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Summer air and stream water temperatures are expected to rise in the state of Wisconsin, U.S.A., over the next 50 years. To assess potential climate warming effects on stream fishes, predictive models were developed for 50 common fish species using classification-tree analysis of 69 environmental variables in a geographic information system. Model accuracy was 56·0–93·5% in validation tests. Models were applied to all 86 898 km of stream in the state under four different climate scenarios: current conditions, limited climate warming (summer air temperatures increase 1° C and water 0·8° C), moderate warming (air 3° C and water 2·4° C) and major warming (air 5° C and water 4° C). With climate warming, 23 fishes were predicted to decline in distribution (three to extirpation under the major warming scenario), 23 to increase and four to have no change. Overall, declining species lost substantially more stream length than increasing species gained. All three cold-water and 16 cool-water fishes and four of 31 warm-water fishes were predicted to decline, four warm-water fishes to remain the same and 23 warm-water fishes to increase in distribution. Species changes were predicted to be most dramatic in small streams in northern Wisconsin that currently have cold to cool summer water temperatures and are dominated by cold-water and cool-water fishes, and least in larger and warmer streams and rivers in southern Wisconsin that are currently dominated by warm-water fishes. Results of this study suggest that even small increases in summer air and water temperatures owing to climate warming will have major effects on the distribution of stream fishes in Wisconsin.
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Water temperature is a key factor determining fish distribution and assemblage composition (Magnuson et al., 1979). In north-temperate streams, summer maximum water temperatures are particularly important, and a reach with a relatively cold summer maximum (cold water) will have an almost completely different fish fauna than a reach with a relatively warm maximum (warm water) (Lyons et al., 2009). Changes in maximum water temperature of only a few degrees can result in major shifts in the occurrence, abundance, survival, growth, size distribution and reproduction of many species (Magnuson et al., 1997; McGinn, 2002; Hughes et al., 2006; Ficke et al., 2007).
Summer maximum water temperature in streams is determined in large part by air temperature (Stefan & Preud’homme, 1993; Erickson & Stefan, 2000). Climate change models for the Laurentian Great Lakes region of east-central North America project increases in annual air temperatures of 1–8° C by the year 2050 (Magnuson et al., 1997), and if these models are correct, then stream summer maximum water temperatures will also increase (Pilgrim et al., 1998; Mohseni et al., 1999), with major consequences for stream fishes (Eaton & Scheller, 1996; Mohseni et al., 2003). The exact relation between air temperature and stream water temperature, however, is complex and influenced by groundwater contributions to streams, stream channel form, riparian vegetation and shading, precipitation and rates of evaporation, such that the water temperatures of individual stream reaches respond differently to a given rise in summer air temperature (Meisner et al., 1988; Erickson & Stefan, 2000; Mohseni et al., 2002; Chu et al., 2008). Anthropogenic factors further influence the air temperature–water temperature relation, and maintenance or rehabilitation of natural vegetation in the stream catchment, particularly the riparian area, can mitigate increases in water temperature and changes in fish assemblages associated with an increase in air temperature (Peterson & Kwak, 1999; Whitledge et al., 2006; Marshall et al., 2008).
Numerous studies have considered the potential effects of climate change on the distribution and abundance of stream fishes in North America (Eaton & Scheller, 1996; McGinn, 2002; Mohseni et al., 2003). These studies have yielded important insights, but most have been relatively coarse in scale, focusing either on broad landscapes where study sites represent only a small fraction of the total stream habitat available (Eaton & Scheller, 1996; Mohseni et al., 2003) or on general patterns within relatively large spatial units such as catchments >500 km2 (Jackson & Mandrak, 2002; Chu et al., 2005, 2008) or thermal isopleths in relation to elevation or latitude (Johnson & Evans, 1990; Meisner, 1990a; Shuter & Post, 1990; Keleher & Rahel, 1996; Rahel et al., 1996; Magnuson et al., 1997; Rahel, 2002; Shuter et al., 2002; Flebbe et al., 2006). Relatively few studies have emphasized the small-scale variation in thermal conditions that may exist within and among individual streams in a region or simultaneously considered the role of other non-thermal habitat factors in determining the distribution of stream fishes, and these studies have tended to cover relatively small geographic areas (Meisner, 1990b; Jager et al., 1999; Battin et al., 2007; Nelson & Palmer, 2007; Nelson et al., 2009; Williams et al., 2009; Steen et al., 2010).
