Linking Landscapes and Habitat Suitability Scores for

North American Journal of Fisheries Management 28:906–918, 2008 Ó Copyright by the American Fisheries Society 2008 DOI: 10.1577/M06-120.1

[Article]

Linking Landscapes and Habitat Suitability Scores for Diadromous Fish Restoration in the Susquehanna River Basin PATRICK M. KOCOVSKY* U.S. Geological Survey, Great Lakes Science Center, Lake Erie Biological Station, 6100 Columbus Avenue, Sandusky, Ohio 44870, USA

ROBERT M. ROSS

DAVID S. DROPKIN

AND

U.S. Geological Survey, Leetown Science Center, Northern Appalachian Research Laboratory, 176 Straight Run Road, Wellsboro, Pennsylvania 16901, USA

JOHN M. CAMPBELL Biology Department, Mercyhurst College, 501 East 38th Street, Erie, Pennsylvania 16546, USA Abstract.—Dams within the Susquehanna River drainage, Pennsylvania, are potential barriers to migration of diadromous fishes, and many are under consideration for removal to facilitate fish passage. To provide useful input for prioritizing dam removal, we examined relations between landscape-scale factors and habitat suitability indices (HSIs) for native diadromous species of the Susquehanna River. We used two different methods (U.S. Fish and Wildlife Service method: Stier and Crance [1985], Ross et al. [1993a, 1993b, 1997], and Pardue [1983]; Pennsylvania State University method: Carline et al. [1994]) to calculate HSIs for several life stages of American shad Alosa sapidissima, alewives Alosa pseudoharengus, and blueback herring Alosa aestivalis and a single HSI for American eels Anguilla rostrata based on habitat variables measured at transects spaced every 5 km on six major Susquehanna River tributaries. Using geographical information systems, we calculated land use and geologic variables upstream from each transect and associated those data with HSIs calculated at each transect. We then performed canonical correlation analysis to determine how HSIs were linked to geologic and land use factors. Canonical correlation analysis identified the proportion of watershed underlain by carbonate rock as a positive correlate of HSIs for all species and life stages except American eels and juvenile blueback herring. We hypothesize that potential mechanisms linking carbonate rock to habitat suitability include increased productivity and buffering capacity. No other consistent patterns of positive or negative correlation between landscape-scale factors and HSIs were evident. This analysis will be useful for prioritizing removal of dams in the Susquehanna River drainage, because it provides a broad perspective on relationships between habitat suitability for diadromous fishes and easily measured landscape factors. This approach can be applied elsewhere to elucidate relationships between fine- and coarse-scale variables and suitability of habitat for fishes.

There are over 2,600 low-head dams on streams throughout Pennsylvania (Pennsylvania Department of Environmental Protection 1996). Many of these dams block passage of anadromous species, such as the American shad Alosa sapidissima, blueback herring Alosa aestivalis, and alewife Alosa pseudoharengus, and the catadromous American eel Anguilla rostrata; these species were once abundant but are now considered functionally extinct in the Susquehanna River drainage of Pennsylvania. Historical ranges of American shad in the Susquehanna River drainage extended into New York (Burgess 1980a), but information on distributions within the individual watersheds is lacking. Alewives extended at least to * Corresponding author: [email protected] Received April 20, 2006; accepted December 7, 2006 Published online June 16, 2008

the confluence with the Juniata River (Burgess 1980b). Blueback herring were collected several kilometers downstream from Harrisburg as recently as 1997 (M. Hendricks, Pennsylvania Fish and Boat Commission [PFBC], personal communication). American eels were historically captured in upstream reaches of all major tributaries of the Susquehanna River (Lee 1980). In the 1990s, efforts were made to inventory dams on primary tributaries to the Susquehanna River (Carline et al. 1994, 1996; Carline and Bukowski 1995), and one goal of these efforts was to remove dams that no longer provided the services for which they were built and thus to open passage for reestablishment of diadromous species. After inventory efforts were concluded, several dams were either retrofitted with passage devices (e.g., fish elevators) or removed entirely from the main-stem Susquehanna River and some of the major tributaries.

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Opening of the York Haven fishway on the main-stem Susquehanna River in 2000 resulted in, on average, over 4,000 American shad (range ¼ 219–16,200) ascending to middle reaches (beyond river kilometer 90) each year. In 2001, thousands of alewives and blueback herring ascended beyond Conowingo Dam, the downstream-most dam on the main-stem Susquehanna River; however, in 2006, no individuals of either species were recorded at Conowingo Dam (M. Hendricks, personal communication). Construction of fish passage facilities (e.g., the Cave Hill Dam in 2001 and the Heishman’s Mill Dam in 2004) and the removal of nonfunctional dams (Good Hope and Black dams in 2002 and 2003, respectively) on Conodoguinet Creek resulted in nearly the entire stream being reopened to passage for diadromous fishes. Several dams were removed from the Conestoga River, the stream with the most blockages, but over a dozen dams remain on its main stem, dozens more remain on thirdorder tributaries, and the majority of the watershed remains closed to passage. Most of the other major tributaries to the Susquehanna River also remained blocked along most of their lengths. Concurrent with dam removal efforts that began in the late 1990s, research emphasis shifted toward determining suitability of the several tributaries for reestablishment of diadromous fishes and prioritizing dam removal to facilitate fish passage. Because the fish species of interest either are currently absent from the watersheds in question or are present only at greatly reduced densities, efforts have been directed primarily toward using habitat suitability indices (HSIs) to predict whether habitat will be suitable for diadromous fishes if passage is reopened. The HSIs for American shad, alewives, blueback herring, and American eels use fine-scale point measurements of depth, velocity, and substrate and sometimes use water chemistry values, such as pH (Pardue 1983; Stier and Crance 1985; Ross et al. 1993a; Carline et al. 1994). Although HSIs are measured on a fine scale, factors or processes at the watershed scale or even the landscape scale may indirectly affect them by altering the timing of delivery, quality, and quantity of water entering streams. Geology (Sharpe et al. 1987) and soils (Swistock et al. 1989) in a watershed can affect water chemistry, and the timing of delivery of water to streams can vary with the amount of forested versus herbaceous land cover or impervious surfaces (Hewlett 1982). If HSIs are indeed reasonable measures of the potential for success of a species in a particular body of water, then whatever relationships exist between landscape-scale factors or processes and HSIs may also exist between landscapescale factors and the species themselves. Bilkovic et al. (2002) provided evidence of land-

