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Lake and Reservoir Management
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Lakeshore and littoral physical habitat structure ina national lakes assessment
Philip R. Kaufmann, David V. Peck, Steven G. Paulsen, Curt W. Seeliger,Robert M. Hughes, Thomas R. Whittier & Neil C. Kamman
To cite this article: Philip R. Kaufmann, David V. Peck, Steven G. Paulsen, Curt W. Seeliger,Robert M. Hughes, Thomas R. Whittier & Neil C. Kamman (2014) Lakeshore and littoral physicalhabitat structure in a national lakes assessment, Lake and Reservoir Management, 30:2, 192-215,DOI: 10.1080/10402381.2014.906524
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Lake and Reservoir Management, 30:192–215, 2014C© Copyright by the North American Lake Management Society 2014ISSN: 1040-2381 print / 2151-5530 onlineDOI: 10.1080/10402381.2014.906524
Lakeshore and littoral physical habitat structure in a nationallakes assessment
Philip R. Kaufmann,1,∗ David V. Peck,1 Steven G. Paulsen,1 Curt W. Seeliger,2
Robert M. Hughes,3 Thomas R. Whittier,4 and Neil C. Kamman5
1US Environmental Protection Agency, Office of Research and Development, National Healthand Environmental Effects Research Laboratory, Western Ecology Division, 200 SW 35th St,
Corvallis, OR 973332Raytheon Information Services, c/o USEPA, 200 SW 35th St, Corvallis, OR 97333
3Amnis Opes Institute and Oregon State University Department of Fisheries & Wildlife, 200SW 35th St, Corvallis, OR 97333
4Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 973315Vermont Department of Environmental Conservation, Water Quality Division, Monitoring,
Assessment and Planning Program, 103 S Main 10N, Waterbury, VT 05671
Abstract
Kaufmann PR, Peck DV, Paulsen SG, Seeliger CW, Hughes RM, Whittier TR, Kamman NC. 2014. Lakeshore andlittoral physical habitat structure in a national lakes assessment. Lake Reserv Manage. 30:192–215.
Near-shore physical habitat is crucial for supporting biota and ecological processes in lakes. We used data from astatistical sample of 1101 lakes and reservoirs from the 2007 US Environmental Protection Agency National LakesAssessment (NLA) to develop 4 indices of physical habitat condition: (1) Lakeshore Anthropogenic Disturbance,(2) Riparian Vegetation Cover Complexity, (3) Littoral Cover Complexity, and (4) Littoral–Riparian Habitat Com-plexity. We compared lake index values with distributions from least-disturbed lakes. Our results form the firstUS national assessment of lake physical habitat, inferring the condition (with known confidence) of the 49,500lakes and reservoirs in the conterminous United States with surface areas >4 ha and depths >1 m. Among thephysical and chemical characteristics examined by the NLA, near-shore physical habitat was the most extensivelyaltered relative to least-disturbed condition. Riparian Vegetation Cover Complexity was good in 46% (±3%), fairin 18% (±2%), and poor in 36% (±2%) of lakes. Littoral–Riparian Habitat Complexity was good in 47% (±3%),fair in 20% (±2%), and poor in 32% (±2%) of lakes. In every ecoregion, habitat condition was negatively relatedto anthropogenic pressures. Gradients of increased anthropogenic disturbance were accompanied by progressivesimplification of littoral and riparian physical habitat. Nationwide, the proportion of lakes with degraded near-shorephysical habitat was equal to or greater than that for excess nutrients and much greater than that for acidic conditions.In addition to better management of lake basins, we suggest an increased focus on protecting littoral and riparianphysical habitat through better management of near-shore anthropogenic disturbance.
Key words: habitat complexity, hydromorphology, riparian disturbance, lake ecology, lake habitat, littoral habitat,monitoring, physical habitat structure, reference condition
Near-shore physical habitat structure has only recently beenaddressed in regional lake monitoring, even though an-thropogenic activities and aquatic and riparian biota areconcentrated in these areas. Monitoring and assessment of
∗Corresponding author: [email protected] versions of one or more of the figures in the article can befound online at www.tandfonline.com/ulrm.
near-shore lake habitat conditions are needed because theseareas are both ecologically important and especially vulnera-ble to anthropogenic perturbation (Schindler and Scheuerell2002, Strayer and Findlay 2010, Hampton et al. 2011). Lit-toral and riparian zones are positioned at the land–water in-terface and tend to be more structurally complex and biolog-ically diverse than either pelagic areas or upland terrestrialenvironments (Polis et al. 1997, Strayer and Findlay 2010).This complexity promotes interchange of water, nutrients,
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and biota between the aquatic and terrestrial compartmentsof lake ecosystems (Benson and Magnuson 1992, Polis et al.1997, Palmer et al. 2000, Zohary and Ostrovsky 2011).
Riparian vegetation and wetlands surrounding lakes increasenear-shore physical habitat complexity (e.g., Christensenet al. 1996, Francis and Schindler 2006) and buffer lakesfrom the influence of upland land use activities (Carpenterand Cottingham 1997, Strayer and Findlay 2010). Anthro-pogenic activities on or near lakeshores can directly or in-directly degrade littoral and riparian habitat (Francis andSchindler 2006). Increased sedimentation, loss of nativeplant growth, alteration of native plant communities, lossof physical habitat structure, and changes in littoral coverand substrate are all commonly associated with lakeshorehuman activities (Christensen et al. 1996, Engel and Peder-son 1998, Whittier et al. 2002, Francis and Schindler 2006,Merrell et al. 2009). Such reductions in physical habitatstructural complexity can deleteriously affect fish (Wagneret al. 2006, Taillon and Fox 2004, Whittier et al. 1997, 2002,Halliwell 2007, Jennings et al. 1999, Wagner et al. 2006),aquatic macroinvertebrates (Brauns et al. 2007), and birds(Kaufmann et al. 2014b).
Despite the growing recognition that protection of near-shore physical habitat structural complexity is essential toconserve biological condition in lakes, the lack of practical,standardized approaches has hindered its quantification andevaluation in large-scale regional and national lake assess-ments. We therefore developed rapid field methods to quan-tify physical habitat structure and near-shore anthropogenicdisturbances (Kaufmann and Whittier 1997, USEPA 2007a)for application in national surveys. Based on data from thesefield methods, we calculated habitat indices that quantify thevariety, structural complexity, and magnitude of areal coverfrom physical habitat elements within the near shore zonesof lakes. Similar indices have been applied both in lakes(Kaufmann et al. 2014a) and streams (Hughes et al. 2010,Kaufmann and Faustini 2012). The structural complexityand variety of habitat quantified by these indices providediverse opportunities for supporting assemblages of aquaticorganisms (Strayer and Finlay 2010, Kovalenko et al. 2012)and foster the interchange of water, nutrients, and organismsbetween compartments within lake ecosystems (Schindlerand Scheuerell 2002, Stayer and Findlay 2010).
We used the methods described herein to assess physicalhabitat condition, interpret biological information, and as-sess lake ecological condition in the US Environmental Pro-tection Agency’s National Lakes Assessment (NLA; USEPA2009). This evaluation of near-shore physical habitat condi-tion was an unprecedented effort at the national scale.
Our objectives in this article are to describe how we evalu-ated near-shore physical habitat data in the NLA and asso-
ciated 4 key physical habitat condition indices with anthro-pogenic disturbances. We summarize key findings concern-ing the extent of lake physical habitat alteration across theUnited States and discuss the potential risks of physical habi-tat alteration to lake biota. This paper also describes how tocalculate the NLA’s physical habitat indices and derive phys-ical habitat condition thresholds relative to least-disturbedconditions.
MethodsAnalytical framework for assessing physicalhabitat condition
We took the following 7 steps to assess physical habitatcondition in US lakes at national and regional levels:
1. surveyed littoral and riparian physical habitat structureand near-shore anthropogenic activities on a nationalprobability sample of lakes and reservoirs;
2. defined ecoregions and classified lakes according to laketype and level of anthropogenic disturbance;
3. calculated a set of physical habitat metrics;4. calculated multimetric indices of physical habitat condi-
tion;5. estimated region-specific expected values for physical
habitat condition indices;6. set criteria for expected physical habitat condition based
on least-disturbed lakes; and7. estimated percentages and confidence bounds for the
number of lakes with physical habitat in good, fair, andpoor condition in the United States and its ecoregions.
National survey of lakes (Step 1)
Study area and site selection
The NLA field sampling effort targeted all lakes and reser-voirs in the conterminous United States with surface areas>4 ha and depths >1 m. Field crews visited 1152 lakesand reservoirs (Fig. 1A) between May and October 2007.Of these, 1028 had been selected as a probability samplefrom the US Geological Survey (USGS)-USEPA NationalHydrography Dataset (NHD) with a spatially balanced, ran-domized systematic design that excluded the Great Lakesand Great Salt Lake (Peck et al. 2013). The remaining 124lakes were hand-selected to increase the number of lakesin least-disturbed condition, which were used to estimatepotential condition and evaluate response of the indices todisturbance (following Stoddard et al. 2006).
Physical habitat data were collected from 1101 of the1152 survey lakes that had surface areas <5000 ha (981probability- and 120 hand-selected lakes; Peck et al. 2013).
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Kaufmann et al.
Figure 1. NLA lakes sampled in 2007, showing (A) 9 aggregated ecoregions and (B) multivariate classification of sample lake types usedto screen least-disturbed lakes (see Table 1). Three major geoclimatic regions were defined as the Eastern Highlands (NAP + SAP), theCoastal Plains and Lowlands (CPL + NPL + SPL + TPL + UMW), and the West (WMT + XER).
Both probability and hand-selected lakes were used to de-velop physical habitat condition indices and the distributionof expected values. A random subset of 90 probability lakeswas visited twice during the sampling period to estimate theprecision of the habitat measurements and indices (Kauf-
mann et al. 2014a.) Depending on the index, 965–973 ofthe 981 probability sample lakes had the requisite data formaking statistical inferences concerning habitat conditionin the national population of ∼49,500 lakes and reservoirs(Peck et al. 2013).
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National lake physical habitat assessment
Field sampling design and methods
Our lake physical habitat field methods (USEPA 2007a,Kaufmann et al. 2014a) produced information concern-ing 7 dimensions of near-shore physical habitat: (1) wa-ter depth and surface characteristics, (2) substrate size andtype, (3) aquatic macrophyte cover and structure, (4) lit-toral cover for biota, (5) riparian vegetation cover and struc-ture, (6) near-shore anthropogenic disturbances, and (7)bank characteristics that indicate lake level fluctuations andterrestrial–aquatic interactions. At each lake, field crewscharacterized these 7 components of near-shore physicalhabitat at 10 equidistant stations along the shoreline. Eachstation included a littoral plot (10 × 15 m) and an adjoiningriparian plot (15 × 15 m). Littoral depth was measured 10 moff-shore at each station. See Kaufmann et al. (2014a) fora description of field methods, our approach for calculatingwhole-lake physical habitat metrics, and an assessment ofhabitat metric precision.
Classifications (Step 2)
Ecoregions
We report findings nationally and by 9 aggregated Omernik(1987) level III ecoregions (Paulsen et al. 2008): the North-ern Appalachians (NAP), Southern Appalachians (SAP),Coastal Plains (CPL), Upper Midwest (UMW), Temper-ate Plains (TPL), Northern Plains (NPL), Southern Plains(SPL), Western Mountains (WMT), and Xeric West (XER;Fig. 1A). We used ecoregions as a first-level classificationfor defining and evaluating near-shore riparian condition in-dicators (RVegQ and its variants) because ecoregions areuseful predictors of landform, climate, and potential naturalvegetation (Omernik 1987, Paulsen et al. 2008). For some as-pects of habitat index development, we grouped ecoregionsinto broader ecoregions: the Eastern Highlands (EHIGH =NAP + SAP), the Plains and Lowlands (PLNLOW = CPL+ UMW + TPL + NPL + SPL), Central Plains (CENPL =TPL + NPL + SPL), and the West (WMT + XER).