Understanding and accounting for both small-scale variation in stream thermal conditions and the influence of non-thermal habitat factors are essential to better predict responses of stream fishes to climate change in the Laurentian Great Lakes region of North America. Here, stream summer maximum water temperatures are naturally highly heterogeneous across the landscape. Cold-water, cool-water and warm-water reaches occur in close proximity and under the same climate conditions because of local variation in geology and groundwater contributions to streams (Lyons, 1996; Wiley et al., 1997; Zorn et al., 2002; Wehrly et al., 2003; Stanfield et al., 2006; Lyons et al., 2009; Diebel et al., 2010). In this region, coarse-scale analyses may miss local features that could allow cold-water habitats and associated fish species to persist in the face of rising air temperatures and consequently overestimate habitat losses for cold-water fishes and gains for warm-water fishes. Fine-scale analyses that look solely at air and water temperatures, however, will be incomplete, and must also consider other important limiting habitat factors such as stream flow, gradient, valley and channel form, geology and land cover to fully understand patterns of species distribution and responses to climate change (Lyons, 1996; Wang et al., 1997, 2000; Diebel et al., 2010; Steen et al., 2010).
Recent advances in database development and management, geographic information systems (GIS) capabilities and statistical analyses have greatly increased the ability to consider small-scale variation in stream conditions across broad landscapes (Fisher & Rahel, 2004; Hughes et al., 2006). It is now possible to develop and apply multiple predictive models of fish occurrence and abundance that encompass hundreds of thousands of discrete stream reaches ranging in length from tens to thousands of metres (Lyons et al., 2009). Such models allow for much more precise predictions of the response of stream fishes to climate change and rising water temperatures. In this paper, models to predict the amount and distribution of suitable stream habitat in relation to catchment, riparian and channel conditions are developed for 50 stream and river fish species in the state of Wisconsin, U.S.A; located in the western portion of the Laurentian Great Lakes region. These models are applied to thousands of discrete reaches encompassing all the streams and rivers in the state under four different thermal conditions in this initial assessment of how climate warming over the next 50 years might influence stream fish distributions.
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The 50 fish species models differed greatly in structure and composition (Table III). The number of terminal branches in the final models ranged from two to 12 with a mean of 6·9. Forty-three species models included a water temperature or an air temperature variable; 11 models had both air and water temperature variables, 25 had just air temperature and seven had just water temperature. Nineteen different environmental variables were used to make the first split or branching point in the tree for the 50 fish models. These were the most important variables in explaining the distribution patterns of species. Catchment area (15 species) and predicted annual median flow (seven species) were the variables most frequently used in the first split. Forty different environmental variables were used for the second splits, and these represented the next most important variables. Once again, catchment area (five species) and predicted annual median flow (six species) were the variables most frequently used to make the splits.
Fish models were moderately to highly accurate for both the model development and validation data sets (Table IV), and all final models explained significantly more of fish distribution patterns than would be expected by chance (Cohen's Kappa, P < 0·001). From the model development step, 48 models were classified as good and two as fair [Ambloplites rupestris (Rafinesque) and Micropterus salmoides (Lacépède)]. Overall accuracy ranged from 61·1 (M. salmoides) to 96·4% [Moxostoma macrolepidotum (Lacépède)] with a mean of 83·0%. Specificity varied from 53·2 (A. rupestris) to 95·9% (M. macrolepidotum) and averaged 80·1%. Sensitivity was particularly high, ranging from 73·0 [Ameiurus melas (Rafinesque)] to 100% (seven species) with a mean of 94·3%. For the validation step, 43 models were classified as good and seven as fair [Notemigonus crysoleucas (Mitchill), Notropis dorsalis (Agassiz), Pimephales promelas Rafinesque, Esox lucius L., Salmo trutta L., Lepomis gibbosus (L.) and Micropterus dolomieu Lacépède]. Overall accuracy ranged from 56·0 (N. crysoleucas) to 93·5% [Ictalurus punctatus (Rafinesque)] with a mean of 77·0%. Specificity varied from 53·3 (S. trutta) to 96·5% (M. dolomieu) and averaged 77·7%. Sensitivity ranged from 50·9 (L. gibbosus) to 90·9% (M. macrolepidotum) with a mean of 70·2%.
