferences for spawning substrate, and so are sensitive to the condition of the river bottom. For example, tributary spawners such as walleye, smelt, and trout prefer silt-free cobble, gravel, rubble, boulder-strewn, or sandy surfaces (Lane et al. 1996c). However, sediment transfer, deposition, and scouring are likely to occur randomly, and may disqualify regions to which fish home in order to spawn. High and low flow regimes impact habitat of the earliest life stages of Lake Erie fish. Fall and winter bring low flow regimes that may result in exposure of certain spawning regions. If this occurs before spawning takes place, the affected regions would become inaccessible to spawning fish (e.g., whitefish spawn in the fall); after eggs have been laid, the result would be desiccation. Spring and summer bring high flows. In the long run, higher flows should produce more tributary habitat for fish, but in the short run their effect is more uncertain. Meion et al. (1996) reported that high run-off events reduced recruitment for walleye spawning in the Maumee and Sandusky rivers. 2.1.2 Winds Winds bring severe storms and high wave energy conditions along Lake Erie shorelines. Spring and winter storms, for example, bring winds out of the northwest and northeast along the longer axes of the lake at speeds up to 50 miles per hour (Herdendorf 1987). Normally, wetland vegetation can attenuate the force of moderate waves to lessen their destructive power. The resulting quiescent environment is desirable spawning and nursery habitat (recall Table 1). But intense lake storms can uproot macrophytes and destroy coastal wetlands (Herdendorf 1992). As a consequence, surviving wetlands in Lake Erie are of limited variety. According to Herdendorf (1992), “coastal marshes of western Lake Erie fall into three categories, depending on the type of protection for aquatic vegetation: (1) coastal lagoons behind barrier beaches, (2) estuarine tributary mouths, and (3) managed marshes protected by earthen and rip-rap dikes.” High winds may also adversely impact offshore spawning habitat. Busch et al. (1975) and, more recently, Roseman et al. (1996) reported that high storm winds washed walleye eggs off offshore reefs, or caused temperature reversals that delayed egg maturation, rendering them more vulnerable to predators or other mishap. 2.1.3 Lake levels Fluctuating water levels are a dominant natural hydrologic phenomenon on the Great Lakes. They constantly rejuvenate Great Lakes coastal wetlands and lead to their high and diverse productivity (Herdendorf 1990, 1992). Aquatic plant diversity is among the highest in Lake Erie (Jude and Pappas 1992). Short term fluctuations are due to wind or atmospheric pressure differences. They average 0.7 m. in the vicinity of the western basin islands, but may reach 2 m. Storm winds along the longitudinal axis of the lake have produced levels 3.1 m. above and 2.4 m. below Low Water Datum at Toledo (LWD for Lake Erie is 173.5 m. above the elevation at Rimouski, Quebec). A bathtub effect produces opposing swings at the northeast corner of the lake at Buffalo, NY. Turning to the seasonal fluctuations, monthly averages within a year have a range of 30 cm. (Lake Superior) to 58 cm. (Lake Ontario). The range is typically 36 cm on Lake Erie, but may be anywhere between 0.3 and 0.6 m. Long term (inter-annual) variations are due to volumetric changes and can be greater than seasonal variations. Erie’s highest recorded monthly

