An expanded fish-based index of biotic integrity for Great Lakes … · 2018. 10. 22. · An expanded fish-based index of biotic integrity for Great Lakes coastal wetlands Matthew

An expanded fish-based index of biotic integrity for GreatLakes coastal wetlands

Matthew J. Cooper & Gary A. Lamberti & Ashley H. Moerke & Carl R. Ruetz III &

Douglas A. Wilcox & Valerie J. Brady & Terry N. Brown & Jan J. H. Ciborowski &Joseph P. Gathman & Greg P. Grabas & Lucinda B. Johnson & Donald G. Uzarski

Received: 25 April 2018 /Accepted: 22 August 2018 /Published online: 10 September 2018# The Author(s) 2018

Abstract Biotic indicators are useful for assessing eco-system health because the structure of resident commu-nities generally reflects abiotic conditions integratedover time. We used fish data collected over 5 years for470 Great Lakes coastal wetlands to develop multi-

metric indices of biotic integrity (IBI). Sampling andIBI development were stratified by vegetation typewithin each wetland to account for differences in phys-ical habitat. Metrics were evaluated against numerousindices of anthropogenic disturbance derived from

Environ Monit Assess (2018) 190: 580https://doi.org/10.1007/s10661-018-6950-6

M. J. Cooper (*)Mary Griggs Burke Center for Freshwater Innovation, NorthlandCollege, 1411 Ellis Ave, Ashland, WI 54806, USAe-mail: [email protected]

G. A. LambertiDepartment of Biological Sciences, University of Notre Dame,Notre Dame, IN, USAe-mail: [email protected]

A. H. MoerkeAquatic Research Laboratory, Lake Superior State University,Sault Ste. Marie, MI, USAe-mail: [email protected]

C. R. Ruetz III,Annis Water Resources Institute, Grand Valley State University,Muskegon, MI, USAe-mail: [email protected]

D. A. WilcoxDepartment of Environmental Science and Biology, SUNYCollege at Brockport, Brockport, NY, USAe-mail: [email protected]

V. J. Brady : L. B. JohnsonNatural Resources Research Institute, University of MinnesotaDuluth, Duluth, MN, USA

V. J. Bradye-mail: [email protected]

L. B. Johnsone-mail: [email protected]

T. N. BrownUnited State Environmental Protection Agency, Mid-continentEcology Division, Duluth, MN, USAe-mail: [email protected]

J. J. H. CiborowskiDepartment of Biological Sciences, University of Windsor,Windsor, ON, Canadae-mail: [email protected]

J. P. GathmanDepartment of Biology, University of Wisconsin-River Falls,River Falls, WI, USAe-mail: [email protected]

G. P. GrabasCanadian Wildlife Service, Environment and Climate ChangeCanada, Toronto, ON, Canadae-mail: [email protected]

D. G. UzarskiInstitute for Great Lakes Research, CMU Biological Station, andDepartment of Biology, Central Michigan University, MountPleasant, MI, USAe-mail: [email protected]

http://crossmark.crossref.org/dialog/?doi=10.1007/s10661-018-6950-6&domain=pdf

water quality and surrounding land-cover variables.Separate datasets were used for IBI development andtesting. IBIs were composed of 10–11 metrics for eachof four vegetation types (bulrush, cattail, water lily, andsubmersed aquatic vegetation). Scores of all IBIs corre-lated well with disturbance indices using the develop-ment data, and the accuracy of our IBIs was validatedusing the testing data. Our fish IBIs can be used toprioritize wetland protection and restoration effortsacross the Great Lakes basin. The IBIs will also beuseful in monitoring programs mandated by the Agree-ment between Canada and the United States of Americaon Great Lakes Water Quality, such as for assessingBeneficial Use Impairments (BUIs) in Great LakesAreas of Concern, and in other ecosystem managementprograms in Canada and the USA.

Keywords Bioassessment . Biotic indicator . Coastalmarsh . Fish .Wetland .Water quality . Land use .

LaurentianGreat Lakes

Introduction

Human activities that alter physical, chemical, or bio-logical processes of aquatic ecosystems also affect thestructure of resident biotic communities (Karr 1981;Fausch et al. 1984). In many ecosystems, biotic indica-tors measured at a single point in time provide a morerepresentative, sensitive, and time-integrated measure ofconditions than typical chemical or physical measures(Karr 1981; Marchant et al. 2006; Reavie et al. 2006).The United States Environmental Protection Agency(USEPA) and other government agencies beganconducting biological assessments of aquatic ecosys-tems in the late 1980s (Plafkin et al. 1989; Karr 1991),and their use continues to increase as new biotic indica-tors are developed (Ruaro and Gubiani 2013). The Indexof Biotic Integrity (IBI) approach, in particular, hasemerged as an effective tool for monitoring aquaticecosystem health (Karr 1981; Belpaire et al. 2000;Ruaro and Gubiani 2013; Simon and Evans 2017). Inthe three decades since the IBI and its applications (e.g.,Rapid Bioassessment Protocol; Plafkin et al. 1989;Barbour et al. 1999) were first introduced, biotic indi-cators have been developed for numerous taxonomicgroups inhabiting a wide variety of aquatic ecosystems,including coastal wetlands of the Laurentian Great

Lakes (e.g., Burton et al. 1999; Seilheimer and Chow-Fraser 2006; Grabas et al. 2012).

Coastal wetlands occur throughout the Great Lakeswhere hydrology (e.g., wave and current energy) issufficiently quiescent for emergent vegetation to persist,and where sediment is conducive to macrophyte growth(Albert et al. 2005). These shallow, productive ecosys-tems provide critical habitat for many fish species ofecological and economic importance (Chubb and Liston1986; Klarer and Millie 1992; Parker et al. 2012). Un-fortunately, half of the coastal wetland area that waspresent before European settlement has been convertedto other land uses (Maynard and Wilcox 1997), andmany remaining wetlands are impacted by invasivespecies, fragmentation, nutrient loading, and hydrologicmanipulation (Bedford 1992; Wilcox 1995; SOLEC2007; Cooper et al. 2012). Therefore, coastal wetlandrestoration and protection are vital components of long-term management of the Great Lakes (Sierszen et al.2012). Broad-scale monitoring is essential for identify-ing wetlands most in need of protection or restoration, toevaluate the effectiveness of restoration projects, and forboth local and regional time-trend analysis.

Government agencies in both Canada and the USAinitiated a process to develop indicators of ecosystemhealth for coastal wetlands and other Great Lakes hab-itats at the State-of-the-Lakes Ecosystem Conferences(SOLEC) in 1998 and 2000. Recommended indicatorsincluded IBIs based on fishes, invertebrates, and plants,even though no broadly accepted protocols were avail-able at the time for any of these groups. Subsequentefforts by members of the Great Lakes Coastal WetlandsConsortium (GLCWC), the Great Lakes EnvironmentalIndicators group (GLEI; Niemi et al. 2007), and othersdeveloped a variety of biotic indicators. Fish-based in-dicators are particularly valuable because fish are sensi-tive to many types of human disturbance; they occupymultiple trophic levels, and the economic and estheticbenefit of protecting fish communities is greater andmore visible than for many other groups (Harris 1995;Oberdorff et al. 2001).

After marked progress in indicator development dur-ing the 2000s, the USEPA initiated a Great Lakes basin-wide coastal wetland monitoring program based onGLCWC protocols in 2011 (Uzarski et al. 2017). Themonitoring program includes sampling of fish, macro-invertebrate, bird, amphibian, and vegetation communi-ties, as well as water quality. Protocols for fish monitor-ing call for sampling fish within discrete mono-

580 Page 2 of 30 Environ Monit Assess (2018) 190: 580

dominant Bvegetation zones^ at each wetland so that IBIscores could be calculated using a habitat-stratified ap-proach (Uzarski et al. 2005, 2017). However, when themonitoring program began in 2011, fish-based IBIs hadbeen developed only for bulrush (Schoenoplectus spp.)and cattail (Typha spp.) vegetation zones (Uzarski et al.2005), which represent only a portion of the habitattypes encountered in Great Lakes coastal wetlands. Fur-thermore, these previously developed IBIs were gener-ated using a much smaller dataset than what is currentlyavailable. We built on the approach of Uzarski et al.(2005) and formulated fish-based IBIs for the mostcommon vegetation types encountered in Great Lakescoastal wetlands using data collected during the first5 years (2011–2015) of the Great Lakes Coastal WetlandMonitoring Program (Uzarski et al. 2017). Our objectivesin the current paper are to describe our approach todeveloping the expanded fish-based IBIs and to test theseIBIs against anthropogenic disturbance gradients derivedfrom water quality and surrounding land-use data for a 5-year period of wetland monitoring. We predicted thatthese refined fish IBIs would accurately reflect the degreeof human disturbance in Great Lakes wetlands.

