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Reach- and Watershed-scale Associations of Crayfish within an Area of Varying Agricultural Impact in West-central Indiana
Jacob L. Burskey and Thomas P. Simon

Southeastern Naturalist, Volume 9, Special Issue 3 (2010): 199–216

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Conservation, Biology, and Natural History of Crayfishes from the Southern US 2010 Southeastern Naturalist 9(Special Issue 3):199–216 Reach- and Watershed-scale Associations of Crayfish within an Area of Varying Agricultural Impact in West-central Indiana Jacob L. Burskey1,* and Thomas P. Simon2 Abstract - We studied the associations of crayfish with reach-scale instream habitat and water quality variables and watershed-scale variables of riparian and watershed land cover, runoff, impervious surfaces, and hydrologic soil type in west-central Indiana. Crayfish assemblage was measured by abundance, species richness, and diversity (Shannon’s H) at 180 sites. The western portion of the study area, within the Interior River Lowland ecoregion, has been heavily impacted by agriculture, while the eastern portion, within the Interior Plateau ecoregion, is more heavily wooded and less impacted. The ability of variables at each spatial extent to predict crayfish assemblage was assessed using multiple linear regression analysis. Reach-scale models were better predictors of crayfish assemblage than watershed-scale models. A variety of habitat and water quality characteristics, principally instream cover, appeared as important predictors. Forested riparian and watershed land appeared as significant watershedscale predictors. Reach-scale models were also better predictors of individual species abundance. Model predictive power was similar when developed separately for the two ecoregions of the study area, indicating that extent of agricultural development had little effect on the ability of variables at a given spatial scale to predict crayfish assemblage. Results indicate that reach-scale variables are more powerful in predicting crayfish assemblage, and important factors to consider in crayfish management are maintaining ample instream cover and intact riparian areas. Introduction Crayfish serve a key role in stream communities, and nearly 50% of the crayfish taxa of the United States and Canada are recognized as endangered, threatened, or of special concern (Taylor et al. 1996). Because restoration and protection efforts tend to focus at the scale where degradation is perceived (Fausch et al. 2002), it is important to understand the relative influences of environmental aspects on faunal assemblages at varying spatial scales. As the availability of remotely sensed data has increased, so has its use in investigating influences of abiotic variables at differing scales on aquatic biota. Such studies have been conducted using fish communities (Brazner et al. 2005, Eikaas et al. 2005, Frimpong et al. 2005, Lammert and Allan 1999, Wang et al. 2003), aquatic macroinvertebrates (Johnson and Goedkoop 2002, Lammert and Allan 1999, Richards et al. 1997, Stewart et al. 2000, Stone et al. 2005, Weigel et al. 2003), and freshwater mussels (McRae et al. 2004) with varying results. 1Aquatic Research Center, Indiana Biological Survey, 6440 Fairfax Road, Bloomington, IN 47401. 22364 East Linden Hill Drive, Bloomington, IN 47401. *Corresponding author - jburskey1@gmail.com. 200 Southeastern Naturalist Vol. 9, Special Issue 3 Associations of crayfish have been shown to correspond with several aspects of the abiotic environment, principally instream refuge cover defined by large substrates, woody debris, undercut banks, root mats and root wads, and submergent and emergent aquatic macrophytes (Hill and Lodge 1994). Hill and Lodge (1994) reported that crayfish abundance was positively correlated with refuge abundance and negatively correlated with predatory fish abundance. Stein and Magnuson (1976) reported that in the presence of a fish predator, crayfish selected larger substrates that provided the most protection from predation. Crayfish are often associated with lower-order streams and can reach high densities in intermittent waters where fish predators are scarce (Flinders and Magoulick 2003). Crayfish are also influenced by factors such as temperature, dissolved oxygen, pH, salinity, and organic and heavy metal contamination (Lodge and Hill 1994). Effects of poor water quality vary by species, and there is evidence of a top-down effect whereby certain species of crayfish can proliferate in contaminated waters where fish populations remain depressed (Seiler and Turner 2004). The goals of this study were as follows: 1) determine the relative abundances of co-occurring crayfish species within a 6-county area of west-central Indiana; 2) assess the ability of habitat variables quantified at reach and watershed scales to predict crayfish assemblage structure and species abundance; and 3) examine the predictive power of reach- and watershed-scale variables in portions of the study area with different land-use patterns. Our hypotheses were that crayfish would be positively associated with high-quality reach habitat containing ample instream cover, a variety of substrates, and an intact riparian corridor and negatively associated with agricultural land use. Methods Study area The study area was located within the boundaries of the Indiana counties of Clay, Greene, Knox, Owen, Sullivan, and Vigo (Fig. 1). The area contains portions of 4 level-three ecoregions defined by Omernik and Gallant (1988). The Interior River Lowland (IRL), Central Corn Belt Plain (CCBP), and Eastern Corn Belt Plain (ECBP) cover the western portion of the study area. These regions are characterized by lower topographic diversity, sediments of glacial till, and a diversity of land use, much of which is agricultural (Omernik and Gallant 1988). On average, 44% of watershed land cover was forest in the IRL, CCBP, and ECBP in this study (Table 1). The Interior Plateau (IP) ecoregion covers the eastern portion of the study area and remained largely unglaciated during the Pleistocene epoch. Topographic diversity is often greater in this ecoregion and much of the Interior Plateau is underlain with thick karst topography where underground caverns, sinkholes, and springs are common. Forested land is more common in the IP, with an average of 82% forest in IP watersheds in this study (Table 1). Agricultural land in the IP is