5

average, 174.9 m., occurred in June, 1986. The lowest, 173.0 m., occurred in February, 1936, for a total swing of almost 2 m. The effect of fluctuating lake levels on coastal wetlands has been quantified by a number of authors (e.g., Bloczynski et al. 1996, Bukata et al. 1988, Busch and Lewis 1982, Lyon et al. 1986). Details in responses differ from wetland to wetland. But there is agreement on general responses. High lake levels tend to kill emergent vegetation and create more open water and possibly submergent plant life within wetlands (see e.g., Jaworski et al. 1979). If high levels persist, the wetland may shift landward and reestablish itself largely as it was originally. However, this freedom rarely exists in Lake Erie, which is bordered by residential property, farmland, and other manmade barriers (e.g., see Jaworski and Raphael 1976). Lower lake levels allow emergent wetland vegetation to reestablish itself from seeds buried in exposed mud banks (Keddy and Reznicek 1986), though in some cases regrowth may require 3 to 5 years to occur (e.g., see Busch and Lewis 1982). The wetland may then also move lakeward, and woody plants may establish themselves up to the landward border. Patterson and Whillans (1985) emphasize that other human activities may create stresses that generate areal or compositional changes in coastal wetlands. When high lake levels combine with the high winds, their synergistic effect can be particularly damaging to wetlands. Jaworski et al. (1979) reported that “water levels and submarine slope appear to be factors controlling wave energy conditions” along some Great Lakes shorelines. “During higher water level conditions the wave energy is stored until the wave is nearer to the shoreline before breaking and expending its energy” (Jaworski et al. 1979). Barrier beaches that normally provide protection may be breached, allowing more of the wave energy to impact vegetation. The result may be erosion or burial of formerly protected wetlands. This type of phenomena worsened during the record high levels of the mid-1980s in North Maumee Bay where Woodtick peninsula was reduced to a narrow sliver or lay below water level in many places. Artificial dikes may have to be reinforced or repaired in order to maintain protection when lake levels are high. When Woodtick was breached, the artificial dikes of the Erie shooting and Fishing Club adjacent to the lagoon had to be reinforced because of erosion due to wave action. 2.1.4 Temperature Rather than being a natural physical process like winds or lake levels or flow rates, temperature, or thermal regime, is itself habitat for fish. Herdendorf et al. (1992) called it a master variable that affects all life processes of fish. Habitat that is otherwise suitable may be rendered less suitable or unsuitable due to temperature. Temperature determines when spawning occurs. It controls hatching and development of eggs, the timing of food availability, and growth rates. Nursery habitat is often warm, protected, near-shore waters. During adult life stages, temperature delineates habitat of one species from that of another (see e.g., Brandt et al. 1980). In Lake Erie during the summer, trout and whitefish inhabit the cold waters of the hypolimnion. Smelt prefer the cool mesolimnetic waters. Perch, shiners, walleyes, shad, and drum prefer the warmer surface waters above the thermocline. After the autumn overturn, a species that was limited to a particular depth might distribute itself throughout the water column. One hypothesis illustrates how temperature-driven phenomena could play an important role in determining the overall structure of the Lake Erie fish community. Alewives suffer high mortality during unusually cold winters (Eck and Wells 1987). The resulting large fluctuations in their population have a pronounced effect on Lake Erie’s trophic dynamics.

6

2.2 Climate Change Under a scenario of climate warming, the impacts on the Great Lakes ecosystem could be dramatic. Models predict that Lake Erie levels could drop by as much as almost 2 m (Hartmann 1990). Mortsch and Koshida (1996) suggest that for Great Lakes coastal wetlands “two of the most likely scenarios are a decline in the mean annual water level and a modification of the seasonal cycle.” A decline in mean annual water level would result from higher temperatures, and, thus, more evapotranspiration and less runoff. Possible modifications of the seasonal cycle are 1) a shorter period of low water in winter, 2) earlier rise of water level in spring, and 3) earlier onset of water level decline. We discuss possible consequences of such changes for tributary flows, lake levels, and temperature in the next three subsections. 2.2.1 Tributary flows For tributary-spawning species, extreme reductions in tributary flows could result in the uncovering of critical river reaches. The fish would be unable to get to these areas. If spawning does take place, the likelihood of uncovering and dessication of eggs might be high (Mortsch and Koshida 1996). With perhaps less extreme reductions, lower tributary flows might still slow the progress of larvae as they are passively transported downstream into tributary mouth nursery areas. As a consequence, they might not reach these food rich areas soon enough to survive. On the other hand, if spring water levels rise earlier, they might persist during spawning and nursery periods. The result of this could be higher production of young of year and enhanced growth and survivorship (Mortsch and Koshida 1996). 2.2.2 Lake levels Hartmann (1990) reports the results (derived by other authors) that a 20 cm lowering of Lake Michigan-Huron levels could affect 64% of all Great Lakes wetlands in the U.S.— especially, though not only, confined wetlands. “Even open shoreline wetland extents could be permanently reduced due to their direct lake level dependence, unsuitable offshore substrates, and steep offshore drop-offs, combined with a resulting reduction in seeds and rhizomes for colonization.” According to Mortsch and Koshida (1996), the result would be decreases in nearshore spawning and nursery grounds. Fish larvae, juvenile, and wetland-dependent fish would be stressed. Water quality would be impaired. However, modifications in the seasonal cycle that cause lower levels would also increase submergent vegetation and the species that prefer this kind of habitat. On the other hand, Meisner et al. (1987) suggest that if water levels were to drop slowly enough, wetland regions might be able to adapt to the changing physical structure of the nearshore zone. Then areas and composition of wetlands might remain the same. Lee et al. (1997) have generated Geographic Information Systems (GIS)-based estimates of the altered shoreline for western Lake Erie under climate change. For a changed climate resulting from a doubling of atmospheric CO2, they calculated that “water volumes would decrease by as much as...20% in Erie, and surface areas would decrease significantly resulting in significant losses of wetlands, freshwater estuaries and embayments.” The surface area of Lake Erie would decrease by 4%. This would lead, for example, to a 32% reduction in the surface area of Inner Bay at Long Point on the north shore of Lake Erie. We note, however, that their analysis did not consider the ability of emergents to colonize exposed substrate, or of changes in geomorphology that might lead to lakeward migration of estuarine wetlands. 2.2.3 Temperature As lake and tributary waters warm, Meisner et al. (1987) report that the range of large and smallmouth bass may be extended northward. That of whitefish and trout will be contracted