Table IV. Fish species model overall per cent accuracy and per cent correct prediction of absence (specificity) and of presence (sensitivity) for the model development and validation data sets
|Overall (%)||Absence (%)||Presence (%)||Overall (%)||Absence (%)||Presence (%)|
|Family Cyprinidae|| || || || || || |
| Campostoma anomalum||76·6||74·1||93·9||73·5||73·8||71·4|
| C. oligolepis||84·0||82·5||100||80·7||82·3||62·5|
| Cyprinella spiloptera||91·6||89·9||97·0||87·9||89·5||82·6|
| Cyprinus carpio||91·1||89·8||93·4||83·7||83·9||82·1|
| Hybognathus hankinsoni||73·3||72·1||89·3||75·5||73·4||75·0|
| Luxilus cornutus||83·6||81·0||87·9||75·8||76·7||74·4|
| Margariscus margarita||90·2||89·0||100||82·8||84·3||70·0|
| Nocomis biguttatus||77·1||72·4||92·3||68·7||68·1||70·4|
| Notemigonus crysoleucas||74·3||72·3||90·7||56·0||56·6||51·2|
| Notropis dorsalis||77·4||75·6||100||76·3||78·1||53·6|
| N. heterolepis||84·0||82·8||100||81·2||81·7||73·1|
| N. percobromus||79·9||79·2||89·3||71·2||72·1||60·7|
| N. stramineus||82·4||81·9||86·0||76·8||78·7||64·0|
| Phoxinus eos||86·5||84·9||100||83·2||84·3||74·4|
| P. erythrogaster||86·0||85·1||100||86·3||87·6||65·2|
| Pimephales notatus||82·5||77·2||98·6||79·8||81·8||72·7|
| P. promelas||86·2||83·8||93·8||71·7||76||58·3|
| Rhinichthys cataractae||90·5||89·8||93·9||77·8||81·8||63·6|
| R. obtusus||85·1||79·9||97·5||76·8||75·3||80·8|
| Semotilus atromaculatus||86·9||81·7||92·5||75·8||74·1||77·8|
|Family Catostomidae|| || || || || || |
| Catostomus commersonii||84·7||89·7||82·4||67·5||63·5||69·3|
| Hypentelium nigricans||75·3||69·2||98·8||79·3||80·1||71·1|
| Moxostoma erythrurum||90·9||88·7||98·4||75·8||78·8||63·2|
| M. macrolepidotum||96·4||95·9||97·1||89·9||89·4||90·9|
|Family Ictaluridae|| || || || || || |
| Ameiurus melas||75·1||75·5||73·0||74·4||74·9||70·2|
| A. natalis||67·3||97·7||82·5||73·4||82·5||80·0|
| Ictalurus punctatus||81·4||79·6||92·7||93·5||94·0||75·8|
| Noturus flavus||75·6||74·4||86·2||79·8||78·9||88·9|
|Family Esocidae|| || || || || || |
| Esox lucius||66·4||60·2||93·2||79·4||82·2||59·7|
|Family Umbridae|| || || || || || |
| Umbra limi||84·7||81·5||90·3||72·6||66·3||81·2|
|Family Salmonidae|| || || || || || |
| Salmo trutta||79·9||82·1||69·6||64·3||53·3||89·3|
| Salvelinus fontinalis||87·8||86·1||98·2||67·0||64·5||71·6|
|Family Gadidae|| || || || || || |
| Lota lota||72·2||70·6||96·2||73·5||69·8||88·5|
|Family Gasterosteidae|| || || || || || |
| Culaea inconstans||86·5||83·1||97·1||76·8||76·1||78·1|
|Family Cottidae|| || || || || || |
| Cottus bairdii||84·7||80·0||98·6||73·7||71·6||80·0|
|Family Centrarchidae|| || || || || || |
| Ambloplites rupestris||63·6||53·2||96·8||75·7||76·5||68·7|
| Lepomis cyanellus||84·5||82·6||91·0||71·9||73·4||66·0|
| L. gibbosus||77·1||73·8||98·1||78·2||80·9||50·9|
| L. macrochirus||65·5||68·8||66·4||66·0||60·4||65·0|
| Micropterus dolomieu||92·5||93·7||92·1||75·3||96·5||54·1|
| M salmoides||61·1||57·8||82·7||71·1||72·9||61·5|
| Pomoxis nigromaculatus||73·8||71·8||87·9||78·3||79·0||67·1|
|Family Percidae|| || || || || || |
| Etheostoma caeruleum||81·7||81·1||91·3||77·6||78·1||69·6|
| E. flabellare||76·0||75·5||78·3||69·7||70·6||64·3|
| E. nigrum||82·5||82·9||82·0||73·7||75·4||71·1|
| E. zonale||80·7||79·3||100||78·4||79·0||69·3|
| Perca flavescens||74·3||69·8||93·3||75·7||77·4||60·5|
| Percina caprodes||88·7||87·6||92·3||77·8||77·0||80·0|
| P. maculata||92·4||92·0||94·0||74·7||77·0||68·0|
| Sander vitreus||83·3||99·0||91·2||92·7||68·3||80·6|
Under current climate conditions, the statewide distribution of the 50 species varied greatly (Table V). Predicted occurrences ranged from a low of 3614 km, representing 4·2% of the total stream length in the state, for Notropis stramineus (Cope), to a high of 68 228 km, representing 78·5% of total stream length, for Semotilus atromaculatus (Mitchill). Seven species were expected to be found in >50% and 17 in <10% of the total stream length in the state. A variety of different predicted distribution patterns was evident, including species that were ubiquitous, e.g. C. commersonii [Fig. 2(a)], or limited to larger rivers, e.g. Sander vitreus (Mitchill) [Fig. 2(b)], the northern portion of the state, Phoxinus eos (Cope) [Fig. 2(c)], or the southern portion of the state, Phoxinus erythrogaster (Rafinesque) [Fig. 2(d)].