largely pasture and grazing land (Omernik and Gallant 1988). Sites were selected using a random design to capture the range of conditions in the area. Each bridge crossing over a wadeable stream shown on a 2010 J.L. Burskey and T.P. Simon 201 Table 1. Mean, standard error (SE), transformation (Trans.), and significance of P-values for variables between Interior River Lowland (IRL) (n = 145) and Interior Plateau (IP) (n = 35) regions. Asterisks denote variables retained for model development. Variable Region Mean SE Trans. t df P % watershed wetlands* IRL 12.57 0.67 arcsin(√x) 4.03 178 <0.001 IP 6.92 0.77 % watershed agriculture IRL 77.49 1.67 arcsin(√x) 7.09 177 <0.001 IP 50.92 3.42 % watershed grass IRL 51.88 1.00 arcsin(√x) 3.19 177 0.002 IP 45.03 1.57 % watershed forest* IRL 44.09 1.76 arcsin(√x) -9.54 178 <0.001 IP 82.44 3.91 Watershed area (acres)* IRL 823.40 9.96 Ln(x) 0.46 178 0.648 IP 813.00 21.70 % impervious area IRL 307.80 19.80 arcsin(√x) 2.94 178 0.004 IP 191.30 1.68 Runoff* IRL 5.00 0.11 3.56 178 0.001 IP 4.13 0.18 % soil B* IRL 41.35 4.68 arcsin(√x) 1.71 178 0.088 IP 24.53 5.85 % soil C IRL 20.83 4.66 arcsin(√x) -1.73 178 0.085 IP 45.61 5.75 % riparian wetlands* IRL 19.26 2.27 arcsin(√x) 3.23 175 0.001 IP 4.42 1.77 % riparian agriculture IRL 64.40 3.96 arcsin(√x) 1.96 175 0.052 IP 48.64 3.61 % riparian grass IRL 41.32 2.28 arcsin(√x) -2.7 175 0.008 IP 54.61 3.89 % riparian forest* IRL 47.28 3.21 arcsin(√x) -3.68 175 <0.001 IP 72.58 5.18 Substrate score* IRL 8.88 0.36 -6.71 177 <0.001 IP 14.26 0.71 Cover score* IRL 9.69 0.26 -2.82 177 <0.001 IP 11.28 0.46 Channel score IRL 12.73 0.30 -6.56 177 <0.001 IP 16.78 0.32 Bank/riparian score IRL 13.2 0.44 -3.73 178 <0.001 IP 16.68 0.60 Riffle/run score* IRL 7.70 0.26 -2.64 178 0.009 IP 9.24 0.51 Oxygen saturation (%)* IRL 87.73 1.90 -1.23 169 0.221 IP 92.37 1.59 pH* IRL 7.83 0.03 -1.54 167 0.126 IP 7.93 0.04 Salinity (mg/L) IRL 0.82 0.41 Ln(x) 0.828 169 0.409 IP 0.17 0.01 Temperature (°C)* IRL 20.23 0.29 Ln(x) 0.97 169 0.333 IP 19.63 0.47 Conductivity (mS/cm)* IRL 1.30 0.58 0.858 169 0.392 IP 0.34 0.02 ORP (mV) IRL 318.30 6.72 2.45 164 0.015 IP 285.40 6.46 TDS (ppm) IRL 0.52 0.05 3.27 169 0.001 IP 0.20 0.01 202 Southeastern Naturalist Vol. 9, Special Issue 3 1:156,000 unit topographic map within the boundaries of each county was marked. Thirty sites within the boundaries of each of the 6 counties were selected randomly from the numbered crossings using a random number generator. Sites were not weighted for county or land area because the large number of sample locations (n = 180 sites) was sufficient for obtaining a representative sample of habitat and crayfish within the study area. This study design also increased the range of stream sizes sampled; thus, we decreased bias that would result from targeted sampling of a particular stream order. Thirty-five sites were within the IP, 140 were within the IRL, four in the CCBP, and one in the ECBP ecoregion (Fig. 1). Ecoregion delineations are not precise, and there is a transition area between zones (Omernik and Gallant 1988). Given the relatively small amount of CCBP and ECBP within the study area, similarity of both to the IRL, and inexact ecoregion delineation, we chose to group the sites within the ECBP and CCBP with those of the IRL. For purposes of analysis, the 145 sites outside of the IP are referred to as IRL sites. Crayfish sampling Crayfish were collected during May and June, 2006. The method for sampling followed Simon (2004), a manual outlining standard operating procedure Figure 1. West-central Indiana study area. Patterns denote ecoregion: light gray = Interior River Lowland, stippled = Central Corn Belt Plain, striped = Eastern Corn Belt Plain, and dark grey = Interior Plateau. Filled circles denote collection sites. 2010 J.L. Burskey and T.P. Simon 203 for collection of burrowing and stream-dwelling crayfish. Open-water habitat was sampled using a Smith Root back-pack unit equipped with an 800-watt generator capable of 300 volts and 3–5 amps. The electrofishing technique allows for quantification of catch-per-unit-effort (CPUE; individuals/minute) and is an effective way to sample crayfish in sluggish water (Simon 2004). Open-water sampling was constrained by stream size and defined using wetted width. Average wetted width (m) was first estimated and multiplied by 15, then the resulting number was rounded up to the nearest 50-m increment and used to define sample reach length. Reach length consisted of a minimum distance of 50 m in smaller streams (<3.34 m wetted width) and a maximum of 200 m in the largest streams encountered in this study (>10 m wetted width). Fifteen times the stream wetted width has been shown to adequately include 2 full habitat cycles (riffle-run-pool sequences; Leopold et al. 1964) and was deemed sufficient to obtain a representative crayfish sample. Stream wetted width averaged 4.9 m (standard deviation of 2.8, range of 2–12.5 m), and sample reach length averaged 90 m (standard deviation of 46.2, range of 50–200 m). Areas near bridges that appeared to be atypical of the stream (wider and deeper water) were not sampled in order to limit the effect of the bridge crossing on the data. Large rocks and woody debris were flipped during sampling to attempt to capture all resident crayfish. Hand nets were used to capture crayfish stimulated by electrofishing and to dislodge them from under cover. Length of time spent on aquatic sampling varied from 300–1800 seconds depending on stream size and habitat complexity. Burrow sampling was done using a spade and bucket to excavate borrows and capture the resident crayfish. Active burrows were those free of plants and debris and usually possessed mud exit holes in the shape of a “chimney.” Excavation of ten burrows or a maximum of 120 minutes of burrow searching was done at each location. All crayfish were preserved in 70% ethanol for laboratory identification. Specimens were identified to species level using Page (1985) and Pflieger (1996). All specimens were deposited in the Crustacean Division of the Indiana Biological Survey, Aquatic Research Center. Assemblage quantification Three measures of crayfish assemblage structure were calculated for each reach. CPUE was calculated in two different ways to accommodate differences in crayfish ecology between burrowing and stream-dwelling species. CPUE for open-water crayfish was defined as individuals captured/ minute using backpack electrofishing methods. Because