7

northward. They note that warmer winters point to greater flourishing of alewife, and decline in whitefish. DeAngelis and Cushman (1990) state that cohort-based or individual-based fish population models (IBMs) appear to best capture the consequences of climate change on fish populations. An IBM is used to show that if threadfin shad hatched early—a circumstance that climate change might induce—they might become too large for their main predator, the largemouth bass, to eat. Magnuson et al. (1990) find that favorable habitats for cool and warmwater fish in Lake Erie would increase. Hartmann (1990) reports that if water temperatures stay above 4 °C year round, regular fall and winter turnovers could become sporadic, dependent on temperature gradients and the ability of the wind to induce turbulent mixing. Hypolimnion chemistry may be altered due to the depletion of oxygen, releasing nutrients and metals from lake sediments as a result. 2.3 Management The last 100 years have seen wide-scale conversion of wetlands for various human uses (Kusler et al. 1994). This has been especially true of Lake Erie coastal wetlands (impact M3 in Table 1 above). “One of the most significant environmental changes in Lake Erie was the draining and filling of the coastal marshes at the western end of the lake. Prior to 1850, an extensive coastal marsh and swamp system, consisting largely of an area known as the Black Swamp, covered an area of about 4,000 km2 between Vermillion, Ohio, and the mouth of the Detroit River and extended up the Maumee Valley into Indiana. This area was largely cleared, drained, and filled to provide agricultural land, lumber, and transportation routes, so that at present only about 100 km2 of coastal marshland remains.” (Herdendorf 1992) Today, most of the remaining marshland is diked to protect against erosion. Busch and Lewis (1982) report that in 1981, 18,236 ha. out of 24,312 ha. of total Lake Erie wetlands within the U.S. were protected either naturally or artificially. This number (generated by the International Lake Erie Regulation Study Board) is based on conditions before the record high water level conditions of 1985-1986, so it is likely that since then more wetlands have been diked. For example, in 1993 a dike was constructed at Metzger Marsh on the south shore of western Lake Erie, where “years of high water levels [had] eroded the barrier beach that once protected [it] from the wave energy of the open lake. What remained was a largely unproductive open water area with shores eroding at the rate of 5 to 10 feet per year” (Bloczynski et al. 1996). The dike constructed in this case was unique in that it was an open dike, and provided for the passage of fish that might attempt to use it for spawning and/or nursery habitat. Most other diked wetlands are closed to the lake, and water levels are managed to produce aquatic plants used by waterfowl. In this latter case water level management decisions often conflict with the use of these regions for spawning and nursery grounds (see e.g., Bloczynski et al. 1996, Herdendorf 1987, 1992). Other management problems are that impoundments limit the upstream reach of spawning fish on certain tributaries, such as walleye on the Maumee (impact M1 in Table 1). Also shoreline development and new farmland areas have still gone forward at the cost of wetland habitat (impact M3). On the other hand, management measures have potential to improve fish and wildlife habitat. Kelso and Hartig (1995) summarize a wide range of such projects. They include recreating spawning substrate (recall impact M4 in Table 1 and efforts at Current River, Lake Superior and Fox River, Lake Michigan). Geiling et al. (1996) report, however, that the subsequent adult population of walleye in Current River has not been demonstrated to have

