North American Journal of Fisheries Management 29:1130–1151, 2009 Ó Copyright by the American Fisheries Society 2009 DOI: 10.1577/M08-118.1
[Article]
Defining and Characterizing Coolwater Streams and Their Fish Assemblages in Michigan and Wisconsin, USA JOHN LYONS* Wisconsin Department of Natural Resources, 2801 Progress Road, Madison, Wisconsin 53716-3339, USA
TROY ZORN Marquette Fisheries Research Station, Michigan Department of Natural Resources, 484 Cherry Creek Road, Marquette, Michigan 49855, USA
JANA STEWART Great Lakes Aquatic Gap Program, U.S. Geological Survey, 8505 Research Way, Middleton, Wisconsin 53562, USA
PAUL SEELBACH, KEVIN WEHRLY,
AND
LIZHU WANG
Institute for Fisheries Research, Michigan Department of Natural Resources, 212 Museums Annex Building, Ann Arbor, Michigan 48109, USA Abstract.—Coolwater streams, which are intermediate in character between coldwater ‘‘trout’’ streams and more diverse warmwater streams, occur widely in temperate regions but are poorly understood. We used modeled water temperature data and fish assemblage samples from 371 stream sites in Michigan and Wisconsin to define, describe, and map coolwater streams and their fish assemblages. We defined coolwater streams as ones having summer water temperatures suitable for both coldwater and warmwater species and used the observed distributions of the 99 fish species at our sites to identify coolwater thermal boundaries. Coolwater streams had June-through-August mean water temperatures of 17.0–20.58C, July mean temperatures of 17.5–21.08C, and maximum daily mean temperatures of 20.7–24.68C. We delineated two subclasses of coolwater streams: ‘‘cold transition’’ (having July mean water temperatures of 17.5–19.58C) and ‘‘warm transition’’ (having July mean temperatures of 19.5–21.08C). Fish assemblages in coolwater streams were variable and lacked diagnostic species but were generally intermediate in species richness and overlapped in composition with coldwater and warmwater streams. In cold-transition streams, coldwater (e.g., salmonids and cottids) and transitional species (e.g., creek chub Semotilus atromaculatus, eastern blacknose dace Rhynichthys atratulus, white sucker Catostomus commersonii, and johnny darter Etheostoma nigrum) were common and warmwater species (e.g., ictalurids and centrarchids) were uncommon; in warm-transition streams warmwater and transitional species were common and coldwater species were uncommon. Coolwater was the most widespread and abundant thermal class in Michigan and Wisconsin, comprising 65% of the combined total stream length in the two states (cold-transition streams being more common than warmtransition ones). Our approach can be used to identify and characterize coolwater streams elsewhere in the temperate region, benefiting many aspects of fisheries management and environmental protection.
Water temperature is a key environmental factor influencing the occurrence and abundance of fishes (Magnuson et al. 1979). In temperate streams, summer maximum water temperature is particularly important in determining habitat suitability for many species, and summer water temperatures are routinely used to classify fishes and their habitats for fisheries and environmental management (Lyons et al. 1996; Stoneman and Jones 1996, 2000; Picard et al. 2003; Wehrly et al. 2003; Seelbach et al. 2006). In North America, * Corresponding author:
[email protected] Received May 12, 2008; accepted March 16, 2009 Published online July 13, 2009
streams with relatively cold summer maximum water temperatures are usually dominated by a small number of ‘‘coldwater’’ species in the families Salmonidae and Cottidae that are unable to tolerate warmer temperatures (Lyons 1996; Lyons et al. 1996; Wehrly et al. 2007). Streams with relatively warm temperatures contain a greater richness of ‘‘warmwater’’ species in the families Cyprinidae, Catostomidae, Ictaluridae, Centrarchidae, and Percidae. These species, while able to survive as individuals at colder temperatures, require warmer temperatures to complete their life cycle and persist as populations (Lyons 1996, 1997). The concepts of coldwater and warmwater are fundamental to stream fisheries management in temperate regions of the world. However, they do not
1130
COOLWATER STREAMS AND FISH ASSEMBLAGES
adequately describe the range of thermal variation as well as associated fish assemblages and fisheries that occurs (Lyons et al. 1996; Wang et al. 2003; Wehrly et al. 2003). Many temperate streams have water temperatures intermediate between coldwater and warmwater, which represent endpoints on a thermal continuum. The fishes and fisheries of these ‘‘coolwater’’ streams have received far less research and management attention, and are thus much more poorly understood than those of either coldwater or warmwater streams. Coolwater streams are often not recognized as a major management category (e.g., Kohler and Hubert 1999) despite the fact that important stream-dwelling game fishes such as walleye Sander vitreus and northern pike Esox lucius are classified as coolwater species (e.g., Kendall 1978). The failure to recognize and distinguish coolwater streams can reduce the efficiency and effectiveness of fisheries management and environmental protection efforts (Wang et al. 2003). For example, many coolwater streams, although warmer than coldwater streams, are still thermally suitable for trout (Wehrly et al. 2003). However, if coolwater streams are lumped with warmwater streams in management classifications, coolwater streams may not receive adequate thermal protection, and opportunities to expand trout fisheries may be missed. Similarly, many coolwater streams are warm enough to support warmwater game fishes (e.g., smallmouth bass Micropterus dolomieu), but if coolwater streams are grouped with coldwater streams, then possible new fisheries may be ignored. Indeed, some coolwater streams can support fisheries based on both coldwater and warmwater species simultaneously, a unique and valuable attribute that may go unrecognized if these streams are classified as either coldwater or warmwater. As another example, the response of coolwater fish assemblages to environmental degradation differs from that of coldwater or warmwater assemblages (Lyons et al. 1996; Wiley et al. 2003; Baker et al. 2005; Zorn et al. 2008), and coolwater streams require their own unique bioassessment indices to determine their biological integrity and underlying ecosystem health (Leonard and Orth 1986; Lyons et al. 1996; Wang et al. 2003). Application of a warmwater or coldwater index to a coolwater stream often leads to an underestimate of biotic integrity (e.g., Wang et al. 2003; Baker et al. 2005), and can result in coolwater streams being misclassified as degraded and subject to unnecessary or inappropriate remediation actions. Recently, coolwater streams have begun to receive more attention and study in North America, particularly in the Great Lakes region where they are common and widespread (Lyons 1996; Stoneman and Jones 1996;
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Wang et al. 2003; Wehrly et al. 2003). Several U.S. state government aquatic management agencies have initiated efforts to assess coolwater stream fish assemblages and to develop and apply coolwater bioassessment indices (e.g., Niemela and Feist 2000, 2002; Aquatic Research Center of the Indiana Biological Survey 2007). Although these studies are thorough and provide much valuable information, they are limited, in our view, by reliance on imprecise or subjective definitions of cool water. Ironically, these definitions were, in large part, developed by the first author during initial efforts to improve thermal classification of Wisconsin streams (Lyons 1996; Lyons et al. 1996). These earlier definitions focused on thermal conditions that were ‘‘marginal’’ for coldwater species such as trout or sculpins, but marginal was not always clearly defined. In some cases, marginal conditions were judged from trout or sculpin populations themselves rather than from temperature data, potentially confounding other factors that limited populations (e.g., habitat or water quality) with thermal suitability. Where quantitative thermal criteria were proposed for cool water, the basis for the actual values was somewhat subjective. We believe that efforts to understand and manage coolwater streams and their fish assemblages would benefit from more rigorous and precise definitions and characterizations. Towards this end, Wehrly et al. (2003) published the first objective and quantitative approach to defining coolwater streams. However, from their results, several alternative coolwater criteria could be justified. They used a statistical analysis of the patterns of differences in fish community similarity, incorporating the occurrence and abundance of all fish species, for 307 stream sites in the Lower Peninsula of the state of Michigan, USA. Sites were grouped into 18C intervals based on July mean water temperature. Pairwise similarities of fish assemblages between sites were calculated within and among groups, and then group mean similarities were plotted relative to each other. Inflection points, where relatively large decreases in mean similarity between groups occurred, were inferred to represent the potential boundaries of thermal classes. This approach, although scientifically defensible and rigorous, produced multiple inflection points that corresponded to different temperature values and perhaps distinct fish assemblages, and thus it did not yield a clear set of thermal boundaries for cool water. Because of the uncertainty inherent in the Wehrly et al. (2003) approach, we undertook a new study, described in this paper, to develop and apply a more precise method to define and characterize coolwater streams and their fish assemblages. We had three main goals. The first was to develop objective, scientifically
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defensible, and management-relevant water temperature criteria that distinguished coolwater streams from coldwater and warmwater streams. We based our criteria on the premise that coolwater streams have water temperatures capable of sustaining an assemblage that may contain both coldwater and warmwater fishes (see also Stoneman and Jones 1996). Thus, an accurate estimate of the distribution of coldwater and warmwater species in relation to summer water temperatures could be used to determine the boundaries of the coolwater stream thermal class. We classified the fishes from our study sites, located in Michigan and Wisconsin, USA, into one of three categories—coldwater, warmwater, or an intermediate group we termed transitional—based on existing laboratory and field data on thermal limits and preferences. We then examined the patterns of occurrence and abundance of the coldwater and warmwater species at our study sites to determine water temperatures that would accurately delineate coolwater streams. Our second goal was to quantify the fish assemblage characteristics of coolwater streams and contrast them with the assemblage characteristics of coldwater and warmwater streams. We postulate that coolwater streams are a transitional habitat type where species traditionally thought of as having fundamentally different thermal requirements can, and often do, coexist, and consequently we expected that coolwater stream fish assemblages would be relatively variable and overlap substantially with the assemblages of both coldwater and warmwater streams. Because of this expected high variability and overlap, coupled with the findings from Wehrly et al. (2003) suggesting that coolwater may contain more than one type of fish assemblage, we further subdivided the coolwater classification into two subclasses, which we termed cool transition and warm transition. We then assessed whether use of these two subclasses provided additional insights relative to the use of a single coolwater class. Our third and final goal was to use the thermal criteria that we developed to map the distribution and quantify the amounts of coldwater, coolwater, and warmwater streams in Michigan and Wisconsin. We limited this analysis to Michigan and Wisconsin because this was the area for which our thermal criteria had been developed and for which we had sufficient information to classify all flowing waters. Michigan and Wisconsin streams are also generally representative of the Great Lakes region (i.e., Ontario, Wisconsin, Michigan, Minnesota, northern Illinois, Indiana, Ohio, and New York). However, the approach we used to define and characterize coolwater streams could be applied anywhere in the temperate zone. Our purposes