burrowing crayfish are usually not collected from open water, the number of crayfish collected from burrows using the spade-plunger method was used as the abundance measure for primary burrowing species. Relative abundance of the most common species (>20 sites) in terms of CPUE for stream species and number of crayfish captured from burrows for primary burrowing species was used to develop individual species prediction models. Species richness was the number of crayfish species found in each reach, including both open-water 204 Southeastern Naturalist Vol. 9, Special Issue 3 and burrowing species. Diversity at each site was calculated using Shannon’s diversity index, which takes into account the relative abundances of each species in relation to the total sample: H = -Σ pi* Ln(pi), where pi is the proportion of the ith species in the total sample, and Ln is natural log. Diversity included both open-water and burrowing crayfish. Watershed-scale variables Watershed-scale variables were quantified using a web-based, watershed delineation program (Choi and Engle 2003). The program uses a double-seed array method based on 30-m resolution digital elevation data. Once the watershed was delineated, land cover was determined using 1992 USGS 30-m resolution National Land Cover layers. Soil associations were based on 1994 Natural Resources Conservation Service data. Hydrologic soil types in the study are mainly B and C, and percentage of each was calculated for the total watershed. Soil types range from A to D, with textural sizes and permeability decreasing from A to D (Soil Conservation Service 1986); thus, soil group B has a larger texture and higher infiltration rate than group C. Percent land use within each watershed in the form of water-wetlands, forest, grass-pasture, and agriculture were calculated. Total watershed area was calculated and used as a proxy for stream size. Percent impervious surfaces within each watershed were calculated from land-use data. The percentage of hydrologic soil group, land use, and USGS average rainfall data were used to calculate average annual runoff depth (Choi and Engle 2003): Q = (P - la)2 / ((P - la) + S), where Q is runoff (in), P is rainfall (in), S is potential maximum retention after runoff begins, and la is initial abstractions. Individual watersheds were imported into ArcView 9.2 (Environmental Systems Research Institute 2006) in order to calculate riparian land cover. A 500-m reach was delineated at each site and a 150-m buffer applied to the stream segment. Riparian land cover within the 150-m segment on each side of the stream was calculated using 1992 USGS 30-m resolution National Land Cover data (Choi and Engle 2003). Reach-scale variables Habitat assessment of instream and riparian areas was done using the qualitative habitat evaluation index (QHEI; Rankin 1995). The QHEI is commonly used in the Midwest United States to evaluate factors of habitat that are important to aquatic life based on in situ field assessment. There are 5 metrics scored at each reach that are based on assessments of substrate, instream cover, channel morphology, pool and riffle quality, bank and riparian quality, and gradient. Gradient was not used in this study because of its lack of variability through most of the study area. Following Frimpong et al. (2005), the bank and riparian score was doubled, providing a maximum score of 20 to make all five individual QHEI metrics equal in scale and retain 2010 J.L. Burskey and T.P. Simon 205 the total QHEI range of 0–100. Reach water quality measures including water temperature, conductivity, oxidation-reduction potential (ORP), total dissolved solids (TDS), salinity, and dissolved oxygen saturation were assessed at each site prior to sampling using a YSI 556 multi-parameter meter (Yellow Springs Instruments Inc., Yellow Springs, OH). Predictive model development Independent variables were compared between the IRL and IP regions using an independent means t-test. Because we were concerned only with describing the differences between abiotic conditions between the IRL and IP, we decided not to control type I error by adjusting P-value threshold because of the increase in type II error that results (Perneger 1998). Prior to model development, all variables were examined and transformations were applied where necessary to achieve normality. For both the watershed and reach models, when two variables correlated at 0.60 or higher (Pearson’s r), a single variable was retained to control for collinearity. Percentage of forest and agricultural land were highly negatively correlated (r = -0.771), and % grass-pasture was highly correlated with % agricultural land (r = 0.686), so forest cover was retained for model development. Percentage of impervious area was highly correlated with runoff (r = 0.650), so runoff was retained. Percentage of soil group B and C were nearly perfectly negatively correlated so % soil group B was retained. Salinity was highly correlated with conductivity (r = 0.991), total dissolved solids (r = 0.991), and oxidation reduction potential (r = 0.669), so conductivity was retained as an overall measure of dissolved particles. Channel score was highly correlated with substrate score (r = 0.733) and bank/riparian score (r = 0.714), so substrate score was retained. Results yielded seven reach-scale and seven watershed-scale variables for further model development (Table 1). Multiple linear regression analysis provided an explanation of relative importance of reach and watershed variables in predicting assemblage structure. Model development was done for 3 measures of crayfish assemblage structure (CPUE, richness, and Shannon's H) to accommodate various responses of crayfish to abiotic variables. Models were developed independently to determine which spatial scale best predicted the dependent variables of assemblage structure and species abundance. Model strength was compared between IRL and IP regions to represent a comparison between heavily agriculturally impacted and less impacted environs. Model-adjusted R-square (R2) values and model significance were used to assess predictive power. The most important independent variables from each model based on standardized regression weights and significance values were identified. All statistical analyses were done using SPSS version 11.0 (SPSS 1999) (alpha = 0.05). Results Crayfish assemblages and habitat structure Crayfish were found at 176 of the 180 