nearshore biological community patterns related to lake ...nearshore biological community patterns...

Prepared by:Reuben R. Goforth, Ph.D. and Stephanie M. Carman

Michigan Natural Features InventoryP.O. Box 30444

Lansing, MI 48909-7944

For:Michigan Department of Environmental Quality

Office of the Great Lakes and Coastal Zone Management Unitand

National Oceanographic and Atmospheric Administration

January 2005

Report Number 2005-01DEQ-CZM Project #03-6217-08

Nearshore Biological Community PatternsRelated to Lake Michigan Shorelines

Nearshore Biological Community PatternsRelated to Lake Michigan Shorelines

Prepared by:Reuben R. Goforth, Ph.D.a and Stephanie M. Carmanb

a Michigan Natural Features InventoryP.O. Box 30444

Lansing, MI

bConservation Services DivisionNew Mexico Department of Game and Fish

PO Box 25112Santa Fe, NM 87504

For:Michigan Department of Environmental Quality

Office of the Great Lakes and Coastal Zone Management Unitand

National Oceanographic and Atmospheric Administration

January 2005

Report Number 2005-01

DEQ-CZM Project #30-6217-08

Nearshore Biological Community Patterns Page-i

Table of ContentsINTRODUCTION .......................................................................................................................................................... 1

METHODS ...................................................................................................................................................................... 3Study Sites ........................................................................................................................................................... 3Physicochemical Habitat and Biological Surveys .............................................................................................. 3Spatial Data ........................................................................................................................................................ 4Statistical Analysis .............................................................................................................................................. 5

RESULTS ........................................................................................................................................................................ 5Summary .............................................................................................................................................................. 5Local Shoreline Type Analyses ............................................................................................................................ 5Spatial Analyses ................................................................................................................................................. 13

DISCUSSION ................................................................................................................................................................ 24

SUMMARY ................................................................................................................................................................... 27

ACKNOWLEDGEMENTS ......................................................................................................................................... 27

LITERATURE CITED................................................................................................................................................. 27

COLOR PLATES ......................................................................................................................................................... 31

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List of TablesTable 1. Sample dates for nearshore areas surveyed in Lake Michigan during the summers of 2003 and 2004......... ....6

Table 2. Mean (±1 S.E.) physicochemical measures for nearshore areas adjacent to modified and intact shorelines ofLake Michigan. Water temperature, dissolved oxygen, conductivity, and pH measures are based on measurementstaken at 2.0 m water depth along the 3.0 m depth contour of study sites....................................................................6

Table 3. Mean (±1 S.E.) benthic macroinvertebrate densities (number of individuals/m2) for nearshore areas adjacentto modified and intact shorelines of Lake Michigan. Samples were collected during summers 2003 and 2004using a Petite Ponar® grab (0.023m2). Study sites include St. Joseph (SJ), Pioneer Park (PP), Whitehall (WH),Silver Lake (SL), Mizpah Park (MP), South Holland (SH), Pere Marquette (PM), and south of the Ludingtonpump storage station (LU). Taxonomic groups include the dipteran Chironomidae (sub-families Chironominae(Chiro); Orthocladiinae (Ortho); Podonominae (Podo); Prodiamesinae (Prodi); and Tanypodinae (Tanypod); andthe tribe Tanytarsini (Tanytar)), the dipteran Ceratopogonidae (Cerato), oligochaete worms (Oligo), water mites(Mites), dreissenid mussels (Dreis), and total benthic macroinvertebrate (Tot_Ben).................................................7

Table 4. Mean densities (±1 S.E.) of native and non-native zooplankton taxa observed in vertical plankton towscollected along the 3 m depth contour of nearshore waters along Lake Michigan shorelines, including SaintJoseph (SJ), Pioneer Park (PP), Whitehall (WH), Silver Lake (SL), Mizpah Park (MP), South Holland (SH),Pere Marquette (PM), and Ludington (LU).. ............................................................................................................8

Table 5. Mean (±1SE) catch per unit effort (CPU) measures for fish species observed in beach seines during surveysof shallow water fish communities associated with Great Lakes shoreline areas. Study sites include St. Joseph(SJ), Pioneer Park (PP), Whitehall (WH), Silver Lake (SL), Mizpah Park (MP), South Holland (SH), PereMarquette (PM), and south of the Ludington pump storage station (LU)..................................................................9

Table 6. Mean (±1SE) catch per unit effort (CPU) measures for fish species observed in gill nets during surveys ofnearshore fish communities associated with eight Great Lakes shoreline areas. Study sites include St. Joseph(SJ), Pioneer Park (PP), Whitehall (WH), Silver Lake (SL), Mizpah Park (MP), South Holland (SH), PereMarquette (PM), and south of the Ludington pump storage station (LU)..................................................................9

Table 7. Percentage of 1.0 km shoreline buffers comprised of urban land uses along the eastern Lake Michiganshoreline. Buffers include a 5 km-long shoreline reach encompassing each study site (local), and 10 km-,25 km-, 50 km-, and 100 km-long shoreline reaches updrift from each study site...................................................16

Table 8. Number of shore structures within 1.0 km shoreline buffers along the eastern Lake Michigan shoreline.Buffers include a 5 km-long shoreline reach encompassing each study site (local), and 10 km-, 25 km-,50 km-, and 100 km-long shoreline reaches updrift from each study site................................................................16

Table 9. Results of linear regressions between biological community measures and the spatial extent of urban landuse (i.e., % buffer area as urban) within 1-km shoreline buffers defined at multiple spatial scales. Buffersincluded a 5 km shoreline encompassing the study site (local), and 10 km-, 25 km-, 50 km-, and 100 km-longshorelines updrift from the study sites. Biological community measures are defined in the Methods section ofthe text. Statistically significant relationships between biological community parameters and the number ofshore structures within a given buffer context are highlighted in gray.....................................................................17

Table 10. Results of linear regressions between biological community measures and the number of shorelinestructures within 1-km shoreline buffers defined at multiple spatial scales. Buffers included a 5 km shorelineencompassing the study site (local), and 10 km-, 25 km-, 50 km-, and 100 km-long shorelines updrift from thestudy sites. Biological community measures are defined in the Methods section of the text. Statisticallysignificant relationships between biological community parameters and the number of shore structures withina given buffer context are highlighted in gray...........................................................................................................18

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List of FiguresFigure 1. Study site locations along the eastern shore of Lake Michigan. ....................................................................... 3

Figure 2. Mean (± 1 S.E.) total densities of A) benthic macroinvertebrates (number of individuals/m2, andB) zooplankton (number of individuals/m3, segregated by shoreline type (modified and intact) for samplescollected along the 3 m depth contour of eastern Lake Michigan during summer 2003 and 2004.........................10

Figure 3. Mean (± 1 S.E.) densities of benthic macroinvertebrate taxonomic groups segregated by shoreline type(modified and intact) for samples collected along the 3 m depth contour of eastern Lake Michigan during summer2003 and 2004. Taxonomic groups include the chironomid subfamilies Chironominae (A), the Orthocladiinae(B), and Tanypodinae (C) and oligochaete worms (D)............................................................................................11

Figure 4. Mean (± 1 S.E.) densities of zooplankton taxonomic groups segregated by shoreline type (modified andintact) for samples collected along the 3 m depth contour of eastern Lake Michigan during summer 2003 and2004. Taxonomic groups include Cyclopoida (A), Calanoida (B), Cladocera (C) and Rotifera (D). ..................... 12

Figure 5. Mean (± 1 S.E.) catch per unit effort (CPU) for shallow water fish captured in beach seine hauls at sitessegregated by shoreline type (modified and intact) in eastern Lake Michigan during summer 2003. Individualspecies at >3 study sites include Alosa pseudoharengus (alewife, Alew), Fundulus diaphanus (banded killifish,Baki), Rhinichthys cataractae (longnose dace, Lodo), and Neogobius melanostomus (round goby, Rogo). Group-ings include overall shallow water piscivores (SWPis), planktivores (SWPlk), benthivores (SWBen), insectivores(SWIns), native fish (SWNat) and introduced fish (SWInt).....................................................................................14

Figure 6. Mean (± 1 S.E.) catch per unit effort (CPU) for nearshore fish captured in gill net hauls at sites segregatedby shoreline type (modified and intact) in eastern Lake Michigan during summer 2003. Individual species andfamilies at >3 study sites include Aplodinotus grunniens (freshwater drum, Frdr), Dorosoma cepedianum (gizzardshad, Gish), catostomids (Cato), salmonids (Salm), and percids (Perco). Groupings include overall nearshorepiscivores (SWPis), planktivores (SWPlk), benthivores (SWBen), native fish (SWNat) and introduced fish(SWInt). ................................................................................................................................................................... 15

Figure 7. Relationships between A) total shallow water fish catch per unit effort (SWTot) and urban land use withinthe 10 km updrift landscape context, B) shallow water planktivorous fish CPU (SWPlk) and urban land usewithin the 50 km updrift landscape context, C) shallow water native fish CPU (SWNat) and urban land usewithin the 10 km updrift landscape context, and D) shallow water introduced species CPU (SWInt) and urbanland use within the 10 km updrift landscape context. .............................................................................................. 19

Figure 8. Relationships between A) total shallow water fish catch per unit effort (SWTot) and the number of shorestructures within the 100 km updrift landscape context, and B) total nearshore fish catch per unit effort and thenumber of shore structures within the 50 km updrift landscape context. ................................................................ 20

Figure 9. Relationships between A) shallow water insectivorous fish catch per unit effort (SWIns) and the number ofshore structures within the 100 km updrift landscape context, B) shallow water benthivorous fish CPU (SWBen)and the number of shore structures within the 100 km updrift landscape context, C) shallow water native fish CPU(SWNat) and the number of shore structures within the 100 km updrift landscape context, and D) shallow waterintroduced species CPU (SWInt) and the number of shoreline structures within the 10 km updrift landscapecontext. ..................................................................................................................................................................... 21

Figure 10. Relationships between A) nearshore piscivorous fish catch per unit effort (NSPis) and urban landusewithin the 10 km updrift landscape context, and B) nearshore planktivorous fish catch per unit effort and urbanland use within the 100 km updrift landscape context. ............................................................................................ 22

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List of Figures (cont.)Figure 11. Relationships between A) nearshore piscivorous fish catch per unit effort (NSPis) and the number of

shore structures within the 100 km updrift landscape context, B) nearshore benthivorous fish CPU (NSBen)and the number of shore structures within the local landscape context, C) nearshore native fish CPU (NSNat)and the number of shore structures within the 100 km updrift landscape context, and D) nearshore introducedspecies CPU (NSInt) and the number of shoreline structures within the 50 km updrift landscape context............23

List of Color PlatesPlate 1. An example of a modified shoreline near Saint Joseph, Michigan, with commerical land use and extensive

shore structure development. Loss of vegetation on areas of the bluff have caused high levels of erosion andsoil loss. .................................................................................................................................................................... 32

Plate 2. An example of a largely intact shoreline near Ludington, Michigan. ................................................................ 32

Plate 3. Removal of benthic samples from the Petite Ponar dredge prior to preservation in ethanol. ............................ 33

Plate 4. Deployment of the zooplankton net to collect vertical plankton tows at the 3.0 m depth contour of study sites. ......................................................................................................................................................................... 33

Plate 5. Zebra mussel clusters and individuals attached to a gastropod shell and leaves of Vallisneria americanafound in the drowned river mouth of the Pere Marquette River where it joins with Lake Michigan. ..................... 34

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INTRODUCTION

Nearshore zones play key roles in Great Lakesecology. They link terrestrial and aquatic environments,facilitating the exchange of energy and materials betweencoastal and pelagic ecosystems. They also providespawning, rearing, foraging, and migratory habitatsessential for most Great Lakes fishes, including manyrecreationally and commercially important species(Goodyear et al. 1984; Lane et al. 1996a; Lane et al.1996b). Other taxa, such as benthic invertebrates,zooplankton, and non-game fish species, are alsocharacteristic of nearshore zones (e.g., Jude and Tesar1985; Evans 1986; Thayer et al. 1997; Madenjian et al.2002; Dettmers et al. 2003) where they support GreatLakes fisheries and contribute to processes that supportother ecosystem services, such as potable water supplies(Daily et al. 1997). Nearshore zones are therefore ofmuch greater significance than their comparativelylimited spatial extent would suggest (Goforth andCarman in press). However, they have been the subjectof comparatively few studies (e.g., Jude and Tesar 1985;Brazner and Beals 1997; Brazner 1997; Garza andWhitman 2004; Goforth and Carman in press), and theirecology and dynamics remain poorly understood,especially in exposed shore areas (Randall and Minns2002). This limited understanding of nearshore ecologylooms large as a serious impediment to ecosystemmanagement and restoration of Great Lakes ecosystems(Goforth and Carman 2003).