Methods

Study area and site selection

Fish communities and water quality variables were sam-pled from 2011 to 2015 at 470 coastal wetlands locatedthroughout all five Laurentian Great Lakes andconnecting channels. Sites were selected according toa probabilistic randomized design developed previouslyby the GLCWC (Uzarski and Otieno 2008; Uzarskiet al. 2017). All coastal wetlands greater than 4 ha inarea with herbaceous vegetation and a surface waterconnection to a Great Lake or connecting channel wereeligible for sampling. Wetlands associated with tribu-taries were included if their hydrology was influencedby Great Lakes water levels and they were locatedwithin 1 km of a shoreline of a Great Lake or connectingchannel. An exception was drowned river mouth wet-lands (Albert et al. 2005) along the eastern shore of LakeMichigan, which often were located farther than 1 kmupstream from the coast but were included becausehydrology in these wetlands is dictated by Lake Mich-igan water levels. The full population of potentiallysampleable wetlands was identified using a spatial

database compiled previously by Albert and Simonson(2004) and Ingram and Potter (2004), who also classi-fied the geomorphic type of each wetland (i.e., riverine,barrier-protected, or lacustrine). A three-way stratifiedsampling approach was used: the 3 strata were GreatLake (N = 5), wetland type (N = 3), and ecoregion (N =3, Bailey and Cushwa 1981; Omernik 1987). Eachwetland was randomly assigned to 1 of the 5 samplingyears, but adherence to the stratification constraintsensured that all strata were proportionally representedin each sampling year. The monitoring program wasdesigned to assess wetlands over a 5-year cycle suchthat 20% of wetlands in each stratum were assigned toeach sampling year (Uzarski et al. 2017).

Wetlands were not sampled if field crews could notsafely reach the site, access through private propertywas denied, a wetland no longer existed at the location,a surface-water connection to the Great Lake orconnecting channel could not be identified (e.g., dikedwetlands), only woody vegetation was found at the site,or if water depth was not appropriate for fish sampling.Fish communities and water quality were sampled frommid-June to early September each year, starting in thesouthern portion of the basin in June and working north-ward as the summer progressed so that sampling coin-cided with maximum vegetation biomass.

Fish sampling

Fish sampling was conducted within discrete vegetationzones at each wetland (Uzarski et al. 2017). Vegetationzones were defined as patches of vegetation in which aparticular macrophyte genus (e.g., Schoenoplectus,Typha) or growth form (e.g., Blily,^ referring tofloating-leaved genera, such as Brasenia spp., Nupharspp., Nymphaea spp.) dominated the plant communitybased on visually assessed coverage estimates. Othermacrophyte genera or growth forms often occurredwithin a given vegetation zone; however, zones werecomprised of at least 75% of the given genus or growthform. The vegetation zone-specific sampling approachwas adopted by the GLCWC to account for habitatvariability among wetlands with different vegetationtypes and to ensure that our IBIs could be used over arange of Great Lakes water levels (Burton et al. 1999;Uzarski et al. 2004). Great Lakes water levels can fluc-tuate dramatically among years, which may create prob-lems because vegetation zones extend downslope dur-ing prolonged (e.g., 2–3 years or more) low-water

Environ Monit Assess (2018) 190: 580 Page 3 of 30 580

periods and upslope when water levels are high (Wilcoxet al. 2002; Gathman et al. 2005). Thus, stratification ofdata collection by vegetation zone allows the IBIs to beused at various water levels (Uzarski et al. 2005, 2017).

At each wetland, we sampled all available vegetationzones with water depths between 20 and 100 cm (theminimum and maximum depths at which our gear func-tioned efficiently) and large enough for three fyke netsto be placed at least 25 m apart to avoid net interference.In wetlands where multiple disjunct smaller patches ofthe same vegetation type were present, these smallerpatches were sampled by placing single nets in differentpatches, as long as they met our minimum-size criterionof 100 m2. For each vegetation zone, three replicate fykenets were set overnight (usually 16–18 h). Fyke netswere set individually (not paired). Two net sizes wereused, depending on water depth at the net location.Small nets (0.9-m wide × 0.5-m tall boxes) were set inwater 20–45 cm deep, and large nets (1.2-m wide × 1.0-m tall boxes) were set in water 45–100 cm deep. Datafrom large and small nets were treated in the same waybecause the main difference between large and smallnets was the box height, which was at or above the watersurface for all sets. Leads (7.3 m long) were attached tothe middle of net boxes and were extended straight outfrom the box. Wings (1.8 m long) were set at 45° anglesto the lead. All mesh was of 0.5 cm bar measurement.Net boxes, wings, and leads were usually set completelywithin the vegetation zone of interest and oriented sothat leads point toward shore. In very dense cattailstands, net boxes were set just outside of the vegetationwith wings and leads extending into the vegetationitself, perpendicular to the leading edge of the vegeta-tion. Fish were identified to species, measured, andcounted in the field. Fish less than 20 mm total lengthwere not counted because the gear was inefficient atcapturing fish that small. Difficult-to-identify specimens(e.g., small cyprinids) were returned to the laboratory foridentification under a dissecting microscope.

We developed IBIs for bulrush (Schoenoplectusspp.), cattail (Typha spp.), lily (Brasenia spp., Nupharspp., Nymphaea spp.), and submersed aquatic vegeta-tion (SAV; primarilyMyriophyllum spp.,Ceratophyllumspp., Chara spp., and Najas spp.). Of all the wetlandssampled for fish by the Great Lakes Coastal WetlandMonitoring Program from 2011 to 2015, 89% had atleast one of these vegetation types. Additional vegeta-tion types (e.g., Phragmites australis, Peltandravirginica, Pontederia spp., Sparganium spp., Juncus

spp., and Zizania spp.) were occasionally sampled butat much lower frequency, and we did not attempt todevelop IBIs for these zones due to small sample sizes.

Water quality

Water quality data were used for both IBI development andtesting butwere not used in the final IBImetric calculations.Dissolved oxygen (% saturation), pH, and specific conduc-tance (μS/cm) were measured at every net location using ahand-heldmeter such as a YSI 6600multi-parameter sonde(Yellow Springs Instruments, Inc., Yellow Springs, OH) orsimilar instrument. Measurements were taken at mid water-column depth, and values from the three net locations wereaveraged for the vegetation zone. Water samples (l L) werecollected at each net location from the middle of the watercolumn and composited into a single sample for eachvegetation zone. Clarity of the composite water samplewas assessed using a 100-cm turbidity tube (Myre andShaw 2006). The composite sample was returned to thelab and analyzed for soluble reactive P (SRP), total P,nitrate-N, ammonium-N, total N, and chlorophyll a usingstandard preservation and analytical methods (USGS 2003;APHA 2005). Additional details on water quality samplingprotocols are provided in Uzarski et al. (2017).

Land-cover

Similarly to water quality, surrounding land-cover datawere used for both IBI development and testing but notin the final IBI metrics. Surrounding land-cover wasquantified using the 2006 National Land-cover Data-base for the U.S. shoreline and the 2005 LandInformation Ontario Database for Canadianwatersheds. We used coastal wetland data layersgenerated previously by Albert and Simonson (2004)and Ingram and Potter (2004) to define the area of eachof our 470 wetlands and then quantified four broad land-cover classes in 1-km and 20-km buffers surroundingeach wetland. The larger buffer represented more gen-eral regional land-cover, and the smaller buffer repre-sented land-cover in the immediate coastal margin, bothof which can affect wetland water quality (Uzarski et al.2005). Land-cover classes included agriculture, devel-oped land, forested land, and wetland, which weexpressed as percentages of total upland area withineach buffer.


Anthropogenic disturbance gradients

We used water quality and land-cover data as surrogatesof anthropogenic disturbance for IBI development andsubsequent testing. We chose this approach over simplymeasuring deviation of metric values from those ofreference sites (i.e., the Breference condition approach^;Bailey et al. 2004) because true reference conditions andreference sites are difficult to identify, and becausemultiple types of human stressors simultaneously affectbiological communities in Great Lakes coastal wetlands(Wilcox et al. 2002; Brazner et al. 2007). Incorporatingvarious dimensions of stress (i.e., water quality andsurrounding land-cover) should, therefore, provide amore comprehensive picture of human disturbance andhelp us to identify, calibrate, and test a robust set ofbiotic indicators. The surrogates of anthropogenic dis-turbance used in IBI development and testing includedindividual water quality and land-cover variables, aswell as multivariate disturbance indices. Individual var-iables included surrounding land-cover, dissolved andtotal N and P concentrations, specific conductance, andturbidity. Multivariate disturbance indices were calcu-lated by combining the above variables into principalcomponents using principal components analysis (PCA)and by summing a set of rank-transformed variables intoa composite index that we refer to as BSumRank^(Uzarski et al. 2005, 2017). Rank-transformed variablesused in SumRank included turbidity, chlorophyll a, totalP, SRP, total N, ammonium-N, nitrate-N, dissolved oxy-gen, pH, specific conductance, the four land-cover clas-ses, and scores from the first principal component of thePCA calculated on the above variables. Higher values ofSumRank indicate higher water quality and less-developed surrounding land (Uzarski et al. 2005, 2017).Because principal components analysis and SumRankare relative indices that vary based on the dataset used,we calculated these separately for each of the four vege-tation zone types for IBI development and testing. Wealso used a previously published landscape-based stressindex, SumRel (Host et al. 2011), as another measure ofpotential anthropogenic disturbance for IBI development.

IBI development

Indices of biotic integrity were developed for each of thefour vegetation types independently of one another. Foreach vegetation type, data were split into Bdevelopment^and Btesting^ datasets, with 50% of wetlands randomly

assigned to each subset for each vegetation type. Thedevelopment dataset was used to identify fish metrics thatcorrelated with anthropogenic disturbance gradients. Thetesting dataset was used to test how well the new IBIscorrelated with anthropogenic disturbance indices.

Metric identification and scoring

Fish data were summarized as catch of each species pernet per night in each vegetation zone at each wetland,which we considered the catch per unit effort (CPUE) forthat zone. Metric identification and IBI developmentgenerally followed previously established methods (e.g.,Karr 1981; USEPA 2002; Lyons 2012) in which (1) alarge set of candidate metrics was calculated; (2) metricswere tested for presence of a relationship with anthropo-genic disturbance or habitat quality; (3) metrics werescreened for adequate range of responses and for highlyredundant metrics; (4) scoring schemes were devised foreach of the final metrics; and (5) the final IBI was testedagainst an independent dataset (i.e., our test dataset).