Table V. Predictions from the 50 fish species models of the stream length and as a percentage of the total stream length in Wisconsin (86 898 km) that would be suitable for 50 fish species under current air and water temperatures, and predictions of the lengths of suitable stream and the per cent change from current climate conditions under three climate warming scenarios. Superscripts indicate the thermal classification of each species (1, warm water; 2, cool water; 3, cold water)
|Fish species||Current climate||Climate warming scenarios|
|Limited warming||Moderate warming||Major warming|
|Length (km)||Per cent of total||Length (km)||Per cent change||Length (km)||Per cent change||Length (km)||Per cent change|
|Family Cyprinidae|| || || || || || || || |
| Campostoma anomalum1||10188||11·7||10188||0||10188||0||10188||0|
| C. oligolepis1||11686||13·4||13966||+16·1||18185||+55·6||18210||+55·8|
| Cyprinella spiloptera1||4871||5·6||5898||+21·1||6528||+34·0||6528||+34·0|
| Cyprinus carpio1||8183||9·4||9534||+16·5||10577||+29·3||10577||+29·3|
| Hybognathus hankinsoni2||44493||51·2||44295||−0·4||42543||−4·4||31117||−30·1|
| Luxilus cornutus1||27156||31·3||19294||−29·0||6244||−77·0||6236||−77·0|
| Margariscus margarita2||23298||26·8||16479||−28·1||4556||−80·4||15||−99·9|
| Nocomis biguttatus1||20804||23·9||17055||−18·0||9145||−56·0||9365||−55·0|
| Notemigonus crysoleucas1||14804||17·0||14804||0||14804||0||14804||0|
| Notropis dorsalis1||9278||10·7||11145||+20·1||11324||+22·1||11324||+22·1|
| N. heterolepis2||17238||19·8||10729||−37·8||778||−95·5||331||−98·1|
| N. percobromus1||7944||9·1||9743||+22·6||10868||+36·8||10868||+36·8|
| N. stramineus1||3614||4·2||5496||+52·1||8180||+126·3||8180||+126·3|
| Phoxinus eos2||35122||40·4||19798||−43·6||87||−99·8||2||−100|
| P. erythrogaster1||14040||16·2||21923||+56·1||21923||+56·1||21923||+56·1|
| Pimephales notatus1||16277||18·7||16695||+2·6||16695||+2·6||16695||+2·6|
| P. promelas1||41734||48·0||39935||−4·3||34374||−17·6||34374||−17·6|
| Rhinichthys cataractae2||15263||17·6||11269||−26·2||3022||−80·2||902||−94·1|
| R. obtusus2||58730||67·6||36554||−37·8||15084||−74·3||11973||−79·6|
| Semotilus atromaculatus2||68228||78·5||59664||−12·6||48617||−28·7||36270||−46·8|
|Family Catostomidae|| || || || || || || || |
| Catostomus commersonii2||29159||33·6||23246||−20·3||11409||−60·9||5085||−82·6|
| Hypentelium nigricans2||11279||13·0||9871||−12·5||6976||−38·2||2269||−79·9|
| Moxostoma erythrurum1||4525||5·2||6229||+37·7||12916||+185·4||15642||+245·6|
| M. macrolepidotum1||6624||7·6||7103||+7·2||7103||+7·2||7103||+7·2|
|Family Ictaluridae|| || || || || || || || |
| Ameiurus melas1||18070||20·8||24536||+35·8||33286||+84·2||33300||+84·3|
| A. natalis1||9165||10·5||18699||+104·0||22694||+147·6||23319||+154·4|
| Ictalurus punctatus1||4861||5·6||5680||+16·8||6446||+32·6||6446||+32·6|
| Noturus flavus1||3940||4·5||16643||+322·4||26415||+570·4||26959||+584·2|
|Family Esocidae|| || || || || || || || |
| Esox lucius2||15275||17·6||14379||−5·9||10114||−33·8||4229||−72·3|
|Family Umbridae|| || || || || || || || |
| Umbra limi2||54901||63·2||51128||−6·8||43640||−20·5||22553||−58·9|
|Family Salmonidae|| || || || || || || || |
| Salmo trutta3||37241||42·9||34296||−7·9||24908||−33·1||4378||−88·2|
| Salvelinus fontinalis3||28802||33·1||16245||−43·6||1618||−94·4||0||−100|
|Family Gadidae|| || || || || || || || |
| Lota lota2||7447||8·6||3478||−53·3||0||−100||0||−100|
|Family Gasterosteidae|| || || || || || || || |
| Culaea inconstans2||60998||70·2||59392||−2·6||52844||−13·4||36898||−39·3|
|Family Cottidae|| || || || || || || || |
| Cottus bairdii3||59599||68·6||46547||−21·9||20936||−64·9||2755||−95·4|
|Family Centrarchidae|| || || || || || || || |
| Ambloplites rupestris1||13759||15·8||13759||0||13759||0||13759||0|
| Lepomis cyanellus1||22080||25·4||24808||+12·4||36368||+64·7||51526||+133·4|
| L. gibbosus1||13094||15·1||13657||+4·3||15121||+15·5||15046||+14·9|
| L. macrochirus1||17592||20·2||17746||+0·9||17746||+0·9||17746||+0·9|
| Micropterus dolomieu1||8172||9·4||8172||0||10905||+33·4||16094||+96·9|
| M salmoides1||20134||23·2||27031||+34·3||27031||+34·3||27031||+34·3|
| Pomoxis nigromaculatus1||9342||10·8||11877||+27·1||13984||+49·7||13984||+49·7|