It is clear that Great Lakes researchers, managers,planners, and conservationists have much work to dowhere nearshore science is concerned. Very few historicalbenchmarks exist, and locations within major regions ofthe Basin that can be considered as reference conditionsare generally lacking (e.g., southern Lake Erie, southernLake Michigan, etc.). Therefore, understandingnearshore dynamics based on contemporary studiespresents many challenges. Although the Great Lakes arelarge bodies of water with complex currents that wouldseemingly dilute inputs from terrestrial sources, theproximity of nearshore zones to shorelines and their roleas an ecotone bridging terrestrial and pelagicenvironments makes them susceptible to the influencesof human land uses in coastal areas. Indeed, multiplestressors related to urban, industrial, and residentialdevelopment of shorelines have dramatically alteredmany Great Lakes nearshore environments (Steedmanand Regier 1987; Busch and Lary 1996; Kelso and Cullis1996; Kelso et al. 1996). The resulting changes inphysicochemical properties have been implicated asdriving factors in the widespread alteration of biologicalcommunities and ecological functions in the Great Lakes(Whillans 1979; Krieger 1984; Kelso et al. 1996; Braznerand Beals 1997). For example, physicochemical habitat

change has been identified as an important factor instructuring fish communities in coastal wetland habitats(Leslie and Timmins 1994; Brazner and Beals 1997),and it is likely to be a significant contributing factor instructuring macroinvertebrate and zooplanktoncommunities in nearshore areas as well (Goforth andCarman in press). There is therefore little doubt that thewholesale physical and chemical alteration of nearshorezones represents a significant impediment to the studyand management of nearshore resources.

Non-native taxa have substantially influenced nativeaquatic communities in the Great Lakes via food webdisruptions, competition for resources (e.g., prey andphysical habitat), and predation (Mills et al. 1993; Buschand Lary 1996; Ricciardi and McIsaac 2000;Vanderploeg et al. 2002; Ratti and Barton 2003). Forexample, Dreissena polymorpha and D. bugensis haveinfluenced benthic invertebrate communities (positivelyand negatively) by increasing colonizable surface area(e.g., Botts et al. 1996; Karatayevetal et al. 1997;Ricciardi et al. 1997; Stewart et al. 1998) and redirectingsources of primary productivity to benthic habitats viadeposition of pseudofeces (Izvekova and Lvova-Katchanova 1992; Roditi et al. 1997; Thayer et al. 1997;Stewart et al. 1998). They have also indirectly competedwith zooplankton (Dettmers et al. 2003) and theamphipod Diporeia hoyi (Dermott and Kerec 1997) forphytoplankton, thus redirecting energy from pelagicenvironments to benthic environments and disruptingfood web structure (Vanderploeg et al. 2002). Thischange in food web dynamics is then projected to fishand other predators that have historically relied on greateraccess to benthic invertebrates now concealed ininterstices of zebra mussel shells and zooplankton/preyfish that have realized a decreased source of primaryproductivity (i.e., phytoplankton) (McIsaac 1996;Haynes et al. 1999). Invasive species such as Dreissenasp. and Neogobius sp. have become well established inmany nearshore areas of the Great Lakes, and thuspresent a second major impediment to understanding andmanaging Great Lakes nearshore ecosystems.

Goforth and Carman (in press) suggested that alteredshorelines may encourage non-native species invasionsuccess in adjacent nearshore areas. Such species areoften habitat generalists that are able to adapt quickly tochanging habitat conditions, especially when competingwith specialist native taxa. While these findings werebased on a pilot study, they nonetheless suggested thepotential cumulative effects of physical, chemical, andbiological stressors on native nearshore biologicalcommunities in nearshore environments. They also implythat management activities aimed at restoring GreatLakes nearshore habitats may have dual benefits inproviding habitats preferred by native taxa that are


simultaneously less favorable to non-native taxa,providing native taxa with a competitive advantage andpotentially addressing some of the industrial andeconomic issues related to non-native invaders. Whilenative biological communities have been shown tobecome altered along shorelines with high levels ofanthropogenic activity compared to intact shorelines(e.g., Brazner 1997; Brazner and Beals 1997; Goforthand Carman in press), complementary work to determinerelationships between non-native communities andshoreline land use has not been performed to date.

Whether native or non-native, the structure ofbiological communities is governed by processes thatresult from interactions of biotic and abiotic factorsoperating over multiple spatial scales (Eadie and Keast1984; Ricklefs 1987; Dunson and Travis 1991; Minns1989). Aquatic ecologists have long recognized that localbiological communities are linked to larger scaleenvironmental factors via the influences of these factorson local habitats in streams (e.g., Hynes 1975; Vannoteet al. 1980; Frissel et al. 1986). Many studies havedemonstrated relationships between stream (e.g.,Osborne and Wiley 1988; McMahon and Harned 1998)and lake (Whittier et al. 1988; Soranno et al. 1996) habitatcharacteristics and the extent of human land uses insurrounding watersheds. Similarly, land use compositionof watersheds has also been implicated as influencinglocal biological communities in these systems,presumably in response to habitat changes resulting fromlandscape alterations (e.g., Reeves et al. 1993; Weaverand Garman 1994; Wichert 1995; Richards et al. 1996;Allan and Johnson 1997; Roth et al. 1997; Goforth et al.2002). These studies demonstrate the great need forconsidering scale as a factor in managing aquaticecosystems to promote long term resource viability andsustainability. As complex littoral environments,nearshore ecosystems are likely driven by similar multi-scale environmental factors of surrounding or adjacentlandscapes, similar to relationships observed in stream(e.g., Allan and Johnson 1997; Richards et al. 1997) andinland lake (e.g., Soranno et al. 1996) ecosystems.Therefore, a multi-scale approach to assessment andmanagement is warranted for these systems.

Relating Great Lakes nearshore communities to bothlocal and larger scale landscape properties of adjacentshorelines has been the subject of few studies (e.g., Kelsoand Minns 1996; Brazner and Beals 1997; Wei et al.2004; Goforth and Carman in press). Meadows et al. (inpress) suggested that local changes in shoreline land useand structure have cumulative impacts on local nearshoreecology via alterations in coastal substrate dynamics thatinfluence habitat distribution and quality in nearshorezones. However, representation of fish species, especiallylarge piscivores, at particular nearshore sites has also

been shown to be primarily related to regional factors(Kelso and Minns 1996; Brazner and Beals 1997). Suchwide ranging and diadromous species are less likely toexhibit predictable community changes among specificlocations because they are more successful in takingadvantage of disparate habitats (Kelso and Minns 1996;McDowall 1996). On the other hand, some smaller, moreshort-lived fish species (e.g., cyprinids) appear to bemore responsive to local habitat factors (Schindler 1987).Macrobenthos have been shown to be responsive to bothlocal and landscape scale environmental properties instreams, so the scale at which they respond toenvironmental change is difficult to predict. Zooplanktondistributions are often dependent upon prevailingcurrents, and may thus be more responsive to larger scalephenomena in Great Lakes nearshore zones. Regardless,it appears that nearshore ecosystems should be studiedwithin a hierarchical spatial context in order to effectivelyidentify the causal factors responsible for structuringresident biological communities (Duarte and Kalff 1990;Brazner and Beals 1997).

Associating aquatic communities with stressorsrelated to urban and industrial activities withincatchments can be difficult (Kelso et al. 1996). At theGreat Lakes Basin scale, cumulative impacts of thesestressors may be significant, although explicitlyidentifying these factors as causal is likely unachievable.A more tenable and manageable land area to explore asa causative agent influencing nearshore ecology is theshoreline. Shorelines may act very similarly to riparianzones of streams and rivers, acting as buffers toanthropogenic activity when they are intact andproviding little to no protection from human land useswhen they are fragmented or characterized by active landuses themselves (e.g., Weller et al. 1998; Gergel et al.2002). In combination with prevailing currents that cancarry materials from updrift areas, shorelines may operateover multiple spatial scales to influence biologicalcommunities at local sites. If such patterns do exist, theycan provide potential landscape indicators for assessingecological integrity of nearshore zones over muchbroader areas of the Basin and act as a foundation foralternative management of shorelines to enhance thelong-term viability of nearshore ecosystems.

We sought to determine whether local nearshorebiological community measures for native and non-native taxa were associated with local and larger scaleshoreline environmental properties, including land covercomposition and the number of shore structures presentwithin specified geographic areas (e.g., revetments, groinfields, jetties, piers, etc). Our primary goal was to providea comprehensive assessment of native and aquaticnuisance species (ANS) community responses to multi-scale shoreline environmental properties based on field


METHODS

Study Sites

surveys of nearshore waters adjacent to local shorelineswith high and low disturbance regimes. The primaryhypothesis of this study was that fish, benthicinvertebrates, and zooplankton native species/community densities and ANS are related to shorelinestructure density and urban land use quantified overlocal and increasingly larger shoreline spatial contexts.We expected ANS densities would be higher and thusreflect greater invasion success in nearshore areasassociated with locally degraded shorelines, while nativetaxa densities would be lower in these same areas. Wealso expected native community density measures to benegatively associated with shore structure densitiesmeasured over increasing spatial scales along shorelines,while ANS densities would be positively related toincreases in shore structure numbers. Finally, weexpected native fish, benthic, and zooplankton densitiesto be negatively related to the spatial extent of urbanland uses quantified within 1-km wide shoreline reachesat progressively larger scales, while ANS densitieswould be positively related to higher urban land usecontributions to these 1-km wide shoreline reaches.

Figure 1. Study site locations along the eastern shoreof Lake Michigan.

Physicochemical Habitat and Biological Surveys

Study sites were located on the eastern shore of LakeMichigan between St. Joseph and Ludington, MI. Weused a two-tiered selection process. Twelve potentialsites were first chosen based on topographic map andaerial photograph (1:16,000 scale) interpretations.Topographic maps were used to identify shorelines withsteep profiles suggesting moderate to high bluffshoreline types. Once the bluff areas were identified,we used aerial photographs to interpret land use andland cover along the shorelines. We identified sixnearshore areas adjacent to modified (i.e., high levelsof human activity and land use, Plate 1) bluff shorelinesand six nearshore areas adjacent to largely intact (i.e.,low levels of human land use and dominated byvegetated land covers and/or dunes, Plate 2) bluffshorelines. The second phase of the selection processinvolved site visits to assess local environmentalcharacteristics and comparability of sites withintreatment classes. Based on the site visits, we selectedfour of the six sites for each shoreline treatment class asstudy sites (eight sites total). The modified sites includednearshore areas south of Saint Joseph (SJ), north ofWhitehall (WH), north of Muskegon at Pioneer Park(PP), and in the vicinity of Silver Lake State Park (SL)(Fig. 1). The intact sites included nearshore areas northof Saint Joseph at Mizpah Park (MP), south of Holland(SH), south of Pentwater adjacent to the Pere MarquetteState Forest (PM), and south of Ludington (LU) (Fig.1).

Study sites were visited once a year for two years(2003 and 2004) to sample local biological communities.At each site, three transects were establishedperpendicular to the shoreline during 2003 using aGarmin 12XL Global Positioning System (GPS) receiver(±10 m accuracy). Transects were established atapproximately 1.0 km increments along the shoreline ateach site. Sampling stations coinciding with the 3.0 mwater depth contour were established along each transectusing the GPS. These transects and sampling stationsprovided a spatial framework for sampling that could beused during both project years.

Physicochemical properties were only measuredduring summer 2003 due to difficulties with the digitalmeters that precluded consistent sampling of waterchemistry during summer 2004. Temperature anddissolved oxygen were measured using a calibrated YSI-55 digital meter, and conductivity and pH were measuredusing an Oakton model pH/Con 10 digital meter.Turbidity was measured using a 200 mm Secchi disk(Fieldmaster®), and was defined as the depth at whichthe black and white quadrant color patterns on the diskcould no longer be discerned visually at the surface.Temperature, dissolved oxygen, pH, and conductivitywere measured at 2.0 m depth at each of the zooplankton/benthos sampling stations. Secchi depth was alsodetermined at the sampling stations at sites where Secchidepth was <3.0 m. For sites with less turbid waters,Sechhi depth was determined at an offshore point along


the transect that was sufficiently deep enough to deploythe disk until it disappeared.

Benthic macroinvertebrate and zooplankton sampleswere collected at all sites during both years of the study.Three benthic invertebrate samples were collected at eachsampling station using a Petite Ponar® grab (0.023 m2,nine total samples/site). Benthic samples were sieved(0.5 mm, Newark Wire Cloth Co., Newark, New Jersey)to remove excess sand and silt, and the remaining samplecontents were washed into a sample storage bottle using95% ethanol (EtOH) (Plate 3). Organisms in benthicsamples were later identified to the lowest practicableand meaningful taxonomic level in the laboratory.Benthic communities were described using measures ofbenthic macroinvertebrate total density (BMTD, numberof individuals/m2), chironomid total density (CTD,number of individuals/m2), and densities ofChironominae, Orthocladiinae, Tanypodinae, andoligocheate worms. Very few non-native benthic taxawere observed; hence, a separate measure of non-nativebenthic macroinvertebrate density was not calculated.