sample locations. Ten species belonging to 4 genera (Cambarus, Fallicambarus, Orconectes, and 206 Southeastern Naturalist Vol. 9, Special Issue 3 Procambarus) were collected. The most frequently occurring species were C. (Tubericambarus) polychromatus Thoma, Jezerinac, and Simon (Paintedhand Mudbug; 122 sites), O. (Tricellescens) immunis (Hagen) (Calico Crayfish; 83 sites), O. (Crockerinus) propinquus (Girard) (Northern Clearwater Crayfish; 83 sites), and O. (Gremicambarus) virilis Hagen (Virile Crayfish; 51 sites). Common but less frequently occurring species included C. (Lacunicambarus) sp. A (Simon 2001; 29 sites), F. (Creaserinus) fodiens (Cottle) (Digger Crayfish; 24 sites), P. (Ortmannicus) acutus (Girard) (White River Crawfish; 22 sites), and C. (Erebicambarus) tenebrosus Hay (Cavespring Crayfish; 21 sites). Two rare species, O. (Faxonius) indianensis (Hay) (Indiana Crayfish) and P. (Girardiella) gracilis (Bundy) (Prairie Crayfish), were collected at 4 sites and 1 site, respectively. Stream crayfish abundance ranged from 0 to 13.51 individuals captured per minute, with a mean of 1.84. Species richness ranged from 0 to 5 species per site, with a mean of 2.4. Diversity ranged from 0 to 2.06, with a mean of 0.81. Table 2 summarizes crayfish assemblage structure for each region. Stream crayfish abundance was the only community variable that differed significantly between regions, with abundance being higher in the IP. Overall, habitat was of higher quality for streams within the IP region as indicated by QHEI metric values and land cover. The IP streams were more heavily forested, had less agriculture, and were less wetland-influenced than streams within the IRL. Similar patterns were seen for land cover within the 150-m riparian buffer. There was significantly more riparian forest, less agriculture, and less wetland influence for IP streams. Instream and riparian habitat scores were significantly higher for IP sites than IRL, and all individual QHEI metrics except riffle/run quality were significantly higher within the IP (Table 1). Reach vs. watershed models and variable importance Reach-scale habitat variables were better predictors of crayfish assemblage structure than watershed-scale within the entire study area. Adjusted R2 values for reach models ranged from 0.108 to 0.388. Watershed adjusted R2 values ranged from 0.027 to 0.188. With all sites grouped together, aquatic crayfish abundance was the only assemblage variable to be significantly predicted by watershed-scale variables (Table 3). Reach models were also stronger when sites were grouped according to region. Table 2. Mean, standard error, transformation, and significance of crayfish assemblage structure values for sites in Interior River Lowland (IRL) (n = 145) and Interior Plateau (IP) (n = 35) regions of the study area. Variable Region Mean Standard error Transformation t df P Abundance IRL 1.5 0.13 Ln(x+1) -4.72 173 less than 0.001 IP 3.2 0.45 Richness IRL 2.4 0.09 -0.451 178 0.652 IP 2.38 0.15 Diversity IRL 0.84 0.05 1.13 178 0.262 IP 0.69 0.08 2010 J.L. Burskey and T.P. Simon 207 Reach-scale models consistently incorporated the same independent variables. The best reach variable in predicting assemblage structure was cover complexity score, appearing as a significant predictor in all three models (Table 3). Cover score correlated significantly with each assemblage variable (r = 0.41–0.53; Fig. 2). Riffle-run score appeared in 3 models as a negative predictor. Conductivity appeared twice as a positive predictor and water temperature once as a positive predictor and once as a negative predictor (Table 3). Watershed-scale models included percentage of forested riparian area as a significant positive predictor of crayfish assemblage. Riparian forest appeared in 3 watershed models (Table 3) and was significantly correlated (r = 0.17–0.29) with species richness and stream crayfish abundance (Fig. 3). Total watershed area appeared in 2 models as a negative predictor of stream crayfish abundance and species richness. Likewise, riffle-run score, a metric Table 3. Identity and relative importance of significant (P < 0.05) predictors of crayfish assemblage structure at reach and watershed scales. Model strength is reported by adjusted R2 and significance values. Significant independent variables are reported along with standardized regression coefficients. Models developed for all locations together (combined), IRL and IP. Dependent Independent variable Adjusted Model variable (standardized regression coefficient) R2 P-value Watershed scale Combined Abundance % riparian forest (0.261), watershed area 0.188 <0.001 (-0.168), % watershed wetlands (-0.163) Richness 0.036 0.065 Diversity 0.027 0.153 IRL Abundance % riparian forest (0.290) 0.146 <0.001 Richness % riparian forest (0.219), 0.174 0.015 % soil group B (-0.283) Diversity 0.013 0.306 IP Abundance 0.14 0.137 Richness Watershed area (-0.466), % soil group B (0.658) 0.223 0.044 Diversity 0.063 0.300 Reach scale Combined Abundance Cover score (0.432), substrate score (0.206), 0.223 <0.001 riffle/run score (-.195) Richness Cover score (0.633), riffle/run score (-.188), 0.338 <0.001 conductivity (0.139) Diversity Conductivity (0.299), cover score (0.255) 0.108 0.002 IRL Abundance Cover score (0.463), riffle/run score (-0.233) 0.178 <0.001 Richness Cover score (0.592), water temperature (0.173) 0.347 <0.001 Diversity Conductivity (0.228), cover score (0.214) 0.087 0.023 IP Abundance 0.006 0.472 Richness Cover score (0.778) 0.365 0.005 Diversity Cover score (0.553), water temperature (-0.392) 0.270 0.040 208 Southeastern Naturalist Vol. 9, Special Issue 3 that increases with greater stream depth, was a negative predictor (Table 3). Negative associations with watershed area and riffle-run score likely reflect a negative relationship with crayfish assemblage and stream size and depth. Models with the most predictive power for individual species abundances incorporated several variables, and 3 reach models and 1 watershed model explained a significant amount of variation in species abundance (Table 4). Reach models were generally stronger than watershed models. Riffle-run score and water temperature were both negative predictors of F. fodiens Figure 2. Significant positive correlative relationship between cover complexity score and stream crayfish abundance, species richness, and diversity (Shannon's H). Figure 3. Significant positive correlative relationship between riparian forest cover and stream crayfish abundance and species richness. 