The initial set of candidate metrics included thosefrom literature sources (Karr 1981; Minns et al. 1994;Wilcox et al. 2002; Uzarski et al. 2005; Lyons 2012;Rabaru and Masese 2012) and metrics representingcatches of the 25 species most frequently collected ineach vegetation type. The full set of candidate metricswas calculated and evaluated for each of the four vege-tation types. Candidate metrics fell into seven broadcategories: (1) diversity-based (alpha diversity, Shannondiversity, evenness); (2) native vs. nonnative speciesoccurrence and abundance; (3) community taxonomiccomposition; (4) feeding ecology; (5) habitat affinity;(6) species attributes (e.g., longevity and relative bodysize); and (7) species sensitivity or tolerance to humandisturbance (see Appendix Table 3 for the full set ofcandidate metrics and Appendix Table 4 for speciesattributes). For most metrics, multiple alternative formu-lations were calculated such as alpha richness vs. %richness or CPUE vs. relative abundance of a species.

Our goal was to use the 2011–2015 developmentdataset to generate a set of IBI metrics for each vegeta-tion type that included 10–15 metrics that each correlat-ed with multiple surrogates of anthropogenic distur-bance (i.e., water quality and land-use variables,SumRank, SumRel, principal components). We used anumber of techniques to identify metrics that varied as afunction of human disturbance. First, Spearman corre-lations were calculated between all candidate fish


metrics and all disturbance indices. High correlation coef-ficients (Spearman’s rho > 0.5) suggested a potential rela-tionship between the metric and the disturbance variable,and we retained those metrics for further evaluation. Sec-ond, we used SumRank scores to identify high-qualitywetlands (20% of wetlands with the highest SumRankscores) and impacted wetlands (20% of wetlands withthe lowest SumRank scores). Mann-Whitney U tests werethen conducted to compare values between these twogroups of wetlands for each candidate fish metric; signif-icant differences suggested an association between themetric and human disturbance. Third, we simply plottedeach fish metric against each multivariate disturbance in-dex to evaluate graphically whether a potential relationshipexisted. These three techniques were used in conjunctionto evaluate each candidate metric and reduce the initial setof candidate metrics to a subset for each vegetation typeusing the development dataset.

Given the extremely large number of comparisonsmade and relationships evaluated, we acknowledge therisk of retaining metrics that had spurious relationshipswith anthropogenic disturbance variables. To mitigatethis risk, we retained only those metrics that exhibitedconsistent patterns with respect to multiple disturbancevariables. Scores of most of the retained metrics wererelated to at least one water quality variable, at least onesurrounding land-cover variable, and at least one of themultivariate disturbance indices (e.g., principal compo-nent scores, SumRank, and/or SumRel). Finally, be-cause multiple alternative formulations were calculatedfor many of the metrics, we chose the formulation thatcorrelated most highly to human disturbance and wasrobust to catches of taxa not targeted in the metric. Forexample, if both CPUE and relative abundance of aparticular fish species correlated with anthropogenicdisturbance indices, we retained the CPUE formulationbecause it is less sensitive to catches of other taxa in thesame habitat. We then screened each of the retainedmetrics to determine whether anomalous catches ofindividual species (e.g., large schools of minnows orjuvenile bullhead) were responsible for relationshipswith anthropogenic disturbance variables. When thisoccurred, the metric was removed from the candidatelist. Remaining candidate metrics were then screened toensure that they represented a sufficiently wide range invalues. We eliminated richness metrics with a range offour or fewer taxa, percent richness metrics with a rangeof less than 25%, CPUE metrics with a range of fewerthan five fish, and relative abundance metrics with a

range of less than 20%. Remaining candidate metricswere then evaluated for redundancy using correlationanalyses (Spearman’s rho > 0.8). While we expected toobserve some correlation among the selected metricsgiven they all were chosen to reflect anthropogenicdisturbance, we removed highly redundant metrics, es-pecially metrics that represented similar aspects of fishcommunity composition. For example, % Carnivore(invertivore+piscivore+zooplanktivore) richness and %Piscivore richness were highly correlated in both thewater lily and SAV zones and represent similar aspectsof fish community structure; therefore, % Carnivorerichness was retained in both of these IBIs.

Next, scoring schemes were devised for each metric bygraphically analyzing scatter plots of fish metric valuesagainst the disturbance indices. In most cases, naturalbreaks observed in the data were used to establish scoringthresholds, and a three-category scoring scheme was de-vised for each metric, corresponding to metric scores of 0,1, or 2, with higher scores implying greater biotic integrity.These steps resulted in a set of IBI metrics, along withscoring thresholds for each of the four vegetation types.Summing of the metric scores for each vegetation zonesampled yielded vegetation zone-level IBI scores.

A final optimization procedure was conducted inwhich metrics that did not improve the overall correlationbetween IBI scores and SumRank were identified, againusing only the development dataset. This step was ac-complished by re-calculating IBI scores and SumRank–IBI correlations (Spearman’s rho) after sequentially leav-ing out each metric. Metrics that did not improve the fit ofthe IBI to the disturbance index were omitted. This stepresulted in a final set of 10metrics for the cattail andwaterlily IBIs, and 11metrics for bulrush and SAV IBIs. Scoreswere then summed across the 10–11 metrics for eachvegetation zone sampled, divided by the total number ofpoints possible for that zone (either 20 or 22), and thenmultiplied by 100 to rescale the zone-level IBI scores to a0–100 point range. Therefore, IBI scores closer to zeroimplied a greater degree of human impact and low habitatquality, whereas IBI scores closer to 100 implied a lowdegree of human impact and high habitat quality.

IBI testing

Using the IBI metrics, scoring thresholds, and re-scalingformulae derived with the development dataset, IBI scoresfor each of the four vegetation types were calculated usingthe test dataset. These IBI scores were then compared to


SumRank scores using Pearson correlation coefficients.We used the SumRank index to test the IBIs because itintegrates both water quality and surrounding land-coversowe considered it themost robust index of anthropogenicdisturbance available. Inter-annual variability in IBI per-formance was evaluated in two ways. First, we calculatedSumRank–IBI correlations for each individual year (2011–2015) using the full dataset (development and testing datacombined) to determinewhether the IBIswere consistentlyrelated to anthropogenic disturbance throughout the 5-yearperiod. Second, we used analyses of covariance(ANCOVA, homogenous slopes model) to test the effectsof year while accounting for the relationship between IBIscores and SumRank.

Biotic integrity categories

After developing and testing the IBIs for each vegetationtype (i.e., different sets of metrics for each of the fourvegetation types), we calculated mean IBI scores at thewetland level by averaging the zone-level scores for eachwetland (0–100 point scale). We then assigned individualwetlands to biotic integrity categories as in Karr et al.(1986), Uzarski et al. (2004), and Lyons (2012). Categoryclass names were Breference quality,^ Bmildly impacted,^Bmoderately impacted,^ Bmoderately degraded,^ andBdegraded.^ Category class boundaries were determinedby assigning wetlands to approximate quintile bins basedon final wetland-level mean IBI scores.

Results

Fish collections

A total of 113 species and over 359,000 fishes werecollected over the 5 years of the study. Mean (±SE)species richness was similar among lakes, ranging from9.5 ± 0.2 species per vegetation zone in Lake Ontario(n = 176 individual vegetation zones) to 10.9 ± 0.5 inLake Superior (n = 78). Mean CPUE (expressed as indi-viduals net-night−1, all species combined) was greatest inLake Michigan (407 ± 159, n = 100 vegetation zones), inpart due to one outlier (15,836 fish net−1 night−1). Afterremoving this outlier, mean CPUE remained greater inLake Michigan (257 ± 52) than in lakes Superior (203 ±40, n = 78), Erie (177 ± 44, n = 81), Huron (150 ± 19, n =217), and Ontario (72 ± 9, n = 176).

IBI metric selection and testing

Metric selection procedures conducted with the devel-opment data yielded 10–11 metrics for each vegetationtype. Most of these metrics had values that correlatedwith multiple disturbance variables, including the mul-tivariate disturbance indices (Table 1). A number ofmetrics functioned well in several of the IBIs(Table 1); metrics ultimately used in all four IBIs includ-ed Nonnative species richness and % Richness of spe-cies particularly sensitive to environmental degradation[sensitivity based on Plafkin et al. 1989 and Lyons 2006,2012]. Metrics selected for three of the four IBIs includ-ed Rock bass CPUE, Black+brown bullhead relativeabundance, and Evenness. While our goal was to iden-tify at least one metric from each of the seven categoriesof integrity for each IBI, we could not identify a habitat-affinity metric for either the bulrush or SAV IBI, adiversity or feeding ecology metric for the cattail IBI,nor a species attribute (e.g., longevity and relative bodysize) metric for the lily IBI (Table 1, Appendix Table 3).