Three zooplankton samples were collected at eachsampling station using a 30-cm-diameter, 90-cm-long,80-µm-mesh plankton net (nine samples/site total).Zooplankton samples were collected by allowing theplankton net to sink to 0.5 m above the lake bottom andthen towing it vertically through the water column (Plate4). Plankton samples were washed from the net into aWhirl-Pak® (Nasco) sample bag using 95% EtOH. Inthe lab, zooplankton samples were washed through a 125µm sieve (Newark Wire Cloth Co., Newark, New Jersey).Following washing, all zooplankton samples werediluted to a known volume of 50 ml. Sub-samples of 2.0ml each were extracted from the 50 ml sample using apipette, and the number of individuals in the sub-samplewas determined. If there were less than 100 individualsin the first sub-sample, additional 2.0 ml sub-sampleswere extracted and processed until at least 100 individualzooplankton were identified across the combined sub-samples. All zooplankton were identified to the lowestpracticable taxonomic level, although statistical analyseswere generally based on taxonomic groups rather thanindividual species. Zooplankton community measureswere calculated based on zooplankton total density(ZTD, number individuals/m3), dreissenid veliger density(DVD, number individuals/m3), and densities ofcyclopoids, Limnocalanus macrurus, eucladocerans, androtifers (number individuals/m3).

Fish communities were only sampled during summer2003 because weather conditions precluded consistentfish sampling during summer 2004. Two methods wereused to assess fish communities. Beach seines (10-m-long, 6.4-mm-mesh) were used to sample shallow waterfish communities (<1.0 m water depth) during twilight

Spatial Data

hours (i.e., 20:30 to 22:30). Three beach seine hauls (10m long parallel to the shore) were collected at the baseof each site transect (nine seine hauls/site total). Fishcollected in the seines were identified to species andreleased after processing. Shallow water fishcommunities were described using catch per unit effortmeasures (CPU; number of individuals/beach seine haul)calculated for species occurring at three or more sites(i.e., Fundulus diaphanous, Rhinichthys cataractae,Notropis hudsonius, and the non-native Alosapseudoharengus and Neogobius melanostomus), allshallow water fish combined (SWTot), planktivores(SWPlk), benthivores (SWBen), insectivores (SWIns),native fish (SWNat), and introduced fish (SWInt).

Scientific gill nets (38.0-m-long, 2.4-m-deep) wereused to sample fish along the 3.0 m depth contour ofstudy sites. The gill nets were comprised of five 7.6 msections, each with a different mesh size (i.e., 2.5 cm,3.8 cm, 5.1 cm, 6.4 cm, and 7.6 cm bar). Gill nets wereset during twilight hours (i.e., 20:30 to 22:30) in anoffshore direction with sampling station points at theshoreward end of the gill net set. Gill nets were fishedfor no more than four hours at a time to minimizesampling induced mortality. At the conclusion of eachgill net set, the elapsed time was recorded and fish wereremoved from the gill net, identified to species, measuredfor length, and released. CPU measures (number fishcaptured/hr) were calculated for individual species andfamily groups (i.e., Aplodinotus grunniens, Dorosomacepedianum, catostomids, salmonids, and percids), allnearshore fish combined (NSTot), piscivores (NSPis),planktivores (NSPlk), benthivores (NSBen), native fish(NSNat), and introduced fish (NSInt).

Existing land cover data (IFMAP 2000) were usedto map land use along the Lake Michigan shoreline. Ashoreline structure data layer was also created bydigitizing shore structures interpreted from digitalorthophotoquads. Spatial analyses to quantify land coverand shoreline structure densities were conducted usingArcView 3.2 Geographic Information Systems (GIS,ESRI 2004) software. Land cover composition and thenumber of shoreline structures present at multiple scalesrelative to study sites were determined using buffer areasdefined as 1.0 km-wide lateral bands along the shoreline.The longitudinal extent of these buffers was defined asone of five shoreline landscape contexts extending northor south from a given survey site, including local scale(a 5.0 km buffer centered on each nearshore study site),and 10 km, 25 km, 50 km, and 100 km updrift from eachsurvey site. The direction (i.e., north or south of a givenstudy site) of the shoreline contexts was determinedbased on mean longshore currents for the study area(Beletsky et al. 1999). Shoreline contexts were spatially


nested so that larger contexts encompassed the areas ofall smaller contexts. The longitudinal and inland extentsof each shoreline spatial context (e.g., 5.0 km long and1.0 km inland, 10 km long and 1.0 km inland, etc.) servedas the boundaries for quantifying the percentage of urbanland use within buffers of each spatial context. Theseshoreline contexts were also used to determine thenumbers of shoreline structures at multiple spatial scalesrelative to the study sites. Both the urban land use andshoreline structure data were used as measures ofshoreline condition in regression analyses with thebiological community data.

Statistical AnalysisA repeated measures analysis of variance (ANOVA)

was used to determine whether benthic and zooplanktoncommunity measures were different between shorelineclasses. For the ANOVAs, biological community datawere log

10 (x+1) transformed to meet the assumption of

equal variance. Multivariate ANOVA (MANOVA) wasused for fish data to detect potential interactions amongthe fish community measures relative to shoreline class.In cases where the MANOVA was significant, individualone-way ANOVAs were conducted using the fishcommunity data to determine which groups exhibiteddifferent CPU between the shoreline classes. Regressionanalysis was used to determine whether overall meanlocal biological community measures were related to thespatial extent of urban land uses and numbers of shorelinestructures within the shoreline buffers describedpreviously. The statistical software package SPSS 12.0(SPSS, Inc.) was used to conduct all statistical analyses.Statistical tests were significant at alpha = 0.05.

across all sites, including two non-native species,Cercopagis pengoi and Dreissena sp. veligers (Table 4).Twenty-three fish species were also observed among thestudy sites, including the introduced species A.pseudoharengus, Osmerus mordax, N. melanostomus,Salmo trutta, and Onchorhynchus tshawytscha (Tables5 and 6).

Local Shoreline Type AnalysesBenthic macroinvertebrate invertebrate community

compositions and densities varied widely within andamong study sites (Table 3). Repeated measures ANOVAindicated that BMTD was not significantly differentbetween shoreline types (F=0.14, p=0.71, Fig. 2a),although it was greater in 2003 vs. 2004 (F=17.55,p<0.001). Four of the 10 observed taxonomic groupsoccurred with sufficient frequency to warrant statisticalcomparisons between modified and intact shorelineclasses, including the chironomid subfamiliesChironominae, Orthocladiinae, and Tanypodinae, andoligochaete worms (Table 3). Densities of Chironominaedid not differ between shoreline classes (F= 0.59,p=0.45), although they were higher in 2003 comparedto 2004 (F=5.17, p=0.03) (Fig. 3a). Orthocladiinaedensities were also not different between shorelineclasses (F=0.76, p=0.76), but were higher in 2003 vs.2004 (F=89.85, p<0.001) (Fig. 3b). Tanypodinaedensities were not different between shoreline classes(F=0.15, p=0.70), although they were greater in 2004vs. 2003 (F=65.00, p<0.001) (Fig. 3c). Finally,oligochaete worm densities were not different betweenshoreline classes (F=0.65, 0.42), but were significantlyhigher during 2003 compared to 2004 (F= 5.49, p=0.02)(Fig. 3d). The only non-native benthic invertebrateobserved was Dreissena polymorpha, and it was onlyobserved in very low densities at two of the eight sites(SJ and SL), thus precluding this group from statisticalanalysis.

Zooplankton densities were moderately variableamong sites, although generally not to the same extentas benthic macroinvertebrates (Tables 3 and 4). MeanZTD was not significantly different between shorelineclasses (F=0.07, p=0.80) (Fig. 2a), although it wassignificantly different between 2003 and 2004 (F=25.11,p<0.001). However, a significant interaction between theyear and shoreline class treatments (F=12.15, p=0.001)suggested that this pattern was not consistent betweenshoreline types (Fig. 2b). Mean ZTD was lower in 2003compared to 2004 for the modified shoreline type(F=29.17, p<0.001), although there was no significantdifference in mean ZTD of intact shorelines betweenyears (F=0.08, p=0.78) (Fig 2b).

Statistical analyses of individual zooplanktontaxonomic groups were restricted to higher levels oforganization in most cases due to the high degree of

RESULTS

SummarySite surveys were primarily conducted during late

June and July of 2003 (benthic macroinvertebrates,zooplankton, fish, and water chemistry) and 2004(benthic macroinvertebrates and zooplankton) (Table 1).For each site, zooplankton and benthic samples werecollected within the same 2-week time frame each year.Water temperatures, dissolved oxygen, conductivity, andpH measures were largely similar among sites (Table2). However, Secchi depth measures varied widelyamong sites, ranging from 2.0 m at PP to 8.2 m at LU(Table 2). Turbidity tended to decrease in a northwarddirection across the study sites and likely reflecteddifferences in the relative productivity of the nearshoreareas. However, there was no statistically significantdifference in Secchi depth between the shoreline classes.

Benthic samples were principally comprised of taxain ten coarse taxonomic groups, including intermittentoccurrences of the introduced species, D. polymorpha(Table 3). Twenty-four zooplankton taxa were observed


Table 2. Mean (±1 S.E.) physicochemical measures for nearshore areas adjacent to modified and intact shorelines ofLake Michigan. Water temperature, dissolved oxygen, conductivity, and pH measures are based on measurements takenat 2.0 m water depth along the 3.0 m depth contour of study sites.

Shoreline

ClassStudy Site

Secchi Depth

(m)

Water

Temperature

(°C)

Dissolved

Oxygen (mg/L)

Conductivity

(µS)pH

Saint Joseph 3.1±0.1 19.0±0.1 9.9±0.6 362.0±81.5 8.2±0.1

Pioneer Park 2.0±0.1 20.3±0.2 9.4±0.1 490.0±92.5 8.2±0.1

Whitehall 4.5±0.1 21.5±0.1 8.5±0.4 497.0±97.0 8.2±0.0

Silver Lake 6.5±0.6 19.4±0.1 9.9±0.1 528.3±0.3 8.4±0.1

Mizpah Park 4.7±0.3 20.6±0.1 10.3±0.6 668.7±2.3 8.4±0.1

South Holland 2.2±0.0 23.4±0.3 10.6±0.3 628.3±16.8 8.5±0.0

Pere Marquette 6.2±0.1 20.4±0.0 9.4±0.1 538.7±0.9 8.4±0.0

Ludington 8.2±0.1 19.3±0.1 9.9±0.0 525.0±2.6 8.5±0.0

Physicochemical Measure

Modified

Intact

Table 1. Sample dates for nearshore areas in Lake Michigan surveyed during thesummers of 2003 and 2004.

Study Site ZooplanktonBenthic

Invertebrates

Shallow

Water Fish

Nearshore

Fish

Ludington29-Jul-03

12-Jul-04

29-Jul-03

12-Jul-0419-Aug-03 19-Aug-03

Mizpah Park24-Jun-03

11-Jul-04

24-Jun-03

11-Jul-0424-Jun-03 24-Jun-03

Pioneer Park30-Jun-03

1-Jul-04

30-Jun-03

1-Jul-0430-Jun-03 30-Jun-03

Pere Marquette30-Jul-03

2-Jul-04

30-Jul-03

2-Jul-0429-Jul-03 29-Jul-03

South Holland14-Jul-03

3-Jul-04

14-Jul-03

3-Jul-0414-Jul-03 14-Jul-03

Silver Lake29-Jul-03

2-Jul-04

29-Jul-03

2-Jul-0429-Jul-03 29-Jul-03

Whitehall1-Jul-03

10-Jul-04

1-Jul-03

10-Jul-0401-Jul-03 01-Jul-03

Taxonomic Group

variability in densities of individual genera and/or specieswithin and among sites, between shoreline classes, andbetween years (Table 4). The cyclopoid group includedfive taxa and tended to be numerically scarce comparedto most other zooplankton groups (Table 4). Totalcyclopoid densities were not different between modifiedand intact shorelines (F=0.21, p=0.65), although theywere consistently higher in 2003 vs. 2004 for bothshoreline classes (F=26.78, p<0.001) (Fig. 4a). Therewas no interaction between year and shoreline type forthe cyclopoid analysis (F=0.27, p=0.61). Only onecalanoid species was detected, Limnocalanus macrurus,

and it generally occurred in small numbers across allsites (Table 4). L. macrurus densities were similar bothbetween shoreline classes (F=0.01, p=0.92) and betweenyears (F=0.103, p=0.75) with no significant interactionbetween the main effects (F=1.76, p=0.19) (Fig. 4b). Theeucladoceran group included six different taxa, althoughBosmina longirostris was much more abundant than anyof the other eucladocerans, and it was a numericallydominant taxon in zooplankton samples across all sites(Table 4). As a group, eucladoceran densities were notdifferent between shoreline classes (F=1.02, p=0.32),although they were lower in 2003 compared to 2004


Tabl

e 3.