8

clearly increased. Minns et al. (1996b) caution that all ecological restoration efforts are experiments. They should be designed not just because there is a need, but with care to incorporate sound science. Recreation and creation of new wetlands habitat has also been attempted. From 19831984 the Big Island Wetland was built on Sandusky Bay, Ohio, to mitigate the loss of a nearby shoreline region to development. It involved construction of a diked wetland and rehabilitation of a wetland area to pre-1950 conditions. The new wetland measured 38 ha and the old 6.8 ha. It was very successful and even won an award from the Ohio Department of Natural Resources. We note that unlike Metzger Marsh, Big Island Wetland permitted no access to fish. As a final example, planners are working in Thunder Bay, on the North shore of Lake Superior to diversify fish habitat in a homogenous floodway (two rivers had been replaced with a single straight channel to avoid flooding of adjacent residential areas). This project, as are others, is taking place under the auspices of one of the Great Lakes Remedial Action Plans. 3. MODELING METHODOLOGY Our goal is to link hydrology to habitat supply to a fish population, LEEM. We first discuss our general approach to modeling the impact of habitat supply limitations in Section 3.1. Then we discuss modeling the dynamics of wetland habitat supply in Section 3.2 by means of two models. 3.1 Modeling habitat supply limits on fish populations LEEM is a simulation model of the Lake Erie fish population that represents 14 key species. It has an annual time step (Locci and Koonce 1997). “Functional characteristics of the model are based on major ecological principles. On the whole, it is a predator-prey model constrained by energy flow and nutrient availability. The model has three major components: [a] nutrient and productivity submodel, a submodel describing the dynamics of age-structured populations of fish, and a submodel of zooplankton, zoobenthos, and zebra mussels.” (Koonce et al. 1996b) According to Minns et al. (1996a), habitat availability exerts its influence on survival through a saturation curve. “In each life-stage, the density-dependent mechanisms are assumed to follow a saturation curve. The basic form of the saturation curves uses actual suitable area scaled against predicted ideal requirements to predict proportional achievement of population process rates key to each life stage: actual/(actual + required) declines as the required space increases relative to actual supply.” Minns et al. (1996a) uses a general functional response curve to scale the shape of the saturation curve: actual b Response = , (1) actual b + a ⋅ required b where a is a scaling factor for the required area and b is a power used to alter the shape of the curve. We adopt this general relationship in LEEM. First, we consider the effect of habitat supply during spawning and nursery life stages. Our hypothesis is that mortality influences of habitat occur primarily during recruitment. To apply the approach of Minns et al. (1996a) to LEEM, we assume that the density-dependence of recruitment on habitat will be a function of the actual area available per individual, i.e., the inverse of the density of the first life-stage, which spans egg deposition, hatching, and early larval development. For each species, we assume that we can estimate the spawning and nursery habitat area and their suitability. Actual availability of habitat is thus

9

HSI , (2) N where HSI , Habitat Suitability Index, is the cumulative area of suitable habitat weighted by a quality index and N is the number of individuals using the habitat. Assuming that an optimum exists for the area per individual, the optimum number of individuals becomes: HSI , (3) N′ = R2 where R 2 = required and we assume a = 1 (from Equation 1). Based on Equations 2 and 3, we can modify Equation 1 to obtain actual =

Response =

=

b

HSI HSI

b

N 1 b

N + 2 ⋅ HSI

b

N′

.

(4)

b

N b

+ 2 ′ N N With the formulation of the saturation curve in Equation 4, N ′ 2 becomes the abundance at which Response decreases by half. As incorporated into LEEM, the equation is 1 HSIExp Si Numberi ,1 = SVi ⋅ Si ⋅ ENVAi ,TI ⋅ , (5) 1 1 + HSINumiHSIExp SiHSIExp where HSIExp = b and HSINumi is N i′ 2 for the ith fish species. The terms SVi , Si , and ENVAi ,TI account for egg survival rate, number of eggs, and environmental variability, respectively. Environmental variability is a time-dependent random variable to simulate the effects of environmental variability on recruitment success. There may also be habitat supply impacts during juvenile and later adult life stages. The second way we can include habitat is through habitat distribution zones that are included with parameters for all species. These habitat preferences enter into the feeding relations. At present, we do not explicitly include amounts of habitat in each zone, but we could expand the definition of the zones if necessary. For example, we could just as easily redefine the habitat zones to include thermal habitat. This way of treating habitat for age 1 and older fish allows the opportunity to consider effects of the availability of adult and juvenile habitat on density dependent feeding interactions. The effect of habitat here is thus on growth. Finally, the lower trophic level constraints on system productivity enable us to include system-wide habitat features associated with nutrient loading and eutrophication. 3.2 Modeling wetland dynamics A general physical categorization of wetland habitat (from approximately the water line lakeward) is emergent vegetation, floating and submergent vegetation, then open water. In the following subsection we discuss the model of Painter and Keddy (1992). In the subsection following that we discuss an application of the hemi-marsh index of wetland health to Metzger marsh, Lake Erie (Bloczynski et al. 1996). We use these models to illustrate how wetland supply can be made dynamic for Lake Erie. 1