Lake types
Littoral habitat elements (e.g., aquatic macrophytes, snags,and substrate) are affected by lithology and water depth, clar-ity, temperature, and ionic strength, factors that were poorlyassociated with the 9 ecoregion classification. We thereforeused a geographically constrained multivariate classifica-tion described by USEPA (appendix A in USEPA 2009) thatincluded variables known to influence those littoral habitatelements to group lakes with similar expectations for littoralcover. Within each of the 3 broader ecoregions, lakes weregrouped by their similarity in size, depth, morphology, el-evation, temperature, precipitation, calcium concentration,latitude, and longitude.
A nonhierarchical cluster analysis (FASTCLUS; SAS Insti-tute 2008) of log10-transformed, standardized data withineach of the 3 broad ecoregions yielded a useful grouping of7 lake types (Table 1; Fig. 1B). Lake-type classification andtype-specific index formulations reduced the among-regionvariation in index values in least-disturbed lakes and reducedambiguity in the response to anthropogenic disturbances.
Anthropogenic disturbance and least-disturbedreference site screening
We examined water chemistry, near-shore anthropogenic in-fluences, and evidence of anthropogenic lake drawdown inall 1101 survey lakes to classify them according to theirlevel of anthropogenic disturbance (low, medium, high).Lakes meeting low-disturbance screening criteria served asleast-disturbed reference sites for best-available condition.Low-disturbance stress (least-disturbed) lakes within eachlake type (A–G in Table 1) were identified on the basis ofchemical variables (total phosphorus, total nitrogen, chlo-ride, sulfate, acid neutralizing capacity, dissolved organiccarbon, and dissolved oxygen in the epilimnion) and directobservations of anthropogenic disturbances along the lakemargin (proportion of lakeshore with nonagricultural influ-ences, proportion of lakeshore with agricultural influences,and the relative extent and intensity of anthropogenic influ-ences of all types together).
For each lake type, a threshold value representing least-disturbed conditions was established as a “pass/fail”criterion for each parameter (Table A1 in USEPA 2010).Thresholds were values that would unlikely be found inleast-disturbed lakes within each region, and the thresholdvalues varied by lake type to account for regional varia-tions in water chemistry and littoral–riparian anthropogenicactivities (Herlihy et al. 2013). A lake was considered least-disturbed if it passed the screening test for all parameters;160 potential least-disturbed reference lakes were identified.We subsequently excluded 11 lakes with large lake levelfluctuations (>10 m horizontal) evident from field mea-surements (Kaufmann et al. 2014a), along with evidence ofdams or lake level controls in field observations or aerialphotos.
Lakes that were not classified as least-disturbed were pro-visionally considered intermediate in disturbance. The in-termediate disturbance lakes were then screened with a setof high-disturbance thresholds applied to the same variables(Table A1 in USEPA 2010). Lakes that exceeded one ormore of the high disturbance thresholds were consideredhighly disturbed. To avoid circularity in defining physicalhabitat alteration, we did not use any of the physical habitatcover complexity indices or their subcomponent metrics indefining lake disturbance classes.
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Kaufmann et al.
Table 1. Multivariate classification of lakes within 3 major ecoregions used in deriving expectations for littoral habitat characteristics inleast-disturbed lakes of the NLA. The aggregated Omernik ecoregions are shown in Fig. 1A; multivariate cluster classification of samplelakes is shown in Fig. 1B.
Climatic Region: (Aggregated Omernik LevelIII ecoregions1) Variables used in lakeclassification
LakeType Description
Eastern Highlands: (NAP, SAP) shorelinedevelopment index, elevation, dissolved calcium,precipitation, latitude and longitude
A Primarily reservoirs in the SAP. Warm and wet climate, generallybuffered by higher calcium concentrations.
B Primarily NAP drainage lakes; also other lakes in the SAP. Coolclimate, low-moderate precipitation, generally low calcium.
Plains and Lowlands: (CPL, NPL, SPL, TPL,UMW) elevation, maximum depth, dissolvedcalcium, water temperature, and precipitation
C Lakes of the eastern plains and Upper Midwest. Somewhat higherprecipitation than for western plains lakes. Considerably deeperthan other lakes in this region.
D Lakes of the southern temperate and eastern coastal plains. Generallyshallower, and lower in calcium than other lakes in the Plains andLowlands, but with particularly warmer, wetter climate.
E Generally shallow lakes of the plains and Upper Midwest. Highercalcium concentrations and lower precipitation than for otherPlains and Lowland lakes.
West: (WMT, XER) latitude, longitude, precipitation,water temperature and dissolved calcium
F Low calcium, deep, largely natural lakes of the intermountain west.
G Reservoirs and natural lakes of the arid west.
1NAP = Northern Appalachians, SAP = Southern Appalachians, UMW = Upper Midwest, CPL = Coastal Plains, NPL = Northern Plains, SPL = Southern Plains, TPL =Temperate Plains, WMT = Western Mountains, XER = Xeric West.
Our screening process identified 149 least-disturbed lakes,775 intermediate lakes, and 177 highly disturbed lakes. Ofthe 149 least-disturbed lakes, 107 were in the UMW, WMT,and NAP aggregated ecoregions (Table 2). Finding least-disturbed lakes that met our screening criteria was difficultin some other ecoregions; only 1, 6, 5, and 3 least-disturbedlakes, respectively, were indentified in the NPL, SPL, TPL,and XER ecoregions. To increase the useable sample size
for estimating expected lake condition, we grouped least-disturbed lakes from the NPL, SPL, TPL into the CentralPlains (CENPL), and the WMT and XER into the West.Due to insufficient numbers of least-disturbed lakes rela-tive to the large amount of lake variability within ecore-gions, we were unable to use totally independent subsetsof lakes for developing and validating models of expectedcondition.
Table 2. Number of NLA sample lakes classified by anthropogenic disturbance stress as least-disturbed, intermediate, andmost-disturbed, based on region-specific low- and high-disturbance screens. Parentheses show subtotals for the Central Plains andWestern US regions formed by combining 2 or more of the 9 NLA aggregated ecoregions.
SITE CLASS
Aggregated Ecoregion least-disturbed intermediate most-disturbed
Northern Appalachians - NAP 28 77 15Southern Appalachians - SAP 14 79 28Upper Midwest - UMW 41 116 5Coastal Plains - CPL 13 74 15(Central Plains - CENPL): (12) (271) (69)
Northern Plains - NPL 1 43 21Southern Plains - SPL 6 105 24Temperate Plains - TPL 5 123 24
(Western US - WEST): (41) (158) (45)Western Mountains - WMT 38 108 14Xeric - XER 3 50 31
Total 149 775 177
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National lake physical habitat assessment
Calculation of lake physical habitat metrics(Step 3)
Near-shore disturbance metrics
Our variable names are from the publicly available NLA2007 dataset released by the USEPA (http://water.epa.gov/type/lakes/NLA data.cfm). We calculated extent ofshoreline disturbance around the lakeshore (hifpAnyCirca)as the proportion of stations at which crews recorded thepresence of at least one of the 12 anthropogenic disturbancetypes as described by Kaufmann et al. (2014a). We cal-culated the disturbance intensity metric hiiAll as the sumof the 12 separate proximity-weighted means for all shore-line disturbance types observed at the 10 shoreline stations(Kaufmann et al. 2014a).
We also calculated subsets of total disturbance intensity bysumming metrics for defined groups of disturbance types.For example, hiiAg sums the proximity-weighted presencemetrics for row crop, orchard, and pasture; hiiNonAg sumsthe proximity-weighted presence metrics for the remaining 9nonagricultural disturbance metrics: (1) buildings; (2) com-mercial developments; (3) parks or manmade beaches; (4)docks or boats; (5) seawalls, dikes, or revetments; (6) trash orlandfill; (7) roads or railroads; (8) power lines; and (9) lawns.
Riparian vegetation metrics
To characterize riparian vegetation, we converted field ob-servations of cover data to mean cover estimates for all thetypes and combinations of vegetation data (Kaufmann et al.2014a). We assigned cover class arithmetic midpoint values(i.e., absent = 0%, sparse = 5%, moderate = 25%, heavy= 57.5%, and very heavy = 87.5%) to each plot’s cover-class observations and then calculated lakeshore vegetationcover as the average of those cover values across all 10 plots.Metrics for combined cover types (e.g., sum of woody veg-etation in 3 layers) were calculated by summing means forthe single types (see Kaufmann et al. 1999, 2014a). Metricsdescribing the proportion of each lakeshore with presence(rather than cover) of particular features were calculated asthe mean of presence (0 or 1) over the 10 riparian plots.
Littoral cover metrics
Metrics describing the mean cover (and mean presence) oflittoral physical habitat features and aquatic macrophyteswere calculated from observations at the 10 littoral plots asdescribed earlier for riparian vegetation. Metrics for com-bined cover types (e.g., sum of natural types of fish cover)were calculated by summing means for single types. Cover-weighted geometric mean substrate diameter indices andtheir standard deviations were calculated for the littoral zoneand separately for the 1 m band of exposed shoreline by first
assigning the geometric mean between the upper and lowerdiameter bounds of each size class for each cover obser-vation, weighting them by their mean cover across the 10stations, and then averaging the weighted cover and comput-ing its variance across size classes (Kaufmann et al. 2014a).This approach is an adaptation of that used for estimat-ing geometric mean diameter from pebble count size-classpercentages in streams (Faustini and Kaufmann 2007, Kauf-mann et al. 2009).
Calculation of summary physical habitatcondition indices (Step 4)
We calculated 4 multimetric indices of physical habitat con-dition and an integrated 5th index that combines the first 3:
RDis IX: Lakeshore Anthropogenic Disturbance Index (in-tensity and extent);
RVegQ: Riparian Vegetation Cover Complexity Index;
LitCvrQ: Littoral Cover Complexity Index;
LitRipCvQ: Littoral–Riparian Habitat Complexity Index;and
LkShoreHQ: Lakeshore Physical Habitat Quality Index.
These indices were defined and calculated independent oftheir association with the lake classification by anthro-pogenic disturbance levels and the variables used to producethose classifications.
Lakeshore Anthropogenic Disturbance Index (RDis IX)
This index was calculated as:
RDis IX = (Disturbance Intensity + Disturbance Extent)/2, (1)
where disturbance intensity was represented by separatesums of the mean proximity-weighted tallies of near-shoreagricultural and nonagricultural disturbance types, and ex-tent was expressed as the proportion of the shore with pres-ence of any type of disturbance. Specifically
RDis IX ={
1 −[
1[1+hiiNonAg+(5×hiiAg)]
]+ hifpAnyCirca
}
2,
(2)
where:
hiiNonAg = proximity-weighted mean disturbance tally(mean among stations) of up to 9 types of nonagriculturalactivities;
hiiAg = proximity-weighted mean tally of up to 3 types ofagriculture-related activities (mean among stations); and
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Kaufmann et al.
hifpAnyCirca = proportion of the 10 shoreline stations withat least 1 of the 12 types of anthropogenic activities presentwithin their 15 × 15 m riparian or 10 × 15 m littoral plots.