Mea

n (±

1 S

.E.)

ben

thic

mac

roin

vert

ebra

te d

ensi

ties

(nu

mbe

r of

indi

vidu

als/

m2 )

for

nea

rsho

re a

reas

adj

acen

t to

high

ly m

odif

ied

and

larg

ely

inta

ctsh

orel

ines

of L

ake

Mic

higa

n. S

ampl

es w

ere

coll

ecte

d du

ring

sum

mer

s 20

03 a

nd 2

004

usin

g a

Pet

ite

Pon

ar®

gra

b (0

.023

m2 )

. St

udy

site

s in

clud

e St

. Jos

eph

(SJ)

, Pio

neer

Par

k (P

P),

Whi

teha

ll (W

H),

Sil

ver L

ake

(SL

), M

izpa

h P

ark

(MP

), S

outh

Hol

land

(SH

), P

ere

Mar

quet

te (P

M),

and

sou

th o

f the

Lud

ingt

on p

ump

stor

age

stat

ion

(LU

). T

axon

omic

gro

ups

incl

ude

the

dipt

eran

Chi

rono

mid

ae (

sub-

fam

ilie

s C

hiro

nom

inae

(C

hiro

); O

rtho

clad

iina

e (O

rtho

); P

odon

omin

ae(P

odo)

; Pro

diam

esin

ae (P

rodi

); a

nd T

anyp

odin

ae (T

anyp

od);

and

the

trib

e Ta

nyta

rsin

i (Ta

nyta

r)),

the

dipt

eran

Cer

atop

ogon

idae

(Cer

ato)

, oli

goch

aete

wor

ms

(Oli

go),

wat

er m

ites

(M

ites

), d

reis

seni

d m

usse

ls (

Dre

is),

and

all

ben

thic

mac

roin

vert

ebra

tes

(Tot

_Ben

).

Sh

ore

lin

e

Cla

ssS

ite

Yea

rC

hir

oO

rth

oP

od

oP

rod

iT

an

yp

od

Ta

ny

tar

Cer

ato

Oli

go

Mit

esD

reis

To

t_B

en

20

03

24

8.4

±1

09

.71

30

.4±

30

.36

.2±

6.2

13

0.4

±1

23

.36

.2±

6.2

6.1

±6

.15

59

.0±

19

6.6

20

04

32

8.5

±5

2.3

8

7.0

±3

4.8

11

5.8

±5

4.2

29

.0±

10

.29

.7±

6.4

56

0.4

±1

29

.9

20

03

53

.1±

33

.08

7.0

±4

4.7

9.7

±9

.74

44

.4±

89

.0

20

04

62

.8±

26

.2

43

.5±

17

.84

.8±

4.8

10

6.3

±2

4.2

20

03

62

8.0

±1

39

.65

8.0

±1

9.2

19

.3±

19

.34

.8±

4.8

96

.6±

70

.24

.8±

4.8

87

4.4

±2

28

.2

20

04

4.8

±4

.8

29

.0±

16

.21

4.5

±1

0.2

4.8

±4

.85

3.1

±2

2.7

20

03

53

.1±

18

.91

40

.1±

10

8.6

9.7

±6

.44

.8±

4.8

23

6.7

±1

25

.1

20

04

14

.5±

10

.2

14

.5±

10

.2

20

03

77

.3±

48

.51

73

.9±

44

.14

8.3

±4

3.1

4.8

±4

.83

23

.7±

12

7.8

20

04

37

6.8

±1

22

.17

2.5

±3

0.7

33

.8±

12

.19

.7±

6.4

48

3.1

±1

36

.4

20

03

91

.8±

28

.53

3.8

±1

5.8

43

.5±

28

.11

73

.9±

39

.7

20

04

87

.0±

50

.2

9.7

±9

.73

3.8

±2

0.2

13

0.4

±6

5.6

20

03

33

3.3

±7

1.0

58

.0±

21

.71

4.5

±1

4.5

44

4.4

±8

9.0

20

04

58

.0±

29

.9

4.8

±4

.89

.7±

6.4

62

.8±

30

.8

20

03

12

5.0

±6

3.3

1

25

.0±

63

.3

20

04

65

.2±

21

.7

16

6.7

±5

4.3

23

1.9

±6

0.1

WH

SL

Mo

dif

ied

MP

Ben

thic

In

ver

teb

rate

Ta

xo

n

SJ

PP

SH

PM

LP

Inta

ct


Tabl

e 4.

Mea

n de

nsit

ies

(±1

S.E

.) o

f nat

ive

and

non-

nati

ve z

oopl

ankt

on ta

xa o

bser

ved

in v

erti

cal p

lank

ton

tow

s co

llec

ted

alon

g th

e 3

m d

epth

con

tour

of n

ears

hore

wat

ers

alon

g L

ake

Mic

higa

n sh

orel

ines

, inc

ludi

ng S

aint

Jos

eph

(SJ)

, Pio

neer

Par

k (P

P),

Whi

teha

ll (W

H),

Sil

ver L

ake

(SL

), M

izpa

h P

ark

(MP

), S

outh

Hol

land

(SH

),P

ere

Mar

quet

te (

PM

), a

nd L

udin

gton

(L

U).

Ori

gin

Ta

xo

no

mic

Gro

up

Ta

xo

nS

JP

PW

HS

LM

PS

HP

ML

U

Ca

lan

oid

aL

imn

oca

lan

us

ma

cru

rus

6.9

9±

2.8

78

5.0

4±

29

.33

39

.25

±1

3.3

08

4.4

4±

15

.08

20

.85

±5

.30

44

.16

±1

1.4

86

1.0

6±

14

.43

8.5

1±

1.0

8

Aca

nth

ocy

clo

ps

vern

ali

s

0.5

8±

0.5

8

34

.34

±2

1.3

2

Dia

cycl

op

s s

p.

2.8

0±

2.2

43

.27

±3

.27

1

.73

±1

.73

3.1

3±

1.9

21

1.4

5±

8.2

9

Eu

cycl

op

s sp

.7

.48

±4

.62

6.5

4±

5.0

7

0.5

8±

0.5

8

Mes

ocy

clo

ps

eda

x8

.91

±4

.79

49

.06

±2

1.9

46

7.0

5±

20

.99

3

2.1

6±

7.5

81

1.4

5±

4.8

47

.90

±4

.47

0.6

3±

0.4

7

Tro

po

cycl

op

s p

rasi

nu

s m

exic

an

us

2.2

6±

1.2

23

2.7

1±

17

.47

65

.42

±2

2.7

71

.73

±1

.73

17

.44

±6

.62

8.1

8±

5.2

23

.82

±1

.93

0.3

5±

0.3

5

Bo

smin

a l

on

gir

ost

ris

35

23

.98

±1

18

4.6

75

79

9.1

9±

12

87

.44

29

65

.83

±3

89

.65

18

52

.54

±3

98

.18

18

09

.99

±2

72

.49

74

37

.88

±8

11

.87

14

41

.34

±2

77

.37

12

9.5

6±

49

.46

Cer

iod

ap

hn

ia s

p.

3

.27

±2

.24

3

.62

±2

.79

Ch

ydo

rus

sp.

59

.37

±1

1.0

94

0.0

7±

14

.95

10

7.1

2±

25

.07

18

2.7

8±

46

.25

37

0.8

3±

60

.32

21

.26

±1

2.7

76

8.1

4±

21

.56

6.0

0±

4.4

6

Da

ph

nia

sp

.9

.58

±5

.80

6.5

4±

3.8

01

1.4

5±

5.9

02

.94

±1

.89

2

2.9

0±

8.0

95

.45

±3

.57

0.3

8±

0.3

8

Eu

bo

smin

a c

ore

go

ni

5

5.6

0±

55

.60

1.6

3±

1.6

30

.87

±0

.87

14

.99

±1

0.1

2

Po

lyp

hem

us

ped

icu

lus

21

.03

±1

0.3

23

.27

±2

.24

1.6

4±

1.6

46

8.1

1±

20

.17

11

.45

±5

.39

99

.76

±3

9.8

31

3.6

3±

3.8

65

.34

±1

.53

Asc

om

orp

ha

sp

.

7

.09

±4

.88

13

7.3

8±

67

.31

Asp

lan

cha

sp

.1

60

.92

±6

5.6

93

22

.18

±6

2.3

72

25

.69

±3

3.5

56

3.9

0±

17

.09

36

7.0

1±

56

.16

54

4.5

9±

17

6.8

53

1.6

2±

11

.75

55

.31

±2

8.9

8

Bra

chio

niu

s p

tero

din

oid

es

10

.63

±6

.71

4.9

1±

3.5

7

2.8

6±

2.4

63

.27

±2

.24

Kel

lico

ttia

lo

ng

isp

ina

72

3.9

3±

15

1.2

33

22

.99

±1

05

.31

84

1.4

2±

26

2.6

65

94

.44

±1

94

.29

12

02

.58

±4

86

.14

37

9.4

2±

20

6.7

93

86

.50

±1

01

.35

28

.45

±6

.07

Ker

ate

lla

sp

.8

5.3

9±

16

.35

89

.95

±2

6.7

41

37

.37

±3

9.2

09

2.5

8±

28

.62

14

8.2

8±

31

.69

26

1.6

7±

18

4.3

16

0.2

4±

30

.66

0.4

5±

0.4

5

Mo

no

styl

a s

p.

1

.64

±1

.64

1.6

4±

1.6

48

.18

±8

.18

Plo

eso

ma

hu

dso

nii

62

.15

±1

2.7

61

81

.53

±5

8.8

01

23

.47

±3

7.2

54

76

.77

±1

77

.72

22

9.2

3±

54

.97

73

.59

±2

2.7

38

3.9

5±

34

.60

4.3

0±

2.3

2

Po

lya

rth

ra s

p.

57

.60

±1

5.5

32

78

.02

±1

39

.95

65

.42

±1

5.3

44

1.3

3±

11

.48

21

7.7

8±

39

.11

51

3.5

2±

22

7.6

51

1.1

8±

5.7

7

Ro

tife

r co

lon

y3

0.1

4±

11

.18

13

.08

±6

.40

14

9.6

4±

67

.21

49

.87

±1

7.0

93

9.2

5±

19

.25

57

7.3

0±

17

5.2

92

0.7

2±

8.7

00

.82

±0

.49

Tri

cho

cerc

a s

p.

23

.97

±6

.52

52

.33

±1

9.5

61

21

.02

±3

1.0

41

3.4

5±

5.4

14

7.1

5±

19

.25

3.2

7±

2.2

45

.72

±4

.15

C

erco

pa

gis

pen

go

i2

.80

±2

.24

6.5

4±

5.0

82

9.4

4±

26

.04

5.1

9±

3.7

7

4.9

1±

2.6

62

.73

±1

.27

D

reis

sen

a s

p.

(Vel

i ger

)4

45

.89

±1

51

.52

19

1.3

4±

40

.96

81

1.9

8±

21

2.6

52

79

.54

±7

9.0

13

95

8.0

7±

13

35

.52

95

9.1

7±

29

7.8

51

23

.75

±2

4.2

57

.12

±1

.87

Nu

mb

er o

f T

ax

a/S

ite

18

20

18

19

19

21

16

14

Mo

dif

ied

Sit

esIn

tact

Sit

es

No

n-N

ati

ve

Ro

tife

ra

Eu

cla

do

cera

Cy

clo

po

ida

Na

tiv

e


Sit

eA

lew

ife

Ba

nd

ed

Kil

lifi

shB

lueg

ill

Em

erald

Sh

iner

Joh

nn

y

Dart

e r

Ju

ven

ile

Salm

on

id

Lon

gn

ose

Dace

Rain

bow

Sm

elt

Ro

un

d

Go

by

Sp

ott

ail

Sh

iner

Wh

ite

Su

cker

Yel

low

Per

ch

LU

1.1

1±

1.4

51.5

6±

3.4

3

MZ

0.2

2±

0.6

70.2

2±

0.6

72.8

9±

3.4

84.4

4±

4.4

516.4

4±

25.5

9

PI

4.2

2±

4.2

92.2

2±

4.5

21.3

3±

1.7

30.4

4±

0.8

85.7

8±

2.9

1

PM

0.8

9±

2.0

30.6

7±

1.0

0

SH

0.6

7±

1.0

00.2

2±

0.6

70.8

9±

1.0

511.7

8±

12.2

7

SJ

0.2

2±

0.6

73.1

1±

7.1

50.8

9±

1.4

51.3

3±

2.8

35.3

3±

7.5

539.7

8±

42.5

70.2

2±

0.6

70.8

9±

1.4

5

SL

0.2

2±

0.6

71.8

9±

2.2

6

WH

1.1

1±

1.4

50.2

2±

0.6

70.4

4±

0.8

80.8

9±

1.7

63.7

8±

2.3

3

Sh

all

ow

Wate

r S

pec

ies

(Bea

ch S

ein

e)

Tabl

e 5.