here were to portray the large quantity and broad distribution of coolwater streams over a large geographic area, emphasizing the importance of this stream type, and to illustrate how our thermal classification framework could be used to develop maps for management applications. Methods Data.—Fish data came from 371 stream sites—252 in Michigan and 119 in Wisconsin—sampled by respective state Department of Natural Resources fisheries research or management crews during 1996– 2000. All sites were large enough to support a viable fish assemblage (based on criteria in Lyons 2006) but small and shallow enough to be sampled effectively by wading (catchment area ¼ 3.2–2,092.0 km2). Sites encompassed the entire range of wadeable stream fish assemblages and habitat conditions in the two states, particularly in regard to geographic location, water temperature and stream flow, riparian and catchment environmental setting, and human impacts. Each site was sampled once during the period 25 May–10 September by daytime electrofishing, the goal being the capture of all fish observed and assessment of the relative abundance of all species present. Sites were surveyed in an upstream direction using standard wading bioassessment techniques (Lyons et al. 1996). A distance of at least 100 m was sampled using either a pulsed DC backpack electroshocker in streams less than 6 m wide or a DC tow barge shocker in streams more than 6 m wide. The entire length and width of the stream was sampled at each site. Fish abundance was expressed as the number of individuals captured per 100 m of stream length sampled. We used stream length rather than surface area or volume as our measure of sampling effort because in Michigan and Wisconsin streams, actual population size correlates better with catch per stream length than with catch per surface area or volume. For example, in a regression analysis, catch per stream length for brown trout Salmo trutta explained 83%, but catch per stream surface area explained only 66% of the variation in estimated brown trout population size at 106 Michigan stream sites (width range ¼ 1.3–69.6 m; T. Zorn, unpublished data). In a second regression analysis, smallmouth bass catch per stream length explained 69% but catch per stream surface area explained only 35% of the variation in estimated smallmouth bass population size at 19 Wisconsin stream sites (width range 7.2–28.7 m; reanalyzed data from Lyons and Kanehl 1993). Finally, in a third regression analysis, total fish catch per stream length explained 59%, but catch per stream surface area explained only 30% of the variation in total fish population size at nine other
COOLWATER STREAMS AND FISH ASSEMBLAGES
Wisconsin stream sites (width range ¼ 1.8–7.9 m; data reanalyzed from Simonson and Lyons 1995). We believe that catch per length better reflects population size because the best fish habitat tends to be concentrated along the banks, and therefore available habitat and fish populations tend to increase more as a function of stream length than stream width, surface area, or volume. However, catch per length is strongly positively related to catch per surface area (r2 ¼ 0.86 for Michigan brown trout, 0.74 for Wisconsin smallmouth bass, 0.84 for Wisconsin total fish), so the measure of sampling effort used probably has relatively little influence on the overall results of our analyses. Summer water temperatures at each study site and for all of the streams in each state were estimated from empirical models that included air temperature, stream network position, catchment size, geology, and land cover as independent variables (generated by geographical information systems [GIS] using procedures outlined in Brenden et al. [2006]). Most of the variation in stream temperature was explained by air temperature, catchment size, and geology. Models differed for Michigan and Wisconsin streams. In Michigan, linear mixed modeling was used to estimate July mean water temperature based on single summers of either maximum–minimum or continuous water temperatures measured during 1989–2005 at 830 stream sites (Wehrly et al., in press). These sites were located throughout Michigan and were matched with landscape attributes and continuous air temperatures at the nearest weather station. Linear mixed models account for both fixed and random effects, and can account for spatial dependency among data. For the fixed-effects component of the Michigan model, July mean water temperatures were modeled as a linear function of six variables: July mean air temperature, catchment area, percent forest, percent surface waters, mean slope, and mean soil permeability. The random-effects component accounted for spatial autocorrelation in water temperature among streams and was estimated from low-rank radial smoothing splines. The overall model had a root mean square error of 2.08C, explained 70% of the variation in observed July water temperature among the 830 sites, and produced unbiased estimates of stream temperature. In Wisconsin, three water temperature measures— June–August mean, July mean, and maximum daily mean (i.e., the warmest daily mean water temperature during the summer)—were estimated from an artificialneural-network predictive model (Roehl et al. 2006; Stewart et al. 2006). This model was developed from 223 sites and tested with independent data from 171 additional sites. Data consisted of single summers of continuous water temperatures measured during 1991–
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2003 at stream sites throughout Wisconsin and matched with continuous air temperature variables from each of the 156 weather stations in the state during 1991–2003 and site-specific information on catchment soil permeability, slope, and land cover. This model predicted daily mean water temperature for each summer day (June 1–August 31) in each year during 1991–2003 for a total of 1,196 daily water temperatures for each site, and these daily estimates were summarized to estimate the ‘‘average’’ June– August mean, July mean, and maximum daily mean water temperatures for the 13-year period. In tests with independent data, the model explained 67% of the variation in observed daily mean water temperature for each summer day over the 13-year period for 31 sites, and predicted July mean water temperatures were within 28C of the observed July mean water temperatures for 55% of 171 sites, and within 48C for 80% of these sites. Predictions from the Wisconsin model were unbiased. Separate temperature models were applied to Michigan and Wisconsin streams for two reasons. First, each state had already independently developed their own temperature model for their state’s waters prior to this study. The workload necessary to develop a new joint Michigan–Wisconsin temperature model would have been substantial and would have constituted a duplication of effort. Second, and more importantly, each state has used and will continue to use their own temperature model for stream classification and other management applications. The relative importance of variables explaining water temperature differs between the states, and each state’s temperature model has been optimized for the area in which it will be applied. Applying a joint Michigan–Wisconsin model to map and estimate the amounts of each stream thermal class would not benefit either state and would lead to confusion wherever a difference in classification occurred between the joint-state model and the individual state model. Although they differed in development and form, the Michigan and Wisconsin temperature models yielded similar results when applied to the same streams. We compared predictions from the two models in 1,833 stream reaches in the Menominee River basin, which drains into Green Bay (Lake Michigan) and straddles the boundary between the Upper Peninsula of Michigan and Wisconsin. There was a significant positive linear relation (Michigan temperature ¼ 10.622679 þ 0.4413 Wisconsin temperature; F ¼ 422.8, r ¼ 0.43, P , 0.0001) between predicted July mean water temperatures from the two temperature models (Figure 1), and predictions from the two models were within 18C for 71% and within 28C for 89% of the reaches.
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FIGURE 1.—Plot of July mean water temperatures predicted by the Michigan stream temperature model versus those predicted by the Wisconsin stream temperature model for 1,833 stream reaches in the Menominee River basin, which drains to Green Bay (Lake Michigan) and straddles the border between the Upper Peninsula of Michigan and northeastern Wisconsin. The line indicates perfect agreement between the two models.
Predicted temperatures from the Michigan model tended to be slightly warmer than predictions from the Wisconsin model in the coldest streams and slightly colder in the warmest streams, but there were no systematic differences in predictions from the two models at intermediate temperatures, which encompassed the vast majority of sites. We assessed the relative degree of human modification to each site using a human stress index developed by Wang et al. (2008) for Michigan streams. This index incorporates data on 27 measures of human disturbance within a GIS framework (as outlined by Brenden et al. 2006)—including catchment and riparian land cover, population and road densities, predicted nutrient loads, fertilizer application rates, point-source discharge densities, and proximity to mining areas—to generate a weighted sum of total human disturbance ranging from 0 (minimal impact) to 100 (severe). We applied this index to Wisconsin using the same data sources as for Michigan except for land cover, which was available for 2001 for Michigan but only for 1992 for Wisconsin (Reese et al. 2002). Based on criteria developed by Wang et al. (2008), we classified the 284 of our sites with index scores of 15 or less as having limited human impacts.