Field procedures classified only 3 types of agricultural dis-turbances, versus 9 types of nonagricultural disturbances,limiting the potential ranges to 0–3 for hiiAg and 0–9 forhiiNonAg. The observed ranges of these variables also dif-fered; hiiAg ranged from 0 to 1.3, whereas hiiNonAg hadan observed range of 0 to 6.4. To avoid under-representingagricultural disturbances and over-representing nonagricul-tural disturbances in the index, we weighted the disturbanceintensity tallies for agricultural land use by a factor of 5 inequation 2. This weighting factor (ratio of observed rangesin nonagricultural to agricultural disturbance types) effec-tively scales agricultural land uses equal in disturbance po-tential to those for nonagricultural land uses. We scaled thefinal index from 0 to 1, where 0 indicates absence of anyanthropogenic disturbances and 1 is the theoretical maxi-mum approached as a limit at extremely high disturbance.We applied a single formulation of the disturbance indexRDis IX to all ecoregions and lake types in the NLA (Fig. 1;Table 1).
Riparian Vegetation Cover Complexity Index (RVegQ)
This index is based on visual estimates of vegetation coverand structure in 3 vegetation layers at the 10 riparianplots along the lake shore. Because the potential vegetationcover differs among regions, we calculated 3 variants ofthe Riparian Vegetation Cover Complexity Index (RVegQ 2,RVegQ 7, or RVegQ 8) for application to different aggre-gated ecoregions (Table 3). The region-specific formula-tions reduce the among-region variation in index values inleast-disturbed lakes and reduce ambiguity in their responseto anthropogenic disturbances. If component metrics hadpotential maximum values >1, their ranges were scaled torange from 0 to 1 by dividing by their respective maximumvalues based on the 2007 NLA data (table 3 in Kaufmannet al. 2014a).
Each variant of the final index was calculated as the meanof its component metric values. Index values range from 0(indicating no vegetative cover at any station) to 1 (40–100%cover in multiple layers at all stations):
RVegQ 2 =[(
rviWoody2.5
)+ rvfcGndInundated
]
2, (3)
RVegQ 7 =[(
rviLowWood1.75
) + rvfcGndInundated]
2, (4)
RVegQ 8 =[(
rviWoody2.5
)+ rvfpCanBig + rvfcGndInundated + ssiNATBedBld
]
4, (5)
where:
rviWoody = sum of the mean areal cover of woody veg-etation in 3 layers: canopy (large and small diametertrees), understory, and ground layers (rvfcCanBig +rvfcCanSmall + rvfcUndWoody + rvfcGndWoody);
rviLowWood = sum of mean areal cover of woody vegetationin the understory and ground cover layers (rvfcUndWoody+ rvfcGndWoody);
rvfcGndInundated = mean areal cover of inundated terres-trial or wetland vegetation in the ground cover layer;
rvfpCanBig = proportion of stations with large diameter(>0.3 m dbh) trees present; and
ssiNATBedBld = sum of mean areal cover of naturally occur-ring bedrock and boulders (ssfcBedrock + sfcBoulders),and where the value of ssiNATBedBld was set to 0 in lakesthat have a substantial extent of constructed seawalls andrevetments (i.e., hipwWalls ≥ 0.10).
We used RVegQ 2 for mesic ecoregions with maximum el-evations <2000 m (NAP, SAP, UMW, and CPL) where treevegetation can be expected in relatively undisturbed loca-tions (Table 3). RVegQ 2 sums the woody cover in 3 lake-side vegetation layers (rviWoody) and includes inundatedgroundcover vegetation (rvfcGndInundated) as a positivecharacteristic.
We used RVegQ 7 for Central Plains ecoregions (NPL,SPL, and TPL). Whereas perennial woody groundcoverand shrubs can be expected on undisturbed lake shore-lines throughout the Central Plains (West and Ruark 2004),the presence or absence of large trees (>5 m high) alonglake margins in this region has ambiguous meaning withoutfloristic information (Barker and Whitman 1988, Johnson2002, Huddle et al. 2011). RVegQ 7 accommodates lackof tree canopy in least-disturbed lakes by summing onlythe lower 2 layers of woody vegetation (rviLowWood) andincludes inundated ground cover vegetation as a positivecharacteristic.
We used RVegQ 8 for the West (WMT and XER), whereclimate ranges from wet to arid and where lakeshores canpotentially grow large diameter riparian trees but may lackvegetated lake shorelines at high elevations, or where rockprecludes vegetation (Table 3). RVegQ 8 sums the woodycover in 3 lakeside vegetation layers and includes inundatedgroundcover vegetation as a positive characteristic; it alsoincludes the proportional presence of large diameter treesaround the lakeshore as a positive characteristic. RVegQ 8includes natural rock as an undisturbed riparian cover typeto avoid penalizing relatively undisturbed lakes in arid areas
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National lake physical habitat assessment
Table 3. Assignment of Riparian Vegetation Cover Complexity, Littoral Cover Complexity, and Littoral–Riparian Habitat Complexity indexvariants by aggregated ecoregion (Fig. 1A) and multivariate lake type (Fig. 1B; Table 1).
Aggregated OmernikEcoregion Lake Type
% of regionsample lakes
Riparian VegetationCover Complexity
Index (RVegQ)
Littoral CoverComplexity
Index (LitCvrQ)
Littoral–RiparianHabitat ComplexityIndex (LitRipCvQ)
NAP B 100 RVegQ 2 LitCvrQ d LitRipCvQ 2d
SAP A 75 RVegQ 2 LitCvrQ c LitRipCvQ 2cB 25 RVegQ 2 LitCvrQ d LitRipCvQ 2d
CPL C 1 RVegQ 2 LitCvrQ d LitRipCvQ 2dD 99 RVegQ 2 LitCvrQ b LitRipCvQ 2b
UMW C 68 RVegQ 2 LitCvrQ d LitRipCvQ 2dE 32 RVegQ 2 LitCvrQ d LitRipCvQ 2d
TPL C 47 RVegQ 7 LitCvrQ d LitRipCvQ 7dD 2 RVegQ 7 LitCvrQ b LitRipCvQ 7bE 51 RVegQ 7 LitCvrQ d LitRipCvQ 7d
NPL C 2 RVegQ 7 LitCvrQ d LitRipCvQ 7dE 98 RVegQ 7 LitCvrQ d LitRipCvQ 7d
SPL C 28 RVegQ 7 LitCvrQ d LitRipCvQ 7dD 8 RVegQ 7 LitCvrQ b LitRipCvQ 7bE 64 RVegQ 7 LitCvrQ d LitRipCvQ 7d
WMT F 76 RVegQ 8 LitCvrQ d LitRipCvQ 8dG 24 RVegQ 8 LitCvrQ d LitRipCvQ 8d
XER F 23 RVegQ 8 LitCvrQ d LitRipCvQ 8dG 77 RVegQ 8 LitCvrQ d LitRipCvQ 8d
or at high elevations above timberline. For lakes with a sub-stantial extent or abundance of constructed seawalls, dikes,or revetments along the shoreline, the substrate metric wasset at 0.
Littoral Cover Complexity Index (LitCvrQ)
This index was based on the station averages for visual es-timates of the areal cover of 10 types of littoral features, in-cluding aquatic macrophytes but excluding manmade struc-tures, within each of the 10 littoral plots (see Kaufmannet al. 2014a). We calculated 3 variants but applied these todifferent lake types rather than to ecoregions (Table 3). Eachvariant of the index was calculated as the mean of its com-ponent metric scores so that index values range from 0 (nocover present at any station) to 1 (very heavy cover at all10 stations). Component metrics with potential maximumvalues >1 within a given lake type were scaled from 0 to 1by dividing by their respective maximum values in the NLA2007 dataset:
LitCvrQ b =[fciNatural +
(fcfcSnag
0.2875
)]
2, (6)
LitCvrQ c =[fciNatural +
(f cf cSnag
0.2875
)+
(amf cF ltEmg
1.515
)]
3, (7)
LitCvrQ d =[(
SomeNatCvr1.5
) +(
f cf cSnag0.2875
)+
(amf cF ltEmg
1.515
)]
3,
(8)
where:
fciNatural = summed areal cover of nonanthropogenic fishcover elements (fcfcBoulders + fcfcBrush + fcfcLedges +fcfcLivetrees + fcfcOverhang + fcfcSnag + fcfcAquatic);
SomeNatCvr = summed cover of natural fish cover elementsexcluding snags and aquatic macrophytes (fcfcBoulders +fcfcBrush + fcfcLedges + fcfcLivetrees + fcfcOverhang);
amfcFltEmg = summed cover of emergent plus floatingaquatic macrophytes (amfcEmergent + amfcFloating); and
fcfcAquatic = total cover of aquatic macrophytes of anytype.
All 3 variants of LitCvrQ include an expression of thesummed cover of naturally occurring fish or macroinver-tebrate cover elements. Snag cover is recognized as a par-ticularly important element of littoral habitat complexity(Christensen et al. 1996, Francis and Schindler 2006, Mi-randa et al. 2010); therefore, we included snags as a separate
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contributing cover component in all 3 variants of the indexand divided cover metrics by their maximum values in the2007 NLA data to make the weightings of snag cover equalto those of the other 2 littoral cover sums. For LitCvrQ cand LitCvrQ d, we increased the emphasis on emergentand floating-leaf aquatic macrophytes relative to other lit-toral components in response to their reported importanceas cover and their sensitivity to anthropogenic disturbancesin many lake types and regions (Radomski and Geoman2001, Jennings et al. 2003, Merrell et al. 2009, Beck et al.2013).
We used LitCvrQ b for lake type D (generally shallow,warm, low conductivity lakes that are almost exclusivelyin the CPL, but also found in the SPL and TPL; Fig. 1B;Tables 1 and 3). We used LitCvrQ c for lake type A, whichconsists primarily of reservoirs in the SAP (Tables 1 and3), where disturbed sites commonly have substantial up-land soil erosion, large water level fluctuations, and bare-soil shorelines. These conditions generate abiotic tur-bidity that suppresses submerged macrophytes, therebydiminishing their common association with anthropogenicnutrient inputs seen in other lake types. LitCvrQ c empha-sizes floating and emergent aquatic macrophytes in addi-tion to snags but still includes submerged aquatic macro-phytes along with other aquatic macrophytes and covertypes in fciNatural. LitCvrQ d excludes submerged aquaticmacrophytes. We applied LitCvrQ d in all lake types ex-cept A and D in the NLA (Table 3), where submergedaquatic macrophytes provide valuable cover but high sub-merged cover is frequently associated with anthropogeniceutrophication (Hatzenbeler et al. 2004, Merrell et al.2009).
Littoral–Riparian Habitat Complexity Index(LitRipCvQ)
We averaged the lake values of the Littoral Cover Com-plexity and Riparian Vegetation Cover Complexity indicesto calculate the Littoral–Riparian Habitat Complexity IndexLitRipCvQ:
LitRipCvQ = (RVegQ n + LitCvrQ x)
2, (9)
where:
RVegQ n = variant of the Riparian Vegetation Cover Com-plexity Index (n = 2, 7, or 8, depending on ecoregion;Table 3); and
LitCvrQ x = variant of Littoral Cover Complexity Index(x = b, c, or d, depending on lake type; Table 3).
Lakeshore Physical Habitat Quality Index(LkShoreHQ)
We calculated an integrated Lakeshore Physical HabitatQuality Index (LkShoreHQ) by averaging the indices of Ri-parian Vegetation Cover Complexity, Littoral Cover Com-plexity, and [lack of] near-shore anthropogenic disturbance:
LkShoreHQ = ((1 − RDis IX) + RV egQ n + LitCvrQ x)
3,
(10)
where RDis IX is calculated from equation 2; n denotes theappropriate variant of RVegQ by ecoregion (Table 3); andx denotes the appropriate variant of LitCvrQ by lake type(Table 3).