10

3.2.1 Wetland model 1 Painter and Keddy (1992) developed a conceptual model that predicts changes in emergent wetland area in response to fluctuating water levels. It takes into account seasonal and previous years’ water levels. They state that “emergent plants respond to water level events as well as competitive forces between themselves and other plant communities such as trees and shrubs which are upslope and aquatic plants which are downslope.” “The landward upper edge of the marsh is determined by high water events. The average water level for the three months surrounding the peak of the growing season was chosen as the critical time period which would move the woody-plant/marsh transition upslope....The upper edge moves inland in response to a single season of high water but is forced back downslope slowly by the reinvading trees and shrubs when lower water levels return. The competitive exclusion of marsh by trees and shrubs begins slowly and escalates over time as the marsh plants are excluded by shading from the tree canopy....” “The lakeward lower edge of the marsh is determined by low water events. The lower edge moves lakeward in response to a single season of low water as muds become exposed and annual marsh plant seeds sprout....When high water returns, the lower edge moves upslope after a few years of flooding.” This model was initially applied to Lake Ontario. The peak of the growing season there occurs in June, so the average water level from May, June, and July were used to determine the landward woody plant/marsh transition. For the lower edge transition from emergent to submergent vegetation, the mean water level for September was chosen. An 18 year delay for the upper and a 3 year delay for the lower edge were chosen. The predicted marsh response varied by +/- 10% among various combinations of delays (e.g., 15 yrs/2 yrs vs. 20 yrs/4 yrs). 3.2.2 Wetland model 2 Wetlands are used by fish, waterfowl, and other mammals. Many diked Lake Erie wetlands are managed for waterfowl production (Herdendorf 1987, 1992; see also Prince et al. 1992). This mixed function of wetlands should be considered when we judge its quality or suitability. First, Weller and Spatcher (1965) and later, several authors (see e.g., Kelso and Hartig 1995, Mortsch and Koshida 1996) suggest 50:50 as an ideal mix between vegetated and open water habitat—i.e., the hemi-marsh condition. The utility of a Metzger marsh in Lake Erie based on this condition was formulated by Bloczynski et al. (1996) as follows: 4OE U= , (6) O+E where O is open water and E is emergent vegetation within the wetland. U is maximized when ratio of open to emergent area within the wetland is 50:50 and is proportional to the size of the wetland. By developing a relationship between water level and open water area, Bloczynski et al. (1996) then related utility U to water level. 4. PRELIMINARY ILLUSTRATIONS In anticipation of detailed habitat supply information (i.e., near-shore bathymetry, substrate characterization, wetland community characterization), we present some preliminary illustrations. In Section 4.1 we illustrate the significance of habitat supply index on Lake Erie fish population. In Section 4.2 we illustrate how the habitat supply index could change over time—i.e., the dynamics of habitat supply.