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scape-scale factors affecting suitability of habitat for anadromous alosines. They investigated relationships between presence of American shad eggs and larvae, instream habitat, and land uses for two streams in Virginia that supported runs of American shad. Densities of American shad eggs and larvae varied with amount of forest cover, and American shad tended to use upstream areas more than downstream areas. Bilkovic et al. (2002) also reported that HSI curves based on instream habitat were less predictive than curves based on land uses. Their results suggest that landscapes can indeed affect suitability of habitat for anadromous alosines. We sought to investigate the relationships between diadromous fish HSIs and land use or geology in six major tributaries of the Susquehanna River. Our objective was to identify potential linkages between landscape-scale factors and habitat suitability in these tributaries. Methods Study sites.—Our study streams were the six largest Susquehanna River tributaries: Conestoga River, Conewago Creek, Conodoguinet Creek, Juniata River, Swatara Creek, and the West Branch of the Susquehanna River (hereafter, West Branch; Figure 1). The Conodoguinet Creek, Swatara Creek, and Juniata River watersheds lie almost entirely within the Ridge and Valley Physiographic Province. The watersheds of Conestoga River and Conewago Creek lie almost entirely within the Piedmont Physiographic Province. Approximately two-thirds of the West Branch watershed lies within the Appalachian Plateaus Province, while the remainder lies within the Ridge and Valley Province. In combination, these six watersheds make up over 27% of Pennsylvania’s land area. Habitat measurements.—Data for habitat suitability models were collected along transects spaced every 5 km beginning at the mouth of each stream and continuing to the third- to second-order transition in all but two watersheds. In the Juniata River watershed, transects continued to the only remaining dams: the Raystown Dam, which forms Raystown Lake on the Raystown Branch; and the Warrior Ridge Dam on the main-stem Juniata River just downstream of the confluence of the Little Juniata River and the Frankstown Branch of the Juniata River. Neither dam is under consideration for removal. In the large West Branch watershed, transects were placed every 10 km and stopped well short of the headwaters. The West Branch upstream from the city of Lock Haven is polluted with acid mine drainage from bituminous coal mines and is incapable of sustaining intact lotic communities

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FIGURE 1.—Major tributaries to the Susquehanna River in Pennsylvania, where habitat suitability for diadromous and catadromous fish species was examined.

beginning a few kilometers upstream from the uppermost transect. Transects were sampled during below bank-full conditions in June 1999 (Conewago Creek, Conodoguinet Creek, and Juniata River) and June 2000 (Conestoga River, Swatara Creek, and West Branch). Sampling was conducted at flows lower than bank full, because bank-full flows are not representative of typical ambient habitat conditions and because of the potential for altered pH values during the acid episodes that are possible in some of these watersheds. Substrate particle size was scored (Klemm and Lazorchak 1995) and current velocity (Marsh-McBirney Model 2000m flowmeter) and depth were measured on each transect at five equidistant points (points 1–5 from left to right, facing upstream). Single water quality measures were taken at point 3 of each transect to quantify temperature, pH, dissolved oxygen, conductivity, and salinity (field measurements with YSI Model 60 pH meter and YSI Model 85 water quality meter). Water samples were collected for laboratory analysis of turbidity (LaMotte Model 2020 nephelometer). To assess benthic macroinvertebrate communities for trophic quality (food resource availability for American eels), benthic samples (pooled 1-min scours at points 1–3 alternating by transect with points 3–5) were taken with a Surber sampler at each transect. Daytime stream drift and zooplankton were quantified

with pooled samples (points 2 and 3 alternating by transect with points 3 and 4) from calibrated Wisconsin (5:1) plankton nets (diameter of opening ¼ 0.25 m; mesh size ¼ 153 lm). A 0.10–5.04-m3 (largely 1–3 m3) volume of water was sampled at transects by holding the net facing into the current from a standing position or from a canoe in deeper waters. Benthic macroinvertebrate samples, preserved in the field in 70% ethanol, were processed in the laboratory using standard techniques for sorting and identification (Peckarsky et al. 1990; Merritt and Cummins 1996). Organisms were identified to genus except for nematodes (phylum), annelids (class), and chironomids (family). Plankton and drift samples, preserved in the field in 10% buffered formalin, were cleared of debris, rinsed, allowed to settle, reduced to a volume of 40–80 mL by siphoning, and enumerated in 20-mL portions on a gridded Petri dish under a dissecting microscope. Density of plankton or drift organisms at each transect was calculated as the total number counted in pooled samples divided by the total volume of river water filtered by the calibrated plankton net. Volume sampled was calculated by multiplying the area of net opening by the linear quantity of water that passed through the net, which was measured using a flowmeter mounted at the net opening.