2010 J.L. Burskey and T.P. Simon 209 abundance. Water temperature and oxygen saturation were significant negative and positive predictors of C. tenebrosus abundance, respectively. Substrate score was a significant positive predictor of O. propinquus abundance. Percentage of riparian and watershed forest and runoff were positive predictors and percentage of watershed wetlands was a negative predictor of O. propinquus abundance. Discussion Crayfish assemblage Capture efficiency and bias can be of concern when attempting to elucidate the relationships between organisms and their environment. Primary, secondary, and tertiary burrowing crayfish differ greatly in habitat patterns (Hobbs 1981), and a variety of sampling procedures need to be used to obtain a representative sample of diversity. We recognized the difficulty in ensuring an unbiased sample of crayfish and, in light of previous studies, incorporated several collection techniques (burrow excavation, hand netting, and electrofishing). Stream-dwelling crayfish can be captured with a variety of techniques including seining, electrofishing, and various trapping methods (Page 1985, Pfleiger 1996). Minnow traps can obtain biased samples of larger crayfish and have decreased efficiency with increasing crayfish densities (Dorn et al. 2005), while quadrat samplers have been shown to be highly Table 4. Identity and relative importance of significant (P < 0.05) predictors of species abundance at the reach and watershed scale. Model strength is reported by adjusted R2 and signifi- cance values. Significant independent variables are reported along with standardized regression coefficients. Superscripts represent primary (1), secondary (2), or tertiary (3) burrowers. Dependent Independent variable Adjusted Model variable (standardized regression coefficient) R2 P-value Watershed scale Cambarus polychromatus1 0.022 0.226 C. sp. A1 0.076 0.311 Fallicambarus. fodiens1 0.085 0.328 Procambarus acutus2 0.011 0.459 C. tenebrosus2 0.142 0.446 Orconectes immunis3 0.054 0.140 O. propinquus3 % riparian forest (0.364), % watershed 0.333 <0.001 forest (0.360), runoff (0.329), % watershed wetlands (-0.253) O. virilis3 0.043 0.665 Reach scale C. polychromatus1 0.014 0.302 C. sp. A1 -0.102 0.550 F. fodiens1 Riffle/run score (-0.575), water temp (-0.385) 0.502 0.013 P. acutus2 -0.080 0.702 C. tenebrosus2 Water temperature (-0.589), dissolved oxygen 0.492 0.014 saturation (0.446) O. immunis3 0.059 0.544 O. propinquus3 Substrate score (0.262) 0.191 0.050 O. virilis3 0.046 0.661 210 Southeastern Naturalist Vol. 9, Special Issue 3 biased toward smaller individuals (Rabeni et al. 1997). Rabeni et al. (1997) compared several capture methods (quadrat samplers, baited traps, hand netting and electrofishing) for stream crayfish and concluded that electrofishing was the most accurate sampling tool for multiple habitats, especially slow-moving water with heavy cover, and produced the highest abundance estimates. Hand netting was biased toward capture of larger individuals, and quadrat sampling was biased toward smaller individuals. The authors recommended hand netting in conjunction with other sampling techniques for abundance estimates. We chose to combine electrofishing and hand netting, with the anticipation that most of the streams in the study area would have slow velocity and ample instream cover. A recent study by Ridge et al. (2008) compared the efficiency of 3 capture methods for primary burrowing crayfish and concluded that burrow excavation, while more labor intensive, was significantly more efficient and had no correlation with habitat quality compared with Norrocky and mist-net trapping. Both efficiency and efficacy of techniques were taken into account when deciding on collection techniques for this study. Previous sampling in Indiana using the methodology of this study (Simon 2004) has yielded several new species records (T.P. Simon, unpubl. data) and has been efficient in both effort and results. Despite the heavy agricultural impacts over much of the study area, the crayfish assemblage in west-central Indiana remains relatively diverse. The crayfish species assemblage in west-central Indiana is a combination of species associated with areas to the west in Illinois and east in Indiana. Page and Mottesi (1995) reported 17 crayfish species in Indiana, and Simon (2001) reported 21 with the possible occurrence of others in the state. Page (1985) noted the presence of 8 species in the glaciated region of Illinois drained by the Wabash River, including O. stannardi Page (Little Wabash Crayfish), an endemic to the Little Wabash River in eastern Illinois which was not collected in this study despite proximity to the Little Wabash River. The lack of O. stannardi records in this study is another confirmation of its endemism to Illinois. Regional heterogeneity of habitats and glacial history is a cause for the relatively high species richness in west-central Indiana (Simon and Thoma 2003). Regions are represented by characteristic crayfish assemblages, and areas that possess both glaciated and unglaciated environments are expected to be more faunistically diverse (Simon 2001). Orconectes indianensis and C. tenebrosus do not occur in adjacent eastern Illinois, but were collected in west-central Indiana. Cambarus tenebrosus was the only crayfish to be limited to the Interior Plateau and surrounding areas to the east. Cambarus polychromatus had not been described at the time of Page’s (1985) publication, but this species also occurs commonly in Illinois (Thoma et al. 2005). Crayfish abundance was significantly greater in the IP, suggesting that conditions are more favorable in this region than in the IRL. Increased abundance of crayfish was likely driven by higher reach habitat quality and increased cover from large cobble and boulder substrates that predominated in the more heavily forested IP. 2010 J.L. Burskey and T.P. Simon 211 Reach vs. watershed scale As both reach and watershed habitat is altered by human activity, it is anticipated that stream biota will respond (Allan 2004, Wang et al. 2003). The connection between watershed land cover, reach habitat, and water quality has been studied, and numerous authors have shown declines in habitat, water quality, and stream biota in agricultural landscapes (Allan 2004, Sponseller et al. 2001, Wang et al. 1997). The associations of aquatic fauna with