Deriving expected index values underleast-disturbed conditions (Step 5)
We used 2 modeling approaches, null models and lake-specific predictive models, to estimate physical habitat ex-pectations under least-disturbed condition. For the null mod-els, we based expectations on the mean and distributionof log10-transformed physical habitat index scores in least-disturbed lakes from each ecoregion, an approach applied inprevious bioassessments (e.g., Stoddard et al. 2005, 2006).We used the null model approach for all ecoregions exceptthe West (WMT and XER).
For the West, we needed an alternative approach to reducethe variability in physical habitat condition indices amongleast-disturbed lakes. Air temperature, precipitation, soils,and lithology vary greatly across the West, resulting inwide variation in potential natural vegetation among least-disturbed lakes. In turn, that variation results in differencesin the amount and complexity of littoral cover, especially forthose elements derived from riparian vegetation. We derivedlake-specific expected values in the West by modeling theinfluence of important nonanthropogenic environmentalfactors in relatively undisturbed lakes, an approach analo-gous to that used to predict least-disturbed conditions formultimetric fish assemblage indices (Pont et al. 2006, 2009,Esselman et al. 2013). We attempted unsuccessfully toderive similar regressions for the combined Central Plainsecoregion.
We used multiple linear regressions (MLR) to calculatelake-specific expected values of the Riparian Vegetation,Littoral Cover, and Littoral–Riparian Cover Complexityindices in the West, employing latitude, elevation, andecoregion (WMT vs. XER) as surrogates for temperature,precipitation, soil, and other characteristics that con-trol potential natural vegetation (Table 4). Ideally, ex-pected cover and complexity would be calculated basedonly on least-disturbed lakes; however, the number of
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Table 4. Multiple linear regression models used for calculating expectations for indices of Riparian Vegetation Cover Complexity, LittoralCover Complexity, and Littoral–Riparian Habitat Complexity for lakes in the Western Mountain and Xeric aggregated ecoregions. Highlydisturbed lakes were excluded from calculations.
Index PredictorsRegressionCoefficients R2
df:model/error RMSE Prob > F
Riparian VegetationCover Complexity:
Log10(RVegQ 8) Intercept −1.2108Lake elevation (m) −0.000037Latitude 0.0126Ecoregion category ( = 1 if WMT) 0.1112 0.236 3/1221 0.172 <0.0001
Littoral CoverComplexity:
Log10(LitCvrQ d) Intercept −0.9738Lake elevation (m) −0.000073 0.050 1/1402 0.314 0.0075
Littoral–RiparianHabitat Complexity:
Log10(LitRipCvQ 8d) Intercept −1.0751Lake elevation (m) −0.000038Latitude 0.0083Interaction of lake elevation andecoregion; where Ecoregion = 1 ifXER
0.000079 0.193 3/1293 0.187 <0.0001
1Excludes the most-disturbed lakes, lakes with water level change >10 m (horizontal), and 16 low Log10(RVegQ 8) outliers (>3 SD from mean of residuals).2Excludes the most-disturbed lakes and those with water level change >10 m.3Excludes the most-disturbed lakes, lakes with water level change >10 m, and 9 low Log10(LitRipCvQ 8d) outliers (>3 SD from mean of residuals).
least-disturbed lakes in the West (Table 2) was toosmall to adequately model expected lake physical habi-tat metrics values in relatively undisturbed lakes acrossthis region. We therefore combined least-disturbed andintermediately disturbed lakes not greatly affected bydrawdown (146 in WMT and 53 in XER) to avoid po-tential bias in modeled expectations by inadequately cov-ering the range of the relevant natural controlling factorsin our MLR. Excluding highly disturbed lakes from theMLRs minimized anthropogenic distortions of the influ-ences of elevation, latitude, and ecoregion on lake physicalhabitat. Including many intermediately disturbed lakes inour regression predictions, however, necessitated a furtheradjustment of expected values, explained in the followingsection.
Condition criteria for near-shore lake physicalhabitat (Step 6)
For the Lakeshore Anthropogenic Disturbance IndexRDis IX, we used uniform criteria for all lakes. For RVegQ,LitCvrQ, and LitRipCvQ we set condition criteria based onthe distribution of values of these indices observed in least-disturbed lakes. We have not yet set condition criteria forLkShoreHQ.
Lakeshore Anthropogenic Disturbance (intensityand extent) – condition criteria
Because RDis IX is a direct measure of anthropogenic ac-tivities, we based criteria for high, medium, and low levelsof disturbance on judgment:
Good (low disturbance): RDis IX ≤ 0.20,
Fair (medium disturbance): RDis IX > 0.20 but ≤ 0.75, and
Poor (high disturbance): RDis IX > 0.75.
Lakes with RDis IX ≤ 0.20 have very low levels of lakeand near-lake disturbance, typically having anthropogenicdisturbance on <8% of their shorelines. Those with RDis IX> 0.75 have very high levels of disturbance, typically havinganthropogenic activities evident on 100% of their shorelines.
Condition criteria for RVegQ, LitCvrQ, and LitRipCvQ
No model perfectly predicts expected values in least-disturbed lakes; rather, the models predict distributions ofvalues. Furthermore, because the screening process for least-disturbed lakes was not perfect, disturbance was anothersource of variation in physical habitat index values in theselakes. Consequently, we set condition criteria for RVegQ,
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Table 5. Mean and standard deviation of habitat index values in regional sets of least-disturbed reference lakes in the aggregatedecoregions of the NLA. Geometric mean and standard deviation are antilogs of mean and SD of log10-transformed index values (LogMeanand LogSD). Parentheses enclose the minimum values (i.e., the most precise models for each index). Bold text marks the least precisemodels. SDs calculated from log-transformed variables are expressions of the proportional variance of these distributions and so aredirectly comparable among regions with different geometric means. A range of ±1SD is calculated by multiplying and dividing thegeomMean by the geomSD. For example, the LogMean ± 1LogSD for the Riparian Vegetation Cover Complexity Index in least-disturbedlakes of the NAP (−0.571 ± 0.128) translates to a range of habitat index values from 0.200 to 0.359: the geometric mean habitat indexvalue of 0.268 multiplied and divided by 1.34, the antilog of 0.128. The multiple 1.34 in this case is equivalent to a CV of 134%.
Aggregated ecoregion Index Ref LogMean Ref LogSD Ref geomMean Ref geomSD
Riparian Vegetation Cover Complexity:NAP NULL Model RVegQ −0.571 (0.128) 0.268 (1.34)SAP NULL Model RVegQ −0.629 0.158 0.235 1.44UMW NULL Model RVegQ −0.598 0.139 0.252 1.38CPL NULL Model RVegQ −0.538 0.169 0.290 1.48CENPL NULL Model RVegQ −0.755 0.158 0.176 1.44West NULL Model RVegQ −0.589 0.198 0.258 1.58West MLR Model
∗ RVegQ OE 0.0603 0.183 1.149 1.52Littoral Cover Complexity:
NAP NULL Model LitCvrQ −0.834 (0.199) 0.147 (1.58)SAP NULL Model LitCvrQ −0.719 0.285 0.191 1.93UMW NULL Model LitCvrQ −0.773 0.231 0.169 1.70CPL NULL Model LitCvrQ −0.524 0.200 0.299 1.58CENPL NULL Model LitCvrQ −0.943 0.338 0.114 2.18West NULL Model LitCvrQ −1.118 0.345 0.076 2.21West MLR Model
∗∗ LitCvrQ OE 0.0031 0.344 1.007 2.20Littoral–Riparian Habitat Complexity:
NAP NULL Model LitRipCvQ −0.669 (0.106) 0.214 (1.28)SAP NULL Model LitRipCvQ −0.637 0.117 0.236 1.31UMW NULL Model LitRipCvQ −0.657 0.120 0.220 1.32CPL NULL Model LitRipCvQ −0.515 0.140 0.305 1.38CENPL NULL Model LitRipCvQ −0.815 0.177 0.153 1.50West NULL Model LitRipCvQ −0.756 0.190 0.175 1.55West MLR Model
∗∗∗ LitRipCvQ OE 0.0531 0.176 1.130 1.50
∗Lake-specific f(Elevation, Latitude, Ecoregion) see Table 4.∗∗Lake-specific f(Elevation) see Table 4.∗∗∗Lake-specific f(Elevation, Latitude, and interaction of Ecoregion × Elevation) see Table 4.
LitCvrQ, and LitRipCvQ with reference to the distribu-tions of these indices in least-disturbed lakes within each ofthe 6 merged ecoregions (Table 5). Specifically, for ecore-gions where we applied the null model, we compared log-transformed index values at each lake with the distribution ofthose values among least-disturbed lakes in their respectiveecoregion.
For the West, we calculated physical habitat index ob-served/expected (O/E) values for each lake by dividing theobserved index value at each lake by the lake-specific ex-pected value derived from regressions (Table 4). In most O/Eapplications, the calculated O/E value directly expresses thedegree of alteration of each lake from values expected underleast-disturbed conditions. Because we calculated expectedvalues in the West using intermediately disturbed lakes inaddition to least-disturbed lakes, we then compared the lake-specific O/E values to the distribution of O/E values in the
41 least-disturbed lakes in that region. This approach car-ries 2 assumptions: (1) the distribution of expected values inthe least-disturbed lakes are accurately predicted from ourMLR based on the set of lakes we used, and (2) the devia-tions of observed values from expected values are responsesto anthropogenic disturbances and are not affected by thevariables used to predict expected values.
We based our definitions of physical habitat alteration onpercentiles of the distribution of physical habitat indicesin least-disturbed lakes (or for the West, the distributionof O/E values in least-disturbed lakes); however, the smallnumber of lakes meeting our low-disturbance criteria inmost regions precluded obtaining reliable percentiles di-rectly from the least-disturbed lake distributions. Conse-quently, we used the central tendency and variance of indexvalues in least-disturbed lakes values to model their dis-tributions and to estimate percentiles from their z-scores
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(Snedecor and Cochran 1980). The log10-transformed in-dex values (and the log10-transformed O/E values for theWest) in the least-disturbed lakes had symmetrical, approx-imately normal distributions. We calculated mean and stan-dard deviations of log10-transformed data, converted themto z-scores, and estimated the 5th and 25th percentiles basedon the log-normal approximation of the index distributionsin least-disturbed lakes within each ecoregion.
Where null models were used to estimate least-disturbedcondition, the index geometric mean and geometric standarddeviation for each regional set of least-disturbed lakes repre-sent the central tendency and dispersion of expected valuesfor sample lakes within each region (Table 5, columns 5and 6). Because means and standard deviations (SD) are alllog values, a range of ±1 SD would be calculated by mul-tiplying and dividing the geometric mean by the geometricSD.
For the MLR O/E models, the geometric mean and SDof O/E values in least-disturbed lakes describe the cen-tral tendency and distribution of O/E values expected underleast-disturbed conditions (Table 5, column 5). The indexgeometric SD for each regional set of least-disturbed lakesdescribes the variation of expected values for sample lakeswithin their respective regions (Table 5, column 6). The geo-metric SDs for MLR-based O/E values in the least-disturbedlakes within regions are also directly comparable to thosebased on the null model.
Lakes with index values (null model) or O/E values (MLRmodel) that are ≥25th percentile for least-disturbed lakeswithin their regions were considered to have habitat in goodcondition (i.e., similar to that in the population of least-disturbed lakes of the region). Similarly, lakes with indexor O/E values <5th percentile of least-disturbed lakes wereconsidered to have poor habitat quality (i.e., they have sig-nificantly lower cover and complexity than observed withinthe subpopulation of least-disturbed lakes of the region).Those with index or O/E values between the 5th and 25thpercentiles of least-disturbed lakes were scored as fair.