Scoring thresholds were established for each of theselected metrics in each of the four IBIs (Table 2). Afterscoring thresholds were established, scores for eachmetric were determined for each vegetation zone in thedevelopment data set. For example, if a bulrush zonehad an average evenness value of 0.6, then that zonewould receive a score of 1 for that metric (Table 2); ifthat same zone had nonnative species richness of 0, thenit would receive a score of 2 for that metric (Table 2).This process was completed for each metric for eachzone sampled—bulrush metrics were applied to the fishdata collected in bulrush zones, cattail metrics wereapplied to data collected in cattail zones, and so on.Metric scores (0, 1, or 2) were then summed across theset of metrics for each vegetation zone and converted toa percentage of total points possible for that zone type(22 points for bulrush and SAV; 20 points for cattail andlily [Table 2]). This yielded zone-level IBI scores foreach of the four vegetation types. The IBI scores forbulrush, cattail, and lily correlated well (r ≥ 0.657,p < 0.001) with SumRank using the developmentdataset (Figs. 1, 2, and 3). The relationship betweenIBI scores and SumRank was less strong for SAV butstill statistically significant (r = 0.483, p < 0.001; Fig. 4).When the IBIs were tested using the test dataset, IBIs forbulrush, cattail, and lily remained strongly correlated(r ≥ 0.615, p < 0.001) with SumRank at the basin-scale(Figs. 1, 2 and 3). For SAV, the correlation between IBI


Table 1 Spearman’s correlation coefficients (rs) between index of biotic integrity (IBI) metrics and multivariate disturbance indices(SumRank, principal component 1 [PC1], SumRel [Host et al. 2011]) for the four vegetation types sampled in the development dataset only

SumRank PC1 SumRel

Bulrush (Schoenoplectus spp.; n = 96)

Evenness 0.041 − 0.072 0.016

Nonnative species richness − 0.097 0.161 0.051

Native Cyprinidae CPUE 0.131 − 0.113 − 0.159Smallmouth bass CPUE 0.243* − 0.121 − 0.216*% Black+brown bullhead 0.258* − 0.197 − 0.172Johnny darter CPUE 0.286** − 0.455*** − 0.287*Common carp CPUE − 0.283** 0.227* 0.250*

% Carnivore (invertivore+piscivore+zooplanktivore) − 0.068 0.080 − 0.034% Richness of high and extra-high temperature spawners − 0.240 0.285** 0.413**

% Richness short-lived species 0.129 − 0.073 − 0.234*% Richness species particularly sensitive to environmental degradation 0.297** − 0.259* − 0.136

Cattail (Typha spp.; n = 72)

% Richness native species 0.609*** − 0.554*** − 0.370**Nonnative species richness − 0.575*** 0.520*** 0.356**

% Native Cyprinidae − 0.177 0.076 − 0.082Rock bass CPUE 0.212* − 0.247* − 0.075% Black+brown bullhead 0.274* − 0.307** − 0.407**% Richness benthic habitat species − 0.417*** 0.350** 0.125

% Richness nest spawners 0.498*** − 0.394** − 0.226*% Richness of high and extra-high temperature spawners − 0.134 0.302* 0.088

% Richness large and extra large species − 0.243* 0.226* 0.078

% Richness species particularly sensitive toenvironmental degradation

0.073 − 0.093 − 0.041

Water lily (Brasenia spp., Nuphar spp., Nymphaea spp.; n = 76)

Evenness 0.341*** − 0.346** − 0.435***Nonnative species richness − 0.559*** 0.524*** 0.388***

Rock bass CPUE 0.527*** − 0.500*** − 0.281**Smallmouth bass CPUE 0.412*** − 0.400*** − 0.110% Black+brown bullhead 0.397*** − 0.383*** − 0.184*Yellow perch CPUE 0.226* − 0.183* − 0.165% Common carp − 0.439*** 0.419*** 0.367***

% Richness carnivore species(invertivore+piscivore+zooplanktivore)

0.432*** − 0.371*** − 0.378***

% Richness vegetation spawners 0.131 − 0.164* 0.144

% Richness species particularly sensitive to environmental degradation 0.228 − 0.258* − 0.211*

Submersed aquatic vegetation (n = 87)

Evenness 0.259* − 0.145 − 0.034Nonnative species richness − 0.242* 0.221* 0.158

% Richness native species 0.315** − 0.339** − 0.241*% Native Cyprinidae 0.189* − 0.255* − 0.099Johnny darter CPUE 0.268* − 0.325** − 0.102


scores and SumRank with the test dataset was againlower than for the other vegetation types but remainedstatistically significant (r = 0.468, p < 0.001; Fig. 4).

Evaluating the IBIs for individual years (develop-ment and test datasets combined) suggested that theIBIs for bulrush, lily, and cattail were robust to inter-annual variation. Relationships between IBI scoresand SumRank were strong for each individual year(Figs. 1, 2, and 3), despite the smaller number ofobservations used in each correlation. The IBI forSAV correlated significantly with SumRank in 2012,2013, and 2015, but not in 2011 or 2014 (Fig. 4).Analyses of covariance revealed that sampling yeardid not have a significant effect on IBI scores forany of the vegetation types (p > 0.3).

Biotic integrity categories

We used mean IBI scores (average of the 1–4vegetation zones sampled within each wetland) toassign wetlands to categories, each representingapproximately 20% of all wetlands sampled(Fig. 5). Category ranges were < 36 = degraded;36 to 45 = moderately degraded; > 45 to 50 = mod-erately impacted; > 50 to 60 = mildly impacted; >60 = reference quality. Lake Erie had the highestpercentage of its wetlands in the degraded catego-ry (56%) whereas Lake Superior had the lowestpercentage of its wetlands in the degraded catego-ry (6%). Lakes Superior and Huron had thehighest percentage of wetlands in the referencecategory (27 and 22%, respectively), whereas lakesErie and Ontario had the lowest percentages ofwetlands in the reference category (2% [1 wetland]and 5% [6 wetlands], respectively).

Discussion

We developed fish assemblage-based IBIs for the fourmost common vegetation types encountered in GreatLakes coastal wetlands using the most extensive datasetcollected to date for these wetlands. Because at least oneof these four vegetation types occurred in 89% of wet-lands sampled for fish basin-wide, our IBIs have thepotential to enhance the utility of existing and futuremonitoring programs greatly. The IBI scores correlatedwell with an index of anthropogenic disturbancethroughout the 5-year period, except for the SAV IBIwhich did not perform well in 2011 or 2014.

While a small subset of metrics was common to allfour IBIs (i.e., Nonnative species richness and % Rich-ness of species particularly sensitive to environmentaldegradation), most metrics pertained uniquely to spe-cific vegetation types. The identification of metrics thatuniquely reflected the condition of different vegetationzones makes sense given that vegetation zonation incoastal wetlands is governed by broad-scale drivers suchas hydrology (Keddy and Reznicek 1986; Lishawa et al.2010), nutrient availability (Lougheed et al. 2001; Croftand Chow-Fraser 2007), wave exposure (Albert et al.2005; Johnston et al. 2007), and sediment characteristics(Albert et al. 2005). These drivers, along with physicalhabitat differences among the vegetation types them-selves, all influence faunal community structure(Burton et al. 2004; Uzarski et al. 2005; Cvetkovicet al. 2010). Much of this variability among vegetationzones is accounted for in the IBIs by the vegetation-specific sampling design and selection of different met-rics for each vegetation type. Furthermore, this samplingdesign allows the IBIs to be used under different lake-level regimes, which addresses previous concerns thatfish IBIs would not be valid for Great Lakes coastal

Table 1 (continued)

SumRank PC1 SumRel

Rock bass CPUE 0.213* − 0.183* − 0.098% Common carp − 0.309** 0.276** 0.177

% Richness carnivore species (invertivore+piscivore+zooplanktivore) 0.160 − 0.145 − 0.091% Richness large and extra large species − 0.276** 0.212* 0.193*

% Richness short-lived species 0.117 − 0.096 − 0.166% Richness species particularly sensitive to environmental degradation 0.240* − 0.250* − 0.345**

*p < 0.1; **p < 0.01; ***p < 0.001


Table 2 Final IBI metrics and scoring thresholds for the fourvegetation zone types. Catch per unit effort (CPUE) was catchnet−1 night−1. Final zone scores are calculated by re-scaling the

sum of all metrics for a vegetation zone to a 100-point scale.Appendix Table 4 contains taxa trait information

Scoring

0 1 2

Bulrush (Schoenoplectus spp.)

Evenness 0–0.4 > 0.4–0.8 > 0.8

Nonnative species richness ≥ 2 1 0

Native Cyprinidae CPUE 0 > 0–50 > 50

Smallmouth bass CPUE < 2 2–5 > 5

% Black+brown bullhead 0 > 0–25 > 25

Johnny darter CPUE 0 > 0–0.34 > 0.34

Common carp CPUE > 2 > 0–2 0

% Carnivore (invertivore+piscivore+zooplanktivore) > 90 40–90 < 40

% Richness of high and extra-high temperature spawners 100 > 82–100 0–82

% Richness short-lived species < 20 20–60 > 60

% Richness species particularly sensitive to environmental degradation 0 > 0–15 > 15

Final score for zone = (sum of metrics / 22) * 100

Cattail (Typha spp.)

% Richness native species < 60 60–< 100 100

Nonnative species richness > 2 1–2 0

% Native Cyprinidae 0–20 > 20–50 > 50

Rock bass CPUE 0 > 0–3 > 3

% Black+brown bullhead 0 > 0–25 > 25

% Richness benthic habitat species > 75 30–75 < 30

% Richness nest spawners 0 > 0–70 > 70

% Richness of high and extra-high temperature spawners 100 60–< 100 < 60

% Richness large and extra-large species > 40 20–40 < 20



Water lily (Nuphar advena sp., Nymphaea odorata sp.)