11

4.1 Effects of habitat limitations on fish community structure 4.1.1 Methods To explore the combined effect of productivity and habitat suitability on Lake Erie fish biomass in LEEM, we varied these two quantities. We varied productivity with five scalar multiples (0.5, 1.0, 1.5, 2.0 and 2.5) of historical phosphorus loading. This range of variation of phosphorus loading resulted in a 5-fold range of primary productivity. We also allowed habitat suitability to vary over five levels with scalar multiples (0.3, 0.5, 0.75, 1.0, 1.5). 4.1.2 Results Total fish biomass varied with changing productivity and habitat suitability (Figure 2). The model simulations suggest much greater sensitivity of fish community structure to variation of habitat suitability than to variation of phosphorus loading. The lowest annual mean fish biomass was obtained with the lowest phosphorus loading, 0.5, and highest habitat suitability, 1.5. The highest annual mean fish biomass was obtained with the highest phosphorous loading, 2.5, and lowest habitat suitability, 0.3. This initially surprising result can be explained as follows. The model simulations indicate that there is a negative association between the proportion of top predator biomass (walleye) and the total fish biomass in the system (Figure 3). As the proportion of top predator biomass increases the total fish biomass decreases. It also indicates that low relative abundance of walleye raises the sensitivity of overall fish biomass to changes in nutrient loading. Loss of habitat suitability affects mostly the walleye population. This increase in the total fish biomass when walleye abundance is low comes from increase of planktivorous fish such as shiners and gizzard shad. 4.2 Dynamics of habitat supply We used the model of Chao et al. (1996) to generate Lake Erie water levels for three scenarios. The first is the historical Great Lakes climate scenario for the sixty years that began in 1928 and ended in 1987. The second scenario is a simulation based on the Geophysical Fluid Dynamics Lab model of a warming transient starting from a normal climate and extending to one in which the atmosphere has double the atmospheric CO2 (generated based on the assumptions of Chao and Hobbs 1996). Thirdly, we generated a lake level regulation scenario. Figures 4 (Wetland Model 1) and 5 (Wetland Model 2) illustrate the behavior of the wetland indices described above under these three scenarios. Wetland Model 1 is the NWRI Index and Wetland Model 2 is the Hemi-marsh index. In Figure 4 Wetland Model 1 has been applied to Lake Erie levels. The climate warming scenario leads to the lowest lake levels and the greatest vertical extent of emergent vegetation. The regulated lake level scenario damps out the impact of the low levels of the 1960s. Figure 5 shows the behavior of Wetland Model 2. With a fixed supply of habitat, the suitability of the habitat varies as the wetland approaches and departs from the ideal hemi-marsh condition. The effect of the low levels of the 1930s and 1960s, as well as the high levels of the early 1970s and mid-1980s can be clearly seen. The ideal lake level for Metzger Marsh was 174.35 m, and the regulated lake level scenario performed best on average. 5.0 CONCLUSIONS Lake Erie fish community managers would benefit from greater understanding of the impact of habitat supply limits on fish population composition. In order to proceed with this approach detailed digital bathymetry is required which could be manipulated with a Geographic Information system (GIS). Also, more mapping of actual areas of different habitat type in Lake Erie is important. This work is scheduled for early next year.

12

ACKNOWLEDGMENT Support for this research was provided by USEPA, Grant R825150010, and NSF, Grant 92ES23780. REFERENCES Bloczynski, J. A., Bogart, W. T., Hobbs, B. F., and Koonce, J. F. 1996. “Irreversible investment in wetlands preservation: making optimal decisions under uncertainty.” Unpublished paper. Case Western Reserve University. Brandt, S. B., Magnuson, J., Crowder, L. 1980. “Thermal habitat partitioning by fishes in Lake Michigan.” Can. J. Fish. Aquat. Sci. 37: 1557-1564. Bukata, R. P., Bruton, J. E., Jerome, J. H. 1988. “An evaluation of the impact of persistent water level changes on the areal extent of Georgian Bay/North Channel marshlands.” Environmental Management 12(3): 359-368. Busch, W-D. N., Scholl, R. L., and Hartman, W. L. 1975. “Environmental factors affecting the strength of walleye (Stizostedion vitreum vitreum) year-classes in western Lake Erie, 19601970.” J. Fish. Res. Bd. Can. 32: 1733-1743. Busch, W-D. N. and Lewis, L. M. 1982. “Some Great Lakes wetland responses to water level variations and the use of these wetlands by fish.” Appendix VII. Great Lakes Fishery Committee, Gananoque, Ontario, pp. 39-59. Chao, P. T. and Hobbs, B. F. 1996. “Decision analysis of shoreline protection under climate change uncertainty.” Water Resources Research. 33(4): 817-829. Chao, P. T., Venkatesh, B. N., and Hobbs, B. F. 1996. The CWRU Great Lakes climate change impact modeling system. Institute for Water Resources, US Army Corps of Engineers, Ft. Belvoir, VA. DeAngelis, D. L. and Cushman, R. M. 1990. “Potential Application of models in forecasting the effects of climate changes on fisheries.” Trans. of the Amer. Fish. Soc. 119: 224-239. Eck, G. W. and Wells, L. 1987. “Recent changes in Lake Michigan’s fish community and their probable causes, with emphasis on the role of alewife (Alosa pseudoharengus).” Can. J. Fish. Aquat. Sci. 44(Suppl. 2): 53Geiling, W. D., Kelso, J. R. M., and Iwachewski, E. 1996. “Benefits from incremental additions to walleye spawning habitat in the Current River, with reference to habitat modification as a walleye management tool in Ontario.” Can. J. Fish. Aquat. Sci. 53(Suppl. 1): 79-87. Hartman, W. L. 1973. “Effects of exploitation, environmental changes, and new species on the fish habitats and resources of Lake Erie.” Great Lakes Fishery Commission Technical Report No. 22. Hartmann, H. 1990. “Climate change impacts on Laurentian Great Lakes levels.” Climatic Change. 17: 49-67. Herdendorf, C. E. 1987. The Ecology of the Coastal Marshes of Western Lake Erie: A Community Profile. Biol. Rep. 85 (7.9) U.S. Fish & Wildlife Service: Washington D. C. Herdendorf, C. C. 1990. “Great Lakes Estuaries.” Estuaries. 13(4): 493-503. Herdendorf, C. E. 1992. “Lake Erie coastal wetlands: An overview.” J. Great Lakes Research. 18(4): 533-551. Herdendorf, C. E., Hakanson, L., Jude, D. J., Sly, P. G. 1992. “A review of physical and chemical components of the Great Lakes: a basis for classification and inventory of aquatic habitats.” Busch, W. D. and Sly, P. G., eds. The Development of an Aquatic Classification system for lakes. CRC Press, Inc.