We emphasize that our designations of good, fair, andpoor are relative to the least-disturbed sites available ineach ecoregion. We define good condition as habitat qual-ity that is well-within the distribution of habitat in least-disturbed sites of the ecoregion, and poor condition ashabitat quality not likely to be found within that distri-bution. Our designations of poor condition do not indi-cate impaired water body status. Conversely, our desig-nations of good condition mean that habitat is similar tothe least-disturbed sites available in a region, which doesnot mean pristine, only the best available, and could berelatively disturbed in extensively and highly disturbedregions.
Population estimation procedures (Step 7)
Following Peck et al. (2013), we estimated percentages andconfidence bounds for the number of lakes with near-shorephysical habitat in good, fair, and poor condition in theconterminous 48 states of the United States and its eco-regions. These lake population estimates employed samplesite weighting factors inversely proportional to each lake’sprobability of being selected for sampling. We report find-ings nationally by the 9 ecoregions and separately for na-tional populations of natural lakes versus reservoirs.
ResultsLeast-disturbed reference distributionsand regressions
Geometric means for RVegQ, LitCvrQ, and LitRipCvQ inleast-disturbed lakes differed among regions (Table 5), butthese unscaled values are not directly comparable becausethe habitat index formulations also differed among regions.The unscaled mean values (Table 5) served as denominatorsby which observed index values at each lake were scaled. Inthe West, the mean modeled O/E values RVegQ, LitCvrQ,and LitRipCvQ among least-disturbed lakes were dividedinto the modeled O/E values observed at each site, whetherleast-, intermediate-, or most-disturbed. Mean O/E valuesfor least-disturbed lakes in the West (Table 5, column 5) were>1.0 for the 3 physical habitat indices (1.149, 1.007, and1.130) because the regression analyses for expected condi-tion included both least- and intermediate-disturbance lakes.This resulted because Littoral Cover Complexity in the least-disturbed lakes tended to be greater than for intermediate-disturbance lakes.
The geometric SDs for least-disturbed lakes of each region(Table 5, column 6) were calculated from log-transformedvariables and therefore are expressions of the proportionalvariance of these distributions. As such, they are directlycomparable as measures of model precision among re-gions with different geometric means, or between null andMLR modeling approaches. The precision in modeling least-disturbed condition using null models was best in the NAP(lowest geometric SD) and worst in the West (highest ge-ometric SD). Null model geometric SDs were generallyhigher (less precise) for LitCvrQ (1.58–2.21) than for RVegQ(1.34–1.58) or LitRipCvQ (1.28–1.55).
The smaller the SD of index values (or O/E values) amongleast-disturbed lakes, the easier it is to confidently distin-guish disturbed lakes from least-disturbed lakes. The MLRmodels for the West were marginally better than null mod-els, but they improved the precision of models for expectedcondition in the West to within the range of those based onnull models in the other regions. The proportions of total
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variance explained by the regression equations derived forthe West were generally low (R2 < 0.25), but the mod-els were highly significant (p < 0.0001) for RVegQ andLitRipCvQ (Table 4), with logSDs 8% lower than thosefor the null model. The equation for LitCvrQ, which usedonly elevation as a dependent variable, explained 5% ofthe total variance in LitCvrQ but was significant at p =0.0075. Although the logSD of the model for estimatingexpected LitCvrQ in least-disturbed lakes of the West wasonly slightly lower than that for the null model, we chose touse it because it set realistically lower expectations in highelevation lakes where there is no natural source for littoralcover features derived from riparian trees.
Physical habitat index expectationsand condition criteria
The (unscaled) 5th and 25th percentiles for least-disturbedlakes (Table 6, columns 3 and 4) are the habitat index valueswe used as condition criteria for the national lake population
estimates. For the null model, these percentiles are in theunits of the physical habitat indices described in equations3–9; for the MLR models, they are O/E values. Althoughdifferences in habitat index criteria values for good, fair, orpoor condition are evident by comparing these values, directcomparisons of the values among regions and indices are notappropriate because the index formulations and the centraltendencies of their expected values in least-disturbed lakesdiffer among regions.
The scaled 5th and 25th percentiles of index values amongleast-disturbed lakes (Table 6, columns 5 and 6) are analo-gous to traditional O/E values. In our case they are calculatedby dividing observed values by the mean in least-disturbedlakes and express the degree of deviation in habitat indexvalues from the central tendency in least-disturbed lakes.Comparisons among scaled percentiles show the relative de-gree of alteration from least-disturbed conditions necessaryfor poor or fair condition classification in various regions oramong indicators that have different ranges and means.
Table 6. Condition criteria for habitat index values and scaled habitat index values based on the distribution of index values inleast-disturbed lakes within each region. The 5th and 25th percentiles, respectively, were set as the upper bounds for poor and faircondition, respectively. These percentiles were estimated, respectively, as the mean of log-transformed index values minus 1.65 and0.67 times the SD of log-transformed habitat index values (see Table 5 for the least-disturbed means and SDs, and see Table 3 for thevariant of each index used). All percentiles are expressed as antilogs of log-transformed values. For the null models, percentiles are in theunits of the physical habitat indices (the O/E percentiles are not). Scaled percentiles in the last 2 columns are directly comparable betweennull and O/E models. Parentheses denote null model estimates that were replaced by lake-specific regression O/E model estimates in theWest.
Region (model) Index Ref 5th Percentile Ref 25th PercentileScaled Ref 5th
PercentileScaled Ref 25th
Percentile
Riparian Vegetation Cover Complexity:NAP (null) RVegQ 0.165 0.220 0.616 0.821SAP (null) RVegQ 0.129 0.184 0.549 0.784UMW (null) RVegQ 0.149 0.204 0.591 0.807CPL (null) RVegQ 0.153 0.224 0.527 0.771CENPL (null) RVegQ 0.096 0.138 0.549 0.784West (null) RVegQ not used (0.035) (0.190) (0.137) (0.736)West (MLR) RVegQ OE 0.573 0.866 0.499 0.753
Littoral Cover Complexity:NAP (null) LitCvrQ 0.069 0.108 0.469 0.735SAP (null) LitCvrQ 0.065 0.123 0.339 0.644UMW (null) LitCvrQ 0.070 0.118 0.416 0.700CPL (null) LitCvrQ 0.140 0.220 0.468 0.735CENPL (null) LitCvrQ 0.032 0.068 0.277 0.594West (null) LitCvrQ not used (0.021) (0.045) (0.270) (0.588)West (MLR) LitCvrQ OE 0.271 0.588 0.272 0.592
Littoral–Riparian Habitat Complexity:NAP (null) LitRipCvQ 0.143 0.182 0.668 0.849SAP (null) LitRipCvQ 0.148 0.193 0.641 0.835UMW (null) LitRipCvQ 0.140 0.183 0.634 0.831CPL (null) LitRipCvQ 0.179 0.245 0.586 0.805CENPL (null) LitRipCvQ 0.078 0.116 0.510 0.761West (null) LitRipCvQ not used (0.085) (0.131) (0.486) (0.746)West (MLR) LitRipCvQ OE 0.578 0.861 0.512 0.762
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The scaled percentile values (condition criteria) also al-low direct comparison between condition criteria derivedfrom null and O/E models. Excluding those for the nullmodel in the West, scaled 25th percentiles for the RVegQ inleast-disturbed lakes showed little variation, ranging from0.75 in the CPL to 0.82 in the NAP (Table 6, column 6).Scaled 5th percentiles of RVegQ in least-disturbed lakes alsoshowed little variation, ranging from 0.50 in the West (O/Emodel) to 0.62 in the NAP (Table 6, column 5). For LitCvrQ,the scaled 25th percentile values for least-disturbed lakeswere >0.7 in the NAP, CPL, and UMW, and lower (∼0.6) inthe SAP, plains, and western regions. Scaled 5th percentilesof LitCvrQ in least-disturbed lakes were greatest (∼0.47) inthe NAP and CPL and lowest (∼0.3) in the CENPL and West.For LitRipCvQ, scaled 25th percentile values were >0.8 forleast-disturbed lakes in all aggregated ecoregions and habi-tat regions except the CENPL and the West (∼0.76). Thelowest scaled 5th percentile values for LitRipCvQ were forleast-disturbed lakes in the CENPL and the West (∼0.51),with all other regions having values ∼0.59–0.67.
Regional distributions of index scoresand scaled index scores for all lakes
Distributions of RDis IX are directly comparable within andamong regions because its formulation and the criteria fordefining low, medium, and high levels were identical ev-erywhere. Interquartile ranges of RDis IX in sample lakesvaried widely among the 9 ecoregions, but extreme valuesspanned nearly the full potential range of the index in everyregion (Fig. 2). Lakeshore Anthropogenic Disturbance wasdistinctly highest for sample lakes in the NPL and lowest inthe WMT and NAP (Fig. 2 unweighted sample statistics).
The sample distributions of unscaled values among regionsare not directly comparable for the other physical habitatindices (RVegQ, LitCvrQ, and LitRipCvrQ; Fig. 3A, 4A,and 5A) because formulations differed among regions. Thescaled index values allow interregional comparisons of thedegree of alteration relative to least-disturbed conditions(Fig. 3B, 4B, and 5B). For example, the unscaled indexRVegQ was highest in the WMT and NAP and lowest in theNPL and XER (Fig. 3A), but the inclusion of natural rockas a riparian cover category in WMT and XER complicatesinterregional comparisons. Similarly, the NPL, SPL, andTPL regions excluded the top layer (height >5 m) in RVegQformulation. After RVegQ was scaled, Riparian VegetationCover Complexity was relatively less altered in the WMT,NAP, UMW, and TPL than in the NPL or XER (Fig. 3B).
The notably high unscaled values of LitCvrQ in the CPL(Fig. 4A) are not directly comparable to those in many ofthe other ecoregions where the index formulation decreasedthe weighting of submerged forms and increased that foremergent and floating macrophytes. After scaling LitCvrQ
WMT XER NPL SPL TPL CPL UMW NAP SAPAggregated Ecoregion
Lake
shor
e A
nthr
opog
enic
Dis
turb
ance
Inde
x(RDis_IX
)
Figure 2. Comparison of Lakeshore Anthropogenic DisturbanceIndex (RDis IX ) values in sample lakes across 9 NLA aggregatedecoregions. Unweighted sample statistics are shown; box midlineand lower and upper ends show median and 25th and 75thpercentile values, respectively; whiskers show maximum andminimum observations within 1.5 times the interquartile rangeabove/below box ends. See Fig. 1 for definitions of aggregatedecoregions.
to facilitate regional comparisons, several ecoregions (e.g.,WMT, CPL, and NAP) had many lakes similar to least-disturbed condition, with sample median LitCvrQ scaledvalues near 1.0 (log10-transformed values near 0 in Fig. 4B).As with the unscaled values, the NPL had the lowest samplemedian.
As was the case for LitCvrQ, the high unscaled LitRipCvQvalues in the CPL (Fig. 5A) are magnified by its dif-ferent treatment of submerged aquatic macrophytes, andmore direct regional comparisons are made by com-paring LitRipCvrQ scaled (Fig. 5B). Median values ofLitRipCvrQ scaled approached 1 (log10 = 0) in theWMT, NAP, CPL and UMW. As was the case for thescaled Littoral Cover Complexity Index, the lowest me-dian LitRipCvrQ scaled was for lakes in the NPL. The NPLmedian log10(LitRipCvrQ scaled) value <−0.5 shows thatmost sample lakes in that region had Littoral-Riparian Habi-tat Complexity Index index values less than one-third of themean value in least-disturbed lakes of this ecoregion (Fig.5B).