|Family Percidae|| || || || || || || || |
| Etheostoma caeruleum1||4111||4·7||5638||+37·1||5638||+37·1||5638||+37·1|
| E. flabellare1||32342||37·2||32342||0||32342||0||32342||0|
| E. nigrum2||60326||69·4||58827||−2·5||48699||−19·3||39277||−34·9|
| E. zonale1||4446||5·1||6435||+44·7||7842||+76·4||7842||+76·4|
| Perca flavescens2||8644||9·9||8117||−6·1||2045||−76·3||0||−100|
| Percina caprodes1||8612||9·9||8612||0||10374||+20·5||13217||+53·5|
| P. maculata1||7863||9·0||6777||−13·8||5755||−26·8||5755||−26·8|
| Sander vitreus2||4832||5·6||4526||−6·3||2004||−55·7||600||−87·6|
Figure 2. Predicted distribution of four representative fishes under current climate conditions. Only stream segments where the species is predicted to occur are shown. (a) Catostomus commersonii, a ubiquitous species, (b) Sander vitreus, a species limited to larger rivers, (c) Phoxinus eos, a species found primarily in northern Wisconsin and (d) Phoxinus erythrogaster, a species found primarily in southern Wisconsin.
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Predicted responses to climate warming varied dramatically among the 50 species. Overall, 23 species declined in distribution, 23 increased and four had no change (Table V), but the losses in habitat by declining species were much greater than the gains in habitat by increasing species. The predicted total combined loss of stream length inhabited by the declining species was 126 441 km (5497 km per species) for the limited warming scenario, 343 034 km (14 915 km per species) for the moderate warming scenario and 484 048 km (21 046 km per species) for the major warming scenario. The total combined gain in stream length inhabited by the increasing species was 66 605 km (2896 km per species) for the limited warming scenario, 127 488 km (5543 km per species) for the moderate warming scenario and 154 537 km (6719 km per species) for the major warming scenario.
Not surprisingly, responses to climate warming differed among the three thermal guilds of fishes. All three of the cold-water species [S. trutta, Salvelinus fontinalis (Mitchill) and Cottus bairdii Girard] declined substantially with increasing air and water temperature, with S. fontinalis extirpated from Wisconsin streams under the major warming climate change scenario. Because S. trutta lacked a water or air temperature component in its model, all its predicted decline was due to the water temperature constraint. Declines in the other two cold-water species were primarily caused by limiting factors in their models rather than the constraints. All 16 of the cool-water species were also predicted to decrease in distribution as climate warmed, with Lota lota (L.) gone from Wisconsin streams under both the moderate and major warming scenarios and P. eos and Perca flavescens (Mitchill) gone under the major warming scenario. Because Hybognathus hankinsoni Hubbs and Esox lucius L. lacked a water or air temperature component in their models, all their predicted declines were due to the water temperature constraint, but declines in the other 14 cool-water species were mainly due to factors within their particular species model. Among the 31 warm-water species, four species were predicted to decline with warmer conditions [Luxilus cornutus (Mitchill), Nocomis biguttatus (Kirtland), P. promelas and Percina caprodes (Rafinesque)], four species showed no change in distribution [Campostoma anomalum (Rafinesque), N. crysoleucas, A. rupestris and E. flabellare] and the remaining 23 species increased in distribution. Of the 23 increasing species, predicted gain varied dramatically from 154 km (0·9% gain) for Lepomis macrochirus Rafinesque, 418 km (2·6%) for Pimephales notatus (Rafinesque) and 479 km (7·2%) for M. macrolepidotum to 23 019 km (584·2%) for Noturus flavus Rafinesque and 29 446 km (133·4%) for Lepomis cyanellus Rafinesque under the major warming scenario. The zoogeography constraint limited the predicted increases of 10 species whereas the stream size constraint limited the predicted increase of seven species. The land-use constraint did not substantially limit increases of any species at a statewide scale, but did restrict predicted local increases of eight species near major urban areas.