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TABLE 1.—Rank importance and importance values for several invertebrate prey taxa used in development of a habitat suitability model for subadult (,400 mm total length) American eels in six tributaries of the Susquehanna River, Pennsylvania. Rank importance source

Taxon Ephemeroptera Trichoptera Plecoptera Odonata Coleoptera Megaloptera Diptera

Ogden (1970)

Lookabaugh and Angermeier (1992)

Denoncourt and Stauffer (1993)

Mean rank

Importance value

1 2 6 4 5 3

1 5 3 2 4

1 3 2 7 4 6 5

1.0 3.3 3.7 4.3 4.3 4.5 5.0

7 6 5 4 3 2 1

Habitat suitability models.—Physical and biotic habitat data from transects were used as input values for calculating (1) HSIs from published models for American shad, blueback herring, and alewives at various riverine life stages and (2) an index we developed for American eel subadults (,40 cm), which are not yet primarily piscivorous (Ogden 1970; Lookabaugh and Angermeier 1992; Denoncourt and Stauffer 1993). The American shad HSI models specify three riverine life stages: spawning adults, fertilized eggs and larvae, and premigratory juveniles (Stier and Crance 1985; Ross et al. 1993a, 1993b, 1997). Two life stages are specified for alewives and blueback herring (collectively referred to as river herrings): (1) spawning adults, developing eggs, and larvae; and (2) juveniles. Habitat variables defining published American shad HSIs are surface water temperature and velocity for spawning adults; surface water temperature for developing eggs and larvae; and surface water temperature, depth, and turbidity for premigratory juveniles. Those defining river herring HSIs in the Pardue (1983) model are (1) substrate type and surface water temperature for spawning adults, developing eggs, and larvae; and (2) zooplankton density, salinity, and surface water temperature for premigratory juveniles. Variables defining river herring HSIs in the Carline et al. (1994) model are substrate type, water velocity, depth, turbidity, pH, and dissolved oxygen. All models were scored at each stream and transect for the specified variables, and mean values were used when more than one measurement was obtained (e.g., 5 values/transect for substrate type, depth, and water velocity). Hereafter, we refer to the American shad (Stier and Crance 1985; Ross et al. 1993a, 1993b, 1997) and river herring (Pardue 1983) models as the U.S. Fish and Wildlife Service (FWS) models and the river herring model of Carline et al. (1994) as the Pennsylvania State University (PSU) model. We developed a food-based HSI model for American eels up to 400 mm total length in mid-Atlantic

rivers and streams. We began by calculating an integrated rank importance of invertebrate food items for American eels by taking the mean of rank importance values from Ogden (1970), Lookabaugh and Angermeier (1992), and Denoncourt and Stauffer (1993). Next, we assigned importance values to each taxon; the most important food item received an importance value of 7 and the least important item received a value of 1 (Table 1). Raw HSI (RHSI) was then calculated using the following formula: RHSI ¼

n X

½ðTij =Nj Þ 3 Vi

ð1Þ

i¼1

where Tij ¼ sum of individuals captured for taxon i at transect j, Nj ¼ sum of all individuals of all taxa captured at transect j, and Vi ¼ importance value for taxon i. Because the HSI values for American shad, alewives, and blueback herring had values between 0 and 1, we standardized RHSI values for American eels to the range from 0 to 1 by linear interpolation. We assigned the value of 1 (most suitable) to the maximum RHSI across all transects and the value of 0 to the minimum RHSI across all transects and then calculated the two-point linear regression equation (standardized HSI ¼ [0.2 3 RHSI] 0.2; R2 ¼ 1.0). The resultant equation was used to calculate standardized HSI scores from RHSI scores for subsequent analysis. Land use and geology.—All land use and geologic data were collected using geographical information systems (GIS). Land use data were calculated from a 30-m resolution map produced as part of the Pennsylvania Gap Analysis program (Myers et al. 2000). The land use map is categorized into eight different land cover types (evergreen forest, deciduous forest, mixed forest, woody transitional, perennial herbaceous, annual herbaceous, water, and barren) within three different categories of human alteration (rural, suburban, and urban) for a total of 24 different classes of land use and human influence. Surficial geology was calculated from a digital version of a

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geologic map of Pennsylvania (Berg 1980). We calculated land use for each of the 24 land use–human influence classes and each of 10 different rock groups: carbonate, quartz, granitic, gneiss–schist, shale, siliciclastic, mudstone, slate, siltstone, and basalt. An existing map of boundaries for small watersheds within Pennsylvania was used to develop maps of boundaries for each of the six river systems studied. The watershed map was used as the overlay for calculating areas of land uses and geology within watersheds (land use, geology, and watershed maps are available from PSU 2008). Transects were geographically referenced using latitude and longitude to overlay with land use, geology, and watersheds. To delineate subwatersheds upstream from each transect, we used digital raster graphics of U.S. Geological Survey (USGS) topographical maps. All GIS work was done using ArcView version 3.2. We calculated land use and geologic data for each subwatershed upstream from each transect. After calculating land use and geologic variables, we used GIS to associate these variables with HSIs. Raw areas of land use and geologic factors upstream from each transect were then converted to proportions for subsequent analysis. The final data set for analysis consisted of three matrices: land use–geology expressed as proportion of total upstream area, HSI calculated by the PSU method, and HSI calculated by the FWS method. Linkages between landscape-scale factors and habitat suitability.—We used canonical correlation analysis (hereafter, CANCOR) to evaluate relationships among the suite of HSIs and the suite of land use and geologic variables for each method of estimating habitat suitability. We chose to use CANCOR, a multivariate analysis, over univariate analyses because it has the advantage of considering all variables in two data sets simultaneously. Univariate methods, such as multiple linear regression, can only assess relationships among individual dependent variables (in this analysis, HSIs) and a set of independent variables (land use and geologic variables); CANCOR considers all HSIs and all land use–geology variables simultaneously, yielding a single comprehensive analysis of the relation between HSIs and the investigated land use–geology variables. Canonical correlation analysis is an extension of multiple regression analysis in which there is more than one dependent variable. In our case, HSI scores are the dependent variables and land use and geologic variables are the independent variables. The primary outcome of CANCOR is a suite of canonical variates, each representing a gradient of variation within one set of variables that is maximally correlated with the gradient in another set of variables (McGarigal et al.