Physical habitat index responsesto anthropogenic disturbance
There were substantial declines in RVegQ, LitCvrQ, andLitRipCvQ from lakes with least to highest anthropogenic
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WMT XER NPL SPL TPL CPL UMW NAP SAPAggregated Ecoregion
WMT XER NPL SPL TPL CPL UMW NAP SAPAggregated Ecoregion
Rip
aria
n V
eget
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n C
over
Com
plex
ity In
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)
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n V
eget
atio
n C
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(Sca
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log 1
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(A)
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Figure 3. Comparison of (A) Riparian Vegetation Cover ComplexityIndex (RVegQ), and (B) scaled values of the same index(log10-transformed) in sample lakes across 9 NLA aggregatedecoregions (RVegQ scaled = RVegQ divided by its geometricmean in least-disturbed lakes within the same region). Unweightedsample statistics are shown. Box midline and lower and upper endsshow median and 25th and 75th percentile values, respectively;whiskers show maximum and minimum observations within1.5 times the interquartile range above/below box ends; dots showoutliers. See Fig. 1 for definitions of aggregated ecoregions.
disturbance in nearly all 9 ecoregions (Fig. 6). Strongcontrasts in RVegQ were evident between high and lowdisturbance lakes in all ecoregions, especially in the CPL,UMW, and WMT (Fig. 6). Among the 3 indices, LitCvrQwas least strongly related to anthropogenic disturbance. Itwas moderately related to disturbance in all regions except
WMT XER NPL SPL TPL CPL UMW NAP SAPAggregated Ecoregion
WMT XER NPL SPL TPL CPL UMW NAP SAPAggregated Ecoregion
Litto
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)
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(A)
(B)
Figure 4. Comparison of (A) Littoral Cover Complexity Index(LitCvrQ), and (B) scaled values of the same index(log10-transformed) in sample lakes across 9 NLA aggregatedecoregions (LitCvrQ scaled = LitCvrQ divided by its geometricmean in least-disturbed lakes within the same region). Unweightedsample statistics are shown. Box midline and lower and upper endsshow median and 25th and 75th percentile values, respectively;whiskers show maximum and minimum observations within1.5 times the interquartile range above/below box ends; dots showoutliers. See Fig. 1 for definitions of aggregated ecoregions.
for the CPL, WMT, and XER, where the relationship wasweak. LitRipCvQ showed steep declines with anthropogenicdisturbance in all ecoregions, especially the UMW, SPL, andWMT (Fig. 6).
Except for the O/E indices in the West, we presentedthe physical habitat indices in Fig. 6 without scaling to
206
National lake physical habitat assessment
Litto
ral–
Rip
aria
n H
abita
t Com
plex
ity In
dex
(LitRipCvQ
)Li
ttora
l–R
ipar
ian
Hab
itat C
ompl
exity
Inde
x (S
cale
d)
log 1
0(LitRipCvQ_scaled)
WMT XER NPL SPL TPL CPL UMW NAP SAPAggregated Ecoregion
WMT XER NPL SPL TPL CPL UMW NAP SAPAggregated Ecoregion
(A)
(B)
Figure 5. Comparison of (A) Littoral–Riparian Habitat ComplexityIndex (LitRipCvQ), and (B) scaled values of the same index(log10-transformed) in sample lakes across 9 NLA aggregatedecoregions (LitRipCvQ scaled = LitRipCvQ divided by itsgeometric mean in least-disturbed lakes within the same region).Unweighted sample statistics are shown. Box midline and lowerand upper ends show median and 25th and 75th percentile values,respectively; whiskers show maximum and minimum observationswithin 1.5 times the interquartile range above/below box ends; dotsshow outliers. See Fig. 1 for definitions of aggregated ecoregions.
highlight the relative differences in potential cover and com-plexity among the disturbance classes in the original unitsof these indices. Within these regions, unscaled index valuesfor different anthropogenic disturbance classes are directlycomparable; however, scaling the indices was necessary forthe 2 ecoregions of the West because the expected valuesunder least-disturbed conditions differ for individual lakes.
Plots of RVegQ, LitCvrQ, and LitRipCvQ for the West (notshown) look similar to the O/E plots (Fig. 6), but they do notdistinguish anthropogenic disturbance categories as clearlyas do the O/E plots, which model part of the natural vari-ability of habitat among lakes throughout the West.
The LkShoreHQ index clearly discriminated reference lakesfrom disturbed lakes in all regions of the United States,showing progressive decreases with increasing anthro-pogenic stress from reference, through intermediate, tohighly disturbed lakes. LkShoreHQ is an integrative mea-sure of habitat complexity and disturbance, and one of itssubcomponents (RDist IX) was used in screening referenceand disturbed sites (Fig. 7). These variables were used with7 water chemistry screens and evidence of anthropogenicdrawdown to classify lake anthropogenic pressures (R, M,and D in Fig. 7). The distinct separation of disturbance levels(Fig. 7) is therefore not only a response of habitat to near-site disturbance. As for the other physical habitat indices, thedeclining levels of LkShoreHQ with disturbance classes area reflection of the covariance of littoral and riparian habitatcomplexity with near-shore anthropogenic disturbance, eu-trophication, chemical perturbation, and basin land-uses anddisturbances associated with the lake water quality screeningvariables.
NLA population estimates for near-shorephysical habitat
Population estimates (Table 7) were based on data from1033 probability-selected lakes representing ∼49,500 lakesin the conterminous United States that have depths >1 m andsurface areas >4 ha. To interpret these populations (Table 7),we estimate, for example, that 34.8% (SE = 2.74%) ofthe total number of lakes have low lakeshore disturbance(Table 7, top left). This means that an estimated 17,226 lakes(±1356) had low values of lakeshore disturbance (RDis IX< 0.20).
We lacked physical habitat data for some of the 1033probability-selected lakes, leading to sets of lakes for whichcondition could not be determined. Physical habitat datawere not collected at the 44 sample lakes that had surfaceareas >5000 ha, and these represent 190 (±77) lakes in thetotal population. Depending on the particular habitat index,missing or incomplete physical habitat data precluded cal-culating habitat condition at an additional 9 to 15 samplelakes, and these represent 131 (±126) to 189 (±39) lakesin the national population. These unassessed lakes represent<0.5% of the national population and ≤1% of lakes in anyecoregion (Table 7).
Population estimates for RDis IX and the 3 physical habi-tat indices show a substantial number and percentage of
207
Kaufmann et al.
R M D R M D R M DNPL SPL TPL
R M D R M DNAP SAP
Lake
Phy
sica
l Hab
itat I
ndex
val
ue
RVegQ LitCvrQ LitRipCvQ
R M D R M DCPL UMW
R M D R M DCPL UMW
R M D R M DCPL UMW
Site Disturbance Class (R M D) = Reference, Intermediate, Highly Disturbed Aggregated Ecoregion (CPL or UMW)
R M D R M DNAP SAP
R M D R M DNAP SAP
RVegQ LitCvrQ LitRipCvQ
R M D R M DWMT XER
R M D R M D R M DNPL SPL TPL
RVegQ LitCvrQ LitRipCvQ
Lake
Phy
sica
l Hab
itat I
ndex
val
ue
Site Disturbance Class (R M D) = Reference, Intermediate, Highly Disturbed Aggregated Ecoregion (NPL, SPL, TPL, WMT, or XER)
RVegQ_O/E LitCvrQ_O/E LitRipCvQ_O/E
R M D R M D R M DNPL SPL TPL
R M D R M DWMT XER
R M D R M DWMT XER
(A)
(B)
Figure 6. Contrasts in physical habitat index values among least-disturbed reference (R), intermediate (M), and highly disturbed (D) lakesin aggregated ecoregions of the US. Unweighted sample statistics are shown; Box midline and lower and upper ends show median and25th and 75th percentile values, respectively; whiskers show maximum and minimum observations within 1.5 times the interquartile rangeabove/below box ends; dots show outliers. (A) NAP, SAP, CPL, and UMW ecoregions; (B) NPL, SPL, TPL, WMT, and XER ecoregions.See Fig. 1 for definitions of aggregated ecoregions.
208
Tab
le7.
Pop
ulat
ion
estim
ates
ofth
epe
rcen
tage
ofla
kes
(sta
ndar
der
rors
inpa
rent
hese
s)in
good
,fai
r,or
poor
cond
ition
with
resp
ectt
oN
LAin
dice
sof
near
-sho
rean
thro
poge
nic
dist
urba
nce
and
habi
tatc
over
com
plex
ity.E
stim
ates
are
prov
ided
for
the
cont
erm
inou
s48
US
stat
esfo
rna
tura
llak
es,f
orm
anm
ade
lake
s(r
eser
voirs
),an
dfo
r9
aggr
egat
edO
mer
nik
ecor
egio
ns(a
dapt
edfr
omU
SE
PA20
09).
Hig
hest
and
low
estp
erce
ntag
esof
the
2po
lar
cond
ition
clas
ses
are
show
nin
bold
font
.Und
erlin
edpe
rcen
tage
sin
dica
tehi
ghes
tdis
turb
ance
orpo
ores
tcon
ditio
n.N
atio
nala
ndre
gion
alla
kesa
mpl
ean
dpo
pula
tion
num
bers
are
show
non
the
botto
m2
row
s.O
nly
prob
abili
tysa
mpl
ela
kes
are
incl
uded
inth
esa
mpl
esi
zefo
rpo
pula
tion
estim
ates
.Phy
sica
lhab
itatfi
eld
mea
sure
men
tsan
dob
serv
atio
nsw
ere
notm
ade
onla
kes
with
surf
ace
area
s>
5000
ha(n
umbe
rof
lake
ssh
own
inla
stro
w)
resu
lting
ina
perc
enta
geof
the
lake
popu
latio
nfo
rw
hich
habi
tatc
ondi
tion
was
nota
sses
sed.
“No
data
”re
pres
entl
akes
that
wer
esa
mpl
ed,b
utha
bita
tdat
aw
ere
notc
olle
cted
orw
ere
dete
rmin
edto
bein
valid
afte
rre
view
.
Ag
gre
gat
edO
mer
nik
Eco
reg
ion
sC
on
dit
ion
US
48U
SU
SC
lass
Sta
tes
Nat
ura
lM
an-M
ade
NA
PS
AP
CP
LU
MW
TP
LN
PL
SP
LW
MT
XE
R
Lak
esho
reA
nthr
opog
enic
Dis
turb
ance
Inde
x(R
Dis
IX)
Low
34.8
(2.7
4)46
.4(3
.69)
18.1
(3.4
0)42
.2(7
.86)
7.7
(4.3
5)16
.4(6
.18)
53.9
(5.0
1)38
.7(8
.29)
0.3
(0.1
9)9.
3(5
.57)
56.3
(7.8
9)9.
8(3
.55)
Med
ium
47.6
(2.7
0)41
.3(3
.56)
56.9
(3.8
9)42
.9(7
.72)
66.2
(7.7
9)64
.6(7
.68)
36.8
(4.7
5)41
.8(6
.83)
64.4
(10.
66)
59.8
(9.5
8)31
.3(6
.84)
57.1
(6.7
9)H
igh
16.9
(1.7
3)11
.9(1
.84)
24.1
(3.1
1)14
.5(3
.75)
24.3
(6.9
9)18
.2(5
.83)
9.0
(2.2
2)18
.2(5
.10)
35.1
(10.
59)
30.3
(8.9
8)12
.0(4
.57)
32.2
(8.1
0)N
otas
sess
ed0.
4(0
.08)
0.3
(0.1
2)0.
6(0
.11)
0.3
(0.2
1)0.
6(0
.24)
0.7
(0.2
3)0.