Spatial patterns of distribution change in response to warming temperatures also varied among fishes. For species that declined in distribution, the tendency was for the predicted range of species to remain roughly stable until the species was nearly extirpated, but for the density of segments predicted to contain the species to decrease steadily with increasingly warm temperatures, as illustrated by the cold-water C. bairdii (Fig. 3) and the cool-water H. nigricans (Fig. 4). For the warm-water species that increased in distribution, two primary patterns were evident. First, some species expanded northwards as climate warmed, especially if they were gamefishes without zoogeographic constraints on potential distribution, as illustrated by Pomoxis nigromaculatus (LeSeur) (Fig. 5). Second, the density of segments predicted to contain the species increased as the species expanded into many more streams within areas where they occurred already, as illustrated by N. flavus (Fig. 6). The new streams that were occupied were generally relatively small and prior to climate warming had been too cold for the species.
Figure 3. Predicted distribution of Cottus bairdii, a cold-water species, under four climate warming scenarios: (a) current conditions, (b) limited warming, (c) moderate warming and (d) major warming. Only stream segments where the species is predicted to occur are shown.
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Figure 4. Predicted distribution of Hypentelium nigricans, a cool-water species, under four climate warming scenarios: (a) current conditions, (b) limited warming, (c) moderate warming and (d) major warming. Only stream segments where the species is predicted to occur are shown.
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Figure 5. Predicted distribution of Pomoxis nigromaculatus, a warm-water species, under four climate warming scenarios: (a) current conditions, (b) limited warming (c) moderate warming and (d) major warming. Only stream segments where the species is predicted to occur are shown.
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Figure 6. Predicted distribution of Noturus flavus, a warm-water species, under four climate warming scenarios: (a) current conditions, (b) limited warming, (c) moderate warning and (d) major warming. Only stream segments where the species is predicted to occur are shown.
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- Materials and methods
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The findings from this study indicate that catchment-scale environmental variables organized within a GIS framework can be used to develop statistical models that accurately predict the distribution of fish species within Wisconsin streams. The most useful environmental variables in the Wisconsin models are related to stream size (catchment area and median annual stream flow), climate (summer air temperatures) and summer water temperatures. Overall accuracy, specificity and sensitivity of the Wisconsin models are similar to or better than values of comparable fish models developed using a variety of statistical approaches (e.g. artificial neural nets, logistic regression and linear discriminant analysis) for other temperate areas of North America, Europe, Australia and New Zealand (Aguilar-Ibarra et al., 2003; Joy & Death, 2004; McKenna, 2005; Drake & Lodge, 2006; Fransen et al., 2006; McKenna et al, 2006; Mugodo et al., 2006; Buisson et al., 2007; Steen et al., 2008, 2010; Diebel et al., 2010). Most of these other studies also found stream size, climate and water temperature to be important predictor variables. Catchment-scale GIS-based models have been found to be useful in predicting effects of human activity, particularly land-use and climate changes, on the occurrence of fishes in streams and rivers throughout North America (Peterson & Kwak, 1999; Van Sickle et al., 2004; Flebbe et al., 2006; Kilgour & Stanfield, 2006; Chu et al., 2008; Nelson et al., 2009; Steen et al., 2010). The models developed in this study provide unique and valuable insights into the changes in fish distribution in Wisconsin streams expected in response to the air and water temperature increases likely to occur as a consequence of global climate change.
The Wisconsin models predict major declines in the occurrence of many fish species in response to a warmer climate. This response was expected for the three cold-water species, which are, by definition, limited by warmer water temperatures. Many previous studies have predicted substantial declines in the distribution and abundance of cold-water species such as salmonids and cottids with rising air and water temperatures (Meisner, 1990a,b; Keleher & Rahel, 1996; Lehtonen, 1996; Nakano et al., 1996; Daufresne et al., 2003; Flebbe et al., 2006; Battin et al., 2007; Williams et al., 2009; Steen et al., 2010). The Wisconsin models also predict that all 16 cool-water species would have reduced distributions with climate warming, and perhaps surprisingly, that many cool-water species would decline to the same or a greater extent than the cold-water species. Relatively few previous studies have examined cool-water species responses to climate change, and results from these studies have been variable. Most studies predicted that cool-water species would decline less than cold-water species (Magnuson et al., 1997; Mohseni et al., 2003) and that a few cool-water species might actually increase in abundance or distribution in certain areas (Lehtonen, 1996; Lappalainen & Lehtonen, 1997; Fang et al., 2004; Chu et al., 2005; Winfield et al., 2008; Steen et al., 2010). An early study of U.S. streams, however, predicted similar declines in occurrence for cold-water and cool-water fishes (Eaton & Scheller, 1996). Cool-water species are known to be less sensitive to increases in maximum water temperatures than cold-water species (Lyons et al., 2009), but the findings from the Wisconsin models suggest that some cool-water species may be similar to cold-water species in sensitivity to changes in other aspects of the thermal regime such as the mean summer water temperature or the duration of the growing season. Thus, in Wisconsin streams, cold-water and cool-water fishes may be similarly vulnerable to climate warming. Many of the cool-water species in this study are at or near the southern edge of their range in Wisconsin, and whether they would decline as greatly in areas further to the north is an important question for further research.