2000). The canonical variates identify gradients in land use and geologic variables that are maximally correlated with gradients in HSIs. We refer readers to McGarigal et al. (2000) and references therein for further details on CANCOR. Before conducting CANCOR, we performed several diagnostic tests to meet the assumptions of the procedure. First, we calculated univariate pairwise correlations between all HSIs, all geologic variables, and all land use variables in all watersheds. Correlation matrices were reviewed for high correlations (R . 0.70) among land use and geologic factors. If a high pairwise correlation was found between two variables, they were combined into a composite variable or one of the variables was removed from subsequent analysis. This step was taken to minimize redundancy and to reduce the dimensionality of the data set. For geologic and land use variables, we selected those variables that were significantly (P , 0.05) correlated with most of the HSIs for most watersheds. Second, we assessed normality of the variables. Some of the geologic variables were either nonnormal or approached nonnormality, which is common for proportions. Attempts to normalize those variables by arcsine transformation were not successful. Despite the deviation of these variables from normality, we proceeded with the analysis using the raw geologic variables, because CANCOR does not explicitly require multivariate normality and because deviations from normality are not a serious concern when CANCOR is used to elucidate potential hypotheses for future testing (McGarigal et al. 2000). We used structure coefficients from CANCOR to identify gradients in land use and geology in the canonical variates and to determine the importance of individual land use and geologic factors in canonical variates (McGarigal et al. 2000). Structure coefficients measure the relationship between an individual variable and a canonical variate in much the same way as a univariate correlation coefficient measures the relationship between two variables, such that higher values indicate a stronger relation (McGarigal et al. 2000). The sign of structure coefficients permits assessment of important gradients by indicating whether the relation between variables is positive or negative. To assess how much of the standardized variance in HSIs was accounted for by the canonical landscape-scale variables, we used redundancy criteria, which are analogous to R2 in multiple linear regression (McGarigal et al. 2000). We reviewed correlations between individual HSIs and canonical variates of land use and geologic variables to determine how strongly each factor was related to HSIs for the different species. Finally, we reviewed univariate correlations between land use and

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TABLE 2.—Summary of percent geology and percent forested land cover in watersheds of six tributaries of the Susquehanna River, Pennsylvania. Geology type Stream Conestoga River Conewago Creek Conodoguinet Creek Juniata River Swatara Creek West Branch of the Susquehanna River

Watershed area (km2)

Carbonate

Shale

Sandstone

Mudstone–siltstone

Forested

1,125 1,335 1,298 8,814 1,479

56.8 6.2 33.4 15.0 14.2

6.3 4.7 58.3 40.2 50.3

18.7 4.1 2.9 29.5 21.2

3.2 51.6 2.6 15.4 11.4

25.9 34.8 33.6 60.7 44.3

18,074

4.5

16.8

74.5

4.2

72.7

geologic factors and HSIs to verify the importance of gradients identified through CANCOR. We performed two separate CANCORs: the first used the matrix of PSU model HSIs (n ¼ 5) and the matrix of geologic and land use variables, and the second used the matrix of FWS model HSIs (n ¼ 8) and the matrix of geologic and land use variables. All watersheds were pooled into a single data set for CANCOR. The minimum recommended sample size for CANCOR is three times the number of observations (transects) as the total variables of the two data matrices (McGarigal et al. 2000). As the number of observations approaches the number of variables in the data set, the canonical correlation approaches a value of 1.0, virtually assuring significance of the first canonical correlation simply by virtue of small sample size (McGarigal et al. 2000); the risk of spurious results decreases as the sample size becomes larger relative to the number of variables in the examined data set. In our data set, we would have needed a minimum of 30 transects from a watershed to meet this assumption (e.g., for the PSU models, there were five landscapescale variables and five HSI variables, for a total of 10 variables; thus, data from 30 transects would have been needed). Only two of our watersheds had at least 30 transects (Conodoguinet Creek and Juniata River). Thus, to minimize the risk of spurious correlations and maximize the potential of finding meaningful relationships between landscape-scale factors and HSIs, we pooled all watersheds for CANCOR. To complement our watershed-scale analyses and elucidate potential links between watershed- and reachscale factors contributing to habitat suitability, we examined the relationship between microcrustacean density and water pH at the time of microcrustacean collections. We plotted microcrustacean density for segments of each water body against average water pH and performed linear regression of log10(microcrustacean density) versus average pH to examine the strength of the relationship.

Results For all watersheds, rural land cover averaged 95.2% (range ¼ 89.2–98.3%). Urban land area averaged 1.8% (range ¼ 0.23–4.10%). Many subwatersheds upstream from transects had no urban or suburban land cover. Forested cover varied from 73% in the West Branch (the upstream-most watershed) to just 26% in the heavily agricultural Conestoga River (the downstreammost watershed; Table 2). Forested cover also generally increased with distance from the river mouths in all watersheds. The four geologic variables retained for analysis included from 67% to 100% of rock formations in each watershed (Table 2). Each of the individual rock types varied from less than 10% to greater than 50% across watersheds. Thus, for all explanatory variables considered, these six watersheds represented a wide range of geology and land use types. Our review of correlation matrices revealed several highly correlated variables. For land use variables, all four forest types in all human use categories (rural, suburban, and urban) had strong or moderately strong positive correlations with two or more of the other forest variables. Furthermore, correlations between forest variables and HSIs were also always concordant; that is, if one measure of forested land use (e.g., deciduous) was positively correlated with an HSI, then so were the other three measures of forest cover. This trend also held across the rural, suburban, and urban categories; whenever a rural category was positively correlated with an HSI, so were the suburban and urban categories. This was the case for all watersheds and all HSIs. Thus, we combined all forest variables from all categories into a single variable (forested). Forested and herbaceous land uses were highly negatively correlated (R ¼ 0.98) and were thus almost entirely redundant measures of the forested to herbaceous gradient. We eliminated the herbaceous variable from further analysis. Barren land use, which includes both naturally occurring and anthropogenic impervious