13

Herdendorf, C. E. and Krieger, K. A. 1988. “Overview of Lake Erie and its Estuaries within the Great Lakes Ecosystem.” Lake Erie and its Estuarine systems: Issues, Resources, Status, and Management. Krieger, K. A., ed. Proceedings of a NOAA Estuary-of-the-Month Seminar. May 4, 1988. Washington D. C. Jaworski, E. and Raphael, C. N. 1976. “Modification of coastal wetlands in southeastern Michigan and management Alternatives.” Michigan Academician. 8(3): 303-317. Jaworski, E., Raphael, C. N., Mansfield, P. J., and Williamson, B. B. 1979. “Impact of Great Lakes water level fluctuations on coastal wetlands.” Dept. of Geography-Geology, Eastern Michigan University, Ypsilanti. Jude, D. J. and Pappas, J. 1992. “Fish utilization of Great Lakes coastal wetlands.” J. Great Lakes Research. 18(4): 651-672. Keddy, P. A. and Reznicek, A. A. 1986. “Great Lakes vegetation dynamics: The role of fluctuating water levels and buried seeds.” J. Great Lakes Research. 12(1): 25-36. Kelso, J. R. M. and Hartig, J. H. 1995. “Methods of Modifying Habitat to Benefit the Great Lakes Ecosystem.” CSTI Occas. Pap. 1: 294 p. Koonce, J. F., Busch, W.-D., Czapla, T. 1996a. “Restoration of Lake Erie: Contribution of water quality and natural resources management.” Can. J. Fish. Aquat. Sci. 53(Suppl. 1): 105-112. Koonce, J. F., Locci, A. B., Knight, R. 1996b. “Contributions of fishery management to changes in walleye and yellow perch populations of Lake Erie.” W. W. Taylor, ed. Great Lakes Fishery Policy and Management: A Binational Perspective. Kusler, J. A., Mitsch, W., Larson, J. 1994. “Wetlands.” Scientific American. pp. 64B-70. Lane, J. A., Portt, C. B., Minns, C. K. 1996a. “Nursery Habitat Characteristics of Great Lakes Fishes.” Canadian Manuscript Report of Fisheries and Aquatic Sciences No. 2338. Lane, J. A., Portt, C. B., Minns, C. K. 1996b. “Adult Habitat Characteristics of Great Lakes Fishes.” Canadian Manuscript Report of Fisheries and Aquatic Sciences No. 2358. Lane, J. A., Portt, C. B., Minns, C. K. 1996c. “Spawning Habitat Characteristics of Great Lakes Fishes.” Canadian Manuscript Report of Fisheries and Aquatic Sciences No. 2368. Lee, D. H., Moulton, R., and Hibner, B. A. 1997. “Climate change impacts on western Lake Erie, Detroit River, and Lake St. Clair water levels.” Great Lakes-St. Lawrence Basin Project. GLERL Contribution #985. Locci, A. B. and Koonce, J. F. 1997. “A theoretical analysis of food web constraints on walleye dynamics in Lake Erie.” in press. Lyon, J. G., Drobney, R. D., and Olson, C. E. 1986. “Effects of Lake Michigan water levels on wetland soil chemistry and distribution of plants in the straits of Mackinac.” J. Great Lakes Research. 12(3): 175-183. Magnuson, J. J., Meisner, J. D., and Hill, D. K. 1990. “Potential changes in the thermal habitat of Great Lakes fish after global climate warming.” Transactions of the American Fisheries Society. 119: 254-264. Mion, J. B., Stein, R. A., and Marschall, E. A. 1998. “River discharge drives survival of larval walleye.” Ecological Applications. 8(1): 88-103. Meisner, J. D., Goodier, J. L., Regier, H. A., Shuter, B. J., Christie, W. J. 1987. “An assessment of the effects of climate warming on Great Lakes basin fishes.” J. Great Lakes Research. 13(3): 340-352.