Overview of analyses.—Our analysis had three parts, described in more detail below. First, we used observed fish distribution data from all 371 sites to define coldwater, coolwater, and warmwater in terms of modeled water temperatures. We used fish abundance data to further subdivide the coolwater thermal-class into cold-transition and warm-transition subclasses. Second, we characterized the fish assemblages of coolwater sites and contrasted them with those of coldwater and warmwater sites, restricting our analysis to the 284 sites with limited human impacts. Third, and finally, we used the appropriate water temperature model to determine the thermal classes and subclasses for each stream in Michigan and Wisconsin, and then we employed GIS to map and calculate the total lengths of each of the classes and subclasses in the two states. Defining coolwater streams.—We defined coolwater streams as those with temperatures suitable for both coldwater and warmwater species, recognizing that coldwater and warmwater species would not necessarily co-occur in all coolwater streams for reasons other than temperature suitability. We used the scientific literature on laboratory water temperature preferences and tolerances (Coutant 1977; Hokanson 1977; Ko-
COOLWATER STREAMS AND FISH ASSEMBLAGES
walski et al. 1978; Beitinger and Magnuson 1979; Magnuson et al. 1979; Ingersol and Claussen 1984; Fields et al. 1987; Wismer and Christie 1987; Peterson 1993; Smale and Rabeni 1995a; Taniguchi et al. 1998; Beitinger et al. 2000; Novinger and Coon 2000) as well as thermal groupings based on field studies of distribution and abundance in relation to water temperature (Neill and Magnuson 1974; McCauley and Kilgour 1990; Mundahl 1990; Lyons 1992, 1996; Eaton et al. 1995; Smale and Rabeni 1995b; Eaton and Scheller 1996; Lyons et al. 1996; Smith and Fausch 1997; Halliwell et al. 1999; Mundahl and Simon 1999; Coker et al. 2001; Zorn et al. 2002, 2004, in press; Wehrly et al. 2003, 2007) to classify the fishes found at our study sites as coldwater, warmwater, or transitional. We applied the following criteria. Warmwater species had laboratory temperature preferenda (if known) greater than or equal to 228C, laboratory critical thermal maxima (if known) greater than or equal to 338C (if acclimation temperature greater than or equal to 158C), and were designated warmwater in all or nearly all field studies. Coldwater species had laboratory temperature preferenda less than or equal to 208C, critical thermal maxima less than or equal to 318C, and were considered coldwater in all or nearly all field studies. Species classified as transitional either had intermediate values for laboratory preferenda and tolerances, or met the warmwater criteria for some attributes and the coldwater criteria for others. We used data from all 371 sites to determine temperature thresholds. We included degraded sites (i.e., disturbance index scores greater than 15) in this portion of the analysis because they often had been warmed by human activities in their watersheds and provided particularly relevant values for the water temperature maxima occupied by coldwater species and the minima occupied by warmwater species. The temperature thresholds that defined coolwater streams were derived from the zone of overlap in water temperatures that could be occupied by both warmwater and coldwater species. The lower temperature boundary for coolwater was the lowest temperature at which warmwater species occurred, and the upper boundary was the warmest temperature at which coldwater species occurred. Because a species might be present as a migrant or stray at a thermally unsuitable site owing only to that site’s proximity to a thermally suitable site, we did not use the most extreme values observed as our coolwater boundaries. Rather, we used the lower 10th percentile of the temperature distribution for warmwater species and the upper 90th percentile of the distribution for coldwater species as our thresholds for defining coolwater. Coolwater streams represent a zone of major change
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in fish faunas and thus have a particularly wide range of potential fish assemblages and fisheries, despite their relatively narrow temperature range (see Wehrly et al. 2003). To characterize these streams and their fish faunas more precisely, we further subdivided coolwater streams into two thermal subclasses, cold transition and warm transition. In cold-transition streams, coldwater species were generally common and warmwater species uncommon, whereas in warm-transition streams, the opposite was true. For our data set, we defined common as more than 10 individuals of a particular thermal guild/100 m of stream length sampled. The upper temperature boundary of the cold-transition subclass was the upper 90th percentile of the temperature distribution for sites where coldwater species were common, and the lower temperature boundary of the warm-transition subclass was the lower 10th percentile of the temperature distribution for sites where warmwater species were common. Because these two criteria gave slightly different values for the temperature boundary between cold-transition and warm-transition streams, we set the boundary as the mean of the two criteria. Characterizing coolwater stream fish assemblages.—We limited this portion of the study to the 284 sites with limited human impacts to avoid major confounding effects of human perturbations on water temperatures and fish assemblages. Here we did not want fish assemblage responses to environmental degradation to overwhelm natural patterns of fish assemblage structure and composition in relation to temperature. Note, however, that the entire Great Lakes landscape has been fundamentally altered since European settlement of the region began over 200 years ago, so none of the sites were free of human disturbance (Becker 1983). Prior to analyses, we classified sites as coldwater, coolwater, or warmwater, and within coolwater as cold transition or warm transition, based on criteria for July mean water temperature developed from the previous analyses that defined coolwater streams. For each site, we calculated 10 fish assemblage variables, the number of species (richness) and the number of individuals (abundance) for each of five attributes of the assemblage: all fishes, native fishes only (Table 1), coldwater fishes, transitional fishes, and warmwater fishes (Table 2). Fish assemblages were contrasted among sites in different thermal classes. All analyses were considered significant if P was less than 0.05. We used a one-way analysis of variance (ANOVA) and a Tukey multiple comparisons test of means (SAS 2003) to test for differences in the 10 fish assemblage variables among both the three-group (i.e., coldwater, coolwater, warm-
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TABLE 1.—Fish species captured at the 252 Michigan and 119 Wisconsin study sites (G ¼ the 10 most frequently encountered game species, N ¼ the 10 most frequently encountered non-game species, and * ¼ 43 species present at more than 5% of sites that were used in multivariate analyses. All species are native to the study area except sea lamprey, alewife, common carp, pink salmon, coho salmon, Chinook salmon, rainbow trout, brown trout, threespine stickleback, and redear sunfish. Carmine shiner and rosyface shiner cannot be distinguished in the field and have very similar ecological requirements and are therefore aggregated here; only rosyface shiners are known from Michigan, but both species occur in Wisconsin. Number of sites Species Lampreys (Petromyzontidae) Chestnut lamprey I. castaneus Northern brook lamprey I. fossor* Southern brook lamprey I. gagei American brook lamprey Lampetra appendix Sea lamprey Petromyzon marinus Gars (Lepisosteidae) Longnose gar Lepisosteus osseus Bowfins (Amiidae) Bowfin Amia calva Herrings (Clupeidae) Alewife Alosa pseudoharengus Gizzard shad Dorosoma cepedianum Minnows (Cyprinidae) Central stoneroller Campostoma anomalum* Largescale stoneroller C. oligolepis Redside dace Clinostomus elongatus Spotfin shiner Cyprinella spiloptera Common carp Cyprinus carpio* Brassy minnow Hybognathus hankinsoni Common shiner Luxilus cornutus* (N) Redfin shiner Lythrurus umbratilis Pearl dace Margariscus margarita Hornyhead chub Nocomis biguttatus* River chub N. micropogon Golden shiner Notemigonus crysoleucas* Emerald shiner Notropis atherinoides Bigmouth shiner N. dorsalis* Blackchin shiner N. heterodon Blacknose shiner N. heterolepis Spottail shiner N. hudsonius Rosyface–carmine shiner N. rubellus–percobromus* Sand shiner N. stramineus Mimic shiner N. volucellus Suckermouth minnow Phenacobius mirabilis Northern redbelly dace Phoxinus eos* Southern redbelly dace P. erythrogaster Finescale dace P. neogaeus Bluntnose minnow Pimephales notatus* (N) Fathead minnow P. promelas* Longnose dace Rhinichthys cataractae* (N) Western blacknose dace R. obtusus* (N) Creek chub Semotilus atromaculatus* (N) Suckers (Catostomidae) Quillback Carpiodes cyprinus White sucker Catostomus commersonii* (N)
Michigan
Wisconsin
1 3 0
3 13 6
0 1
8 0
0
1
2
1
0 1
1 1
13 0 1 5 19 0 68 1 0 20 1 4 4 0 5 2 2
22 14 4 17 32 11 71 0 7 51 0 16 0 17 4 11 0
5 3 1
16 14 2
0 8 1 5
5 14 8 5
45 6
44 27
53 112
37 49
151
76
0
2
167
108
TABLE 1.—Continued. Number of sites Species Creek chubsucker Erimyzon oblongus Lake chubsucker E. sucetta Northern hog sucker Hypentelium nigricans* Smallmouth buffalo Ictiobus bubalus Bigmouth buffalo I. cyprinellus Silver redhorse Moxostoma anisurum River redhorse M. carinatum Golden redhorse M. erythrurum Shorthead redhorse M. macrolepidotum* Greater redhorse M. valenciennesi Bullhead catfishes (Ictaluridae) Black bullhead Ameiurus melas* Yellow bullhead A. natalis* Brown bullhead A. nebulosus Channel catfish Ictalurus punctatus Stonecat Noturus flavus* Tadpole madtom N. gyrinus Northern madtom N. stigmosus Pikes (Esocidae) Grass pickerel Esox americanus* Northern pike E. lucius* (G) Mudminnows (Umbridae) Central mudminnow Umbra limi* (N) Trout (Salmonidae) Pink salmon Oncorhynchus gorbuscha Coho salmon O. kisutch* Chinook salmon O. tshawytscha Rainbow trout O. mykiss* (G) Brown trout Salmo trutta* (G) Brook trout Salvelinus fontinalis* (G) Pirate perches (Aphredoderidae) Pirate perch Aphredoderus sayanus Codfish (Gadidae) Burbot Lota lota* Topminnows (Fundulidae) Blackstripe topminnow Fundulus notatus New World silversides (Atherinopsidae) Brook silverside Labidesthes sicculus Sticklebacks (Gasterosteidae) Brook stickleback Culaea inconstans* Threespine stickleback Gasterosteus aculeatus Sculpins (Cottidae) Mottled sculpin Cottus bairdii* (N) Slimy sculpin C. cognatus Temperate basses (Moronidae) White bass Morone chrysops Sunfishes (Centrarchidae) Rock bass Ambloplites rupestris* (G) Green sunfish Lepomis cyanellus* (N) Pumpkinseed L. gibbosus* (G) Warmouth L. gulosus Orangespotted sunfish L. humilis Bluegill L. macrochirus* (G) Longear sunfish L. megalotis Redear sunfish L. microlophus Smallmouth bass Micropterus dolomieu* (G) Largemouth bass M. salmoides* (G) Black crappie Pomoxis nigromaculatus* Perches (Percidae) Greenside darter Etheostoma blenniodes Rainbow darter E. caeruleum* Iowa darter E. exile Fantail darter E. flabellare* Least darter E. microperca Johnny darter E. nigrum* (N)
Michigan
Wisconsin
3 1
0 1
42 0 0 0 1 4 3 0
22 1 1 1 1 13 20 7
6 9 3 2 12 0 1
28 26 2 8 23 6 0
40 24
2 19
109
48
2 27 4 78 113 90
0 3 0 12 42 26
4
0
23
8
0
3
1
0
29
27
2
0
82 4
48 4
1
0
44 80 46 3 0 56 5 1
35 33 26 0 1 52 2 0
17 66 9
27 20 19
6 44 0 6 1 108