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TABLE 3.—Correlations between landscape-scale variables or habitat suitability index (HSI) scores and canonical variates of landscape-scale factors (LS1–LS4) from canonical correlation analysis of American shad, alewife, blueback herring, and American eel HSIs in six major tributaries of the Susquehanna River, Pennsylvania. Structure coefficients are used to describe correlations between landscape-scale variables and canonical variates; correlation coefficients are used to describe relations between HSIs and canonical variates. Canonical variate Variable Landscape-scale factors Forested (% of area) Carbonate (%) Shale (%) Sandstone (%) Mudstone–siltstone (%) HSIs for: American shad adults American shad eggs and larvae American shad juveniles Alewife adults, eggs, and larvae Alewife juveniles Blueback herring adults, eggs, and larvae Blueback herring juveniles American eels

surfaces, was a very small proportion of total land area and was zero for several small watersheds; this category was therefore also eliminated from analysis. Thus, we were able to reduce our original set of 24 land use variables to the single forested variable. For geology, only 3 of 10 rock types (carbonate, shale, and sandstone) were major contributors in five of the six watersheds and 75–93% of the subwatersheds (i.e., upstream from transects). All had significant univariate correlations with HSIs; thus, all three were retained for CANCOR. Mudstone and siltstone each occurred in about half of the watersheds but rarely occurred together within a watershed. Both types have the same potential effect of contributing fine sediments to streams; hence, we used the combined measure of total mudstone–siltstone to represent these variables. Granite, basalt, quartz, gneiss, and schist were absent from 50% to 70% of subwatersheds and were either uncommon in or entirely absent from four of the six watersheds. Combining all of these formations and creating a composite variable still resulted in zero values for half of the subwatersheds, indicating that this composite variable was highly skewed. Because of these problems, we excluded these rock types from further analysis. Canonical Correlation Analysis FWS models.—The first four pairs of canonical variables were significant (Wilk’s lambda ¼ 0.174, P , 0.0001; for each canonical pair, P , 0.0001), indicating significant joint correlation between the canonical variates of the HSI and land use–geology data sets. Redundancy analysis indicated that 24% of

LS1

LS2

LS3

LS4

0.3764 0.4099 0.2514 0.7497 0.2318

0.1798 0.4523 0.0673 0.5193 0.9129

0.0253 0.3453 0.1741 0.1258 0.2636

0.5831 0.4523 0.8282 0.1406 0.1907

0.0853 0.1533 0.4095 0.2569 0.0020 0.2173 0.0184 0.0636

0.4712 0.4041 0.1779 0.5091 0.0483 0.3597 0.1015 0.3780

0.0194 0.0723 0.0759 0.0657 0.5238 0.1404 0.4750 0.0933

0.0974 0.1911 0.1286 0.0256 0.0349 0.1638 0.0211 0.1802

the standardized variance in HSIs was explained by the first four canonical variates of land use–geology (hereafter, landscape-scale [LS] variates, LS1–LS4). The LS1 and LS2 accounted for 82% of the total explained variation. The LS3 accounted for 12% of the variation, and LS4 accounted for 6%. Structure coefficients (Table 3) indicated that several important factors and gradients in land use and geology were significantly related to HSIs. The highest structure coefficient on LS1 was for sandstones, indicating that proportion sandstone exerted an important effect on LS1. Carbonates and forested land cover had moderate structure coefficients, indicating a moderate contribution to LS1. Opposite signs of coefficients on LS1 for sandstone and carbonate revealed a gradient from sandstone- to carbonatedominated geology, which agrees well with distributions of these rock types within Ridge and Valley Province watersheds and in portions of the Appalachian Plateaus and Piedmont provinces (i.e., sandstones in headwaters, carbonates in valley floors). Similarly, a secondary gradient of proportion forested cover can be inferred, given the moderately high structure coefficient and the strong negative correlation between herbaceous cover and forested cover. Correlations between individual HSIs and LS variates (Table 3), when viewed in concert with the importance of individual factors and gradients on the canonical axes, reveal how HSIs and landscape-scale factors are related. For example, the positive correlation between juvenile American shad HSI and LS1 and the negative structure coefficient for the relation between carbonate rock and LS1 suggest a negative

913


influence of carbonate rock on HSIs for juvenile American shad. Similarly, the negative correlation coefficient between the alewife HSI (for adults, eggs, and larvae) and LS1 and the negative structure coefficient for carbonate rock and LS1 suggest a positive relationship between this alewife HSI and proportion carbonate rock. Viewed across all LS variates, generally positive relationships were demonstrated between carbonate rock and HSIs for American shad adults, eggs, and larvae; all life stages of alewives; and blueback herring adults, eggs, and larvae. Negative relationships were observed between carbonate rock and the HSIs of American shad juveniles, blueback herring juveniles, and American eels. A negative correlation was apparent on LS2 between mudstone– siltstone and HSIs of American shad (all life stages), alewives (adults, eggs, and larvae), and blueback herring (adults, eggs, and larvae). A positive correlation was apparent for American eels and mudstone– siltstone. Forested cover was positively correlated with American shad juvenile HSI and negatively correlated with HSIs for alewife adults, eggs, and larvae and blueback herring adults, eggs, and larvae. Univariate correlations between land use–geologic factors and HSIs generally agreed with gradients of importance identified from the canonical axes, but a few exceptions were apparent. The association between juvenile blueback herring HSI and carbonate rock was not supported by the univariate correlation, which was low and positive. Likewise, the association between the HSI for blueback herring (adults, eggs, and larvae) and forested cover was not supported by univariate correlations. PSU models.—All five pairs of canonical variables were significant (Wilk’s lambda ¼ 0.25, P , 0.0001; for the canonical pairs, P , 0.0001 for LS1–LS4 and P ¼ 0.0063 for LS5). Redundancy analysis indicated that 23% of the standardized variance in HSIs was explained by LS1–LS5. The LS1–LS3 accounted for about 30% of the explained variation. The LS4 accounted for 6% of the explained variation, and LS5 accounted for the remaining 4%. No important correlations were evident between HSIs and LS5, so we do not discuss this variate further. Structure coefficients (Table 4) indicated a weak to moderate gradient in forest land use on three of the four interpreted axes. Moderate gradients were also evident for carbonate geology on three of four axes. A strong gradient in shale was evident from LS1, and a moderate gradient was identified from LS2. Strong gradients in sandstone and mudstone–siltstone were evident from LS3 and LS4, respectively. As with the FWS models, opposite signs of structure coefficients for sandstone and carbonate supported the upstream-to-downstream