Mea

n (±

1SE

) ca

tch

per

unit

effo

rt (

CPU

) m

easu

res

for

fish

spe

cies

obs

erve

d in

bea

ch s

eine

s du

ring

sur

veys

of

shal

low

wat

er f

ish

com

mun

ities

asso

ciat

ed w

ith G

reat

Lak

es s

hore

line

area

s. S

tudy

site

s in

clud

e St

. Jos

eph

(SJ)

, Pio

neer

Par

k (P

P), W

hite

hall

(WH

), S

ilver

Lak

e (S

L),

Miz

pah

Park

(MP)

,So

uth

Hol

land

(SH

), P

ere

Mar

quet

te (

PM),

and

sou

th o

f th

e L

udin

gton

pum

p st

orag

e st

atio

n (L

U).

Sit

eB

row

n

Tro

ut

Ch

inook

Salm

on

Fre

shw

ate

r

Dru

m

Giz

zard

Sh

ad

Gold

en

Red

hors

e

Lon

gn

ose

Su

cker

Riv

er

Red

hors

e

Rou

nd

Wh

itef

ish

Sil

ver

Red

hors

eW

all

eye

Wh

ite

Su

cker

Yel

low

Per

ch

LU

0.2

2±

0.1

90.1

1±

0.1

80.7

4±

0.7

90.1

1±

0.1

80.1

1±

0.1

8

MZ

0.0

8±

0.1

40.0

8±

0.1

40.0

8±

0.1

40.0

9±

0.1

50.4

1±

0.2

80.5

9±

0.4

00.3

3±

0.3

7

PI

0.0

9±

0.1

50.1

7±

0.1

50.0

9±

0.1

50.5

2±

0.2

6

PM

0.1

5±

0.1

30.0

7±

0.1

3

SH

1.4

0±

0.6

40.6

9±

0.4

90.2

8±

0.2

90.0

9±

0.1

50.1

0±

0.1

8

SJ

0.1

6±

0.2

70.0

8±

0.1

40.1

8±

0.3

11.5

3±

0.9

3

SL

0.1

4±

0.2

5

WH

1.7

4±

0.8

00.0

8±

0.1

40.1

9±

0.1

7

Nea

rsh

ore

Sp

ecie

s (G

ill

Net

)

Tabl

e 6.

Mea

n (±

1SE

) cat

ch p

er u

nit e

ffor

t (C

PU) m

easu

res

for f

ish

spec

ies

obse

rved

in g

ill n

ets

duri

ng s

urve

ys o

f nea

rsho

re fi

sh c

omm

uniti

es a

ssoc

iate

dw

ith e

ight

Gre

at L

akes

sho

relin

e ar

eas.

Stu

dy s

ites

incl

ude

St. J

osep

h (S

J), P

ione

er P

ark

(PP)

, Whi

teha

ll (W

H),

Silv

er L

ake

(SL

), M

izpa

h Pa

rk (M

P), S

outh

Hol

land

(SH

), P

ere

Mar

quet

te (

PM),

and

sou

th o

f th

e L

udin

gton

pum

p st

orag

e st

atio

n (L

U).


Den

sity

(N

umbe

r/m3 )

IntactModifiedShoreline Type

B.

A.

Den

sity

(N

umbe

r/m2 ) 2003

2004

0

100200

300

400500

600

0

2000

4000

6000

8000

1000020032004

Figure 2. Mean (± 1 S.E.) total densities of A) benthic macroinvertebrates (number ofindividuals/m2) and B) zooplankton (number of individuals/m3) segregated by shoreline type(modified and intact) for samples collected along the 3 m depth contour of eastern LakeMichigan during summer 2003 and 2004.

(F=56.16, p<0.001) (Fig. 4c). There was a nearlysignificant interaction between year and shoreline typedue to the comparably lower degree of variability in meaneucladocern densities between 2003 and 2004 for theintact shoreline class (F=3.06, p=0.09) (Figure 4c).

Rotifers also comprised a large portion ofzooplankton samples, including seven taxa that werewidely distributed among the sites and three additionaltaxa that were only found at a few sites (Table 4). Asignificant interaction between year and shoreline type(F=8.56, p=0.005) indicated that ANOVAs had to besegregated by year. Rotifer densities were significantlygreater in 2004 vs. 2003 for the modified shoreline class(F=17.99, p<0.001), although they were not significantlydifferent between years for the intact shoreline class(F=0.02, p=0.89) (Fig. 4d). Overall rotifer densities were

also not significantly different between nearshore areasassociated with modified vs. intact shorelines (ANOVAF=0.07, p=0.80) (Fig. 4d).

Non-native zooplankters were represented by C.pengoi and Dreissena sp. veligers. Very few C. pengoiwere detected at sites (Table 4), and there was nosignificant difference in densities of C. pengoi betweenshoreline classes (F=1.51, p=0.22) or between years(F=1.91, p=0.17). There was also no significantinteraction between year and shoreline class for thisanalysis (F=2.86, p=0.1). Veligers often comprised verylarge portions of zooplankton samples (Table 4). Asignificant interaction between year and shoreline type(F=5.75, p=0.02) indicated that ANOVAs had to beconducted separately by year. Nearshore areas adjacentto modified shorelines had greater densities of veligers


Density (Number Individuals/m2)

Inta

ctIn

tact

Mod

ified

Mod

ified

Shor

elin

e Ty

peSh

orel

ine

Type

A.

B.

C.

D.

0100

200

300

400

050100

150

0204060

020406080100

2003

2004

2003

2004

2003

2004

2003

2004

Fig

ure

3. M

ean

(± 1

S.E

.) d

ensi

ties

of

bent

hic

mac

roin

vert

ebra

te ta

xono

mic

gro

ups

segr

egat

ed b

y sh

orel

ine

type

(m

odif

ied

and

inta

ct)

for

sam

ples

col

lect

edal

ong

the

3m d

epth

con

tour

of

east

ern

Lak

e M

ichi

gan

duri

ng s

umm

er 2

003

and

2004

. Ta

xono

mic

gro

ups

incl

ude

the

chir

onom

id s

ubfa

mil

ies

Chi

rono

min

ae(A

), th

e O

rtho

clad

iina

e (B

), a

nd T

anyp

odin

ae (

C)

and

olig

ocha

ete

wor

ms

(D).


Density (Number/m3)

Inta

ctIn

tact

Mod

ified

Mod

ified

Shor

elin

e Ty

peSh

orel

ine

Type

A.

B.

C.

D.

2003

2004

050100

150

200

250

2003

2004

020406080100

0

2000

4000

6000

8000

2003

2004

0500

1000

1500

2000

2500

2003

2004

Fig

ure

4. M

ean

(± 1

S.E

.) d

ensi

ties

of

zoop

lank

ton

taxo

nom

ic g

roup

s se

greg

ated

by

shor

elin

e ty

pe (

mod

ifie

d an

d in

tact

) fo

r sa

mpl

es c

olle

cted

alo

ng th

e3

m d

epth

con

tour

of e

aste

rn L

ake

Mic

higa

n du

ring

sum

mer

200

3 an

d 20

04.

Taxo

nom

ic g

roup

s in

clud

e C

yclo

poid

a (A

), C

alan

oida

(B),

Cla

doce

ra (C

) and

Rot

ifer

a (D

).


in 2004 compared to 2003 (F=8.51, p=0.005), althoughthis was not the case for nearshore areas along intactshorelines (F=0.01, p=0.97). There was no differencein veliger densities between shoreline types (F=0.08,p=0.79).

Most shallow water fish were only found at three orfewer sites (Table 5). The most common fish observedamong sites were N. hudsonius, A. pseudoharengus, andF. diaphanus (Table 5). A MANOVA conducted usingdata for all shallow water fish species at three or moresites indicated that the shallow water fish communityvaried between modified and intact shoreline types(ë=0.83, p=0.03). Individual ANOVAs for each of thesefish species indicated that A. pseudoharengus (F=2.43,p=0.12), R. cataractae (F=2.33, p=0.13), N.melanostomus (F=0.00, p=1.00), and N. hudsonius(F=1.18, p=0.28) CPU measures were not significantlydifferent between the shoreline types (Fig. 5a). This wasalso true for SWTot (F=0.77, p=0.39) (Fig. 5a). Only F.diaphanus CPU was different between shoreline types,with greater CPU in nearshore areas adjacent to modifiedshorelines (F=0.77, p=0.39) (Fig. 5a).

MANOVA indicated an overall difference in shallowwater fish mean CPU between shoreline types based ontrophic classifications (ë=0.83, p=0.03). Mean SWPisand SWPlk were greater in nearshore areas adjacent tomodified shorelines (F=5.06, p=0.03, and F=3.88,p=0.05, respectively) (Fig. 5b). Both SWBen and SWInsmean CPU were similar between the shoreline types(F=0.35, p=0.56 and F=1.26, p=0.27, respectively) (Fig.5b). Mean SWNat and SWInt were not significantlydifferent between shoreline types (F=1.66, p=0.20 andF=1.68, p=0.20, respectively) (Fig. 5c).

All but one nearshore fish species (A. grunniens)occurred at three or fewer sites (Table 6). Thus, all speciesbut A. grunniens and D. cepedianum were grouped intofamily groups for analysis (i.e., catostomids, salmonids,and percids). A MANOVA indicated that nearshore fishexhibited no differences in CPU between shorelineclasses based on these taxonomic groupings (ë=0.73,p=0.29) (Fig. 6a). A MANOVA conducted using thenearshore fish data grouped according to species’ trophicstatus indicated no overall difference in the nearshorefish community between shoreline types based on trophicstatus (ë=0.86, p=0.39) (Fig. 6b). Both NSNat (F=0.01,p=0.93) and NSInt (F=1.37, p=0.25) were also notsignificantly different between shoreline types (Fig. 6c).

respectively), while the greatest percentage of urban landuse occurred within the local landscape context at SJ(83%). The variability in urban land use within multi-scale buffers was judged to provide an appropriate basisfor conducting regression analyses to detect relationshipsbetween biological community measures and urban landuse of buffers quantified over multiple spatial scales.

Shore structures were also very prominent featuresof the buffers defined at different scales (Table 8). Thenumber of shore structures generally increased withincreasing spatial scale of landscape contexts for eachstudy site. Shore structures ranged in number from nonein the local landscape context at PP to 461 in the buffercomprising the largest landscape context for the samesite. As was the case for the urban land use analyses, thevariability in the number of shore structures over multiplelandscape contexts for each site was judged to providean adequate basis for conducting regression analyses todetect relationships between biological communitymeasures and the number of shore structures withinbuffers of landscape contexts over multiple spatial scales.

Regression analyses of benthic community data withurban land use and shore structure density were limitedto mean BMTD and CTD. The greatest variability inBMTD was explained by the extent of urban land useswithin 1.0 km shoreline buffers of the local, 10 kmupdrift, and 25 km updrift landscape contexts (Table 9).Although these relationships were not statisticallysignificant, the degree of variability in mean BMTDexplained by urban land use dropped precipitously atthe 50 km and 100 km updrift landscape contexts (Table9). Urban land use of shoreline buffers over all landscapecontexts explained very little of the variation in meanCTD (Table 9). Variability in CTD explained by urbanland use was limited to a maximum of 13% observed forthe 100 km updrift landscape context.

Shore structures of the two largest landscape contextsexplained the greatest degree of variation in mean BMTD(Table 10). There was a nearly significant relationshipbetween BMTD and the number of shoreline structureswithin the 50 km updrift landscape context (R2=0.45,p=0.07), and the degree of variation explained by thenumber of structures within the 100 km updrift landscapecontext, though not statistically significant, was muchgreater than the three smallest landscape contexts (Table10). The number of shore structures within shorelinebuffers explained <5% of the variation in mean CTDover all landscape contexts (Table 10).

The extent of urban land use within the shorelinebuffers explained very little variability in the ZTD dataset(Table 9). The coefficients of determination for theseanalyses were generally R2<0.07, and the greatestvariability in mean ZTD explained by urban land usewas limited to 12% for the 100 km updrift landscape

Spatial Analysis ResultsUrban land uses were a prominent feature of the

shoreline buffers over almost all landscape contexts(Table 7). The lowest percentages of urban land useoccurred in buffers of the local and 10 km updriftlandscape contexts for the SH study site (9% and 8%,


CPU

(Num

ber/S

eine

Hau

l)

Fish Community Measure

C.

B.

ModifiedIntact

0

10

20

30

SWNat SWInt

ModifiedIntact

0

5

10

15

20

25

SWPis SWPlk SWBen SWIns

0

10

20

30

Alew Baki Lodo Spsh Rogo Total

ModifiedIntact

A.