Evenness < 0.5 0.5–0.75 > 0.75

Nonnative species richness > 2 > 0–2 0

Rock bass CPUE < 2 2–6 > 6

Smallmouth bass CPUE 0 > 0–3 > 3

% Black+brown bullhead < 5 5–30 > 30

Yellow perch CPUE 0 > 0–10 > 10

% Common carp > 3 > 0–3 0

% Richness carnivore species (invertivore+piscivore+zooplanktivore) < 50 50–75 > 75

% Richness vegetation spawners < 15 15–40 > 40




wetlands because fluctuating water levels impart toomuch variability in habitat and community structure(Wilcox et al. 2002). For lakes Superior, Michigan,and Huron, the 5-year sampling period included 3 yearsof relatively low lake levels (2011–2013) as well as aperiod of rapidly rising lake levels (2014–2015) whenwater levels increased by approximately 0.5 m (Fig. 6).Water levels in lakes Erie and Ontario also varied overthe sampling period (Fig. 6), although fluctuations inLake Ontario levels were moderated by regulation of theoutflow by the Moses-Saunders Dam (Wilcox and Xie2007). The consistent performance of the IBIs over the5-year period, which included marked water-level vari-ation, suggests that the IBI approach is robust to inter-annual water-level fluctuations. However, as waterlevels continue to fluctuate in the future, IBI metricsshould continue to be periodically re-evaluated to ensuretheir continued accuracy. Year-to-year changes in waterlevels have been shown to affect vegetation, inverte-brate, and fish communities in these habitats (Gathmanet al. 2005; Gathman and Burton 2011; Cooper et al.2014; Langer et al. 2018). Other potential sources ofinter-annual variation in fish communities such asshifting seasonal temperatures or changes in wave ener-gy and currents along Great Lakes shorelines may alsonecessitate periodic re-evaluation of IBI metrics.

Although we developed IBIs for the four most com-mon vegetation types encountered, the strength of

relationships between IBI scores and anthropogenic dis-turbance varied. The weak correlations between theSAV-zone IBI and disturbance indices indicates thatrefinement of SAV metrics with additional data is desir-able. It may also reflect the fact that the umbrella termBSAV^ encompasses a variety of growth forms, whichcreate different habitat conditions for fish, thusimparting a greater variability in the fish communityfor this vegetation type compared to the other vegetationtypes sampled. Nevertheless, we included the SAV-zoneIBI in composite scores because it correlated significant-ly with disturbance indices using both the developmentand testing datasets and for three of the 5 years whenrelationships were evaluated on a per-year basis.

Wetlands in Lake Erie and western Lake Ontario,which had the highest percentages of surrounding agri-culture and developed land, along with the poorest waterquality, had more wetlands in the Bmoderately degraded^and Bdegraded^ categories, based on IBI scores, thanother regions of the Great Lakes (Fig. 5). This finding isconsistent with previous studies evaluating anthropogen-ic impacts to coastal habitats throughout the Great Lakes(e.g., Danz et al. 2007; Trebitz et al. 2007; Morrice et al.2008). Wetlands in the southern portion of the GreatLakes basin are more degraded than those to the northbecause the physical setting of the lower lakes providesfor better agricultural growing conditions, and conse-quently a larger human population has settled in the south

Table 2 (continued)

Scoring

0 1 2

Submersed aquatic vegetation

Evenness < 0.2 0.2–0.80 > 0.80

Nonnative species richness > 3 1–3 0

% Richness native species < 75 75–95 > 95

% Native Cyprinidae < 20 20–60 > 60

Johnny darter CPUE 0 > 0–2 > 2

Rock bass CPUE 0 > 0–5 > 5

% Common carp > 5 > 0–5 0

% Richness carnivore species (invertivore+piscivore+zooplanktivore) < 50 50–80 > 80

% Richness large and extra-large species > 40 > 8–40 0–8

% Richness short-lived species < 20 20–70 > 70

% Richness species particularly sensitive to environmental degradation < 5 5–20 > 20



(Huang and Méthot 2015). Lake Superior wetlands,which tend to have the least surrounding agriculture anddeveloped land and highest water quality, had relativelyhigh IBI scores in general. Wetlands in lakes Huron andMichigan tended to have moderate IBI scores, except fornorthern Lake Huron, which had scores more similar toLake Superior, reflecting the higher water quality (Chow-Fraser 2006) and the considerably lower proportion ofagriculture and developed land in that region.

Finer scale geographic patterns in IBI scores alsoappeared to reflect the level of anthropogenic distur-bance to which wetlands were subjected. For example,degraded wetlands in Lake Superior were located closeto the Duluth-Superior Harbor near the city of Duluth,Minnesota and immediately adjacent to eitherresidential/commercial development or commercial shipdocks. Degraded wetlands in Lake Michigan occurrednear highly agricultural or urbanized areas. One of these

SumRank

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E. 2013r=0.494

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Lake SuperiorLake HuronLake Michigan

Legend

Lake ErieLake Ontario

Fig. 1 Relationships between bulrush zone IBI scores andSumRank indices calculated for the development (a) and testdatasets (b), as well as for individual years (c–g). Developmentand test data were combined for the individual year analyses.

Separate SumRank values were calculated for each comparison.Pearson correlations were calculated between IBI and SumRankscores


was located at the mouth of the Galien River in southernMichigan, which has a highly agricultural watershedand chronically high turbidity and dissolved N. Anotherdegraded Lake Michigan wetland was located in south-ern Green Bay, an area with high turbidity and nutrientconcentrations from the outflow of the Fox River (Sagerand Richman 1991; De Stasio and Richman 1998).Brazner and Beals (1997) found lower fish diversityin wetlands located in the more eutrophic southern

Green Bay compared with the northern portion ofthe bay. Seilheimer and Chow-Fraser (2006) alsonoted a gradient in the structure of fish communitiesfrom southern Green Bay to northern Green Bay andreported lower Wetland Fish Index scores in thesouthern bay. Our fish IBI reflected this north-to-south gradient of declining water and habitat qualityin Green Bay (Fig. 5).

SumRank

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Fig. 2 Relationships between cattail zone IBI scores andSumRank indices calculated for the development (a) and testdatasets (b), as well as for individual years (c–g). Developmentand test data were combined for the individual year analyses.



The gradient of increasing IBI scores from west toeast in Lake Ontario (Fig. 5) coincides with decreasingareal extent of agricultural and urban land-cover andimproving coastal water quality along the same longitu-dinal gradient. The coastal region from Toronto aroundthe west end of Lake Ontario to Buffalo-Niagara is oneof the most densely populated areas in the Great Lakesbasin (Huang and Méthot 2015). Agricultural land-cover is also remarkably high in this region, and a mean

of 44% of the area within our 20-km buffers surround-ing wetlands situated between Toronto and Buffalo-Niagara were devoted to agriculture compared to thebasin-wide mean of 29%. Past work has shown thatcoastal and nearshore water quality in the western halfof Lake Ontario is consistently more degraded (e.g.,higher dissolved nutrient concentrations, dissolvedsolids, turbidity) than in the eastern half of the lake(Cvetkovic and Chow-Fraser 2011; Makarewicz et al.

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Fig. 3 Relationships between water lily zone IBI scores andSumRank indices calculated for the development (a) and testdatasets (b), as well as for individual years (c–g). Developmentand test data were combined for the individual year analyses.



2012). Previous work also has shown that coastal wet-land fish communities in western Lake Ontario tend tobe less diverse and comprised of more tolerant speciesthan those of eastern Lake Ontario (Uzarski et al. 2005;Cvetkovic and Chow-Fraser 2011). Our IBI scores cap-tured this west-to-east gradient of increasing biotic in-tegrity, with a transition occurring at approximately the79th meridian, which lies just east of the Toronto andBuffalo-Niagara metropolitan areas (Fig. 5).

Similar to Lake Ontario, IBI scores in Lake Erie alsotended to increase from west to east, corresponding to agradient of improving water quality from Lake Erie’swestern to eastern basins (Conroy et al. 2008; Kane et al.2014). Lake Erie wetlands with relatively high IBIscores (i.e., Bmildly impacted^ wetlands) occurred inthe Rondeau and Long Point Provincial Parks in Cana-da, and Presque Isle in Pennsylvania, all of which are

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Fig. 4 Relationships between submersed aquatic vegetation zoneIBI scores and SumRank indices calculated for the development(a) and test datasets (b), as well as for individual years (c–g).Development and test data were combined for the individual year

analyses. Separate SumRank values were calculated for eachcomparison. Pearson correlations were calculated between IBIand SumRank scores


protected areas known for their high floral and faunaldiversity (Herdendorf 1987; Tiner et al. 2014).

Comparison to other fish-based indicators in GreatLakes coastal wetlands

Seilheimer and Chow-Fraser (2006) developed the Wet-land Fish Index (WFI) using fish and water quality datafrom 40 Great Lakes coastal wetlands. The WFI wasbased on a partial canonical correspondence analysis toordinate fish species along environmental axes (primar-ily water quality features) to estimate species-environment associations and niche breadth. Althoughthe WFI differs from our approach, a number of ourmetrics are consistent with the species-environment co-efficients derived by Seilheimer and Chow-Fraser(2006). For example, common carp (Cyprinus carpio)and goldfish (Carassius auratus) both received very lowcoefficients in the WFI, indicating their tolerance ofpoor water quality. Similarly, the presence of thesespecies reduced the scores for each of our IBIs becausethey are both nonnative, which influenced at least onemetric in each of our IBIs, and because common carpwas included specifically as a metric in three of our fourIBIs (Table 2). We identified rock bass (Ambloplitesrupestris) as an indicator of high quality habitat in threeof our four IBIs; this species was similarly associated

with good water quality in Seilheimer and Chow-Fraser(2006) and was found to have potential as a basin-wideindicator of coastal wetland health by Brazner et al.(2007). Seilheimer and Chow-Fraser (2006) found thata number of native cyprinids were associated withmoderate-to-good water quality in Great Lakes coastalwetlands [e.g., blackchin shiner (Notropis heterodon),mimic shiner (N. volucellus), blacknose shiner(N. heterolepis), bluntnose minnow (Pimephalesnotatus), emerald shiner (N. atherinoides), and goldenshiner (Notemigonus crysoleucas)]. These native cypri-nids contributed to metrics in our IBIs as well. Forexample, % Native Cyprinidae and Native CyprinidaeCPUE were found to be useful metrics in cattail, bul-rush, and SAV-zone IBIs. Other authors also have foundmetrics related to native cyprinid species to be usefulindicators of habitat quality in lakes (Minns et al. 1994;Randall and Minns 2002), wetlands (Wilcox et al.2002), and streams (Karr 1981; Fausch et al. 1984;Raburu and Masese 2012), likely due to the group’sgenerally high sensitivity to turbidity and other formsof environmental degradation (Whittier and Hughes1998; Trebitz et al. 2007).