TABLE 4.—Correlations between landscape-scale variables or habitat suitability index (HSI) scores and canonical variates of landscape-scale factors (LS1–LS4) from canonical correlation analysis of American shad, alewife, blueback herring, and American eel HSIs in six major tributaries of the Susquehanna River, Pennsylvania. Structure coefficients are used to describe correlations between landscape-scale variables and LS1–LS4; correlation coefficients are used to describe relations between HSIs and LS1–LS4. Canonical variate Variable Landscape-scale factors Forested (% of area) Carbonate (%) Shale (%) Sandstone (%) Mudstone–siltstone (%) HSIs for: Blueback adults Blueback herring juveniles Alewife adults Alewife juveniles American eels

LS1

LS2

LS3

LS4

0.2539 0.4733 0.8209 0.3725

0.0135 0.5927 0.4682 0.3168

0.4782 0.3630 0.2499 0.8141

0.3549 0.0068 0.1714 0.2560

0.2633

0.2693

0.2564

0.8754

0.1413

0.4343

0.1410

0.1452

0.0026 0.4649 0.3444 0.0558

0.1260 0.1783 0.0085 0.3772

0.4684 0.2939 0.1893 0.0765

0.0722 0.0153 0.0980 0.1762

gradient in these types. A positive association between American eel HSI and mudstone–siltstone was apparent from LS2. Correlations between individual HSIs and LS variates (Table 4) revealed a positive relationship between carbonate rock and HSIs for all alewife life stages and adult blueback herring based on LS1 and LS2. A negative effect of carbonate rock on juvenile blueback herring HSI was present on the LS3 axis. A negative effect of carbonate rock on the American eel HSI was also evident from LS2. There were no consistent relationships between forest gradients and any HSI, but a weak negative relationship was evident for the alewife HSI on LS1. Univariate correlations between land use–geologic variables and species- and life-stage-specific HSIs agreed with identified gradients of importance from CANCOR. Correlation coefficients were of the same sign as identified gradients of importance between HSIs and canonical variates. Relationship Between Microcrustacean Density and pH Both microcrustacean density and water pH were lowest on average in the West Branch watershed and highest on average in the Conewago Creek watershed (Figure 2). Water pH accounted for 30% of the variation in log10(microcrustacean density) (P ¼ 0.035). Water pH was generally positively correlated with proportion of carbonate rocks in surficial geology

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FIGURE 2.—Microcrustacean density (number of individuals/m3; bars) and average water pH (black circles) plotted in relation to location within watersheds of Susquehanna River, Pennsylvania, tributaries: Conestoga River (CNS), Conewago Creek (CNW), Conodoguinet Creek (CND), Juniata River (JUN), Swatara Creek (SWA), and West Branch of the Susquehanna River (WBS; W ¼ west of Lock Haven, E ¼ east of Lock Haven). Data collected from upper (U), middle (M), and lower (L) reaches of streams (except WBS) are indicated.

upstream of the point sampled. Conewago Creek watershed was an exception, which had the highest average pH and the lowest proportion of carbonate rock (Table 2). Macroinvertebrate drift paralleled the general trend for microcrustaceans; highest values occurred in Conewago Creek, and lowest values were observed in West Branch (Table 5). The general trend in the six watersheds was for high microcrustacean density to be associated with high water pH, which itself was associated with a high proportion of carbonate rock, providing the link between watershed-scale geology and a reach-scale factor used in the PSU model for river herrings. Discussion Evaluating the potential success of restoration efforts for anadromous clupeids and catadromous American eels in the main-stem Susquehanna River and its

tributaries requires an understanding of factors operating at many different scales. Instream habitat is an important fine-scale factor that will determine whether these species have a high or low probability of reestablishing populations after fish passage is reopened. Instream habitat and physicochemical factors are determined in part by factors and processes that occur at the landscape (e.g., Wiley et al. 1997; Bulger et al. 2000; Kocovsky 2004) or riverscape (Fausch et al. 2002) scale, extending at least to the boundaries of watersheds and in some cases (e.g., acid deposition; Lynch et al. 2002), beyond. Thus, a more-comprehensive understanding of whether instream habitat is sufficient, as estimated by HSIs, relies on an understanding of how factors operating at watershed and landscape scales affect instream habitat and the finer-scale factors used to estimate habitat suitability. This analysis was undertaken to (1) identify hypotheses for future research into relationships between geology or land use and HSIs and (2) assist in decisions regarding removal of dams to allow passage of diadromous fishes. We chose to use a multivariate approach for this analysis, because this approach considered all factors and all HSIs simultaneously in a single analysis. Univariate methods (e.g., multiple linear regression) that could be used to relate individual HSIs to landscape-scale factors would provide input for only a single species. A multivariate approach provides a more-holistic and more-complete evaluation, because it addresses the potential suite of HSIs for several species simultaneously. A direct comparison of outcomes from the two different HSI models is hampered by the fact that the models predicted habitat suitability for different life stages. For example, the PSU models applied only to adults and juveniles, whereas the FWS models were applicable to eggs and larvae in addition to adults and