reach- and watershed-scale factors have been studied by several authors with varying conclusions. Frimpong et al. (2005) showed that watershed-scale variables were better predictors of stream fish integrity than were reach-scale variables within the Eastern Corn Belt Plain in Indiana. Watershed models explained about 15% more variation in fish integrity, and adjusted R2 values ranged from 0.25 to 0.93 for reach models and 0.09 to 0.86 for watershed models. McRae et al. (2004) identified both reach- and watershed-scale variables as having strong predictive influences on freshwater mussel integrity, distribution, and abundance in the Eastern Corn Belt Plain in Michigan. In that study, stepwise regression models (R2 = 0.63 for total mussels, R2 = 0.51–0.86 for individual species) identified reach habitat and substrate composition as important reach-scale variables and surficial geology as the most important watershed-scale variable. Wang et al. (2003) investigated stream fish assemblages in the nonagricultural Northern Lakes and Forests ecoregion in Wisconsin, Minnesota, and Michigan and concluded that watershed-scale variables (19–24% of variance) predicted the most assemblages in degraded areas and reach-scale variables (25–51% of variance) predicted the most in pristine areas. Within the same ecoregion, Weigel et al. (2003) determined that both watershed and reach variables influenced aquatic macroinvertebrate structure, with reach-scale variables having the most influence on individual species abundances. The watershed-scale models explained 10–24% of variance, and reach-scale models 35–42% of variance in macroinvertebrate traits. In this study, reach-scale models consistently explained more variance in assemblage structure than did watershed-scale measures, suggesting that reach-scale variables have stronger influences on crayfish assemblages. The importance of reach- over watershed-scale variables is supported by other studies focusing on assemblages of benthic macroinvertebrate communities in agricultural landscapes. Richards et al. (1997) studied macroinvertebrate life-history traits within an agricultural environment and concluded that reach-scale variables had greater influence. Fourteen of fifteen macroinvertebrate traits were significantly predicted by reach-scale variables (concordance values >68%), while watershed-scale models significantly predicted just four of fifteen traits. Stewart et al. (2000) studied streams within a heavily altered landscape in Northwest Indiana using multidimensional scaling and Bray- Curtis similarity analyses, and concluded instream habitat and local-scale land use had stronger influences on macroinvertebrate communities than physiochemical variables and watershed-scale land use. Both studies highlighted 212 Southeastern Naturalist Vol. 9, Special Issue 3 the importance of reach-scale abiotic factors such as stream size, availability of shallow slow-water refugia, and substrate quality on the structure and function of macroinvertebrate communities. In a study of Orconectes williamsi Fitzpatrick (Williams Crayfish), a stream-dwelling crayfish from Missouri and Arkansas, Westoff et al. (2006) found that no watershed-scale abiotic variables significantly influenced density, but reach-scale variables of channel morphology and instream macrophyte growth did. Our study adds evidence to support the importance of reach-scale factors over watershed-scale factors in influencing crayfish communities, and our results are similar to macroinvertebrate studies in agricultural landscapes. Models were largely unable to predict individual species abundance with statistical significance, with just three significant species models. Reach-scale models were again more powerful predictors than watershedscale models. Successful models identified abiotic variables that reflect species accounts from other authors who commented on distribution and habitat (Page 1985, Pflieger 1996). Cambarus tenebrosus occupied small, rocky, spring-fed creeks and did not occur over fine substrates. The species was found at highest numbers in clear, cold, well-oxygenated streams, and this relationship was reflected in a positive association with low water temperature and high dissolved oxygen. Fallicambarus fodiens was most abundant in high quality flooded areas adjacent to slow-flowing deep streams. Orconectes propinquus was most common in streams with medium flow and large gravel-cobble substrates. The species was not generally found in streams with fine substrates and macrophyte growth, which was reflected in the significant positive association of O. propinquus with substrate score. The positive association of O. propinquus with wooded riparian and watershed land may also be related to instream substrate. Intact woodland helps to stabilize soils and limit impacts from fine substrate deposition (Allan 2004) and may in turn allow a stream to maintain larger substrates, which is an important microhabitat feature allowing O. propinquus to prosper. It is worthy to note the absence of cover score as a significant predictor in the three significant individual species models. It may be that at the species level other abiotic factors (temperature, dissolved oxygen) or biotic factors take precedence in governing abundance when substantial cover is available. The lack of strong relationships between abiotic factors and crayfish assemblage may be attributable to more than one cause. Previous authors have incorporated measures of surficial geology into watershed-scale assessments, which may be particularly important when dealing with land-use influences. Richards et al. (1996) found surficial geology to have strong influences on benthic macroinvertebrates and that it overshadowed the influence of land cover through regulation of stream morphology and hydrology. McRae et al. (2004) found measures of surficial geology the most effective watershed-scale predictors of freshwater mussel distribution. Lodge and Hill (1994) discussed factors governing crayfish assemblages and proposed that the effects of density-independent abiotic factors do not exert controls on crayfish density 2010 J.L. Burskey and T.P. Simon 213 above a threshold value. If abiotic factors such as pH, dissolved oxygen, and temperature are sufficient to support crayfish, then density-dependent factors (refuge abundance) and biotic variables limit crayfish density. This could be the reason for the lack of physiochemical variables