0 11 5 30 1 65
COOLWATER STREAMS AND FISH ASSEMBLAGES
TABLE 1.—Continued. Number of sites Species
Michigan
Wisconsin
Banded darter E. zonale Yellow perch Perca flavescens* (G) Logperch Percina caprodes* Blackside darter P. maculata* Slenderhead darter P. phoxocephala Walleye Sander vitreus Drums (Sciaenidae) Freshwater drum Aplodinotus grunniens
0 19 13 53 0 9
11 23 26 25 2 7
0
3
water) and four-group (i.e., coldwater, cold-transition, warm-transition, warmwater) stream thermal classifications. We also used the same analyses to compare individual abundances of the 10 most frequently encountered game fish species and the 10 most frequently encountered nongame or ‘‘forage’’ (i.e., potential prey for many of the game fish) species (Table 1). All variables based on numbers of individuals were log transformed (with 1 added to each number to prevent undefined values for catches of zero) to better approximate normality. We used indicator species analysis (Dufreˆne and Legendre 1997; implemented through PC-ORD software; McCune and Mefford 1999) to identify species characteristic of a particular thermal-class. Indicator species analysis calculates the relative frequency of occurrence and abundance of a species within each class and then across the entire data set to determine an indicator score for each species for each class. Indicator scores range from 0 to 100, higher scores signifying greater value as an indicator of a particular class. A score of 100 occurs if a species is a perfect indicator of a class (i.e., found in high abundance at all sites within one class but absent from all other sites in all other classes). A score of 0 occurs if a species has no value as indicator because it is found at exactly the same abundance and proportion of sites within each of the classes. We used a Monte Carlo technique to determine if individual indicator scores were significantly better than would be expected if species and abundances were randomly distributed among classes. We ran indicator species analysis for the 43 ‘‘most widespread’’ species that occurred at 5% or more of the sites (Table 1) for both the three-group (i.e., cold, cool, warm) and the four-group (i.e., cold, cold-transition, warm-transition, warm) thermal classification. We also examined the relative importance of summer water temperature in explaining patterns of species richness and fish abundance within thermalclasses. For each thermal class or subclass separately,
1137
we correlated (Spearman rank) each of the 10 fish assemblage, 10 game fish abundance, and 10 nongame fish abundance variables with July mean temperature (SAS 2003). Because of the large number of correlations (30), we applied a Bonferroni adjustment and used a stricter P-value threshold of 0.0017 (¼0.05/ 30) for statistical significance to reduce the chance of type I errors. Finally, we applied nonmetric multidimensional scaling (NMDS; McCune and Mefford 1999) to explore overall patterns of fish assemblage composition in relation to summer water temperature and thermal class. In NMDS, sites are ordered and scored along two or more ordination axes, each of which is based on the ranks of dissimilarities or ‘‘distances’’ among sites in species composition and abundance. Sites that have similar scores on the axes have similar fish assemblages. Species are also scored based on the ranks of their distances in abundance and distribution among sites; species with similar scores have similar patterns of abundance and distribution. Studies with both simulated and real data have demonstrated that NMDS is an excellent technique for revealing and explaining patterns in species assemblages among sites (Kenkel and Orlo´ci 1986; Minchin 1987). We ran a two-axis NMDS on the log-transformed abundances of the most widespread species using a Bray–Curtis distance measure (McCune and Mefford 1999). Monte Carlo techniques were used to determine if the overall ordination and the individual ordination axes accounted for a significantly greater proportion of the distances among sites than would a random reordering of the data. We correlated site scores on each axis with July mean water temperature, and plotted and compared sites scores among stream thermal-classes. Estimating the amount and distribution of thermal classes.—All mapped streams in Michigan and Wisconsin (based on 1:100,000 scale National Hydrography Data) were partitioned into ‘‘reaches,’’ lengths of stream that extended from a tributary confluence or lake–impoundment (or the stream’s source) downstream to the next tributary confluence or lake– impoundment (or the stream’s mouth; Brenden et al. 2006). The appropriate (Michigan or Wisconsin) temperature model was run for each reach using reach-specific data on air temperature; stream network position; and catchment size, geology, and land cover to estimate July mean water temperature. Each reach was assigned to a thermal class (and subclasses within the coolwater class) based on the criteria developed previously in this study to define coolwater streams. The distribution of thermal classes was mapped using GIS, and the total length of each thermal class was
1138
LYONS ET AL.
TABLE 2.—Mean laboratory temperature preferences and tolerances (where acclimation temperature 158C), thermal guild assignments from previous field studies (number of studies given after assignment; Cd ¼ cold, Cl ¼ cool, Wm ¼ warm), and our thermal classification as warm, cold, or transitional (Trans) for the 99 study species. Species were classified as transitional if they did not meet the criteria for either warmwater or coldwater species. See Methods for sources of data. Species Lampreys Chestnut lamprey Northern brook lamprey Southern brook lamprey American brook lamprey Sea lamprey Gars Longnose gar Bowfins Bowfin Herrings Alewife Gizzard shad Minnows Central stoneroller Largescale stoneroller Redside dace Spotfin shiner Common carp Brassy minnow Common shiner Redfin shiner Pearl dace Hornyhead chub River chub Golden shiner Emerald shiner Bigmouth shiner Blackchin shiner Blacknose shiner Spottail shiner Rosyface–carmine shiner Sand shiner Mimic shiner Suckermouth minnow Northern redbelly dace Southern redbelly dace Finescale dace Bluntnose minnow Fathead minnow Longnose dace Western blacknose dace Creek chub Suckers Quillback White sucker Creek chubsucker Lake chubsucker Northern hog sucker Smallmouth buffalo Bigmouth buffalo Silver redhorse River redhorse Golden redhorse Shorthead redhorse Greater redhorse North American catfishes Black bullhead Yellow bullhead Brown bullhead Channel catfish Stonecat Tadpole madtom Northern madtom Pikes Grass pickerel
Laboratory preferred temperatures (8C)
Laboratory critical thermal maxima (8C)
Thermal guilds from previous field studies
Thermal classification for this study
No data No data No data No data 13.6–14.3
No data No data No data No data 30.0–31.0
Cl-1, Wm-3 Cd-2, Cl-3 Cl-1 Cd-3, Cl-2 Cd-2, Cl-2
Warm Trans Trans Trans Trans
25.3–33.1
No data
Wm-5
Warm
No data
37.0
Wm-5
Warm
16.0–25.0 28.5–31.0
29.4–34.6 31.7
Cd-1, Cl-1, Wm-1 Cl-1, Wm-3
Trans Warm
22.0–29.0 No data No data 29.4–31.0 27.4–32.0 No data No data No data No data No data No data 22.3–23.7 22.0–24.0 No data No data No data 25.0–29.0 22.2–27.7 No data No data No data 25.3 No data No data 29.0 20.9–29.0 No data No data No data
35.8–37.7 No data 32.6 No data 38.0–39.0 No data 31.9–35.7 No data No data 35.6 30.9 33.0–36.8 34.3–37.6 36.6 No data No data 32.8–34.0 35.3 32.0–37.0 No data No data 29.0 35.9 28.5 31.9–37.9 33.2–40.2 31.4 29.5–31.9 35.7
Cl-2, Wm-5 Wm-1 Cd-1, Cl-2, Wm-1 Wm-4 Wm-6 Cd-1, Cl-2, Wm-1 Cl-2, Wm-4 Cl-1, Wm-3 Cd-3, Cl-3 Cl-1, Wm-4 Cl-1, Wm-4 Cl-2, Wm-4 Cl-1, Wm-5 Wm-4 Cl-2, Wm-3 Cl-3, Wm-3 Cl-1, Wm-3 Wm-2 Wm-6 Wm-5 Wm-2 Cd-3, Cl-3, Wm-1 Wm-2 Cd-3, Cl-3 Wm-6 Wm-6 Cl-3, Wm-3 Cd-1, Cl-3, Wm-2 Cl-4, Wm-2
Warm Warm Trans Warm Warm Trans Warm Warm Trans Warm Trans Warm Warm Warm Trans Trans Warm Warm Warm Warm Warm Trans Warm Trans Warm Warm Trans Trans Trans
No data 22.4–24.1 No data No data 16.9–29.2 No data No data No data No data No data No data No data
37.2–38.8 31.6–36.1 No data No data 30.8 No data No data No data No data No data No data No data
Cl-1, Wm-3 Cl-4, Wm-3 Wm-2 Wm-2 Cl-1, Wm-5 Wm-2 Wm-2 Cl-2, Wm-4 Cl-1, Wm-3 Cl-1, Wm-5 Wm-5 Cl-1, Wm-5
Warm Trans Warm Warm Trans Warm Warm Warm Warm Warm Warm Warm
No data 27.6–28.8 24.9–31.0 25.2–30.5 25.1 No data No data
38.1 36.4–37.9 No data 35.5–42.1 No data No data No data
Wm-5 Cl-1, Wm-4 Cl-1, Wm-5 Wm-6 Wm-5 Wm-5 Wm-2
Warm Warm Warm Warm Warm Warm Warm
26.0
No data
Wm-3
Warm
1139
COOLWATER STREAMS AND FISH ASSEMBLAGES
TABLE 2.—Continued. Species Northern pike Mudminnows Central mudminnow Trout Pink salmon Coho salmon Rainbow trout Chinook salmon Brown trout Brook trout Pirate perches Pirate perch Codfish Burbot Topminnows Blackstripe topminnow New World silversides Brook silverside Sticklebacks Brook stickleback Threespine stickleback Sculpins Mottled sculpin Slimy sculpin Temperate basses White bass Sunfishes Rock bass Green sunfish Pumpkinseed Warmouth Orangespotted sunfish Bluegill Longear sunfish Redear sunfish Smallmouth bass Largemouth bass Black crappie Perches Greenside darter Rainbow darter Iowa darter Fantail darter Least darter Johnny darter Banded darter Yellow perch Logperch Blackside darter Slenderhead darter Walleye Drums Freshwater drum
Laboratory preferred temperatures (8C)
Laboratory critical |thermal maxima (8C)
Thermal guilds from previous field studies
Thermal classification for this study
19.0–24.0
30.8–33.3
Cl-4, Wm-3
Trans
No data
No data
Cl-3, Wm-4
Trans
9.3–12.8 11.4–16.6 11.3–20.0 11.7 12.5–18.8 16.0–18.0
No data 28.7–29.7 26.7–29.8 25.1 25.0–30.0 29.8
Cd-5 Cd-5 Cd-7 Cd-5 Cd-7, Cl-1 Cd-7
Cold Cold Cold Cold Cold Cold
No data
No data
Wm-3
Warm
21.2
No data
Cd-3, Cl-4
Trans
No data
38.3–41.6
Cl-1, Wm-3
Warm
No data
36.0
Cl-1, Wm-4
Warm
No data No data
No data 33.5–34.6
Cd-1, Cl-4 Cd-1, Cl-1
Trans Trans
No data 12.0–13.0
30.9 26.3–29.4
Cd-6, Cl-2 Cd-6
Cold Cold
27.8–31.0
35.3
Wm-4
Warm
22.8–30.6 27.3–30.6 26.0–31.5 No data No data 30.5–32.3 No data No data 30.0–31.3 29.0–32.0 21.7–24.6
No data 35.8–37.9 35.1–37.5 No data 36.4 36.3–41.4 37.8 No data 36.3–36.9 36.3–40.9 34.9
Cl-2, Wm-6 Cl-1, Wm-6 Cl-1, Wm-5 Wm-4 Wm-4 Wm-7 Wm-4 Wm-2 Cl-2, Wm-6 Wm-6 Cl-1, Wm-5
Warm Warm Warm Warm Warm Warm Warm Warm Warm Warm Warm
No data No data No data No data No data 24.5 No data 20.1–27.0 No data No data No data No data
31.2–34.5 32.1–38.4 No data 31.3–37.7 No data 30.1–37.4 No data 35 No data No data No data 34.8–35.0
Cl-1, Wm-4 Cl-1, Wm-4 Cl-1, Wm-4 Cl-1, Wm-4 Cl-1, Wm-3 Cl-2, Wm-4 Wm-2 Cl-4, Wm-2 Cl-1, Wm-5 Cl-1, Wm-4 Wm-1 Cl-3, Wm-3
Warm Warm Warm Warm Warm Trans Warm Trans Warm Warm Warm Trans
26.5–31.3
34.0
Wm-5
Warm
calculated as the sum of the lengths of all stream reaches within that class. Results Defining Coolwater Streams Our data set contained a diverse array of species having a variety of distribution patterns in relation to water temperature. The 371 study sites yielded a total of 99 species: 78 from the 252 Michigan sites, and 83
from the 119 Wisconsin sites (Table 1). Of these 99 species, we classified 65 as warmwater, eight as coldwater, and 26 as transitional (Table 2). Modeled water temperatures at our sites ranged from 14.18C to 24.28C for June–August mean (Wisconsin only), 14.5– 24.78C for July mean (both states), and 17.6–27.58C for maximum daily mean (Wisconsin only). Comparison of the distribution of species in different thermal-classes (Table 3) resulted in the following
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LYONS ET AL.