TABLE 5.—Summary of total plankton densities and invertebrate taxa richness (number of species) in plankton–drift samples collected in 1999–2000 at transects on tributaries of the Susquehanna River, Pennsylvania. Lock Haven (Figure 1) marks the approximate downstream boundary of chronic effects of acid mine pollution on aquatic biota in the West Branch of the Susquehanna River. Plankton–drift density (organisms/m3)

Total taxa richness

Tributary

Median

Range

Mean

Range

West Branch of the Susquehanna River Downstream of Lock Haven Upstream of Lock Haven Juniata River Conodoguinet Creek Conestoga River Conewago Creek Swatara Creek

9.8 4.6 46.9 46.0 94.8 128.5 58.4

4.3–41.8 1.4–27.5 11.3–264.6 8.4–342.6 16.7–890.6 13.2–8,396.1 18.7–169.2

14.0 6.2 13.2 14.0 11.2 18.3 17.0

8–21 2–8 7–18 6–22 7–15 8–27 5–30


juveniles. Despite this incongruence, we can identify several consistencies in our results. A positive effect of carbonate rock on HSIs for adult alewives and adult blueback herring was identified for both models, as was a negative effect of carbonate rock on HSIs for juvenile blueback herring and American eels. Another consistency was identification of a negative relationship between forested land use and adult alewife HSI scores. To our knowledge, no rigorous assessments have been conducted to address which method (PSU or FWS) is superior for predicting habitat suitability for the species considered here. Thus, differences between methods with respect to importance of various land use and geologic factors cannot be used to conclude which factors most influence HSIs or to infer superiority of one method over another. Conversely, similarities do provide some justification for inferring importance of factors that were significant correlates in both methods. The general agreement between methods regarding gradients in carbonate rock and HSIs suggests an influence of carbonate rock on habitat suitability for these species and life stages. Our analysis of the potential relationships between the fine-scale HSIs and landscape-scale factors suggests two possible mechanisms by which landscapes affect habitat suitability for most alosine species and life stages. One is the importance of the acid neutralization ability, which is strongly related to stream pH. The positive relationship between proportion carbonate rock and HSIs (except HSIs for juvenile American shad, juvenile blueback herring, and American eels) implies that the ability to buffer against acid episodes is a potentially important factor. Carbonate rocks produce water of high alkalinity and high pH, providing a strong buffer against acid inputs. The success of other species in Pennsylvania has similarly been linked to the buffering capacity of carbonate rock (Kocovsky 2004; Kocovsky and Carline 2006). Hendrey (1987) also linked declines in alosines to acid episodes, which are rare in well-buffered systems, lending further support to the pH buffering hypothesis. For the PSU model, pH was one of the reach-scale factors used in calculating HSI; therefore, our analysis links the landscape-scale measure of carbonate rock to the reach-scale factors of pH and habitat suitability. Conversely, no measure of pH was used in the FWS model, yet the same positive relationship between carbonate rock and HSIs was evident. Examination of stream productivity is another, morestraightforward, way of linking geology to HSIs. Our data generally showed that a higher proportion of carbonate was associated with higher pH, which in turn supported higher densities of microcrustaceans (Figure 2), a required food for alosines that was an input for the

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FWS river herring model. The downstream segments of the two streams entering the Susquehanna River from the east, Conestoga River and Swatara Creek, had lower average densities of microcrustaceans than we would have expected based on their high pH levels. The lower halves of these streams were different from the upper halves (and all segments of the other streams) in containing higher numbers of the invasive jellyfish Craspedacusta sowerbyi in plankton and drift samples. Maximum individual transect density of C. sowerbyi was 10 organisms/m3 in Swatara Creek and 55 organisms/m3 in the Conestoga River. This jellyfish is a specialist zooplankton predator, and a single medusa can consume as much as 190 zooplankton/d (Spadinger and Maier 1999). Thus, predation may be a factor accounting for the lack of correspondence between pH and average total microcrustacean density in these two streams. Craspedacusta sowerbyi were not detected at transects of the upstream reaches of Conestoga River and Swatara Creek, and microcrustacean densities were higher in those areas. When we excluded the lower reaches of Conestoga River and Swatara Creek and reanalyzed the relationship between zooplankton density and pH, the R2 increased from 0.30 to 0.58, which lends greater support to the productivity hypothesis and to our contention that pH and zooplankton density are positively related. A shortcoming of our analysis is that the HSIs are univariate in nature; they are calculated for individual species instead of for suites, assemblages, or guilds of species (see Vadas and Orth [2001] for a method of calculating guild HSIs). An HSI for a given species does not consider the HSIs of other species and cannot be adjusted for anticipated outcomes of ecological interactions with other species. Should one or more species of diadromous fishes recolonize, interactions among the diadromous species and between diadromous species and resident species assemblages, many of which have changed drastically relative to the period when diadromous species inhabited these streams, may result in habitat use occurring at less than the full potential predicted by HSIs. For example, a study in a tributary of the Rappahannock River, Virginia, found that alewives and blueback herring used the same spawning and rearing areas (O’Connell and Angermeier 1997), which suggests the potential for ecological interactions. Evaluations of HSIs for American shad (Ross et al. 1993a, 1993b) revealed that adults spawned in areas of higher temperature and lower water velocity than predicted by some HSIs. This casts some doubt on the accuracy of HSIs. Another complicating factor is that the assemblages of species that currently inhabit these streams are much different than historical assemblages. The introduction