14

Minns, C. K., Randall, R. G., Moore, J. E. Cairns, V. W. 1996a. “A model simulating the impact of habitat supply limits on northern pike, Esox lucius, in Hamilton Harbor, Lake Ontario.” Can. J. Fish. Aquat. Sci. 53(Suppl. 1): 20-34. Minns, C. K., Kelso, J. R. M., and Randall, R. G. 1996. “Detecting the response of fish to habitat alterations in freshwater ecosystems.” Can. J. Fish. Aquat. Sci. 53(Suppl. 1): 403414. Mortsch, L. and Koshida, G. 1996. “Effects of fluctuating water levels on Great Lakes coastal wetlands.” Mortsch, L. and Mills, B., eds. Great Lakes-St. Lawrence Basin Project Progress Report #1 Painter, S. and Keddy, P. 1992. “Conceptual emergent marsh response to water level regulation.” Unpublished paper. NWRI, Environment Canada. Patterson, N. J. and Whillans, T. H. 1985. “Human interference with natural water level regimes in the context of other cultural stresses on Great Lakes wetlands.” In Coastal Wetlands, ed. H. H. Prince and F. M. D’Itri, pp. 209-239. Chelsea, MI: Lewis Publishers Inc. Prince, H. H., Padding, P. I., and Knapton. 1992. “Waterfowl use of Laurentian Great Lakes.” J. Great Lakes Research. 18(4):673-699. Roseman, E. F., Taylor, W. W., Hayes, D. B., Haas, R. C., Knight, R. L., Paxton, K. O. “Walleye egg deposition and survival on reefs in Western Lake Erie (USA).” Ann. Zool. Fennici 33: 341-351. Weller, M. W. and Spatcher, C. E. 1965. “Role of habitat in the distribution and abundance of marsh birds.” Iowa Agricultural and Home Economics Exp. Station Special Report 43, Ames, Iowa, 31 p.

15

Exploratory Modeling Framework

GCM Precip. Scenario

oC,

Tributaries, Great Lakes Hydrologic System (GLERL, CWRU STELLA Models)

Hydrologic Management

Flows, Levels, Winds, Temperatures

Habitat

Ecological Community, Population System Indices (IJC/CWRU Lake Erie Ecological Model)

Habitat Quantity, System Quality (HSI Models)

Habitat Management

Pollution, Nutrient, & Fisheries Management

Figure 1

8.00

Total Mean Annual Fish Biomass

7.00 6.00 5.00 4.00 3.00 2.5

2.00

2 1.5

1.00 1 0.00 0.3

0.5

0.5

0.75

1

Habitat Suitability Figure 2

16

1.5

Phosphorous Loading

7.00

Hi P

6.00 5.00

(108 Kg)

Total Mean Annual Fish Biomass

8.00

Lo P

Lo HSI

4.00

Hi P 3.00

Lo P

2.00

Hi HSI

1.00 0.00 0.0000

0.0020

0.0040

0.0060

0.0080

0.0100

Proportion of Walleye

Figure 3

17

0.0120

NWRI index for Lake Erie wetlands 1.6

1.4

Meters vertical extent

1.2

1 2xCO2 0.8

1xCO2 Regul LL

0.6

0.4

0.2

0 1940

1945

1950

1955

1960

1965 Year

Figure 4

18

1970

1975

1980

1985

1990

Hemi-Marsh index for Metzger Marsh, Lake Erie 1.2

1

0.8

Index

2xCO2 1xCO2

0.6

Regul LL

0.4

0.2

0 1920

1930

1940

1950

1960 Year

Figure 5

19

1970

1980

1990