TABLE 3.—Water temperature criteria for classifying Michigan and Wisconsin streams into thermal classes and subclasses. Class and subclass
Jun–Aug mean
Jul mean
Maximum daily mean
Coldwater Coolwater Cold transition Warm transition Warmwater
,17.0 17.0–20.5 17.0–18.7 18.7–20.5 .20.5
,17.5 17.5–21.0 17.5–19.5 19.5–21.0 .21.0
,20.7 20.7–24.6 20.7–22.6 22.6–24.6 .24.6
water temperature thresholds for coolwater streams: June–August mean, 17.0–20.58C; July mean, 17.5– 21.08C; and maximum daily mean, 20.7–24.68C. Note that June–August and maximum daily mean temperatures were estimated only for Wisconsin streams, so thresholds for these two temperature measures were defined from only Wisconsin data. Coldwater streams had temperatures colder than these thresholds, and warmwater streams had temperatures that were warmer. Coolwater streams were further subdivided into cold-transition streams (which had summer means of 17–18.78C, July means of 17.5–19.58C, and maximum daily means of 20.7–22.68C) and warm-transition streams (which had summer means of 18.7–20.58C, July means of 19.5–21.08C, and maximum daily means of 22.6–24.68C). Based on the July mean threshold, 113 (30.5%) of our 371 study sites were coldwater, 182 (49%) coolwater, and 76 (20.5%) warmwater. Among the 182 coolwater sites, 101 (55%) were cold transition and 81 (45%) warm transition. Of the 284 leastimpacted sites, 98 (34%) were coldwater, 144 (51%) coolwater, and 42 (15%) were warmwater. Among the 144 least-impacted coolwater sites, 79 (55%) were cold transition and the remaining 65 (45%), warm transition. Characterizing Coolwater Stream Fish Assemblages Coldwater, coolwater, and warmwater streams had major differences in their fish assemblages, and most fish assemblage, game, and nongame variables differed among stream thermal-classes. Coolwater sites were generally intermediate in species richness and fish abundance. Warmwater sites had significantly higher total, native, and warmwater species richness, as well as greater warmwater fish abundance, than coolwater sites which, in turn, had significantly higher values than coldwater sites (Table 4). In contrast, coldwater sites had significantly more coldwater species and greater abundances of coldwater fish, rainbow trout, brook trout, and mottled sculpin than coolwater sites, which had significantly higher values than warmwater sites. Coolwater and warmwater sites did not differ in transitional species richness and abundances of com-
mon shiner, bluntnose minnow, and johnny darter, but both thermal classes had significantly more than coldwater sites. Abundances of northern pike, rock bass, green sunfish, bluegill, smallmouth bass, and largemouth bass did not differ between coldwater and coolwater sites, but both thermal classes had significantly lower abundances of these species than warmwater sites. Coolwater sites had significantly higher transitional fish, western blacknose dace, creek chub, and white sucker abundances than either coldwater or warmwater sites, which did not differ from each other for these variables. Clear differences in fish assemblages and species abundances among thermal-groups were also apparent when the coolwater class was broken into the coldtransition and warm-transition subclasses (Table 5). All five of the fish assemblage species richness and three of the five fish assemblage abundance variables differed significantly between cold-transition and warm-transition sites. Rainbow trout and brook trout abundances were higher in cold-transition streams than warm-transition sites, but abundances of the other eight game fishes did not differ among the two subclasses. Common shiner and central mudminnow abundances were higher in warm-transition streams than coldtransition sites, but the abundances of the other eight nongame fishes did not differ. Cold-transition and warm-transition sites were similar to each other, and were generally higher than either coldwater or warmwater sites for the abundances of transitional fish, western blacknose dace, creek chub, and white sucker. For most other variables, cold-transition and warmtransition sites had values intermediate between coldwater and warmwater sites, but cold-transition sites tended to be more similar to coldwater sites than to warm-transition sites, and warm-transition sites tended to be more similar to warmwater sites than to coldtransition sites. Indicator species analysis revealed that most of the 43 most widespread species were significantly associated with a particular thermal class or subclass, but that none of the species had a high indicator value (Table 6). In other words, although most species were more frequently encountered and abundant within one of the thermal classes than would be expected by chance, the presence or abundance of a particular species at a site would have only limited value in predicting the thermal class of that site. Rock bass, an indicator of warmwater conditions, had the highest value for both the threethermal-class (62) and the four-thermal-class–subclass (52) analyses. Summer water temperature appeared to explain more of the variation in species richness and abundance within the coolwater thermal class than in the other
1141
COOLWATER STREAMS AND FISH ASSEMBLAGES
TABLE 4.—Mean values and results of ANOVA tests for differences in fish assemblage, game species, and nongame species variables at 284 coldwater, coolwater, and warmwater sites with limited human impact. The actual (untransformed) mean values are given for the abundance variables, but the tests were performed on log-transformed data. Within rows, means followed by the same letter indicate that the transformed values were not significantly different. Mean value Variable
Total species Native species Coldwater species Transitional species Warmwater species Total fish Native fish Coldwater fish Transitional fish Warmwater fish Northern pike Rainbow trout Brown trout Brook trout Rock bass Pumpkinseed Bluegill Smallmouth bass Largemouth bass Yellow perch
Cold (N ¼ 98)
Cool (N ¼ 144)
ANOVA results Warm (N ¼ 42)
Fish assemblage species richness 6.1 x 10.7 y 13.6 z 5.1 x 9.8 y 13.1 z 2.2 z 1.5 y 0.45 x 3.0 y 4.7 z 4.8 z 1.0 x 4.5 y 8.4 z Fish assemblage abundance (individuals 73.6 z 155.7 z 34.5 z 71.9 z 41.1 z 36.5 y 28.4 y 69.1 z 4.0 x 50.5 z
per 100 m) 77.4 z 53.9 z 1.8 x 28.1 y 47.6 y
Game species abundance (individuals per 100 m) 0.02 y 0.11 y 0.31 z 8.8 z 4.1 y 0.04 x 6.1 zy 21.2 z 1.3 y 14.7 z 3.6 y 0.03 x 0.05 y 0.50 y 2.8 z 0.08 y 0.68 z 0.52 z 0.09 y 1.1 y 2.9 z 0.002 y 0.13 y 0.62 z 0.22 y 0.43 y 1.3 z 0.11 y 0.90 z 0.74 z
Nongame species abundance (individuals per 100 m) Common shiner 0.85 y 19.5 z 14.2 z Bluntnose minnow 0.20 y 3.2 z 2.1 z Longnose dace 4.6 z 5.6 z 1.6 z Western blacknose dace 5.5 y 14.6 z 4.8 y Creek chub 6.2 y 18.3 z 5.6 y White sucker 5.4 y 15.7 z 5.1 y Central mudminnow 1.3 z 1.9 z 1.2 z Mottled sculpin 8.7 z 5.4 y 0.49 x Green sunfish 0.36 z 1.2 z 2.3 z Johnny darter 0.63 y 5.4 z 4.2 z
thermal classes. Overall, more of the fish assemblage, game fish, and nongame fish variables were significantly related to summer water temperatures within the coolwater thermal class than within either the coldwater or the warmwater thermal class. However, all correlations were relatively weak. For the coolwater sites, July mean water temperature was significantly positively correlated with total species (r ¼ 0.32), native species (0.35), and warmwater species (0.43) richness as well as warmwater fish (0.37), green sunfish (0.30), and johnny darter (0.38) abundance, and was negatively correlated with coldwater species richness (0.34) and brook trout abundance (0.32). In contrast, July mean water temperature was not correlated with any fish variables for the coldwater sites, and positively correlated with only warmwater species richness (0.53) and smallmouth bass abundance
F
P
33.56 36.55 28.63 10.56 59.81
,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001
1.74 0.80 55.54 10.56 59.81
0.1778 0.4496 ,0.0001 ,0.0001 ,0.0001
11.30 21.72 5.20 26.14 46.34 5.10 17.70 20.03 9.15 2.50
,0.0001 ,0.0001 0.0060 ,0.0001 ,0.0001 0.0066 ,0.0001 ,0.0001 0.0001 0.0839
15.82 9.09 1.96 8.54 9.92 12.76 0.45 14.89 8.76 20.39
,0.0001 0.0002 0.1432 0.0003 ,0.0001 ,0.0001 0.6371 ,0.0001 0.0002 ,0.0001
(0.57) for the warmwater sites. Within the two subclasses of coolwater, July mean water temperature was not significantly correlated with any of the fish variables. Based on the NMDS ordination, summer water temperature accounted for a significant proportion of the similarity in fish assemblage composition among the stream sites. Each of the two ordination axes yielded greater explanatory power than would be expected if they merely represented a random reordering of the data (Monte Carlo permutation [N ¼ 249] tests: P , 0.004), and together they explained 65% of the variation in the original distance matrix of species abundances among the sites. The first axis, which explained 41% of the variation, was significantly negatively correlated with July mean water temperature (r ¼ 0.68). Coldwater species had positive scores on
1142
LYONS ET AL.