identified in predictive models in this study. Indeed, cover availability was identified as the strongest predictor of crayfish assemblage, but R2 values were still low compared to studies concerning other fauna (see discussion above), suggesting that other factors may be governing crayfish assemblage. The interaction of abiotic factors and biotic factors of food availability, inter- and intraspecific competition for resources (refuges, food, thermal niche), and predation by both aquatic and terrestrial fauna are likely governing crayfish assemblage and individual species abundances, and this interaction may underscore the relatively low predictive strength of models in this study. Importance of individual abiotic variables The importance of cover availability as a positive predictor of crayfish assemblage in this study is supported by previous research. Cover is important for juvenile crayfish to avoid predation and for adult crayfish to avoid cannibalism during molts (Flinders and Magoulick 2003, Lodge and Hill 1994, Stein and Magnuson 1976). A variety of cover types (woody debris, larger substrates, macrophytes, shallows, etc.) is an important aspect of crayfish diversity because different crayfish species have been shown to utilize different habitat throughout their life stages, and habitat partitioning by separate species can allow for coexistence and increased diversity (Rabeni 1985). Agricultural land use degrades streams by erosion of stream banks and increasing sedimentation, which decreases the amount of larger substrates that are important refugia for benthic fauna such as crayfish (Hill and Lodge 1994). The deposition of fine sediments likely had substantial negative impacts on stream crayfish abundance in the agriculturally dominated IRL. Loss of habitat heterogeneity as streams are channelized and riparian buffers removed also leads to loss of instream cover as stream sinuosity and inputs of woody debris decrease (Allan 2004). While watershed-scale models generally had low predictive power, watershed- scale variables of forested land cover and watershed area appeared as significant assemblage predictors. Crayfish were negatively associated with watershed area (stream size) and positively associated with forested riparian and watershed land. Flinders and Magoulick (2003) found crayfish to have greater densities in smaller intermittent streams than in perennial streams and attributed the pattern largely to reduced predation risk. Larger streams support more fish predators that negatively impact crayfish population size (Hill and Lodge 1994, Stein and Magnuson 1976). Loss of riparian buffers causes loss of habitat heterogeneity, increases bank erosion and sediment deposition, increases water temperatures, and decreases inputs of allochthonous materials (Allan 2004). Maintaining an intact riparian area appeared to be especially important for maintaining healthy crayfish assemblages. 214 Southeastern Naturalist Vol. 9, Special Issue 3 Conclusion Because conservation strategies tend to focus on subjective scales, it is important to investigate influences at varying spatial scales (Fausch et al. 2002). Multiple regression analysis indicated that reach-scale variables were stronger predictors of crayfish assemblage structure and individual species abundances than watershed-scale variables. Cover availability was the most consistent reach-scale predictor and was significantly positively associated with each assemblage model. Previous crayfish studies indicate that cover can be a limiting resource and is important for predation and cannibalistic avoidance (Lodge and Hill 1994, Stein and Magnuson 1976). Stream crayfish abundance was significantly positively associated with riparian forest and negatively associated with catchment size at the watershed-scale. The negative association with watershed area is a reflection of an inverse relationship of crayfish abundance with stream size, largely due to increased predation risk from fish in larger streams. Intact riparian cover helps maintain water and habitat quality and limits the effects of agriculture (Allan 2004). Generally low regression values compared to similar studies for other fauna indicate that the interaction of biotic and abiotic factors may be governing crayfish assemblage. Future studies may benefit from including measures of surficial geology at the watershed-scale and incorporating biotic variables (competition, predation risk) in analyses. Acknowledgments The Indiana Biological Survey Aquatic Research Center and the Indiana State University Department of Ecology and Organismal Biology supported this research. We would like to thank Dr. Peter Scott and Dr. John Whitaker for providing helpful reviews, and Stuart Welsh for publication support. The publication of this manuscript was supported, in part, by the US Geological Survey Cooperative Research Unit Program, including the West Virginia Cooperative Fish and Wildlife Research Unit. Literature Cited Allan, J.D. 2004. Landscapes and riverscapes: The influence of land use on stream ecosystems. Annual Review of Ecology, Evolution, and Systematics 35:257–284. Brazner, J.C., D.K. Tanner, N.E. Detenbeck, and S.L. Batterman. 2005. Regional, watershed, and site-specific environmental influences on fish assemblage structure and function in western Lake Superior tributaries. Canadian Journal of Fisheries and Aquatic Sciences 62:1254–1270. Choi, J., and B.A. Engel. 2003. Real-time watershed delineation system using web- GIS. Journal of Computing in Civil Engineering 17:189–196. Dorn, N.J., R. Urselles, and J. Trexler. 2005. Evaluating active and passive sampling methods to quantify crayfish density in a freshwater wetland. Journal of the North American Benthological Society 24:346–356. Eikaas, H.S., A.R. McIntosh, and A.D. Kliskey. 2005. Catchment- and site-scale influences of forest cover and longitudinal forest position on the distribution of a diadromous fish. Freshwater Biology 50:527–538. Environmental Systems Research Institute. 2006. ArcView GIS 9.2 ESRI Inc., Redlands, CA. 2010 J.L. Burskey and T.P. Simon 215 Fausch, K.D., C.E. Torgersen, C.V. Baxter, and W.H. Li. 2002. Riverscapes to landscapes: Bridging the gap between research and conservation of stream fishes. BioScience 52:483–498. Flinders, C.A., and D.D. Magoulick. 2003. Effects of stream permanence on crayfish community structure. American Midland Naturalist 149:134–147. Frimpong, E.A., T.M. Sutton, B.A. Engle, and T.P. Simon. 