We found no positive relationship between speciesrichness and habitat quality for any of the vegetationtypes, which is consistent with Seilheimer and Chow-Fraser (2006). They found a weak negative relationship

Fig. 5 Biotic integrity categories based on fish IBI scores (meansof IBI scores from all vegetation zones sampled per wetland, bothdevelopment and testing data sets). Categories correspond to ap-proximate quintiles of the full set of IBI scores for the basin. The

downstream boundary for the region was a line through WolfeIsland in the St. Lawrence River, from Kingston, Ontario to CapeVincent, New York


between water quality and fish species richness. Thisresult is likely because tolerant and often nonnative

species tend to colonize degraded habitats and increaselocal species richness. Not surprisingly, therefore, we

Fig. 6 Great Lakes water levels(m above sea level, mASL) basedon monthly mean levels, 2005–2015. Arrows representapproximate sampling periods(summers, 2011–2015). Datadownloaded from the NOAAGreat Lakes EnvironmentalResearch Laboratory Water LevelDashboard (https://www.glerl.noaa.gov/data/dashboard/data/)


https://www.glerl.noaa.gov/data/dashboard/data/

https://www.glerl.noaa.gov/data/dashboard/data/

identified useful richness-basedmetrics based onwheth-er species were native, and all four IBIs included at leastone such metric. Others also have found that accountingfor native vs. nonnative species improved richness-based metric performance in fish IBIs (Minns et al.1994; Thoma 1999; Wilcox et al. 2002; Lyons 2012).

The fish-based IBIs developed by Uzarski et al.(2005) and evaluated further by Bhagat et al. (2007)included metrics for bulrush and cattail zones in GreatLakes coastal wetlands. We identified many of the samemetrics as these previous efforts, with notable modifi-cations and additions. In our bulrush zone IBI, Evennesswas selected as a diversity metric instead of Total taxonrichness. Neither Total catch net-night−1 nor % Insecti-vores (Uzarski et al. 2005) was retained. We identified% Carnivore as a metric, similar to the % Carnivorerichness metric of Uzarski et al. (2005); interestinglyhowever, the relationship between this metric and hab-itat quality was negative and nonlinear. Domination ofan assemblage by just one or two carnivore taxa fre-quently resulted in > 90% carnivore relative abundance,which generally occurred in the most disturbed wet-lands. Therefore, the scoring scheme for this metricwas designed to detect fish assemblages that were dras-tically out of trophic balance compared to assemblageswith a broader trophic base.

Our cattail zone IBI also differed from that of Uzarskiet al. (2005) in a number of ways. We did not retainInsectivorous Cyprinidae richness but included % Na-tive Cyprinidae. The cattail zone IBI of Uzarski et al.(2005) included six metrics related to Centrarchidae.Weretained one of these, Rock bass CPUE, but the otherswere redundant and only marginally indicative of hab-itat quality in our analysis. Centrarchids did, however,contribute substantially to % Richness nest spawners,which was a useful indicators of habitat quality forcattail zones in our study. We also incorporated metricsrelated to species longevity, body size, and speciessensitivity to anthropogenic disturbance, none of whichwere considered by Uzarski et al. (2005). Despite thesemodifications, we consider our current set of indices tobe expanded and updated versions of the preliminaryIBIs developed by Uzarski et al. (2005).

Synopsis and applications

Anthropogenic stressors such as nutrient and sedimentloading, which are generally related to human activitieson the landscape, are important drivers of fish

community structure in Great Lakes coastal wetlands(Brazner and Beals 1997; Seilheimer and Chow-Fraser2006; Trebitz et al. 2009). Macrophytes also affect fishcommunity structure in these habitats (Brazner andBeals 1997; Cvetkovic et al. 2010). Thus, we used anapproach that leveraged the information contained infish–habitat quality relationships while also accountingfor the effects of macrophyte structure by samplingwithin discrete vegetation zones.

We developed the fish-based IBIs using a combina-tion of water quality and land cover data for 470 coastalwetlands spanning the Great Lakes basin. By includinglocally collected water quality data, we incorporatedwetland-scale variation in habitat quality that cannotbe detected when using only landscape-scale data dur-ing IBI development. Additionally, the development ofseparate IBIs for different vegetation types is of partic-ular importance in Great Lakes coastal wetlands becausethe number of vegetation types (thus, the degree ofhabitat variation) differs from wetland to wetland, andcan differ within a given wetland from year to year ifwater levels fluctuate dramatically. The fact that differ-ent metrics were found to be useful in different vegeta-tion types supports the idea that different fish speciesprefer different vegetation structure. Thus, we feel thatthe multiple vegetation type approach to IBI develop-ment is appropriate for representing the fish communi-ties of Great Lakes coastal wetlands. Management agen-cies who are interested in using these methods but findthe multi-zone sampling approach to be overly demand-ing on their time and resources may find it reasonable toreduce their effort by sampling in, and calculating IBIsfor, only selected vegetation types.

Potential applications for the IBIs developed hereinclude ranking fish community condition among wet-lands to assist managers in identifying and prioritizingwetlands in need of restoration, tracking the efficacy ofrestoration investments throughout the Great Lakes, andassessing fish-related beneficial use impairments (BUIs)within Great Lakes Areas of Concern (see Annex 1 ofthe 2012 Great Lakes Water Quality Agreement). TheIBIs also can be used by government agencies in Canadaand the USA to report on State of the Lakes EcosystemConference (SOLEC) indicators and meet other obliga-tions under the Great Lakes Water Quality Agreement(e.g., Annex 2, Lakewide Management; Annex 7, Hab-itat and Species; and Annex 10, Science). Each of theseannexes contain requirements to monitor ecosystemhealth, and our fish-based IBIs are potential tools to


accomplish this monitoring. Given that approximately50% ofGreat Lakes coastal wetlands have been lost, andremaining wetlands suffer from a variety of humanimpacts, growing emphasis has been placed onprotecting and restoring these ecosystems. Furthermore,as the last barrier to watershed runoff that enters theopenwaters of the Great Lakes, coastal wetlands occupya critical position in protecting water quality and har-boring unique biodiversity. Our approach provided in-dices that offer a robust means for classifying the con-dition of fish communities in wetlands, which can guideresearchers and managers in identifying locations mostin need of protection and restoration and for trackingconditions through time.

Acknowledgments Funding for the wetland monitoring pro-gram that generated data for this study was provided by the GreatLakes National Program Office under the United States Environ-mental Protection Agency, grant number GL-00E00612-0. Al-though the research described in this work has been partly fundedby the United States Environmental Protection Agency, it has notbeen subjected to the agency’s required peer and policy review andtherefore does not necessarily reflect the views of the agency andno official endorsement should be inferred. This paper is Contri-bution Number 103 of the Central Michigan University Institutefor Great Lakes Research. We thank the many students, researchassistants, and technicians who participated in the project. Supportfor MJC was provided by an Arthur J. Schmitt Presidential Fel-lowship, an NSF-IGERT GLOBES Fellowship, and a Center forEnvironmental Science and Technology-Bayer Fellowship fromthe University of Notre Dame.