TABLE 5.—Mean values and results of ANOVA tests for differences in fish assemblage, game species, and nongame species variables at 284 coldwater, cool-transition (CT), warm-transition (WT), and warmwater sites with limited human impacts. See Table 4 for additional details. Mean value Variable
Total species Native species Coldwater species Transitional species Warmwater species
Cold (N ¼ 98)
6.1 5.1 2.2 3.0 1.0
x x z x w
CT (N ¼ 79)
WT (N ¼ 65)
Fish assemblage species richness 8.8 y 13.0 z 7.7 y 12.3 z 1.9 z 1.1 y 4.1 yx 5.5 z 2.9 x 6.4 y
ANOVA results Warm (N ¼ 42)
F
P
13.6 z 13.1 z 0.45 x 4.8 zy 8.4 z
31.47 34.34 25.42 10.13 55.84
,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001
Total fish Native fish Coldwater fish Transitional fish Warmwater fish
Fish assemblage abundance (individuals per 100 m) 73.6 zy 155.4 zy 156.1 z 77.4 y 34.5 y 55.8 y 91.4 z 53.9 y 41.1 z 58.8 z 9.5 y 1.8 x 28.4 y 68.7 zy 69.8 z 28.1 y 4.0 x 28.0 y 76.8 z 47.6 z
3.05 3.12 47.50 7.90 41.87
0.0290 0.0265 ,0.0001 ,0.0001 ,0.0001
Northern pike Rainbow trout Brown trout Brook trout Rock bass Pumpkinseed Bluegill Smallmouth bass Largemouth bass Yellow perch
Game species abundance (individuals per 100 m) 0.02 y 0.11 y 0.11 y 0.31 z 8.8 z 7.1 y 0.45 x 0.04 x 6.1 zy 34.4 z 5.2 zy 1.3 y 14.7 z 5.7 y 1.1 x 0.03 x 0.05 y 0.29 y 0.74 y 2.8 z 0.08 y 0.60 zy 0.77 z 0.52 zy 0.09 x 0.33 yx 2.0 y 2.9 z 0.002 y 0.03 y 0.25 y 0.62 z 0.22 x 0.26 zy 0.63 zy 1.3 z 0.11 z 0.53 z 1.4 z 0.74 z
7.51 16.36 4.13 21.97 31.44 3.66 14.26 14.06 7.58 1.67
,0.0001 ,0.0001 0.0069 ,0.0001 ,0.0001 0.0129 ,0.0001 ,0.0001 ,0.0001 0.1727
Nongame species abundance (individuals per 100 m) 0.85 x 13.2 yx 27.0 z 14.2 zy 0.20 y 1.3 zy 5.5 z 2.1 z 4.6 z 7.6 z 3.2 z 1.6 z 5.5 y 15.2 z 13.9 z 4.8 y 6.2 y 19.6 z 16.7 z 5.6 y 5.4 y 16.1 z 15.1 z 5.1 y 1.3 y 1.3 y 2.6 z 1.2 y 8.7 z 7.6 zy 2.8 yx 0.49 x 0.36 y 0.28 y 2.3 z 2.3 z 0.63 y 3.4 y 7.9 z 4.2 z
15.22 8.18 1.32 7.64 10.07 10.90 2.89 11.05 9.49 26.03
,0.0001 ,0.0001 0.2965 ,0.0001 ,0.0001 ,0.0001 0.0356 ,0.0001 ,0.0001 ,0.0001
Common shiner Bluntnose minnow Longnose dace Western blacknose dace Creek chub White sucker Central mudminnow Mottled sculpin Green sunfish Johnny darter
this axis (coho salmon, brook trout, and rainbow trout having the highest values), and warmwater species had negative scores (common carp, stonecat, and bluegill having the lowest values; Table 7). Thus, coldwater species tended to be found at relatively cold stream sites, and warmwater species tended to be found at relatively warm stream sites. The second axis, which explained 24% of the variation, was not correlated with July mean water temperature. Only 5 of the 43 species had negative scores, brown trout (coldwater species) having the lowest value. The remainder had positive scores, carmine–rosyface shiner (warmwater), bigmouth shiner (warmwater), and northern redbelly dace (transitional) having the highest values. A plot of site scores from the NMDS ordination indicated that site thermal classification could only partially explain the pattern of similarity in fish assemblages among sites (Figure 2). Warmwater and coldwater sites could be largely distinguished based on
their scores for the first axis. All but one of the 42 warmwater sites (98%) had scores less than 0.25, whereas all but six of the 98 (94%) coldwater sites had scores greater than 0.25. However, coolwater sites encompassed the entire range of scores along the first axis and completely overlapped with all warmwater and coldwater sites as 71 of 144 sites (49%) had scores less than 0.25 and 73 sites (51%) had scores greater than 0.25. For the two subclasses of coolwater, most warm-transition sites had scores less than 0.25 (49 of 65; 75%), and most cold-transition sites (55 of 78; 70%) had scores greater than 0.25. However, for each subclass, the breadth of scores was very wide and encompassed nearly the entire range of scores on the first axis. Stream thermal class had no clear relation with the second ordination axis; all thermal classes and subclasses encompassed essentially the entire range of scores along the second axis with no obvious clustering of sites.
1143
COOLWATER STREAMS AND FISH ASSEMBLAGES
TABLE 6.—Results of indicator species analysis for the 43 most widespread species (those found at .5% of the 284 limited-impact sites) in the study area. Listed for each species are the thermal class or subclass with which they have a significant (P , 0.05) association, followed by their indicator value (maximum possible ¼ 100). ‘‘None’’ signifies no significant association with class or subclass. The second column is based on a three-group analysis of coldwater, coolwater, and warmwater sites, the third column on a fourgroup analysis of coldwater, cold-transition, warm-transition, and warmwater sites. Species
Three groups
Four groups
Northern brook lamprey Central stoneroller Common carp Common shiner Hornyhead chub Golden shiner Bigmouth shiner Rosyface–carmine shiner Northern redbelly dace Bluntnose minnow Fathead minnow Longnose dace Western blacknose dace Creek chub White sucker Northern hog sucker Shorthead redhorse Black bullhead Yellow bullhead Stonecat Grass pickerel Northern pike Central mudminnow Coho salmon Rainbow trout Brown trout Brook trout Burbot Brook stickleback Mottled sculpin Rock bass Green sunfish Pumpkinseed Bluegill Smallmouth bass Largemouth bass Black crappie Rainbow darter Fantail darter Johnny darter Yellow perch Logperch Blackside darter
None Coolwater (10) Warmwater (12) Warmwater (31) Warmwater (31) None Warmwater (8) Warmwater (17) None Warmwater (24) None None Coolwater (30) Coolwater (32) Coolwater (35) Warmwater (31) Warmwater (12) None None Warmwater (19) Warmwater (18) Warmwater (24) None Coldwater (15) Coldwater (33) Coolwater (25) Coldwater (44) Coldwater (11) None Coldwater (32) Warmwater (62) Warmwater (24) Warmwater (18) Warmwater (40) Warmwater (28) Warmwater (27) Warmwater (7) Warmwater (18) Coolwater (15) Warmwater (35) Warmwater (15) Warmwater (11) Warmwater (35)
None Warm transition (7) Warmwater (16) Warm transition (26) Warmwater (23) None None Warmwater (14) None Warmwater (17) None None Warm transition (28) Warm transition (31) Warm transition (31) Warmwater (24) Warmwater (9) None Warm transition (7) Warmwater (15) Warmwater (14) Warmwater (20) Warm transition (20) Coldwater (12) Coldwater (28) Cold transition (20) Coldwater (35) Coldwater (9) None Coldwater (24) Warmwater (52) Warmwater (19) Warmwater (13) Warmwater (31) Warmwater (24) Warmwater (20) None Warm transition (13) Warm transition (13) Warm transition (36) Warmwater (11) None Warmwater (27)
Estimating the Amount and Distribution of Thermal Classes Based on predicted July mean water temperatures, coolwater streams were estimated to be the most common thermal class of streams in Michigan and Wisconsin (Table 8; Figure 3). Of the total 164,680 km of streams in the two states, coldwater comprised 19.4%; coolwater, 64.9%; and warmwater, 15.7%.