The Wisconsin models gave highly variable responses to climate warming for the 31 warm-water species, with four species predicted to decline in distribution, four to have no change and 23 to increase. Among the 23 increasing species, gains ranged from minimal to very large. Declines in the distribution of warm-water species in response to climate warming are somewhat counterintuitive, and may be a spurious result. The climate warming scenarios represent combinations of climate, water temperature and other environmental variables that did not exist within the data set used to build the models, and extrapolating beyond the model development conditions may yield unreliable results. This is a potential problem for all climate change modelling, as the expected future climate conditions have no current precedents, and there are many irresolvable uncertainties in terms of ecosystem response to climate change (Schindler et al., 2008; Johnson & Weaver, 2009). Thus, all projections and conclusions from this and previous studies should be viewed with caution. That being said, it is possible that some warm-water species will decline as climate warms, as has been proposed in several previous studies (Eaton & Scheller, 1996; Magnuson et al., 1997; Peterson & Kwak, 1999; Whitledge et al., 2006). In certain stream segments, water temperatures may become too warm for those warm-water species that prefer relatively cool-water temperatures compared to other warm-water species. The cold-water:cool-water:warm-water species classification is a simplification of a thermal continuum, and there are differences among species within each thermal guild in temperature tolerances (Eaton & Scheller, 1996; Lyons et al., 2009). The four Wisconsin warm-water species predicted to decline with climate warming are arrayed on the colder end of the thermal tolerance continuum within the warm-water species guild, and thus they may decrease while other warm-water species increase.
The four warm-water species predicted to have no change in their distributions in response to climate warming all lack water temperature or air temperature variables in their models. Thus, their predicted stable distribution may be unrealistic and caused by a limitation in model sensitivity to climate change. The absence of temperature variables in the model, however, may also reflect the relatively low importance of water and air temperatures as limiting factors for these species in Wisconsin streams under current conditions. It may be that other factors such as habitat volume, stream size and flow, as reflected in stream velocity and habitat structure (e.g. formation of riffle habitats), including stream gradient and valley slope variables, or channel morphology and bottom substratum, as reflected in geology and land cover variables, are more important than temperature in determining the distribution of these species in Wisconsin streams. It is likely, however, that water temperature is a more apparent limiting factor elsewhere in their range, and in those areas these species may experience a substantial change in distribution in response to climate warming.
In the same vein, for the 23 warm-water species predicted to become more widespread, the great range in the magnitude of increase among species can be explained by the relative importance of water temperature as a factor limiting each species distribution. Wisconsin streams that are currently too cold for warm-water fishes tend to be relatively small headwaters (Lyons, 1996; Lyons et al., 1996, 2009). Some warm-water species, such as N. flavus and L. cyanellus, are capable of living in small streams, but other warm-water species are not (see Table I). As these headwaters become warmer, warm-water species adapted for small-stream life are predicted to occupy them, leading to a relatively large increase in their distributions. These headwaters, however, remain unsuitable for warm-water species requiring relatively large stream and river habitats, such as P. nigromaculatus and M. macrolepidotum, because they are precluded by stream size rather than water temperature. As climate warms, these warm-water river species are predicted to expand into those relatively few river habitats that are currently cold water or cool water, but not into smaller streams, so their overall increase in distribution is modest.
For most non-game warm-water species, movement barriers further limit predicted increases in distribution. Many areas of northern Wisconsin are currently unsuitable for warm-water non-game species restricted to southern Wisconsin, such as P. erythrogaster. As climate warms, the models for many of the southern species predict that stream conditions will become suitable in northern Wisconsin, but the species will be unable to colonize these streams because of waterfalls and dams that block upstream movement towards the north. This is illustrated by N. flavus (Fig. 6). Presently, the model for this species predicts that habitat is unsuitable in a large portion of northern Wisconsin. As air and water temperatures increase, many streams in this area are predicted to become suitable, but the area is inaccessible to colonization because of a series of impassable barriers to upstream movement. This non-game species is not routinely used for bait in Wisconsin and is unlikely to be moved around barriers by anglers or government river management agencies. Thus, although N. flavus is expected to greatly expand its occurrence in southern and central Wisconsin as climate warms, it is predicted to remain absent from much of northern Wisconsin.