2(0
.20)
0.2
(0.1
4)0.
2(0
.19)
0.6
(0.2
3)0.
2(0
.12)
1.0
(0.4
9)N
oda
ta0.
26(0
.13)
0.2
(0.1
6)0.
3(0
.21)
01.
2(0
.90)
00
1.1
(0.7
6)0
00.
2(0
.16)
0R
ipar
ian
Veg
etat
ion
Cov
erC
ompl
exity
Inde
x(R
VegQ
)G
ood
45.5
(2.7
1)50
.4(3
.73)
38.4
3.71
)65
.9(7
.34)
41.8
(8.6
3)22
.2(5
.30)
54.4
(5.3
9)56
.1(6
.98)
7.1
(4.7
5)36
.8(9
.22)
47.5
(8.5
4)34
.0(8
.20)
Fair
17.8
(2.1
6)16
.4(2
.92)
19.7
(3.2
0)8.
4(2
.76)
27.6
(8.2
7)24
.3(6
.60)
20.3
(4.6
7)2.
7(0
.95)
7.8
(4.0
8)22
.1(7
.39)
24.2
(7.3
1)20
.2(7
.98)
Poor
35.9
(2.5
2)32
.6(3
.33)
40.8
(3.7
2)25
.3(7
.05)
28.8
(7.8
5)52
.8(7
.05)
25.0
(4.2
6)40
.0(6
.70)
84.5
(6.5
2)40
.5(9
.71)
27.3
(7.2
0)44
.9(7
.91)
Not
asse
ssed
0.4
(0.0
8)0.
3(0
.12)
0.6
(0.1
1)0.
3(0
.21)
0.6
(0.2
5)0.
7(0
.23)
0.2
(0.2
0)0.
2(0
.14)
0.2
(0.1
9)0.
6(0
.23)
0.2
(0.1
2)1.
0(0
.49)
No
data
0.3
(0.1
4)0.
2(0
.16)
0.5
(0.2
4)0
1.2
(0.9
0)0
01.
08(0
.76)
0.4
(0.2
7)0
0.9
(0.6
0)0
Litt
oral
Cov
erC
ompl
exity
Inde
x(L
itC
vrQ
)G
ood
58.7
(2.6
7)61
.6(3
.44)
54.6
(4.0
9)46
.1(7
.64)
62.0
(8.4
9)57
.0(7
.38)
58.5
(5.1
7)61
.9(7
.05)
47.4
(8.5
9)57
.6(8
.59)
76.0
(7.0
2)68
.5(7
.00)
Fair
20.5
(2.1
5)20
.8(2
.88)
20.0
(3.2
6)29
.2(7
.85)
20.2
(7.0
3)11
.9(3
.88)
26.1
(4.6
2)16
.0(5
.26)
20.1
(7.2
6)20
.5(7
.56)
11.9
(4.4
6)10
.4(3
.02)
Poor
20.1
(2.1
6)17
.1(2
.45)
24.5
(3.7
0)24
.3(7
.13)
16.0
(6.0
6)30
.4(7
.59)
15.1
(3.2
4)20
.8(6
.40)
31.7
(10.
06)
21.4
(7.2
2)11
.7(5
.95)
20.2
(6.7
1)N
otas
sess
ed0.
4(0
.08)
0.3
(0.1
2)0.
6(0
.11)
0.3
(0.2
1)0.
6(0
.24)
0.7
(0.2
3)0.
2(0
.20)
0.2
(0.1
4)0.
2(0
.19)
0.6
(0.2
3)0.
2(0
.12)
1.0
(0.4
9)N
oda
ta0.
3(0
.13)
0.2
(0.1
6)0.
4(0
.21)
01.
2(0
.90)
00
1.1
(0.7
6)0.
6(0
.37)
00.
2(0
.16)
0L
ittor
al–R
ipar
ian
Hab
itatC
ompl
exity
Inde
x(L
itR
ipC
vQ)
Goo
d46
.8(2
.84)
52.3
(3.7
9)38
.83.
98)
50.9
(8.0
3)43
.8(9
.04)
38.1
(7.3
7)50
.3(5
.62)
60.7
(6.7
6)9.
6(5
.61)
41.5
(9.4
8)55
.9(8
.43)
31.5
(7.3
6)Fa
ir20
.1(2
.36)
18.7
(3.1
0)22
.2(3
.54)
17.8
(7.7
6)6.
2(1
.72)
27.3
(7.1
1)24
.1(4
.56)
7.4
(2.3
8)31
.4(1
5.56
)39
.1(9
.67)
9.7
(3.7
7)17
.0(7
.46)
Poor
32.4
(2.4
7)28
.5(3
.08)
37.9
(3.8
9)30
.9(7
.33)
48.2
(8.7
3)33
.9(7
.02)
25.3
(4.4
0)30
.5(6
.33)
57.9
(14.
06)
18.8
(6.3
0)33
.3(8
.14)
50.6
(8.3
2)N
otas
sess
ed0.
6(0
.08)
0.3
(0.1
2)0.
6(0
.11)
0.3
(0.2
1)1.
8(0
.26)
0.7
(0.2
3)0.
2(0
.20)
1.3
(0.1
4)0.
2(0
.19)
0.6
(0.2
3)0.
4(0
.12)
1.0
(0.4
9)N
oda
ta0.
4(0
.14)
0.2
(0.1
6)0.
6(0
.24)
01.
2(0
.90)
00
1.1
(0.7
6)1.
0(0
.48)
00.
9(0
.60)
0L
ake
Popu
latio
nan
dSa
mpl
eSi
zes
Popu
latio
nSi
ze49
,546
29,3
0820
,238
5226
4690
7009
15,5
6263
2726
6031
4841
2280
2N
o.la
kes
sam
pled
1028
437
591
9311
610
414
813
767
129
155
84
No.
lake
sno
tas
sess
ed44
1034
26
151
22
84
4
209
Kaufmann et al.
Lake
shor
e Ph
ysic
al H
abita
t Qua
lity
Inde
x(LkShoreHQ)
R M D R M D R M D R M D R M DWest CENPL CPL UMW EHIGH
Site Disturbance Class (R M D) = Reference, Intermediate, Highly Disturbed Aggregated Ecoregion (West, CENPL, CPL, UMW, or EHIGH)
Figure 7. Contrasts in integrated Lakeshore Physical Habitat Quality Index (LkShoreHQ) values among least-disturbed reference (R),intermediate (M), and highly disturbed (D) lakes in aggregated ecoregions of the US. Unweighted sample statistics are shown. Box midlineand lower and upper ends show median and 25th and 75th percentile values, respectively; whiskers show maximum and minimumobservations within 1.5 times the interquartile range above/below box ends; dots show outliers. West = Western US (WMT + XER),CENPL = Central Plains (NPL + SPL + TPL), CPL = Coastal Plains, UMW = Upper Midwest, and EHIGH = Eastern US Highlands (NAP+ SAP) ecoregions. See Fig. 1 for definitions of aggregated ecoregions.
lakes in the conterminous United States with high lakeshoreanthropogenic disturbance and near-shore physical habitathaving low cover complexity (poor condition), but the re-sults differ by region and lake type (Table 7). Although 35%of the target lake population exhibited RDis IX ≤ 0.20 and17% had RDis IX > 0.75, natural lakes had less lakeshoredisturbance (46% low, 12% high) than did manmade lakes(18% low, 24% high). Nationally, 46% of lakes had highRVegQ whereas 36% had very low RVegQ (Table 7). Withrespect to LitCvrQ, 59% of lakes had high and 20% had lowvalues. For LitRipCvQ, 47% of lakes were in good condi-tion (high complexity) and 32% were in poor condition (lowcomplexity). Natural lakes tended to have higher values ofall 3 physical habitat complexity indices than did manmadelakes.
Population estimates of RDis IX were lowest in the WMTand UMW and greatest in the NPL and XER ecoregions(Table 7). The NAP, TPL, and UMW ecoregions had thehighest percentages of lakes with RVegQ in good condition.Despite having the highest percentage of lakes with RVegQcomparable to least-disturbed condition (66%), the NAPhad the lowest percentage (46%) with LitCvrQ comparableto least-disturbed condition. Although the WMT had thesecond lowest percentage of lakes with high RDis IX, itranked below the NAP, TPL, and UMW in terms of thepercentage of lakes with RVegQ in good condition. Amongall 9 ecoregions, the NPL had the highest percentages of
lakes in poor condition for all 4 physical habitat indicesand the lowest percentage of lakes in good condition for allindices except LitCvrQ, for which it had the second lowestpercentage.
DiscussionImplications of uncertainty in modelingcondition in least-disturbed lakes
The power to discriminate altered habitat from that in least-disturbed lakes depends on the magnitude of the alterationrelative to the central tendency and dispersion in the distribu-tion of index scores among least-disturbed lakes, measuredby the reference means and SDs (Table 5). The effect ofthis interaction can be illustrated by box-and-whisker plots(Fig. 6). Reductions in RipVegQ and LitRipCvQ in disturbedlakes are large relative to the median and the magnitudeof their dispersion among least-disturbed (reference) lakesin nearly all the ecoregions, giving us confidence in thecondition classifications for these indices. Conversely, re-ductions in LitCvrQ among disturbed lakes are small rela-tive to the median and the magnitude of dispersion amongleast-disturbed (reference) lakes in nearly all the ecoregions,particularly the West (WMT and XER) and CENPL (NPL,SPL, and TPL) ecoregions. LitCvrQ index alterations aretherefore, more difficult to discern, and the net effect is tounderestimate habitat alteration.
210
National lake physical habitat assessment
Variation in natural conditions not captured by the expectedcondition regressions (Table 4) contributes to the uncertaintyin the LitCvrQ condition classifications in the West. Con-sequently, population estimates of the percentages of lakesin poor condition with respect to LitCvrQ may be under-estimated in the West. The CENPL ecoregions have both alarge SD and a substantial amount of anthropogenic distur-bance in the combined set of least-disturbed lakes used toset condition criteria. Inclusion of substantial amounts of an-thropogenic disturbance in the set of least-disturbed CENPLlakes reduces the central tendency of LitCvrQ among thoselakes. Compared with other ecoregions, larger reductions inLitCvrQ must be observed in sample lakes of the CENPL be-fore we would classify them in poor condition with respectto that index. Again, the result of these limitations is thatour population estimates of the percentages of lakes havingpoor LitCvrQ are likely underestimates for ecoregions of theCENPL.
Lake physical habitat condition in the US
Among all the chemical and physical condition indicatorsmeasured in the NLA, degradation of RVegQ and LitRipCvQwere the most widespread (USEPA 2009). Physical habitatcondition in lakes of every ecoregion was negatively associ-ated with the gradient of anthropogenic disturbance of basinsand near-shore zones expressed by the classification of least-disturbed reference, intermediate, and highly disturbed lakes(Fig. 6). The numbers of US lakes with poor littoral or ripar-ian habitat condition exceeded those with excessive nutrients(nitrogen and phosphorus), turbidity, low dissolved oxygen,or acidity (USEPA 2009). Although physical and chemicaldegradation were more widespread in manmade lakes, therelative ranking of the types of degradation by their extentwas similar for both lake types.
The ecological impact of physical habitat degradation onlakes can be quantified by evaluating the relative risk tolake biota when physical habitat degradation occurs (VanSickle 2013). In the relative risk context, we refer to phys-ical and chemical habitat degradation as stressors on biota,recognizing that these proximal stressors are in-turn influ-enced by more distal pressures such as near-shore, basin,and landscape-scale anthropogenic disturbances (Allen et al.1999), as they are in streams (Saly et al. 2011, Marzin et al.2013, Macedo et al. forthcoming).