TABLE 7.—Scores for the 43 most widespread species along the two axes of the nonmetric multidimensional scaling ordination of the 284 limited-impact sites. Species
Axis 1
Axis 2
Northern brook lamprey Central stoneroller Common carp Common shiner Hornyhead chub Golden shiner Bigmouth shiner Rosyface–carmine shiner Northern redbelly dace Bluntnose minnow Fathead minnow Longnose dace Western blacknose dace Creek chub White sucker Northern hog sucker Shorthead redhorse Black bullhead Yellow bullhead Stonecat Grass pickerel Northern pike Central mudminnow Coho salmon Rainbow trout Brown trout Brook trout Burbot Brook stickleback Mottled sculpin Rock bass Green sunfish Pumpkinseed Bluegill Smallmouth bass Largemouth bass Black crappie Rainbow darter Fantail darter Johnny darter Yellow perch Logperch Blackside darter
0.108 0.548 0.937 0.500 0.668 0.577 0.721 0.781 0.139 0.634 0.327 0.184 0.186 0.316 0.331 0.656 0.610 0.558 0.739 0.844 0.745 0.667 0.250 1.024 0.685 0.023 0.861 0.384 0.000 0.265 0.789 0.679 0.526 0.842 0.715 0.647 0.766 0.673 0.512 0.542 0.387 0.525 0.655
0.309 0.517 0.099 0.461 0.495 0.154 0.655 0.712 0.527 0.473 0.387 0.341 0.323 0.314 0.149 0.200 0.383 0.206 0.251 0.366 0.140 0.140 0.381 0.147 0.164 0.537 0.189 0.242 0.508 0.041 0.167 0.008 0.257 0.143 0.185 0.139 0.304 0.329 0.413 0.275 0.174 0.496 0.271
Within the coolwater class, cold-transition streams were more common (58.6% of all coolwater streams) than warm-transition streams (41.4%). The two states differed in their proportions of the three thermal classes; coolwater streams were relatively more common in Wisconsin, and coldwater streams were relatively more common in Michigan. Within Michigan’s 77,722 km of streams, coldwater accounted for 32.3%; coolwater, 54.1%; and warmwater, 13.6%. Within Wisconsin’s 86,958 km of streams, coldwater accounted for only 7.9%; coolwater, 74.5%; and warmwater, 17.6%. In Michigan, 54.2% of the coolwater length belonged to the cold-transition subclass and 45.8% to the warm-transition subclass, whereas in Wisconsin the respective values were 61.9% and 38.1%.
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FIGURE 2.—Plot of the site scores from the nonmetric multidimensional scaling ordination of the 284 sites in the study area with limited human impact using the abundance of the 43 most widespread species. The general trend in stream temperature and species composition is given next to each axis.
Geographic patterns were apparent in the distribution of the thermal-classes and subclasses. Coolwater streams were ubiquitous in the two states, but were most common in the eastern Lower Peninsula and southwestern Upper Peninsula of Michigan and in northern and western Wisconsin (Figure 3). Coldwater streams dominated northern and western portions of the Lower Peninsula and northern parts of the Upper Peninsula and were common in localized areas of northern and western Wisconsin. Warmwater streams were concentrated in the southeastern Lower Peninsula and in southeastern Wisconsin. For the two subclasses of coolwater, cold-transition streams were most common in the central Lower Peninsula and throughout the Upper Peninsula, and along the Lake Superior shoreline and in the western half of Wisconsin (Figure
4). Warm-transition streams were found mainly in the southeastern Lower Peninsula and in northern and eastern Wisconsin. Discussion Defining Coolwater Streams We have developed new and ecologically meaningful definitions for stream thermal-classes and subclasses in Michigan and Wisconsin that should be generally useful throughout the Great Lakes region. However, our values differ somewhat from previously published thermal criteria for this geographic area. Lyons et al. (1996) defined coolwater streams in Wisconsin as having summer maximum daily mean water temperatures of 22–248C, similar to our warm transition subclass of coolwater (22.6–24.68C), but excluding a
TABLE 8.—Lengths of all streams (1:100,000-scale national hydrography data) in Michigan and Wisconsin within each of the thermal classes and subclasses. Michigan
Wisconsin
Total
Class and subclass
km
%
km
%
km
%
Coldwater Coolwater Cold transition Warm transition Warmwater
25,103 42,023 22,767 19,256 10,596
32.3 54.1 29.3 24.8 13.6
6,906 64,790 39,881 24,909 15,262
7.9 74.5 45.9 28.6 17.6
32,010 106,813 62,648 44,165 25,858
19.4 64.9 38.0 26.8 15.7
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FIGURE 3.—Map (1:100,000-scale national hydrography data) showing the distributions of coldwater, coolwater, and warmwater streams in Michigan and Wisconsin based on summer water temperature predictions.
large fraction of what we consider coolwater in the cold-transition subclass. Our broader thermal range for coolwater was the result of our broader biological definition of coolwater: temperatures suitable for both coldwater and warmwater species. Lyons et al. (1996) defined coolwater as temperatures ‘‘marginal’’ for trout, which is roughly equivalent to our biological definition of the warm-transition subclass: temperatures where coldwater species are generally uncommon. The Lyons et al. (1996) 22–248C coolwater definition was later used in Minnesota (Niemela and Feist 2000, 2002) and expanded to 22–268C for use in Indiana (Aquatic Research Center of the Indiana Biological Survey 2007). An upper bound of 268C for coolwater encompasses much of what we have defined as warmwater (.24.68C), and we believe that it is too high. The analyses of Wehrly et al. (2003) generally support our cold-transition and warm-transition thermal boundaries. Wehrly et al. (2003) proposed that coolwater streams in Michigan had July mean temperatures of 198C to less than 228C, which differs from our
values of 17.5–218C. They based their temperature thresholds on two inflection points in a plot of fish community similarity values (Figure 2 in Wehrly et al. 2003), but their analysis yielded multiple inflection points, at approximately 17, 18, 19, 20, 21 and 228C, depending on which groups of sites were considered. They choose inflection points that defined coolwater as the temperature range within which coldwater species went from generally common to uncommon to absent, which matches well with our warm-transition subclass (19.5–218C). However, their results also indicate major fish community shifts as water temperatures increase from 178C to 198C, corresponding to our cooltransition subclass (17.5–19.58C). Our general procedure for defining temperature threshold values for different thermal-classes should work elsewhere in the temperate zone. The key requirement is a large dataset on summer water temperature and fish abundance from stream sites encompassing the entire range of summer water temperatures, stream sizes, climate, gradient, geology, and land cover found within the region of interest
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(Seelbach et al. 2006). The precise temperature criteria that define coolwater may vary slightly among regions because of differences in fish faunas and their distribution in response to available summer water temperatures. Although the concept of coolwater is recognized and used outside of the Great Lakes region (e.g., Leonard and Orth 1986), we have been unable to find specific thermal definitions of coolwater from other geographic areas. Stream thermal classification can be based on either measured or modeled water temperatures. In our analyses, we used modeled rather than measured temperatures, both to allow inclusion of sites where measured temperatures were lacking and to reduce the variance that would be introduced by including measured temperatures collected under substantially different climatic conditions (e.g., unusually hot versus unusually cold summers). Our models standardized climatic influences across years and estimated water temperatures under ‘‘average’’ climate conditions. However, because the models did not fit the observed water temperature data perfectly, water temperatures were predicted from the models with error. The models were largely unbiased, so prediction errors were equally likely in either direction (i.e., too warm or too cold) and would tend to offset each other in the overall distribution of modeled water temperatures. Thus, thermal criteria estimates derived from the distribution were unlikely to be biased too high or low, but there was uncertainty associated with each estimate. The magnitude of this uncertainty cannot be determined without complex simulation modeling, which is beyond the scope of this study. Characterizing Coolwater Stream Fish Assemblages For most stream classification applications, we recommend using the two subclasses rather than the single coolwater class. Fish assemblage structure and composition were more variable and sensitive to water temperature in coolwater streams than they were in coldwater or warmwater streams (see also Zorn et al. 2002). Coolwater fish assemblages overlapped extensively with those of coldwater and warmwater streams, and coolwater streams had no clearly diagnostic species. Dividing the coolwater stream class into the cold-transition and warm-transition subclasses resulted in less-variable and more easily described and distinguished fish assemblages. We have noted a misperception among some fisheries biologists and anglers, probably due, at least in part, to the lack of a consistent definition and fisheries characterization of coolwater, that only coldwater streams can support good coldwater fisheries, only warmwater streams can support good warmwater
fisheries, and coolwater streams have little fisheries value. However, our fish assemblage analyses highlight the fisheries potential of coolwater streams. Both coldwater and cold-transition sites are capable of supporting high abundances of coldwater fishes; indeed, the greatest average abundance of brown trout occurs in cold-transition streams. Coldwater fishes have lower abundance at warm-transition sites and are essentially absent from warmwater sites during the summer. Although warmwater streams have no potential for year-round coldwater fisheries, they may be important habitats for coldwater species outside of the summer (Meyers et al. 1992). Warmwater and warm-transition sites are capable of supporting high abundances of warmwater fishes, although warmwater sport fish abundances tend to be highest at warmwater sites. Populations of warmwater fishes are much lower at cold-transition sites and generally absent at coldwater sites. Estimating the Amount and Distribution of Thermal Classes Our temperature models indicate that, although long neglected as a management category by fisheries managers, coolwater streams are the most abundant thermal class in Michigan and Wisconsin. Given broad climate, glacial geology, and land cover patterns, it is likely that coolwater streams are generally widespread and common in the Great Lakes region. The distribution and abundance of coolwater streams is uncertain elsewhere, but such streams, as we have defined them biologically, undoubtedly occur throughout much of temperate North America. Within the Great Lakes region, there is substantial variation in the relative amounts and distributions of stream thermal classes. A comparison of thermal classes between Michigan and Wisconsin illustrates this variation. Although both states had huge amounts of coolwater stream habitat, their relative proportions of thermal classes and subclasses differed. Some of this divergence may be an artifact of the different water temperature models used in the two states, but we believe that most of the variation in proportions is real and occurs because the unique climate and glacial geology of each state (Omernik and Gallant 1988; Albert 1995) produces distinctive patterns of stream temperatures. In particular, the unglaciated Driftless Area of southern and western Wisconsin has no equivalent in the wholly glaciated landscape of Michigan. Several potential sources of error could have influenced our thermal classification results, including uncertainty in the temperature models, and temporal and spatial variation in actual water temperatures at our
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FIGURE 4.—Map (1:100,000-scale national hydrography data) showing the distributions of the two subclasses of coolwater streams—cold transition and warm transition (see text)—in Michigan and Wisconsin in relation to those of coldwater and warmwater streams.