Figure 5. Mean (± 1 S.E.) catch per unit effort (CPU) for shallow water fish captured in beachseine hauls at sites segregated by shoreline type (modified and intact) in eastern Lake Michiganduring summer 2003. Individual species at >3 study sites include Alosa pseudoharengus (alewife,Alew), Fundulus diaphanus (banded killifish, Baki), Rhinichthys cataractae (longnose dace, Lodo),and Neogobius melanostomus (round goby, Rogo). Groupings include overall shallow waterpiscivores (SWPis), planktivores (SWPlk), benthivores (SWBen), insectivores (SWIns), nativefish (SWNat) and introduced fish (SWInt).


CPU

E(N

umbe

r/Gill

Net

Set

)

Fish Community Measure

C.

B.

ModifiedIntact

ModifiedIntact

A.

0.0

0.5

1.0

1.5

2.0

Frdr Gish Cato Salm Perco Total

0.0

0.5

1.0

1.5

NSPis NSPlk NSBen

ModifiedIntact

0.0

0.2

0.4

0.6

0.8

1.0

NSNat NSInt

Figure 6. Mean (± 1 S.E.) catch per unit effort (CPU) for nearshore fish captured in gill net haulsat sites segregated by shoreline type (modified and intact) in eastern Lake Michigan during summer2003. Individual species and families at >3 study sites include Aplodinotus grunniens (freshwaterdrum, Frdr), Dorosoma cepedianum (gizzard shad, Gish), catostomids (Cato), salmonids (Salm),and percids (Perco). Groupings include overall nearshore piscivores (SWPis), planktivores(SWPlk), benthivores (SWBen), native fish (SWNat) and introduced fish (SWInt).


Study Site Local 10 km 25 km 50 km 100 km

Saint Joseph 83 81 46 34 35

Pioneer Park 48 51 48 45 43

Whitehall 28 29 35 40 42

Silver Lake 21 15 20 25 35

Mizpah Park 19 24 41 28 28

South Holland 9 8 20 26 26

Pere Marquette 32 20 18 23 33

Ludington 26 29 33 26 34

Buffer Landscape Context

Study Site Local 10 km 25 km 50 km 100 km

Saint Joseph 47 96 111 129 129

Pioneer Park 0 90 123 209 461

Whitehall 7 19 93 155 420

Silver Lake 51 48 120 234 396

Mizpah Park 20 38 107 126 126

South Holland 1 4 66 173 300

Pere Marquette 23 47 117 229 346

Ludington 13 20 46 140 279

Buffer Landscape Context

Table 7. Percentage of 1.0 km shoreline buffers comprisedof urban land uses along the eastern Lake Michiganshoreline. Buffers include a 5 km-long shoreline reachencompassing each study site (local), and 10 km-, 25km-, 50 km-, and 100 km-long shoreline reaches updriftfrom each study site.

Table 8. Number of shore structures within 1.0 kmshoreline buffers along the eastern Lake Michiganshoreline. Buffers include a 5 km-long shoreline reachencompassing each study site (local), and 10 km-, 25km-, 50 km-, and 100 km-long shoreline reaches updriftfrom each study site.

context (Table 9). Urban land use within shoreline buffersalso explained very little of the variation in DVD overmost landscape contexts (Table 9). Similar to ZTDregressions, the greatest amount of variability in DVDwas explained by the extent of urban land use within thelargest landscape context (R2=0.26, p=0.20).

Shore structures within shoreline buffers explainedrelatively little of the variation in ZTD over all landscapecontexts (Table 10). The local shoreline contextexplained the greatest degree of variability in ZTD(R2=0.18), and the remaining landscape contextsexplained <3% of the variation in ZTD (Table 10). Shorestructures within the three smallest landscape contextsexplained <5% of the variation in DVD for each analysis,and numbers of shore structures within the two largestlandscape contexts explained comparatively much largerdegrees of variation, although neither was statisticallysignificant (Table 10).

Mean SWTot was positively related to the spatialextent of urban land uses within the 10 km updriftlandscape context (Fig. 7a). It also exhibited a nearlysignificant relationship to urban land uses of the locallandscape context (Table 9). Urban land use within thetwo largest spatial contexts explained very little of thevariation in SWTot (Table 9). In contrast, mean SWTotshowed a significant negative relationship with thenumber of shore structures within the 100 km updriftlandscape context (Fig. 8a).

Relationships between shallow fish trophic groupsand urban land uses varied greatly based on landscapecontext, while relationships between these groups andshore structures of shoreline buffers were more similar.Mean SWIns was positively related to urban land usewithin buffers of the local and 10 km updrift landscapecontexts (Table 9). Mean SWPlk was positively relatedto urban land use within the 50 km updrift landscapecontext (Table 9 and Fig. 7b). Mean SWBen was not

significantly related to urban land uses of any landscapecontext, although the smaller landscape contextsexplained more variability in SWBen than the largesttwo landscape contexts (Table 9). In contrast, both SWInsand SWBen were negatively related to the number ofshore structures in the 100 km updrift landscape context(Fig. 9a-b). Similarly, although SWPlk was notsignificantly related to the number of shore structureswithin any landscape context, the greatest amount ofvariation in SWPlk was explained by the number of shorestructures within the 100 km updrift context (Table 9).

Mean SWNat exhibited significant positiverelationships with the spatial extent of urban land use inlocal and 10 km updrift buffers (Table 9 and Fig. 7c). Incontrast, mean SWNat was negatively related to thenumber of shoreline structures within the largest buffercontext (Table 10 and Fig. 9c). Regression analysisshowed that SWInt was positively related to urban landuse of the local, 10 km updrift, and 25 km updrift buffercontexts (Table 9 and Fig. 7d). Mean SWInt also showeda significant positive relationship with the number ofshore structures in the 10 km updrift buffer context (Table10 and Fig. 9d).

Urban land uses within shoreline buffers explainedvery little of the variation in mean NSTot (Table 9).Although not statistically significant, urban land usewithin the largest spatial context explained the greatestdegree of variation in NSTot (Table 9). Mean NSTotshowed a significant negative relationship with thenumber of shore structures within the 50 km updriftlandscape context (Table 10 and Fig. 8b). Shorestructures within the remaining buffer contexts explained<28% of the variation in NSTot (Table 10).

Mean NSPis was positively related to urban landuses of the local and 10 km updrift landscape contexts(Fig. 11a), and urban land use of the two largest buffercontexts accounted for very little variation in mean NSPis


Tabl

e 9.

Res

ults

of

line

ar r

egre

ssio

ns b

etw

een

biol

ogic

al c

omm

unit

y m

easu

res

and

the

spat

ial

exte

nt o

f ur

ban

land

use

(i.e

., %

buf

fer

area

as

urba

n)w

ithi

n 1-

km s

hore

line

buf

fers

def

ined

at m

ulti

ple

spat

ial s

cale

s. B

uffe

rs in

clud

ed a

5 k

m s

hore

line

enc

ompa

ssin

g th

e st

udy

site

(lo

cal)

, and

10

km-,

25

km-,

50

km-,

and

100

km

-lon

g sh

orel

ines

upd

rift

fro

m th

e st

udy

site

s. B

iolo

gica

l com

mun

ity

mea

sure

s ar

e de

fine

d in

the

Met

hods

sec

tion

of

the

text

.St

atis

tica

lly

sign

ific

ant r

elat

ions

hips

bet

wee

n bi

olog

ical

com

mun

ity

para

met

ers

and

the

num

ber

of s

hore

str

uctu

res

wit

hin

a gi

ven

buff

er c

onte

xt a

rehi

ghli

ghte

d in

gra

y.

R2

Fp

R2

Fp

R2

Fp

R2

Fp

R2

Fp

ZT

D0.0

50.3

20.5

90.0

20.1

20.7

40.0

20.1

50.7

20.0

60.3

70.5

70.1

20.8

30.4

0

DV

D0.0

90.6

20.4

60.0

40.2

30.6

50.0

50.3

30.5

90.0

10.0

70.8

00.2

62.1

00.2

0

BM

TD

0.3

02.6

10.1

60.3

43.0

40.1

30.2

62.0

70.2

00.0

70.4

60.5

20.0

10.0

30.8

6

CT

D

0.0

60.3

60.5

70.0

40.2

20.6

50.0

00.0

10.9

50.0

70.4

70.5

20.1

30.9

00.3

8

SW

Tot

0.4

75.2

90.0

60.5

36.6

50.0

40.3

63.3

30.1

20.0

30.1

80.6

90.0

50.3

10.6

0

SW

Pl k

0.0

40.2

80.6

20.0

80.5

20.5

00.2

41.9

00.2

20.6

310.1

90.0

20.3

53.2

90.1

2

SW

Ben

0.1

51.0

90.3

40.2

21.6

60.2

50.2

72.2

00.1

90.0

00.0

20.9

00.1

71.2

40.3

1

SW

Ins

0.5

06.0

60.0

50.5

67.5

30.0

30.3

43.0

90.1

30.0

40.2

30.6

50.0

40.2

20.6

6

SW

Int

0.5

16.2

30.0

50.6

08.9

90.0

20.6

19.4

50.0

20.2

31.8

10.2

30.0

00.0

10.9

3

SW

Nat

0.5

47.0

60.0

40.5

98.7

00.0

30.3

43.1

40.1

30.0

50.3

00.6

00.0

20.1

40.7

2

NS

Tot

0.0

00.0

00.9

90.0

20.0

90.7

70.0

70.4

70.5

20.0

40.2

30.6

50.1

10.7

30.4

3

NS

Pis

0.5

98.4

60.0

30.6

29.8

00.0

20.2

82.2

90.1

80.0

10.0

30.8

60.0

10.0

80.7

9

NS

Pl k

0.1

40.9

70.3

60.1

41.0

10.3

60.1

51.0

80.3

40.1

00.7

00.4

40.4

85.4

50.0

6

NS

Ben

0.2

41.8

70.2

20.1

20.8

30.4

00.0

00.0

10.9

20.0

80.5

00.5

10.0

10.0

60.8

2

NS

Nat

0.1

81.2

70.3

00.2

51.9

90.2

10.2

82.3

50.1

80.0

30.1

70.7

00.1

71.2

30.3

1

NS

Int

0.0

30.1

80.6

90.0

80.5

10.5

00.1

20.8

10.4

00.2

10.2

80.6

10.0

50.2

90.6

1

Sh

all

ow

Wate

r F

ish

Nea

rsh

ore

Fis

h

Com

mu

nit

y

Mea

sure

Loca

lT

axon

om

ic G

rou

p

Zoop

lan

kto

n

Ben

thic

Macr

oin

ver

teb

rate

s

50

100

Lan

dsc

ap

e C

on

text

10

25


R2

Fp

R2

Fp

R2

Fp

R2

Fp

R2

Fp

ZT

D0.1

81.2

80.3

00.0

10.0

70.7

90.0

10.0

50.8

30.0

20.1

50.7

10.0

10.0

60.8

2

DV

D0.0

10.0

40.8

50.0

40.2

40.6

40.0

10.0

40.8

60.2

41.9

10.2

20.3

22.7

90.1

5

BM

TD

0.0

70.4

50.5

30.0

60.4

10.5

50.0

30.1

80.6

90.4

54.8

30.0

70.3

33.0

10.1

3

CT

D

0.0

10.0

70.8

10.0

20.0

90.7

70.0

00.0

00.9

60.0

40.2

50.6

40.0

10.0

70.8

0

SW

Tot

0.1

40.9

90.3

60.3

12.6

70.1

50.0

50.3

20.6

00.3

63.3

30.1

20.6

19.5

40.0

2

SW

Pl k

0.2

62.0

80.2

00.2

01.4

60.2

70.1

00.6

30.4

60.0

90.5

70.4

80.3

32.9

90.1

4

SW

Ben

0.1

30.8

70.3

90.0

90.6

20.4

60.0

10.0

80.7

80.5

26.3

90.0

50.8

635.7

40.0

01

SW

Ins

0.1

51.0

20.3

50.2

82.3

50.1

80.0

30.2

00.6

70.3

83.6

50.1

10.5

88.2

00.0

3

SW

Int

0.0

20.1

10.7

60.5

57.3

40.0

40.2

01.4

50.2

70.1

61.1

60.3

20.2

62.0

90.2

0

SW

Nat

0.1

41.0

00.3

60.3

02.5

70.1

60.0

30.2

00.6

70.3

63.3

50.1

20.5

36.8

20.0

4

NS

Tot

0.1

61.1

20.3

30.1

10.7

40.4

20.2

72.2

20.1

90.5

47.1

40.0

40.1

61.1

50.3

2

NS

Pis

0.2

51.9

50.2

10.2

41.8

90.2

20.0

00.0

00.9

80.4

34.5

20.0

80.6

08.9

70.0

2

NS

Pl k

0.1

20.7

90.4

10.2

11.5

90.2

50.1

71.2

00.3

20.0

10.0

30.8

70.0

30.1

50.7

1

NS

Ben

0.5

88.4

20.0

30.4

14.2

30.0

90.2

62.1

20.2

00.1

30.9

30.3

70.0

30.9

30.3

7

NS

Nat

0.0

00.0

10.9

40.1

10.7

20.4

30.0

00.0

10.9

30.3

73.5

90.1

10.5

26.5

20.0

4

NS

Int

0.0

10.0

80.7

90.0

00.0

10.9

30.3

02.6

10.1

60.4

85.5

80.0

60.3

93.8

10.1

0

Sh

all

ow

Wate

r F

ish

Nea

rsh

ore

Fis

h

50

100

Zoop

lan

kto

n

Ben

thic

Macr

oin

ver

teb

rate

s

Lan

dsc

ap

e C

on

text

Taxon

om

ic G

rou

pC

om

mu

nit

y

Mea

sure

510

25

Tabl

e 10

. Res

ults

of

linea

r re

gres

sion

s be

twee

n bi

olog

ical

com

mun

ity m

easu

res

and

the

num

ber

of s

hore

line

stru

ctur

es (

i.e.,

reve

tmen

ts, j

ettie

s, g

roin

fiel

ds, e

tc.)

with

in 1

-km

sho

relin

e bu

ffer

s de

fine

d at

mul

tiple

spa

tial s

cale

s. B

uffe

rs in

clud

ed a

5 k

m s

hore

line

enco

mpa

ssin

g th

e st

udy

site

(loc

al),

and

10

km-,

25

km-,

50

km-,

and

100

km

-lon

g sh

orel

ines

upd

rift

from

the

stud

y si

tes.