The models developed here are powerful new tools to explore potential impacts of climate change and other anthropogenic factors such as land-use changes on stream fishes, but they must be used with caution. Their greatest value lies in the level of detail and spatial precision they provide for predictions. Their strengths, however, should not blind readers to their limitations. They are ecological models, and as such represent major oversimplifications of the complex suite of variables that determine the suitability of a particular stream segment for a particular fish species. Although they have a relatively high level of accuracy compared to other similar models, they still misclassify from 6·5 to 44% of species occurrences under current conditions. Their use in predicting future fish distributions assumes that the observed relations between species and environmental variables are causative, which is probably not always to be the case. This modelling exercise also presumes that relations between species and their environment will remain constant with rising temperatures, which is unlikely (Davis et al., 1998). Finally, the models do not directly address biotic interactions (e.g. predation, competition and disease) that may limit species distributions, nor how these biotic interactions may change with a warming climate. Given these limitations, model predictions should be viewed as indices of the relative magnitude and spatial pattern of species distribution changes in response to a warming climate rather than absolute estimates of the exact amounts and locations of changes.
Along with the limitations of the species models the simplicity of the climate change scenarios that were explored must also be considered. Although the summer air temperature increases used here represent reasonable approximations of the range of warming predicted by a variety of climate models, it is certain that not all streams will experience the same increase in water temperature for a given air temperature increase as was assumed for the species models. For any increase in summer air temperature, streams will vary in their increase in water temperature based on their stream flow, relative groundwater inputs, stream channel morphology, solar radiation inputs and riparian and catchment vegetation and land use (Wehrly et al., 2009). Moreover, climate change will involve more than just increases in air temperature and will probably include changes in the amount, timing and form (i.e. rain v. snow) of precipitation and the intensity and duration of storm events and climate extremes (e.g. hot spells and droughts) (Magnuson et al., 1997). These changes will in turn influence stream flows, groundwater inputs, channel morphology, solar radiation, vegetation and land use and ultimately water temperatures and habitat suitability for fishes. Finally, climate warming will probably affect more than just summer water temperatures, including reducing the severity of winter water temperatures, which could improve fish overwinter survival, and altering the timing, rate and variability or spring water temperature rises and autumn water temperature declines, which could modify fish spawning, egg development and hatching and larval growth and survival. These other water temperature changes may be as or even more important than increases in summer water temperatures in determining stream habitat suitability and distribution patterns for some species. A new research initiative in Wisconsin is attempting to develop improved stream flow and water temperature models that are more sensitive to these other aspects of climate change. These new flow and temperature models will be applied to more detailed and realistic climate change scenarios to provide more accurate inputs for the species models in order to generate better predictions of climate change effects on fish species distributions.
Overall, the results of the current study predict that even small increases in summer air and water temperatures owing to climate warming will have major effects on the distribution of common stream fishes in Wisconsin. An equal number of species will increase as will decrease, but there will be a net loss of species habitat because other limiting environmental factors and barriers to dispersal and colonization will prevent expanding warm-water species from fully replacing declining cold-water and cool-water species. Consequently, some streams will experience a decline in species richness, as cold-water and cool-water fishes are lost but are not fully replaced by warm-water fishes. Fisheries opportunities will probably also decrease as S. trutta and S. fontinalis, popular cold-water gamefishes, disappear from small streams but warm-water gamefishes, none of which thrive in small streams, do not expand to take their place. Species changes will be most dramatic in small geographically isolated headwater streams in northern Wisconsin that currently have cold to cool summer water temperatures and are dominated by cold-water and cool-water fishes. There, cold-water and cool-water species will decline and eventually disappear as climate warms. Only a small subset of the warm-water species, however, will be able to replace them. These small streams lack suitable habitat for many warm-water species, and distance and barriers to movement will impede their colonization by southern Wisconsin warm-water species (e.g. P. erythrogaster, N. flavus and E. flabellare) for which the habitat would be suitable. Species changes are likely to be least in larger and warmer streams and rivers in southern Wisconsin that are already dominated by warm-water fishes.
The authors thank T. Brenden, A. Cooper, L. Hinz, P. Kanehl, A. Martin, C. Smith and S. Westenbroek for assistance in collecting, compiling and processing the data used to generate the species models and stream segment characteristics. Helpful reviews of earlier versions of this manuscript were provided by J. McKenna, J. Schaeffer and three 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; Federal Aid in Sport Fish Restoration Project F-80-R, Project F-95-P, studies SSMP and SSCN and the Wisconsin Department of Natural Resources.