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of smallmouth bass Micropterus dolomieu and walleyes Sander vitreus for recreational angling added top predators to systems that historically contained only adult American eels as top predators. Juvenile smallmouth bass are known predators of stocked American shad larvae in the Susquehanna River basin (Johnson and Dropkin 1992; Johnson and Ringler 1995). The unintentional introduction of the banded darter Etheostoma zonale and its subsequent spread throughout the entire Susquehanna River drainage (Denoncourt et al. 1975) have resulted in behavioral and habitat shifts in native darter species (Gray 1998). Predation by smallmouth bass and walleyes on alosines and other ecological changes caused by shifts in the species assemblage may have greatly reduced the probability of successful reestablishment of diadromous fish runs. Two additional problems are (1) how changes in physical characteristics of watersheds affect anadromous and catadromous fishes and (2) how removal of one or several dams affects habitat. We cannot know with certainty what flow or thermal regimes were like when American shad, alewives, and blueback herring were abundant 300 years ago, before human activity resulted in drastically altered streams and landscapes. Urbanization, farming, roads, air pollution, and point source or non-point-source additions all affect the timing, quality, and quantity of water delivered to streams and have probably changed the flow and temperature regimes. The habitat suitability models we evaluated use point measurements of various instream features and do not consider the flow, discharge, or temperature regime. Removal of dams will result in changes in water depth, velocity, sedimentation (Hart et al. 2002), and possibly other factors used to calculate HSIs. Over the short term, velocities will probably increase, depths will decrease, and plankton densities will decrease in the immediate vicinity of the former dam sites, resulting in lower habitat suitability for some life stages and higher habitat suitability for others; longterm changes, however, are less predictable. The upstream or downstream extent of changes depends largely on the height of the dam and the type and amount of sediment present upstream from the dam (Pizzuto 2002). Reestablishment of a stable channel downstream from the dam site may take only a few years, whereas the reestablishment of upstream stability may require several years or decades. Thus, once a dam is removed, habitat suitability downstream (and perhaps upstream) may be affected and dynamic for many years. Many of the dams on Susquehanna River tributaries are low-head dams and, as such, upstream and downstream effects on habitat or HSIs could be small. Presumably, removal of any dam will restore

more-natural flow and thermal regimes and thus will improve habitat conditions for anadromous and catadromous fishes. However, as discussed above, these river systems are anthropogenically modified versions of historical systems and are characterized by altered thermal and flow regimes and biotic communities. Habitat suitability indices also do not consider effects of phenomena that in the past either did not occur or occurred at much lower levels; for example, acid episodes are common during spring (Sharpe et al. 1987), when American shad, blueback herring, and alewives spawn. Hendrey (1987) identified acid episodes as a plausible hypothesis for declines in American shad, alewives, and blueback herring throughout eastern North America. More-recent research has shown that American shad are affected by acid episodes (Leach and Houde 1999). O’Connell and Angermeier (1997) also observed that pH in Virginia tributaries to the Rappahannock River was at times below the lethal limit for blueback herring larvae present in streams, which supports the hypothesis that acid episodes are an agent of population declines. Considering (1) the hypothesized (and, in a few cases, demonstrated) negative effects of acid episodes on American shad and blueback herring, (2) that Pennsylvania’s rainfall is among the most acidic in North America (Lynch et al. 2002), and (3) that Pennsylvania streams are highly susceptible to acid episodes owing to predominance of siliciclastic geology (Sharpe et al. 1987) and generally acidic soils (Ciolkosz et al. 1989), the potential success of recolonization of these species in Pennsylvania streams may be entirely unrelated to the suitability of instream habitat measured at fine scales. Management Implications The outcomes of this analysis can be used by decision makers, agencies, and other stakeholders in prioritizing removal of dams to permit fish passage in the Susquehanna River system. Simultaneously considering HSIs, geology, and land use upstream of candidate dams along with the present results will permit a more-holistic view of the factors potentially relating to the successful reestablishment of clupeids and American eels. For example, the positive relationships between alewife HSIs and the proportion of carbonate rock upstream of a particular point in a watershed suggest that recolonization of alewives has a higher probability of success in a watershed with a large proportion of carbonate rock (e.g., Conestoga River) than in one with a lower proportion of carbonate rock (e.g., West Branch). Conversely, HSIs for American eels are often negatively related to propor-


tion of carbonate rock and thus may have a lower probability of successful colonization than alewives within the same watersheds. The HSI scores tend to bear this out, but other biological factors that are not included in HSI calculation (e.g., the general tendency for American shad to migrate farther upstream than alewives or blueback herring) must also be considered; for example, a stream with a high HSI may be beyond the distance typically traveled by blueback herring. Although our results are specific to this system, our statistical methods and the approach of using input from multiple scales could be applied elsewhere. This analysis has provided evidence that factors operating at landscape scales are related to and may affect habitat suitability for anadromous clupeids and catadromous American eels that were once common in Pennsylvania tributaries of the Susquehanna River but that were extirpated after the construction of dams. It is a widely held belief that removal of dams, while perhaps not sufficient, is a necessary step toward reestablishment of American shad, alewives, blueback herring, and American eels in hundreds of kilometers of streams throughout their native ranges in Pennsylvania. Given the many uncertainties regarding the potential for successful restoration of diadromous fish runs after dam removal, an adaptive management approach in which successive dam removals follow rigorous assessment of previous dam removals seems prudent for native fish restoration in Susquehanna River tributaries. Acknowledgments We thank C. Frese for assistance with collection of data in the field. Digital raster graphics of USGS topographical maps for watershed delineations were provided by S. Hoffman (USGS Water Resources Division). Data on upstream movements of American shad, blueback herring, and alewives were provided by M. Hendricks (PFBC). We thank A. Shiels, D. St. Pierre, and two anonymous reviewers for constructive comments that improved the manuscript. Reference to trade names does not imply endorsement by the U.S. Government. References Berg, T. M. 1980. Geologic map of Pennsylvania: 1:250,000. Commonwealth of Pennsylvania, Department of Environmental Resources, Bureau of Topographic and Geologic Survey, Harrisburg. Bilkovic, D. M., C. H. Hershner, and J. E. Olney. 2002. Macroscale assessment of American shad spawning and nursery habitat in the Mattaponi and Pamunkey rivers, Virginia. North American Journal of Fisheries Management 22:1176–1192. Bulger, A. J., B. J. Cosby, C. A. Dolloff, K. N. Eshleman,

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