Bio

logi

cal c

omm

unity

mea

sure

s ar

e de

fine

d in

the

Met

hods

sec

tion

of th

ete

xt.

Stat

istic

ally

sig

nifi

cant

rel

atio

nshi

ps b

etw

een

biol

ogic

al c

omm

unity

par

amet

ers

and

the

num

ber o

f sho

re s

truc

ture

s w

ithin

a g

iven

buf

fer c

onte

xt a

rehi

ghlig

hted

in g

ray.


% U

rban

Lan

d U

se

C.

SWTot SWNat

SWIntSWPlk

A.

0102030405060

020

4060

8010

00.

0

1.0

2.0

3.0

4.0

5.0 20

3040

50

B.

D.

0102030405060

020

4060

8010

00.

0

2.0

4.0

6.0

8.0 0

2040

6080

100

y =

0.64

4x -

4.80

R2 =

0.5

3p

= 0.

04

y =

0.14

3x -

3.64

R2 =

0.6

3p

= 0.

02

y =

0.50

0x -

3.76

R2 =

0.5

9p

= 0.

03

y =

0.08

1x -

0.21

1R

2 = 0

.60

p =

0.02

Fig

ure

7. R

elat

ions

hips

bet

wee

n A

) to

tal s

hall

ow w

ater

fis

h ca

tch

per

unit

eff

ort (

SW

Tot)

and

urb

an la

nd u

se w

ithi

n th

e 10

km

upd

rift

land

scap

eco

ntex

t, B

) sha

llow

wat

er p

lank

tivo

rous

fish

CP

U (S

WP

lk) a

nd u

rban

land

use

wit

hin

the

50 k

m u

pdri

ft la

ndsc

ape

cont

ext,

C) s

hall

ow w

ater

nat

ive

fish

CP

U (

SW

Nat

) an

d ur

ban

land

use

wit

hin

the

10 k

m u

pdri

ft l

ands

cape

con

text

, and

D)

shal

low

wat

er i

ntro

duce

d sp

ecie

s C

PU

(S

WIn

t) a

ndur

ban

land

use

wit

hin

the

10 k

m u

pdri

ft la

ndsc

ape

cont

ext.


Figure 8. Relationships between A) total shallow water fish catch per unit effort (SWTot)and the number of shore structures within the 100 km updrift landscape context, and B) totalnearshore fish catch per unit effort and the number of shore structures within the 50 kmupdrift landscape context.

NST

otC

PUSW

TotC

PU

Number of Shore Structures

A.

B.

y = -0.129x + 55.387R2 = 0.61p = 0.02

y = -0.015x + 3.941R2 = 0.54p = 0.04

0

20

40

60

80

100 200 300 400 500

0.0

1.0

2.0

3.0

120 150 180 210 240


Num

ber o

f Sho

re S

truc

ture

s

C.

SWIns SWNat

SWIntSWBen

B.

D.

0.0

2.0

4.0

6.0

8.0 10

020

030

040

050

00102030405060

100

200

300

400

500

0.0

2.0

4.0

6.0

8.0 0

2040

6080

100

01020304050 100

200

300

400

500

A.

y =

-0.1

0x +

44.

90R

2 = 0

.58

p =

0.03

y =

-0.0

2x +

9.3

6R

2 = 0

.86

p <

0.00

1

y =

-0.0

9x +

39.

24R

2 = 0

.53

p =

0.04

y =

-0.0

6x +

0.0

8R

2 = 0

.55

p =

0.04

Figu

re 9

. R

elat

ions

hips

bet

wee

n A

) sha

llow

wat

er in

sect

ivor

ous

fish

cat

ch p

er u

nit e

ffor

t (SW

Ins)

and

the

num

ber o

f sho

re s

truc

ture

s w

ithi

n th

e 10

0km

upd

rift

lan

dsca

pe c

onte

xt, B

) sh

allo

w w

ater

ben

thiv

orou

s fi

sh C

PU

(S

WB

en)

and

the

num

ber

of s

hore

str

uctu

res

wit

hin

the

100

km u

pdri

ftla

ndsc

ape

cont

ext,

C) s

hall

ow w

ater

nat

ive

fish

CP

U (S

WN

at) a

nd th

e nu

mbe

r of s

hore

str

uctu

res

wit

hin

the

100

km u

pdri

ft la

ndsc

ape

cont

ext,

and

D)

shal

low

wat

er in

trod

uced

spe

cies

CP

U (

SW

Int)

and

the

num

ber

of s

hore

line

str

uctu

res

wit

hin

the

10 k

m u

pdri

ft la

ndsc

ape

cont

ext.


0.0

0.2

0.4

0.6

0.8

25 30 35 40 45

NSP

lkN

SPis

% Urban Land Use

A.

0.0

0.5

1.0

1.5

2.0

0 15 30 45 60 75 90B.

y = 0.019x - 0.274R2 = 0.62p = 0.02

y = -0.027x +1.030R2 = 0.48p = 0.06

Figure 10. Relationships between A) nearshore piscivorous fish catch per unit effort (NSPis)and urban landuse within the 10 km updrift landscape context, and B) nearshoreplanktivorous fish catch per unit effort and urban land use within the 100 km updriftlandscape context.


Num

ber o

f Sho

re S

truc

ture

s

C.

NSPis NSNat

NSIntNSBen

B.

D.

A.

y =

-0.0

04x

+ 1.

43R

2 = 0

.60

p =

0.02

y =

-0.0

29x

+ 1.

460

R2 =

0.5

8p

= 0.

03

y =

-0.0

04x

+ 1.

933

R2 =

0.5

2p

= 0.

04

y =

-0.0

02x

+ 0.

411

R2 =

0.4

8p

= 0.

06

0.0

0.1

0.2

0.3

0.4 12

015

018

021

024

0

0.0

1.0

2.0

3.0

015

3045

60

0.0

0.5

1.0

1.5

2.0

2.5 10

020

030

040

050

0

0.0

0.5

1.0

1.5

2.0 10

020

030

040

050

0

Fig

ure

11.

Rel

atio

nshi

ps b

etw

een

A) n

ears

hore

pis

civo

rous

fish

cat

ch p

er u

nit e

ffor

t (N

SP

is) a

nd th

e nu

mbe

r of s

hore

str

uctu

res

wit

hin

the

100

kmup

drif

t lan

dsca

pe c

onte

xt, B

) ne

arsh

ore

bent

hivo

rous

fis

h C

PU

(N

SB

en)

and

the

num

ber

of s

hore

str

uctu

res

wit

hin

the

loca

l lan

dsca

pe c

onte

xt, C

)ne

arsh

ore

nati

ve f

ish

CP

U (

NS

Nat

) an

d th

e nu

mbe

r of

sho

re s

truc

ture

s w

ithi

n th

e 10

0 km

upd

rift

land

scap

e co

ntex

t, an

d D

) ne

arsh

ore

intr

oduc

edsp

ecie

s C

PU

(N

SIn

t) a

nd th

e nu

mbe

r of

sho

reli

ne s

truc

ture

s w

ithi

n th

e 50

km

upd

rift

land

scap

e co

ntex

t.


(Table 9). In contrast, NSPis showed a nearly significantnegative relationship with shore structures within the 50km updrift buffer and a significant negative relationshipwith urban land use within the 100 km updrift buffer(Fig. 10a), while the smaller buffer contexts accountedfor only moderate to very little variation in NSPis (Table10). Mean NSPlk exhibited a nearly significant positiverelationship with urban land use within the largest updriftlandscape context (Fig. 10b). The smaller buffer contextseach accounted for <16% of the variation in NSPlk (Table9). Shore structures of all landscape contexts explained<22% of the variation in NSPlk (Table 10). Mean NSBenwas not significantly related to urban land uses of anylandscape context, although the smallest landscapecontext explained the greatest degree of variability inNSBen (Table 9). Mean NSBen exhibited a significantnegative relationship with shore structures of the locallandscape context (Fig. 11b), and the degree of variabilityin NSBen explained by shore structures decreased withincreasing landscape context (Table 10).

Mean NSNat and NSInt were not significantlyrelated to urban land uses of buffers over all landscapecontexts (Table 9). Among the landscape contexts, the10 km and 25 km updrift described the greatest degreeof variation in mean NSNat relative to urban land use(Table 9). Shore structures within buffers generallyexplained <22% of the variation in NSIntCPU (Table9). Mean NSNat exhibited a significant negativerelationship to the number of shoreline structuresquantified over the largest landscape context (Fig. 11c),and very little variation in NSNat was explained in eachof the smallest landscape context regression analyses(Table 10). NSInt exhibited a nearly significant negativerelationship to shore structures of the 50 km updriftlandscape context (Fig. 11d), and the smallest twolandscape contexts explained <2% of the variation inmean NSInt (Table 10).

DISCUSSION

Fish, benthic macroinvertebrate, and zooplanktoncommunities of eastern Lake Michigan bluff shorelinesvaried greatly within and among sites, and for benthosand zooplankton, between years. However, with theexception of Sechhi depth, site physicochemicalmeasures varied little among sites. Despite the variabilityin turbidity among sites, Secchi depths were statisticallysimilar between shoreline classes and tended to increasewith increasing latitude regardless of shoreline condition.This suggested that local shoreline condition was not asignificant factor in determining the turbidity of adjacentnearshore waters. Turbidity reflects both organic andinorganic materials suspended in the water column, bothof which influence biological communities in significant

ways. The absence of consistent patterning of nearshoreturbidity with local shoreline condition suggests thatlocal factors may not play a significant role in structuringnearshore communities via pathways mediated bysuspended organic and inorganic materials (e.g.,productivity), presumably due to the actions ofalongshore currents. However, sediment and nutrientinputs from nearby updrift tributary confluences mayhave had significant influences on local nearshorecommunities of some sites. Although beyond the scopeof this study, the potential for organic and inorganicmaterials associated with tributary confluences toinfluence local nearshore biological communities shouldbe addressed in future research.

We expected benthic macroinvertebrate communitymeasures to differ between shoreline classes, and theabsence of significant differences in these measures wassurprising. Although few historical studies exist thatfocus on relationships between nearshore benthos andshoreline environmental properties (e.g., Garza andWhitman 2004, Goforth and Carman in press), they dosuggest that nearshore benthic communities respond toshoreline land use or manipulation at relatively localscales (i.e., <10 km). In contrast to the local shorelineanalyses, the spatial analyses showed some agreementwith these past studies by demonstrating that greatervariability in BMTD was explained by urban land usesof the smaller landscape contexts (i.e., <25 km updrift)compared to larger landscape contexts (i.e., >50 kmupdrift). This is likely a result of local changes in sanddistribution and stability mediated by shore structuressimilar to the findings of Garza and Whitman (2003).However, significant regressions of BMTD with thenumber of shore structures within the 50 km updriftlandscape context also suggested that there may becumulative influences of shore structures on localmacrobenthos, presumably because of collectivechanges in substrate movement and distribution thatinfluence local nearshore benthic habitats.

Sand substrates dominated almost every samplingstation, suggesting similar habitat availability amongsites. However, there were subtle differences in theparticle sizes of these sands which were anecdotallynoted, although not quantified as part of the study. Theseapparently subtle changes in substrate particle size likelyconstituted considerable differences in habitatavailability from the perspective of benthicmacroinvertebrates (Winnell and Jude 1984). Somenearshore sites were also characterized by pockets ofaccumulated organic debris that may have served asislands where benthos congregated due to the greaterfood resource availability within the larger context ofthe lake bed. The number, size, distribution, andavailability of sand patches of differing particle sizes,


as well as organic debris islands, are likely to be of greatimportance in determining benthic productivity anddistribution in nearshore zones. However, our samplingregime did not stratify according to substrate particlesize or organic debris concentrations, and therefore highvariability due to random sampling error may havemasked responses of benthos to local changes inshoreline condition. Further research focusing on therelative importance of these microhabitat features istherefore important for better understanding hownearshore benthos are influenced by local habitats. Inaddition, studies focusing on the physical factors thatdetermine the spatial distribution of such microhabitatswithin nearshore zones is needed to better understandhow biologically relevant habitats are influenced byshoreline change.