2005. Spatial-scale effects on relative importance of physical habitat predictors of stream health. Environmental Management 36:899–917. Hill, A.M., and D.M. Lodge. 1994. Diel changes in resource demand: Competition and predation in species replacement among crayfishes. Ecology 75:2118–2126. Hobbs, H.H. Jr. 1981. The crayfishes of Georgia. Smithsonian Contributions to Zoology 318:1–549. Johnson, R.K., and W. Goedkoop. 2002. Littoral macroinvertebrate communities: Spatial scale and ecological relationships. Freshwater Biology 47:1840–1854. Lammert, M., and J.D. Allan. 1999. Assessing biotic integrity of streams: Effects of scale in measuring the influence of landuse/cover and habitat structure on fish and macroinvertebrates. Environmental Management 23:257–270. Leopold, L.B., M.G. Woolman, and J.P. Miller. 1964. Fluvial Processes in Geomorphology. W.H. Freeman, San Francisco, CA. 522 pp. Lodge, D.M., and A.M. Hill. 1994. Factors governing species composition, population size, and productivity of cool-water crayfish. Nordic Journal of Freshwater Research. 69:111–136. McRae, S.E., J.D. Allan, and J.B. Burch. 2004. Reach- and catchment-scale determinants of the distribution of freshwater mussels (Bivalvia: Unionidae) in southeastern Michigan, USA. Freshwater Biology 49:127–142. Omernik, J.M., and A.L. Gallant. 1988. Ecoregions of the Upper Midwest States. EPA 600/3-88/037. US Environmental Protection Agency. Corvallis, OR. 56 pp. Page, L.M. 1985. The crayfishes and shrimps (Decapoda) of Illinois. Illinois Natural History Survey Bulletin 33:335–448. Page, L.M., and G.B. Mottesi. 1995. The distribution and status of the Indiana crayfish, Orconectes indianensis, with comments on the crayfishes of Indiana. Proceedings of the Indiana Academy of Science 104:103–111. Perneger, T.V. 1998. What's wrong with Bonferroni adjustments. British Medical Journal 316:1236–1238. Pflieger, W.L. 1996. The Crayfishes of Missouri. Missouri Department of Conservation, Jefferson City, MO. 152 pp. Rabeni, C.F. 1985. Resource partitioning by stream-dwelling crayfish: The influence of body size. American Midland Naturalist 113:20–29. Rabeni, C.F., K.J. Collier, S.M. Parkyn, and B.J. Hicks. 1997. Evaluating techniques for sampling stream crayfish (Paranephrops planifrons). New Zealand Journal of Marine and Freshwater Research 31:693–700. Rankin, E.T. 1995. Habitat indices in water resource quality assessments. Pp. 181– 208, In W.S. Davis and T.P. Simon (Eds.). Biological Assessment and Criteria. Lewis Publishers, Boca Raton, fl. 415 pp. Richards, C., L.B. Johnson, and G.E. Host. 1996. Landscape-scale influences on stream habitats and biota. Canadian Journal of Aquatic Sciences 53:295–311. Richards, C., R.J. Haro, L.B. Johnson, and G.E. Host. 1997. Catchment and reachscale properties as indicatiors of macroinvertebrate species traits. Freshwater Biology 37:219–230. Ridge, J., T.P. Simon, D. Karns, and J. Robb. 2008. Comparison of three burrowing crayfish collection methods based on relationships with species morphology, seasonality, and habitat quality. Journal of Crustacean Biology 28:466–472. 216 Southeastern Naturalist Vol. 9, Special Issue 3 Seiler, S.M., and A.M. Turner. 2004. Growth and population size of crayfish in headwater streams: Direct and indirect effects of acidity. Freshwater Biology 49:870–881. Simon, T.P. 2001. Checklist of the crayfish and freshwater shrimp (Decapoda) of Indiana. Proceedings of the Indiana Academy of Science 110:104–110. Simon, T.P. 2004. Standard operating procedures for the collection and study of burrowing crayfish in Indiana. I. Methods for collection of burrowing crayfish in streams and terrestrial habitats. Miscellaneous Papers of the Indiana Biological Survey Aquatic Research Center 2:1–16. Simon, T.P., and R.F Thoma. 2003. Distribution patterns of freshwater crayfish (Decapoda: Cambaridae) in the Patoka River basin of Indiana. Proceedings of the Indiana Academy of Science 112:175–185. Soil Conservation Service. 1986. Urban Hydrology for Small Watersheds. TR-55, Second Edition. SCS, United States Department of Agriculture, Washington, DC. 164 pp. SPSS, Inc. (1999). SPSS base 10.0 for windows user's guide. SPSS, Inc., Chicago IL. Sponseller, R.A., E.F. Benfield, and H.M. Valett. 2001. Relationships between land use, spatial scale, and stream macroinvertebrate communities. Freshwater Biology 46:1409–424. Stein, R.A., and J.L. Magnuson. 1976. Behavioral response of crayfish to a fish predator. Ecology 57:751–761. Stewart, P.M., J.T. Butcher, and T.O. Swinford. 2000. Land use, habitat, and water quality effects on macroinvertebrate communities in three catchments of a Lake Michigan-associated marsh system. Aquatic Ecosystem Health and Management 3:179–89. Stone, M.L., M.R. Whiles, J.A. Webber, K.W. Willard, and J.D. Reeve. 2005. Macroinvertebrate communities in agriculturally impacted southern Illinois streams: Patterns with riparian vegetation, water quality, and in-stream habitat quality. Journal of Environmental Quality 34:907–917. Taylor, C.A., M.L. Warren, J.F. Fitzpatrick, Jr., H.H. Hobbs, Jr., R.F. Jezerinac, W.L. Pflieger, and H.W. Robison. 1996. Conservation status of crayfishes of the United States and Canada. Fisheries 21(4):25–38. Thoma, R.F., R.F. Jezerinac, and T.P. Simon. 2005. Description of new burrowing crayfish, Cambarus (Tubericambarus) polychromatus from Indiana, Illinois, Ohio, and Michigan. Proceedings of the Biological Society of Washington 118:326–336. Wang, L., J. Lyons, P. Kanehl, and R. Gatti. 1997. Influences of watershed land use on habitat quality and biotic integrity in Wisconsin streams. Fisheries 22:6–12. Wang, L., J. Lyons, P. Rasmussen, T. Seelbach, T. Simon, M. Wiley, E. Kanehl, S. Niemela, and P.M. Stewart. 2003. Watershed, reach, and riparian influences on stream fish assemblages in the Northern Lakes and Forest Ecoregion, USA. Canadian Journal of Fisheries and Aquatic Sciences 60:491–505. Weigel, B.M., L. Wang, P.W. Rasmussen, J.T. Butcher, P.M. Stewart, T.P. Simon, and M.W. Wiley. 2003. Relative influence of variables at multiple spatial scales on stream macroinvertebrates in the Northern Lakes and Forest Ecoregion, USA. Freshwater Biology 48:1440–1461. Westoff, J.T., J.A. Guyot, and R.J. DiStefano. 2006. Distribution of the imperiled Williams’ Crayfish (Orconectes williamsi) in the White River drainage of Missouri: Associations with multi-scale environmental variables. American Midland Naturalist 156:273–288.