Appendix

Table 3 Candidate fish assemblage-based metrics for IBI development (catch per unit effort, CPUE; smallmouth bass, SMB; largemouthbass, LMB). See Appendix Table 4 for taxonomic names of fishes

1 – Diversity

Total species richness Omnivore species richness

Shannon diversity %Omnivore species richness

Evenness Omnivore CPUE

%Omnivore

2 – Native vs. non-native Invertivore species richness

%Invertivore species richness

Native species richness Invertivore CPUE

%Native species richness %Invertivore

Native species CPUE

%Native Benthic invertivore species richness

%Benthic invertivore species richness

Non-native species richness Benthic invertivore CPUE

%Non-native species richness %Benthic invertivore

Non-native species CPUE

%Non-native Invertivorous Cyprinidae species richness

%Invertivorous Cyprinidae species richness

3a – Taxonomic community structure Invertivorous Cyprinidae CPUE

%Invertivorous Cyprinidae

Centrarchidae species richness

%Centrarchidae species richness

Centrarchidae CPUE 5 – Habitat affinity

%Centrarchidae

Adult benthic habitat species richness

Centrarchidae species richness, excl. SMB & LMB %Adult benthic habitat species

%Centrarchidae species richness, excl. SMB & LMB Adult benthic habitat CPUE

Centrarchidae CPUE, excl. SMB & LMB %Adult benthic habitat


Table 3 (continued)

%Centrarchidae, excl. SMB & LMBAdult vegetation habitat species richness

Cyprinidae species richness%Cyprinidae species richness %Adult vegetation habitat speciesCyprinidae CPUE%Cyprinidae Adult vegetation habitat CPUENative Cyprinidae species richness %Adult vegetation habitat%Native Cyprinidae species richnessNative Cyprinidae CPUE%Native Cyprinidae Nest spawner richness

%Nest spawner richnessNotropis species richness%Notropis species richness Nest spawner CPUENotropis CPUE %Nest spawners%Notropis

Vegetation spawner richness%Vegetation spawner richness

3b – 25 most common species (CPUE and Rel. Ab.) Vegetation spawner CPUEWhite sucker %Vegetation spawnersBlack bullheadRock bass 6 – Species attributesAlewifeSmallmouth bass Long-lived species richnessLongnose gar %Long-lived species richnessLargemouth bass Long-lived species CPUEBluegill %Long-lived speciesLepomisBlack+brown bullhead Short-lived species richnessBanded killifish %Short-lived species richnessBlacknose shiner Short-lived species CPUEBluntnose minnow %Short-lived speciesBowfinEmerald shiner Large and and XLarge species richnessGolden shiner %Large and XLarge species richnessJohnny darter Large and XLarge species CPUEPumpkinseed %Large and Xlarge speciesRound gobySand shiner Small species richnessSpottail shiner %Small species richnessYellow perch Small species CPUEBlackchin shiner %Small speciesBrook silversidesBrook stickleback High and XHigh temperature spawner species richnessCommon carp %High and XHigh temperature spawner species richnessCommon shiner High and XHigh temperature species CPUE

%High and XHigh temperature spawner species4 – Feeding ecology

7 – Sensitivity/tolerance to human disturbancePiscivore species richness%Piscivore species richness Number of species particularly sensitive to environmental degradationPiscivore CPUE %Species richness particularly sensitive to environmental degradation%Piscivore Species particularly sensitive to environmental degradation CPUE

%Particularly sensitive to degradationCarnivore species richness%Carnivore species richness Number of species particularly tolerant to environmental degradationCarnivore CPUE %Species particularly tolerant to environmental degradation%Carnivore Species particularly tolerant to environmental degradation CPUE

%Particularly tolerant to degradation


Tab

le4

Classificationof

fish

taxa.Fishtaxa

wereclassified

into

adulthabitat(H

AB),adultfood

(FOOD),lifespan(LIFE),TROPHIC,totallength

(LENGTH),spaw

ning

habitat

(SPA

WN_H

),spaw

ning

temperature

(SPA

WN_T

),overalltolerance(TOL),andendemism

(ENDEM)groups.Sources

forHAB,LONG,TROPHIC,LENGTH,SPA

WN_H

,and

SPAWN_T

inform

ationincludeGoldstein

andSimon

(1999),Halliw

elletal.(1999),Hubbs

etal.(2004),Pflieger(1975),Simon

(1999),andWhittier

andHughes(1998).Sources

for

FOODandTOLincludePlafkinetal.(1989)andLyons

(2006,2012)

Trait

HAB

FOOD

LIFE

TROPHIC

LENGTH

Traitcodes

bt,benthic

fh,fish

hg,>

15yrs.

ff,filter

feeder

xl,>

600mm

md,mud

mi,macroinvertebrates

md,5–15

yrs.

gf,generalist

lg,301–600

mm

pl,pool

ov,omnivore

lw,<

5yrs.

in,insectiv

ore

md,101–300mm

pz,pelagic

pt,plants

iv,invertiv

ore

sm,0–100

mm

rf,riffle

pz,phytoplankton.

ov,omnivore

rk,rock

pv,piscivore

su,surface

Speciesname

Com

mon

name

vg,vegetation

Alosa

pseudoharengus

Alewife

pzpz

lwff

md

Ambloplites

rupestris

Rockbass

vgov

md

pvmd

Ameiurus

melas

Black

bullh

ead

btov

md

inmd

Ameiurus

melas

orA.nebulosus

Black

orbrow

nbullh

ead

btov

lwin

md

Ameiurus

natalis

Yellowbullh

ead

btov

md

inmd

Ameiurus

nebulosus

Brownbullh

ead

btov

lwin

md

Amia

calva

Bow

fin

hyfh

md

pvlg

Anguilla

rostrate

American

eel

btov

md

pvxl

Aphredoderussayanus

Pirateperch

md

ovlw

insm

Aplodinotus

grunniens

Freshwater

drum

btmi

hgiv

lg

Carassius

auratus

Goldfish

btov

md

ovmd

Carpiodes

cyprinus

Quillb

ack

btov

md

ovlg

Catostomidae

Suckers

btov

md

ovmd

Catostomus

commersonii

Whitesucker

btov

md

ovmd

Centrarchidae

Sunfishes

vgov

md

inmd

Lepomisgulosus

Warmouth

vgov

md

pvmd

Coregonus

artedi

Lakecisco

pzpz

md

ffmd

Cottusbairdii

Mottledsculpin

gnov

lwin

sm

Cottuscognatus

Eastern

slim

ysculpin

btov

lwin

sm

Couesiusplum

beus

Lakechub

gnov

lwin

md

Culaeainconstans

Brook

stickleback

vgov

lwin

sm

Cyprinella

spilo

ptera

Spotfinshiner

btov

lwin

sm


Tab

le4

(contin

ued)

Cyprinidae

Carps

andminnows

btov

md

ovlg

Cyprinuscarpio

Com

mon

carp

btov

md

ovlg

Dorosom

acepedianum

Gizzard

shad

pzpz

lwov

lgEsoxam

ericanus

Grass

pickerel

vgfh

lwpv

md

Esoxlucius

Northernpike

vgfh

md

pvlg

Esoxmasquinongy

Muskellu

nge

vgfh

hgpv

xlEsoxniger

Chain

pickerel

vgfh

md

pvlg

Etheostom

aDarters

btov

lwin

smEtheostom

acaeruleum

Rainbow

darter

btmi

lwin

smEtheostom

achlorosomum

Bluntnose

darter

btmi

lwin

smEtheostom

aexile

Iowadarter

btov

lwin

smEtheostom

amicroperca

Leastdarter

btmi

lwin

smEtheostom

anigrum

Johnny

darter

btmi

hgin

smFundulusdiaphanous

Bandedkillifish

suov

lwin

smGam

busiaaffin

isWestern

mosquito

fish

sumi

lwin

smGasterosteusaculeatus

Threespinestickleback

vgov

lwin

smGym

nocephalus

cernuus

Eurasianruffe

plov

md

ovmd

Hybognathus

hankinsoni

Brassyminnow

vgov

lwov

smIctaluridae

North

American

catfishes

btov

md

pvlg

Ictaluruspunctatus

Channelcatfish

btov

md

pvlg

Ictio

buscyprinellus

Bigmouth

buffalo

md

ovmd

inlg

Labidesthessicculus

Brook

silverside

pzov

md

insm

Lepisosteidae

Gars

sufh

hgpv

lgLepisosteusoculatus

Spottedgar

sufh

hgpv

xlLepisosteusosseus

Longnosegar

sufh

hgpv

lgLepisosteusplatostomus

ShortnoseGar

sufh

hgpv

lgLepomis

Sunfishes

vgmi

md

inmd

Lepomiscyanellus

Green

sunfish

vgfh

md

inmd

Lepomisgibbosus

Pumpkinseed

vgov

md

inmd

Lepomishumilis

Orangespottedsunfish

vgov

lwin

smLepomismacrochirus

Bluegill

vgmi

md

inmd

Lepomismegalotis

Longear

sunfish

vgov

md

insm

Lota

lota

Burbot

btfh

md

pvlg

Luxiluschrysocephalus

Stripedshiner

vgov

md

insm

Luxiluscornutus

Com

mon

shiner

vgov

md

insm

Lythrurusum

bratilis

Redfinshiner

vgov

lwin

smMargariscus

margarita

Pearld

ace

pzov

lwin

smMicropterus

dolomieu

Smallm

outh

bass

rkov

md

pvmd

Micropterus

salmoides

Largemouth

bass

vgov

md

pvmd

Minytremamelanops

Spottedsucker

btov

md

inmd

MoroneAmericana

Whiteperch

pzov

md

pvmd

Moronechrysops

Whitebass

suov

lwpv

md

Moxostomaanisurum

Silver

redhorse

btov

md

inmd


Tab

le4

(contin

ued)