Most benthic macroinvertebrates exhibiteddifferences in densities between the study years, oftenby an order of magnitude in size. This was not surprisinggiven that the 3.0 m depth contour of Great Lakesnearshore zones is subject to constant disturbance fromwave and current activity (Garza and Whitman 2004),and it is likely that local aquatic communities fluctuateon daily, weekly, seasonal, and annual bases (Braznerand Beals 1997). The great variability in benthicmacroinvertebrate densities between years suggests thatlong-term datasets reflecting annual, or even seasonal,variations in benthic communities are needed to betterunderstand the how benthic communities respond tochanges in nearshore environmental properties.

We were surprised to see very few instances ofbenthic ANS during our surveys. Only a few adultDreissena sp. were observed among all samples, and noinvasive amphipods were detected. This contrasts withGoforth and Carman’s (in press) observations of highdensities of dreissenids in nearshore zones alongmodified shorelines of Lake Erie and the western sideof Lake Michigan. These nearshore areas weredominated by many large cobbles and boulders within amatrix comprised chiefly of clay, although the Lake Eriesite is known to have been dominated by sand historicallybefore shore structures designed to protect bluffs alteredthe substrate regime, diverting sand away from thenearshore zone (Meadows et al. in press). Large, hardsubstrates were almost entirely absent from our surveysites along the eastern Lake Michigan shoreline andlikely contributed to the general absence of adultDreissena sp., although veligers were observed ratherprominently in zooplankton samples. Althoughdreissenids have been observed in some soft-bottomedhabitats of the Great Lakes, the high energy of thenearshore zone of eastern Lake Michigan, combined withthe lack of large, stable substrates, is likely to haveinterfered with successful settlement and subsequent

maturation of veligers. In comparison, dreissenids werepresent in large numbers in the quieter waters of drownedriver mouths in close vicinity to the nearshore areas wesurveyed (Plate 5). Thus, maintaining naturally active,dynamic sandy nearshore areas and shorelines likelyhelps to discourage the spread and establishment ofdreissenids in these nearshore areas. However, continuedand expanded modifications of the Lake Michiganshoreline may lead to a similar “sand starved” conditionnow apparent in other places of the basin (e.g., Garzaand Whitman 2004, Meadows et al in press), and thuspotentially facilitate the establishment of current andfuture ANS.

It was not surprising that zooplankton communitymeasures were similar between the shoreline classes andwere not related to urban land uses or number of shorestructures along shorelines at multiple spatial scales.Although zooplankton distributions are generallyconsidered to be heavily dependent upon larger scalefeatures of water bodies such as wind and currentdirections, there has also been some evidence to suggestthat local zooplankton communities may be influencedby local nearshore environmental and ecologicalproperties. High local densities of dreissenids caninfluence zooplankton densities via indirect competitionfor phytoplankton (Dettmers et al. 2003, Goforth andCarman in press). For example, nearshore zones that havebecome sand-starved as a result of shoreline land useand engineering can provide greater availability ofsubstrates suitable for settling dreissenid veligers andcan facilitate such localized changes in zooplanktoncommunities (Goforth and Carman in press). However,localized shifts in plankton availability in response tofeeding dreissenids was not a factor in this study forreasons explained earlier. Thus, a further benefit ofmaintaining nearshore areas that are naturally dominatedby sand substrates is lowered susceptibility to dreissenid-mediated changes in food web structure.

Similar densities of the calanoid L. macrurusbetween shoreline classes was probably the most notableresult based on zooplankton data analyses. L. macrurusis considered to be an indicator of oligotrophic conditionsbecause it is a cold water stenotherm requiring highdissolved oxygen concentrations (Gannon andStemberger 1978). This species has been used as anindicator of ecosystem recovery in the Lake Erie Basindue to its intolerance of cultural eutrophication (Kane etal. 2004), and its consistent presence and abundance inLake Michigan samples included in the present studysuggests that local shoreline condition does notsignificantly influence trophic condition of adjacentnearshore waters. However, it should be noted thatcurrent densities of L. macrurus are considerably lowerthan those reported historically for Lake Michigan


(Evans 1986), suggesting that there has been some lossof biological integrity in Lake Michigan based on thehistory of decline in this species. However, it appearsthat L. macrurus is currently distributed rather unifomlyalong the eastern Lake Michigan shoreline, suggestingthat cumulative impacts, rather than specific shorelinereaches, are likely to be responsible for observeddecreases in abundance.

Surprisingly few non-native zooplankton specieswere observed in this study. While densities of C. pengoiwere very low during 2003 and 2004, this was thedominant zooplankter observed in samples collected atSJ during summer 2000 (Goforth et al. 2002). Reasonsfor this difference in abundance of C. pengoi betweenthe two studies are unclear, although they may reflectgreater current rates of predation on this species byplanktivorous fish (Bushnoe et al. 2003). Dreissena sp.veligers were present at all sites, and the general absenceof adult dreissenids suggests that veligers originated atupdrift, offshore, or drowned river mouth locations. Verylittle habitat suitable for adult dreissenids was detectedduring reconnaissance visits updrift of the survey sites,suggesting that the primary sources for veligers weremore likely from offshore locations and tributaries. Whilewe cannot comment on occurrences of adult dreissenidsin offshore locations, it was clear that extensive coloniesof adults were present in the protected drowned rivermouths (e.g., Plate 5). These populations likely servedas sources for many of the veligers seen in nearshorezooplankton samples. However, as discussed earlier, veryfew adult dreissenids were observed in benthic samplesand reconnaissance visits outside study areas, so theabsence of suitable habitat in these sandy nearshore zonesappears to be adequate for discouraging colonization bydreissenids.

Responses of fish communities to local shorelinecondition and multi-scale anthropogenic properties ofshoreline buffers were much more consistent with ourexpectations than either benthic macroinvertebrates orzooplankton. Our results generally concur with those ofKelso and Minns (1996) and Brazner and Beals (1997)in that larger fish tended to be more responsive to larger-scale shoreline features while smaller fish tended to bemore responsive to smaller-scale shoreline properties.Larger fish species appear to be better at changing theirmovements and behaviors to take advantage ofalternative habitats when others become sub-optimal,making them much less dependent upon specific localsites for long-term viability of populations (Kelso andMinns 1996). In contrast, smaller species are not capableof comparable changes in behavior, and are thusconsidered to be more influenced by local habitatchanges (Schindler 1987). While this may superficiallysuggest that local site management is unimportant for

sustaining recreational and commercial fisheries thatdepend on nearshore habitats, small fish that areinfluenced by smaller scale phenomena nonetheless serveas important forage for the game species, and viabilityof these forage fish is therefore highly desirable. Thus,management strategies to enhance nearshore resourcesustainability over multiple spatial scales will be neededto preserve not only valuable fisheries, but also nativebiodiversity and prey for game fish.

While the Secchi depth, benthic macroinvertebrate,and zooplankton analyses described previously did notappear to indicate significant differences in relativeproductivity between shoreline classes, SWPis andSWPlk were higher for nearshore areas adjacent tomodified shorelines. NSPis CPU was also more closelyrelated to smaller scale landscape contexts and may havereflected NSPis tracking of SWPlk as a prey resources.While SWPlk densities (as well as SWPis and NSPis)would be expected to be greater in response to increasedlocal availability of phytoplankton and zooplankton thatcould result from increased nutrient loading fromadjacent shoreline land uses, Secchi depth andzooplankton analyses did not suggest this to be the casein our study. It is possible that local zooplanktonpopulations may fluctuate widely over the short term,while shallow water fish populations remain moreconstant over time, enduring zooplankton “feast orfamine” cycles that were not detected in the current studyor switching facultatively to other food sources (e.g.,benthic invertebrates) when zooplankton become scarce.This residential existence hypothesis for small non-gamefish could not be tested within the context of our study,although it does appear that these communities are moreresponsive to local vs. larger scale properties ofshorelines, with the exception of cumulative shorelineinfluences that appear to operate at larger scales tonegatively influence local shallow water fishcommunities. Few fish species were common to morethan a few sites despite the relatively small geographicrange of the study area and the considerable superficialsimilarity in habitat conditions among sites. However, itis worth noting that beach seine samples varied greatly,often even among replicate samples taken in relativelyclose proximity of one another. There may be more subtlechanges in Great Lakes shallow water habitats thatinfluenced local distributions of small non-game fish andjuvenile game fish. As with benthos, more focused studyof relationships between shallow water fish and potentialmicrohabitats of Great Lakes nearshore zones iswarranted.

While we expected N. melanostomus and otherintroduced fish species to be more abundant in nearshorewaters adjacent to modified shorelines, this was not thecase. N. melanostomus CPU was not different between


ACKNOWLEDGEMENTS

LITERATURE CITED

the shoreline classes, presumably because of the generalsimilarity in shallow water habitat among all sites. Inother locations where nearshore areas have become sandstarved (e.g., central Lake Erie), habitat conditions havechanged to become favorable for N. melanostomus, andit has become established as the dominant shallow waterbenthic species as a result (Meadows et al. in press).None of the sites included as part of this study exhibitedevidence of sand starvation, and similar to adultdreissenids, suitable habitat for N. melanostomus waslacking at survey sites. Most individuals observed weresmall and may have been actively dispersing and/ordisplaced juveniles seeking appropriate habitat oroccupying suboptimal habitat. As with dreissenids,populations of N. melanostomus in the drowned rivermouths may have served as sources of juveniles that werethen transported or moved to nearshore areas where theyformed sink populations that were unstable and had lowlong-term viability. This provides another example of acase where maintenance of natural sand dynamics inthese systems decreases the potential for establishmentand spreading of ANS that depend on large, more stablesubstrates for habitat.

We thank Amy Derosier, Rachel Harris, ColleenMcLean, Jensen Rodney, and Frank Magyar for manylong hours in the field conducting surveys and in thelaboratory processing field samples. We also thankStephanie Swart and Helen Enander for their support indeveloping the GIS data layers. We thank the Departmentof Fisheries and Wildlife at Michigan State Universityfor allowing us to use the 18’ Whaler that made oursampling of the nearshore zone of Lake Michiganpossible. Finally, we thank the Michigan Great LakesProtection Fund, the Michigan Department ofEnvironmental Quality’s Office of the Great Lakes andCoastal Zone Management Unit, and the NationalOceanic and Atmospheric Administration for providingthe generous financial support that made this workpossible.

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SUMMARY

Benthic macroinvertebrates and zooplanktoncommunities did not exhibit significant responses to localshorelines, and most of the variation explained in thesemeasures was attributed to urban land use and/or thenumber of shoreline structures within shoreline buffersof larger landscape contexts updrift from the study sites.In contrast, shallow water fish exhibited greater responsesto smaller scale shoreline condition and urban land use,and nearshore piscivorous fish appeared to track withthe prey fish patterns. However, fish communities alsoexhibited negative relationships with increasing numbersof shore structures within larger landscape contexts.These patterns of response suggest that nearshore foodwebs in sand-based systems integrate responses ofmultiple trophic levels to environmental propertiesoperating at multiple spatial scales. Although themechanisms influencing different components of the foodweb were not evident, there is little doubt that sustainingnearshore biodiversity of the Great Lakes will requiremanagement of resources at multiple spatial scales. Thegeneral absence of adult benthic ANS despite theavailability of substantial pools of juvenile ANS in easternLake Michigan, including N. melanostomus andDreissena sp., suggests that sand-based nearshore areasdiscourage successful colonization of these species dueto the lack of large, stable substrates and high energy ofthe wave zone. This suggests that maintaining naturalsand dynamics in the nearshore zones of eastern LakeMichigan should be a management priority.


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COLOR PLATES


Plate 1. An example of a modified shoreline near Saint Joseph, Michigan, with commerical landuse and extensive shore structure development. Loss of vegetation on areas of the bluff have causedhigh levels of erosion and soil loss.

Plate 2. An example of a largely intact shoreline near Ludington, Michigan.


Plate 3. Removal of benthic samples from the Petite Ponar dredge prior to preservation in ethanol.

Plate 4. Deployment of the zooplankton net to collect vertical plankton tows at the 3.0 m depthcontour of study sites.


Plate 5. Zebra mussel clusters and individuals attached to a gastropod shell and leaves of Vallisneriaamericana found in the drowned river mouth of the Pere Marquette River where it joins with LakeMichigan.

nearshore biological community patterns related to lake ...nearshore biological community patterns...

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