study sites. Our estimates of coolwater stream occurrence are based on water temperature models and are thus not always correct. The Michigan model explained 70% and the Wisconsin model 67% of the variance in observed temperatures, which provides an approximation of the potential for misclassification in the thermal classification of a particular stream site. However, because the models are unbiased, many of these site-specific errors should ‘‘cancel out’’ in the statewide classifications, and the overall error for the statewide totals should be less. Another source of error in the thermal classification is year-to-year variation in stream water temperatures owing to climate variation. The water temperature models estimate a mean annual condition for each stream site, but in reality, most years will differ from the mean. Annual variation in maximum summer water temperatures is likely to be greatest in small warm streams with little groundwater input and least in cold streams with substantial groundwater input (Wehrly et
al. 2003). Particularly for sites that have maximum water temperatures near a thermal threshold, relatively warm summers may elevate water temperatures and shift the site into a warmer thermal class, whereas relatively cold summers may reduce water temperatures and shift the site into a cooler thermal class. The fish assemblage at the site may vary year to year in response to this temperature variation (Wichert and Lin 1996). The fish assemblage at a site with a maximum water temperature near a thermal threshold may differ from that expected based on the thermal classification depending on the availability of thermal refugia from extremes. For example, a site with a mean maximum water temperature near the upper end of the coldtransition subclass might have actual maximum water temperatures within the range of the warm-transition subclass or even the warmwater class during warmerthan-average summers. Based on the thermal classification, coldwater fishes would be expected to be
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common at the site, but during warmer-than-average summers, thermal conditions might be unsuitable for coldwater fishes. If colder refugia (e.g., cold springs or tributaries) were accessible, then coldwater species could probably persist through the warmer-thanaverage summers (e.g., Baird and Krueger 2003), and the fish assemblage would likely reflect the mean conditions. However, if thermal refugia were unavailable, then coldwater fish might disappear and the fish assemblage might more closely reflect the warmerthan-average conditions (e.g., Wichert and Lin 1996). Consequently, the fish assemblage observed during sampling might not match the assemblage predicted from the classification. The potential magnitude of this error is difficult to estimate as we cannot consistently assess the availability of thermal refugia at the scale of our analysis. However, the error may not be particularly significant. Because our classification was developed from a large and diverse data set, we undoubtedly included sites with and without thermal refugia in our characterization of fish assemblages within each thermal class and subclass. Thus, the expected assemblage for each class or subclass is based on a combination of sites where the assemblage reflected the mean thermal conditions and sites where the assemblage was more influenced by extremes. Therefore, the relative influence of annual fluctuations in maximum water temperatures in regard to thermal refugia was at least partially taken into account. A final source of uncertainty is within-day variation in water temperature. Some sites, particularly on small warm streams with little groundwater input, may have large within-day fluctuations in water temperature, whereas other sites, mainly on coldwater streams with substantial groundwater input and larger streams and rivers with substantial thermal mass, may have small within-day fluctuations (Zorn et al. 2002; Wehrly et al. 2003). At sites with large temperature fluctuations, the maximum instantaneous water temperature may be high enough to preclude certain species, even though the daily, monthly, and summer mean water temperatures are suitable (Wehrly et al. 2007). The shortest time frame our temperature models could predict was daily mean, so we could not distinguish among sites that had similar daily or monthly means but highly disparate within-day temperature fluctuations. Sites with high within-day temperature fluctuations could have a fish fauna more characteristic of a warmer thermal class than their mean temperature would indicate. The magnitude of this source of error in our statewide projections is impossible to estimate, but, based on the types of sites with the highest within-day fluctuations (Wehrly et al. 2003; Seelbach et al. 2006), most likely affects sites on small streams classified as
warm transition or warmwater. Uncertainty owing to within-day temperature fluctuations could be reduced if models that predicted maximum instantaneous water temperature or within-day water temperature fluctuation were incorporated into the thermal classification. However, based on preliminary attempts, we have thus far been unable to predict instantaneous water temperature or within-day water temperature fluctuation with sufficient accuracy or lack of bias. Management Implications Our results suggest that coolwater streams are more common and widespread than many managers realize, and thus they warrant increased study by aquatic research institutions and greater recognition and attention from natural resources management and environmental protection governmental agencies. Efforts to study and manage coolwater streams require an accurate and efficient procedure to classify streams objectively based on their thermal regime. At large spatial scales, some sort of catchment-based, GISdriven modeling procedure to estimate water temperatures will be necessary to process the large number of stream reaches present (Brenden et al. 2006; Seelbach et al. 2006). However, for small catchments or individual streams, direct field measurement of summer temperatures may be practical. In such cases, sitespecific continuous temperature data from the entire summer are essential, although creative development of single-measurement temperature indicators (e.g., Stoneman and Jones 1996) can reduce the number of continuously recording thermographs required. Classifications based on measured temperatures must also account and correct for year-to-year variation in climate that causes annual variation in summer water temperatures (e.g., Pilgrim et al. 1998). Recognizing and delineating coolwater classes and subclasses in stream classification can improve the effectiveness and efficiency of fisheries management and environmental protection. For example, in Michigan, our four-level thermal classification has been applied to support new flow protection legislation by identifying stream reaches where water diversions or groundwater pumping are most likely to threaten fisheries through increases in water temperature (Zorn et al. 2008). In Wisconsin, the precise definition of cold-transition and warm-transition streams has been essential to development of new fish-based bioassessment tools and has been used to help set expectations for smallmouth bass abundance (Lyons, unpublished data). In both states, the four-level thermal classification has guided selection of sites for fisheries surveys and has been embraced by resource managers. Possible future fisheries applications include determination of
COOLWATER STREAMS AND FISH ASSEMBLAGES
sites most appropriate for fish stocking, instream and riparian habitat improvement, and specific fishing regulations. Broader potential uses encompass establishment of thermal-class-specific environmental regulations and biocriteria. Acknowledgments We thank the many biologists and technicians who helped collect the fish assemblage data, especially Chuck Bassett and Paul Kanehl. We also recognize the assistance of Travis Brenden, Arthur Cooper, Karen Koval, Alex Martin, Matthew Mitro, Chris Smith, Paul Steen, and Steve Westenbroek in data processing, database management, and temperature model development. Helpful comments on earlier versions of this manuscript were provided by Amanda Bell, Mark Hazuga, Greg Searle, Dan Sullivan, Brian Weigel, and four anonymous reviewers. Support for this study was provided by a U.S. Environmental Protection Agency ‘‘Science to Achieve Results’’ grant (R830596) through the National Center for Environmental Research; the U.S. Geological Survey, National GAP Analysis Program, Great Lakes Aquatic Gap Project; Game and Fish Protection Fund Project 230552 for Michigan; Federal Aid in Sport Fish Restoration Project F-80-R, study 230738 for Michigan and Project F-95-P, study SSMP, for Wisconsin; and the Michigan and Wisconsin Departments of Natural Resources. Reference to trade names does not imply endorsement by the U.S. Government. References Albert, D. A. 1995. Regional landscape ecosystems of Michigan, Minnesota, and Wisconsin: a working map and classification, fourth revision. U.S. Forest Service General Technical Report NC-178. Aquatic Research Center of the Indiana Biological Survey. 2007. Development of coolwater index of biotic integrity expectations for use in streams and rivers of Indiana and review of existing data. Indiana Biological Survey, Technical Report 2007-01, Bloomington. Baird, O. E., and C. C. Krueger. 2003. Behavioral thermoregulation of brook and rainbow trout: comparison of summer habitat in an Adirondack River, New York. Transactions of the American Fisheries Society 132:1194–1206. Baker, E. A., K. E. Wehrly, P. W. Seelbach, L. Wang, M. J. Wiley, and T. Simon. 2005. A multimetric assessment of stream condition in the Northern Lakes and Forest Ecoregion using spatially explicit statistical modeling and regional normalization. Transactions of the American Fisheries Society 134:697–710. Becker, G. C. 1983. Fishes of Wisconsin. University of Wisconsin Press, Madison. Beitinger, T. L., and J. J. Magnuson. 1979. Growth rates and temperature selection of bluegill, Lepomis macrochirus.
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darter for Colorado Plains streams. Transactions of the American Fisheries Society 126:676–686. Stewart, J., M. Mitro, E. A. Roehl, Jr., and J. Risley. 2006. Numerically optimized empirical modeling of highly dynamic, spatially expansive, and behaviorally heterogeneous hydrologic systems, part 2. Pages 1–8 in Proceedings of the 7th International Conference on Hydroinformatics, Nice, France. Stoneman, C. L., and M. L. Jones. 1996. A simple method to classify stream thermal stability with single observations of daily maximum water and air temperatures. North American Journal of Fisheries Management 16:728–737. Stoneman, C. L., and M. L. Jones. 2000. The influence of habitat features on the biomass and distribution of three species of southern Ontario stream salmonines. Transactions of the American Fisheries Society 129:639–657. Taniguchi, Y., F. J. Rahel, D. C. Novinger, and K. G. Gerow. 1998. Temperature mediation of competitive interactions among three fish species that replace each other along longitudinal stream gradients. Canadian Journal of Fisheries and Aquatic Sciences 55:1894–1901. Wang, L., T. Brenden, P. Seelbach, A. Cooper, D. Allan, R. Clark, and M. Wiley. 2008. Landscape-based identification of human disturbance gradients and reference conditions for streams in Michigan. Environmental Monitoring and Assessment 141:1–17. Wang, L., J. Lyons, P. Rasmussen, P. Seelbach, T. Simon, M. Wiley, P. Kanehl, E. Baker, S. Niemala, and P. Stewart. 2003. Watershed, reach, and riparian influences on stream fish assemblages in the Northern Lakes and Forest Ecoregion. Canadian Journal of Fisheries and Aquatic Sciences 60:491–505. Wehrly, K. E., T. O. Brenden, and L. Wang. In press. Comparison of statistical approaches for predicting stream temperatures across heterogeneous landscapes. Journal of the American Water Resources Association 45:. Wehrly, K. E., L. Wang, and M. Mitro. 2007. Field-based estimates of thermal tolerance limits for trout: incorporating exposure time and temperature fluctuation.
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