Moxostomabreviceps

Shortheadredhorse

btov

md

inmd

Moxostomaerythrurum

Goldenredhorse

btov

md

inmd

Moxostomavalenciennesi

Greater

redhorse

btov

md

inlg

Neogobius

melanostomus

Round

goby

btov

lwov

md

Nocom

isbigutta

tus

Horneyheadchub

btov

lwin

smNotem

igonus

crysoleucas

Goldenshiner

vgov

md

ovmd

Notropis

Shiners

vgov

lwin

smNotropisanogenus

Pugnoseshiner

gnpt

lwin

smNotropisatherinoides

Emeraldshiner

pzmi

lwin

smNotropisbifrenatus

Bridled

shiner

vgov

lwin

smNotropisheterodon

Blackchin

shiner

suov

lwin

smNotropisheterolepis

Blacknose

shiner

vgov

lwin

smNotropishudsonius

Spottailshiner

gnov

lwin

smNotropisrubellu

sRosyfaceshiner

rfmi

lwin

smNotropisstramineus

Sand

shiner

vgov

lwin

smNotropisvolucellu

sMim

icshiner

vgov

lwin

smNoturus

gyrinus

Tadpolemadtom

btov

lwin

smNoturus

miurus

Brindledmadtom

btmi

lwin

smOncorhynchustshawytscha

Chinook

salm

onpz

ovmd

pvxl

Osm

erus

mordax

Rainbow

smelt

pzov

md

ivmd

Perca

flavescens

Yellowperch

btov

md

inmd

Percina

caprodes

Logperch

btov

lwin

smPercopsisom

iscomaycus

Trout-perch

btov

lwin

smPetromyzon

marinus

Sealamprey

pzfh

lwpv

lgPhoxinuseos

Northernredbelly

dace

btpt

lwgf

smPhoxinusneogaeus

Finescaledace

btmi

md

insm

Pimephalesnotatus

Bluntnose

minnow

suov

lwov

smPimephalesprom

elas

Fatheadminnow

suov

lwov

smPom

oxis

Black

orwhitecrappie

vgov

md

pvmd

Pom

oxisannularis

Whitecrappie

pzov

md

pvmd

Pom

oxisnigrom

aculatus

Black

crappie

btmi

md

inlg

Proterorhinus

marmoratus

Tubenosegoby

btov

lwin

smPungitiu

spungitius

Ninespine

stickleback

vgov

lwin

smRhinichthys

obtusus

Western

blacknosedace

rfmi

lwgf

smRhinichthys

cataractae

Longnosedace

rfmi

lwin

smSander

Pikeperches

btfh

hgpv

lgSander

vitreus

Walleye

btfh

hgpv

lgScardinius

erythrophthalmus

Rudd

btmi

lwov

md

Semotilu

satromaculatus

Creek

chub

btov

md

gfmd

Umbra

Mudminnows

vgov

lwin

smUmbralim

iCentralmudminnow

vgov

lwin

sm


Tab

le4

(contin

ued)

Trait

SPA

WN_H

SPA

WN_T

TOL

ENDEM

Traitcodes

bc,broadcast

xh,extra

high

(>21)

i,intolerant

n,nonnative

cv,under

rocks

hg,high(16–20)

m,intermediate

y,nativ

elb,livebearer

md,moderate(11–15)

t,tolerant

ns,nestsor

redds

lw,low

(4–10)

pl,pool

rk,riffles

orshoals

Speciesname

Com

mon

name

vg,vegetation

Alosa

pseudoharengus

Alewife

bcmd

mn

Ambloplites

rupestris

Rockbass

nshg

my

Ameiurus

melas

Black

bullh

ead

nsxh

my

Ameiurus

melas

orA.nebulosus

Black

orbrow

nbullh

ead

nsxh

ty

Ameiurus

natalis

Yellowbullh

ead

nsxh

ty

Ameiurus

nebulosus

Brownbullh

ead

nsxh

ty

Amia

calva

Bow

fin

nshg

my

Anguilla

rostrate

American

eel

bchg

my

Aphredoderussayanus

Pirateperch

nsxh

my

Aplodinotus

grunniens

Freshw

ater

drum

bcxh

mn

Carassius

auratus

Goldfish

vghg

tn

Carpiodes

cyprinus

Quillb

ack

bcxh

my

Catostomidae

Suckers

rkmd

ty

Catostomus

commersonii

Whitesucker

rkmd

ty

Centrarchidae

Sunfishes

nshg

my

Lepomisgulosus

Warmouth

nsxh

my

Coregonus

artedi

Lakecisco

bclw

my

Cottusbairdii

Mottledsculpin

cvlw

iy

Cottuscognatus

Eastern

slim

ysculpin

cvlw

my

Couesiusplum

beus

Lakechub

bchg

my

Culaeainconstans

Brook

stickleback

nshg

my

Cyprinella

spilo

ptera

Spotfinshiner

cvxh

my

Cyprinidae

Carps

andminnows

vgxh

mu

Cyprinuscarpio

Com

mon

carp

vgxh

tn

Dorosom

acepedianum

Gizzard

shad

bcmd

my

Esoxam

ericanus

Grass

pickerel

vglw

my

Esoxlucius

Northernpike

vglw

my

Esoxmasquinongy

Muskellu

nge

vglw

my

Esoxniger

Chain

pickerel

vglw

my

Etheostom

aDarters

vgmd

my

Etheostom

acaeruleum

Rainbow

darter

nsmd

my

Etheostom

achlorosomum

Bluntnose

darter

vgmd

my

Etheostom

aexile

Iowadarter

vgmd

my

Etheostom

amicroperca

Leastdarter

vgmd

my

Etheostom

anigrum

Johnny

darter

cvmd

my


Tab

le4

(contin

ued)

Fundulusdiaphanous

Bandedkillifish

vgxh

ty

Gam

busiaaffin

isWestern

mosquito

fish

lbxh

mn

Gasterosteusaculeatus

Threespinestickleback

nshg

my

Gym

nocephalus

cernuus

Eurasianruffe

vgmd

mn

Hybognathus

hankinsoni

Brassyminnow

vghg

my

Ictaluridae

North

American

catfishes

cvxh

my

Ictaluruspunctatus

Channelcatfish

cvxh

my

Ictio

buscyprinellus

Bigmouth

buffalo

vghg

my

Labidesthessicculus

Brook

silverside

bcxh

my

Lepisosteidae

Gars

vghg

my

Lepisosteusoculatus

Spottedgar

vghg

my

Lepisosteusosseus

Longnosegar

vghg

my

Lepisosteusplatostomus

ShortnoseGar

vghg

my

Lepomis

Bluegill

orpumpkinseed

nshg

my

Lepomiscyanellus

Green

sunfish

nshg

ty

Lepomisgibbosus

Pumpkinseed

nshg

my

Lepomishumilis

Orangespottedsunfish

nshg

mn

Lepomismacrochirus

Bluegill

nshg

my

Lepomismegalotis

Longear

sunfish

nshg

iy

Lota

lota

Burbot

bclw

my

Luxiluschrysocephalus

Stripedshiner

rkxh

my

Luxiluscornutus

Com

mon

shiner

rkxh

my

Lythrurusum

bratilis

Redfinshiner

nsxh

my

Margariscus

margarita

Pearld

ace

rkmd

my

Micropterus

dolomieu

Smallm

outh

bass

nshg

my

Micropterus

salmoides

Largemouth

bass

nshg

my

Minytremamelanops

Spottedsucker

rkhg

my

MoroneAmericana

Whiteperch

bchg

mn

Moronechrysops

Whitebass

bchg

my

Moxostomaanisurum

Silver

redhorse

rkhg

my

Moxostomabreviceps

Shortheadredhorse

bchg

my

Moxostomaerythrurum

Goldenredhorse

rkhg

my

Moxostomavalenciennesi

Greater

redhorse

rkhg

iy

Neogobius

melanostomus

Round

goby

rkmd

tn

Nocom

isbigutta

tus

Horneyheadchub

nshg

iy

Notem

igonus

crysoleucas

Goldenshiner

vghg

ty

Notropis

Shiners

vgxh

my

Notropisanogenus

Pugnoseshiner

vgxh

iy

Notropisatherinoides

Emeraldshiner

rkxh

my

Notropisbifrenatus

Bridled

shiner

vgxh

iy

Notropisheterodon

Blackchin

shiner

vgxh

iy

Notropisheterolepis

Blacknose

shiner

vgxh

iy

Notropishudsonius

Spottailshiner

rkxh

my


Tab

le4

(contin

ued)

Notropisrubellu

sRosyfaceshiner

bcxh

iy

Notropisstramineus

Sand

shiner

vgxh

my

Notropisvolucellu

sMim

icshiner

vgxh

iy

Noturus

gyrinus

Tadpolemadtom

cvxh

my

Noturus

miurus

Brindledmadtom

cvxh

iy

Oncorhynchustshawytscha

Chinook

salm

onns

lwm

nOsm

erus

mordax

Rainbow

smelt

bclw

mn

Perca

flavescens

Yellowperch

vghg

my

Percina

caprodes

Logperch

nshg

my

Percopsisom

iscomaycus

Trout-perch

bchg

my

Petromyzon

marinus

Sealamprey

nslw

mn

Phoxinuseos

Northernredbelly

dace

vghg

my

Phoxinusneogaeus

Finescaledace

cvhg

my

Pimephalesnotatus

Bluntnose

minnow

nsxh

ty

Pimephalesprom

elas

Fatheadminnow

cvxh

ty

Pom

oxis

Black

orwhitecrappie

nshg

my

Pom

oxisannularis

Whitecrappie

nshg

my

Pom

oxisnigrom

aculatus

Black

crappie

bclw

my

Proterorhinus

marmoratus

Tubenosegoby

rkmd

tn

Pungitiu

spungitius

Ninespine

stickleback

nsmd

my

Rhinichthys

obtusus

Western

blacknosedace

nshg

ty

Rhinichthys

cataractae

Longnosedace

rkhg

iy

Sander

Pikeperches

rklw

my

Sander

vitreus

Walleye

rklw

my

Scardinius

erythrophthalmus

Rudd

vghg

tn

Semotilu

satromaculatus

Creek

chub

nsmd

ty

Umbra

Mudminnows

vgmd

ty

Umbralim

iCentralmudminnow

vgmd

ty


Open Access This article is distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestrict-ed use, distribution, and reproduction in any medium, providedyou give appropriate credit to the original author(s) and the source,provide a link to the Creative Commons license, and indicate ifchanges were made.

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An expanded fish-based index of biotic integrity for Great Lakes … · 2018. 10. 22. · An expanded fish-based index of biotic integrity for Great Lakes coastal wetlands Matthew