Certainly basin- and landscape-scale pressures affect lakephysical, chemical, and biological habitat, but the relativeand attributable risk analyses reported by USEPA (2009) andVan Sickle (2013) did not focus on those distal pressures.USEPA (2009) reported that the highest risk to zooplank-ton and phytoplankton assemblages nationally was associ-ated with degradation of RVegQ around lakes. RVegQ had
a relative risk of 3 for biological plankton assemblages,meaning that lakes with poor riparian vegetation cover com-plexity were 3 times more likely to exhibit poor biologi-cal conditions than those that did not have poor ripariancover complexity (USEPA 2009). Degradation of LitCvrQ,LitRipCvQ, excess nitrogen and phosphorus, and turbidityhad relative risks between ∼2 and 2.5 (USEPA 2009), show-ing the potential importance of both littoral–riparian covercomplexity and water quality for protecting or improvingbiotic condition. Van Sickle (2013) bundled similar stressorsin the NLA and found the relative risk to lake biotic con-dition from degradation of combined lake physical habitatstressors (∼3) was greater than that for chemical stressors,including excess nutrients (∼2).
Although the apparent importance of lakeshore and littoralhabitat to planktonic assemblages may be unexpected, mostUS lakes are small and relatively shallow; 80% of the lakesin the NLA population have surface areas <0.5 km2 (USEPA2009). Especially in small or shallow lakes, pelagic waterquality and plankton can be directly or indirectly affected bylakeshore disturbance and littoral habitat conditions. Ripar-ian vegetation may buffer lakes from diffuse inputs of nutri-ents, sediment, and toxins (Allen et al. 1999, Wetzel 2001).Even in lakes of moderate size, primary production may bedominated by littoral processes, including nutrient uptake byaquatic macrophytes (Wetzel 1975). Littoral aquatic macro-phytes can also affect dissolved nutrient concentrations andprovide cover and structure that influences phytoplankton,zooplankton, macrobenthos, and fish (Wetzel 1975, 2001,Strayer and Findlay 2010). Thus, littoral cover complexitycan directly influence lake plankton by providing cover andstructure for planktonic organisms and by moderating waterquality. Riparian vegetation cover complexity can positivelyinfluence plankton through inputs of structural elements thatprovide littoral structure (e.g., snags) and by buffering thelake from upland and lakeside anthropogenic activities, dis-turbances, and contaminant inputs.
The ecological importance of physical or chemical habitatdegradation as stressors to lake biota is dependent on boththe extent of the stressors and the severity of biological im-pacts when the stressors are present (Van Sickle et al. 2006).Attributable risk is derived by combining the relative ex-tent and the relative risk into a single number. Conceptually,attributable risk is an estimate of the proportion of poorbiological conditions that could be reduced if a particularstressor were restored from poor condition to fair or bet-ter condition, assuming that the relative risk association iscausative, stressors are independent, and the stressor effectsare reversible (Van Sickle 2013). Other important stressors(e.g., excess nutrients, fine sediment, or toxins) can be mod-erated by riparian buffers, and the recovery of biota after ri-parian rehabilitation will also depend on the extent to whichassociated stressors are reduced.
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The attributable risk value represents the importance of apotential stressor, and ranking such risks can be useful forpolicy makers and managers. The NLA estimated the at-tributable risk of various lake stressors to biological condi-tion expressed by the planktonic O/E index (USEPA 2009,Van Sickle 2013). Reduction in RVegQ had the highest at-tributable risk for phytoplankton and zooplankton taxa loss(43%) among the stressors examined. Thus, we expect lakeplanktonic taxa richness would be most improved by im-proving conditions of lakeshore vegetation cover and com-plexity, including inundated wetland vegetation. Removingriparian degradation as a stressor could potentially restore43% of ∼11,000 (4700) lakes in poor biological conditionnationally to intermediate or good condition, or approxi-mately 9.5% of lakes nationwide.
Related research
Many studies at smaller spatial scales corroborate our na-tional findings associating littoral and riparian habitat sim-plification with anthropogenic development around lakes(Kaufmann et al. 2014b). To date, however, no results fromother national probability surveys of lake physical habitat areavailable for direct comparison with the NLA results. Inter-estingly, a recent assessment of the ecological condition ofwadeable streams in the United States (Paulsen et al. 2008)showed results similar to those we report for lakes. Both as-sessments found the greatest percentages of near-shore (ornear bank) disturbance, poor riparian vegetation cover com-plexity, and poor littoral or instream cover complexity in theNPL. They both identified the WMT as the region with theleast near-bank disturbance and lowest percentage of waterswith poor physical habitat condition. The relative rankingof stressors by extent and severity was also similar betweenthe national lake and stream surveys (Paulsen et al. 2008,Van Sickle 2013). Both showed that physical habitat sim-plification was a pervasive stressor with high relative risk tobiological condition.
The results of Miranda et al. (2010) are similar to ours,although their survey concerned the condition of 494 ran-domly selected US public reservoirs ranging from 200 to131,000 ha selected from the National Inventory on Damsdatabase (NID 2008). In addition, they based their assess-ment on the expert opinion of 221 lake managers respondingto an online survey. They reported that well-established ri-parian zones and wetlands were absent around many reser-voirs, and the littoral zones of reservoirs were generallybarren, lacking woody debris and native aquatic plants. Insome cases, reservoirs contained excessive growth of non-native aquatic vegetation. Miranda et al. (2010) also citeddegradation of reservoir physical habitat by fluctuating wa-ter levels and drawdown that limited access to backwatersand reduced shoreline–riparian interaction.
We can also compare a smaller-scale probability survey oflakes in the Northeastern United States with the NLA re-sults. The physical habitat field methods were similar in the2 surveys (Kaufmann et al. 2014a), but the Northeasternlakes survey included smaller lakes than surveyed by NLAin 2007. In addition, results were based on slightly differentformulations of lakeshore disturbance indices and differentcondition criteria than used for the NLA (Kaufmann et al.2014a, 2014b). Despite these differences, major results andconclusions concerning the importance of habitat simplifi-cation and its association with anthropogenic disturbanceswere similar between the 2 surveys.
Whittier et al. (2002) reported substantially greater ripar-ian and littoral habitat structural complexity in the 27%(±9%) of the lake population in the Northeast having nodirect evidence of shore zone anthropogenic activities com-pared with those having more shoreline anthropogenic dis-turbance. Conversely, the 23% (±10%) of Northeast lakeswith large amounts of lakeshore anthropogenic disturbancehad half the riparian woody cover and lower littoral habitatcomplexity. Whittier et al. (2002) concluded that althoughstressors such as nonnative fish introductions, mercury con-tamination, and lakeshore physical habitat alteration werenot a focus for lake management at that time, they wereas widespread as eutrophication and more extensive thanacidification in Northeastern lakes. Brauns et al. (2007) andUSEPA (2009), respectively, emphasized the need to rec-ognize physical habitat alteration as an important stressormeriting education, management, and regulatory attentionfor European and US lakes.
Kaufmann et al. (2014b) found that the strongest relation-ships with fish and bird assemblages in the Northeasternlake survey were with LkShoreHQ. Compared with its sub-components and with basin road density, LkShoreHQ wasthe best predictor of intolerant fish species richness, tolerantfish species richness, the percentage of neotropical migrantbirds, and the percentage of tolerant bird taxa in the near-shore lake environment of lakes in the Northeastern lakesurvey (Kaufmann et al. 2014b).
Kaufmann et al. (2014a) reported good precision andsignal:noise ratio for LkShoreHQ in the NLA, with ra-tio of repeat visit standard deviation:potential range ratio(SDrep/Rgpot) = 0.05 and S/N = 4.2. We believe that Lk-ShoreHQ has great potential in regional and national lakeassessments as an integrated index that combines the di-mensions of anthropogenic disturbance, cover, and struc-tural complexity in the terrestrial and aquatic compartmentsof the near-shore lake environment. The strong associa-tion of LkShoreHQ with the disturbance gradients we used(Fig. 7) may be somewhat accentuated because field mea-sures of riparian anthropogenic disturbance contributing toLkShoreHQ were also among the many variables used to
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screen least- and most-disturbed lakes. To avoid circularityin assessments, condition criteria for LkShoreHQ could beset based on biotic tolerances (e.g., Bryce et al. 2010, Whit-tier and Van Sickle 2010) or in the manner we described forthe other lake habitat complexity indices, but by employingreference sites that do not use lakeshore disturbance in theirscreening.
Summary and recommendationsOur NLA findings show widespread alteration and degra-dation of near-shore riparian and littoral habitat in lakesof the conterminous 48 US states. Lake physical habitatsimplification was associated with basin and near-shoreanthropogenic disturbances in all regions. Many smaller-scale studies have associated lakeshore anthropogenicdisturbances with habitat simplification and reduction in bi-ological condition (Kaufmann et al. 2014b). Our results vali-date the generality of responses to known stressors reportedin those studies. In addition, the more intensive, smaller-scale research suggests plausible mechanisms underlyingthe associations we observed. The weight of evidence fromcombining this knowledge increases the potential to makereasonable decisions concerning conservation and rehabili-tation of physical habitat quality and biological condition inlakes and watersheds that have not been extensively studied.
Of the 8 types of in-lake or near-shore physical and chemi-cal degradation examined by the NLA (USEPA 2009), poorquality (low) lake Riparian Vegetation Cover Complexitywas identified as the most widespread. Attributable risk anal-yses suggest that eliminating the effects of poor lakeshorehabitat cover could improve the biological condition in manylakes nationwide (USEPA 2009). Therefore, in addition tobetter managing basin land use, we suggest that lake man-agers at local, state, and national levels should better protectand rehabilitate littoral and riparian physical habitat throughbetter management of anthropogenic disturbance in near-shore areas. Anthropogenic disturbance along lakeshores(such as tree removal and residential construction) impairslakeshore and shallow water habitats, affecting terrestrialand aquatic biota alike (Hughes et al. 2014a). Many statesand the USEPA are promoting low impact development andmaintenance or restoration of green infrastructure to pro-tect and rehabilitate more natural hydrology and to improvebiological and physical and chemical habitat conditions insurface waters (USEPA 2007b, Hughes et al. 2014b).
AcknowledgmentsThe field data used in our study were collected byFederal and State agency collaborators, USEPA regionalstaff, and contract crews from TetraTech, GLEC, and Dyna-
mac too numerous to mention here, but their conscientiousfield work made our paper possible. We thank C. Burch-Johnson for preparing the maps; John Van Sickle, TonyOlsen, and Tom Kincaid for technical advice concerning de-sign, statistics and analysis; and Susan Holdsworth, SarahLehmann, and Amina Pollard for NLA project managementand funding. Our manuscript was improved as a result ofrecommendations from Bob Ozretich, Ekatrina Kovalenko,Kellie Merrell, Chuck Hawkins, and 5 anonymous review-ers. The manuscript was reviewed by the National Healthand Environmental Effects Research Laboratory’s WesternEcology Division and approved for publication. Approvaldoes not signify that the contents reflect the views of theAgency, nor does mention of trade names or commercialproducts constitute endorsement or recommendation for use.
FundingFunding for data analysis and manuscript preparation waslargely from the USEPA Office of Research and Develop-ment in support of the EPA Office of Water’s National LakesAssessment through cooperative agreement #CR-818606with Oregon State University and contract GSFU594G withRaytheon Information Services. Bob Hughes was supportedin manuscript preparation during sabbatical in Brazil byFulbright Brasil, Companhia Energetica de Minas Gerais(CEMIG), and Fundacao de Amparo a Pesquisa do Estadode Minas Gerais (FAPEMIG) grant 00011/09.
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