An invader in salmonid rearing habitat: current and future distributions of smallmouth bass (Micropterus dolomieu) in the Columbia River Basin

Abstract

Invasive species and climate change are leading threats to freshwater ecosystems. In the Columbia River Basin (CRB), nonnative fishes are a critical consideration in salmon recovery, yet managers lament a lack of distribution information. Combining a species distribution model (SDM) with environmental DNA (eDNA), we locate range boundary regions of nonnative smallmouth bass (Micropterus dolomieu) and evaluate its overlap with native salmonids. A combination of thermal, hydrological, and geomorphic variables predict that smallmouth bass is distributed across ∼18 000 river kilometres and overlaps with 3%–62% of rearing habitat of salmonids (species-dependent) in the CRB. Under a moderate climate change scenario, smallmouth bass is predicted to expand its range by two-thirds (totaling ∼30 000 river kilometres) by 2080. Basin-wide models were sufficiently accurate to identify upstream invasion extents to within 15 km of the eDNA-based boundary, and including eDNA data improved model performance at critical range boundary regions without sacrificing broadscale model performance. Our study highlights how eDNA approaches can supplement large geospatial data sets to result in more accurate SDM predictions, guiding nonnative species management.

Full text

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1An invader in salmonid rearing habitat: current and future distributions of smallmouth
2bass (Micropterus dolomieu) in the Columbia River Basin
3 Erika S. Rubenson1*
4 Julian D. Olden1#
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61 School of Aquatic and Fishery Sciences, University of Washington, 1122 NE Boat Street,
7 Seattle, Washington 98195, USA.
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9*E-mail: esuther@uw.edu
10 *Phone: 206-685-9582
11 #E-mail: olden@uw.edu
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22 Keywords: Invasive species, salmon conservation, range boundary, eDNA, species distribution
23 model
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24 Abstract:
25 Invasive species and climate change are leading threats to freshwater ecosystems. In the
26 Columbia River Basin (CRB), nonnative fishes are a critical consideration in salmon recovery,
27 yet managers lament a lack of distribution information. Combining a species distribution model
28 (SDM) with environmental DNA (eDNA), we locate range boundary regions of nonnative
29 smallmouth bass (Micropterus dolomieu) and evaluate its overlap with native salmonids. A
30 combination of thermal, hydrological, and geomorphic variables predict that smallmouth bass is
31 distributed across approximately 18 000 river kilometers and overlaps with 3-62% of rearing
32 habitat of salmonids (species dependent) in the CRB. Under a moderate climate change scenario,
33 smallmouth bass is predicted to expand its range by two-thirds (totaling ca. 30 000 river
34 kilometers) by 2080. Basin-wide models were sufficiently accurate to identify upstream invasion
35 extents to within 15 km of the eDNA-based boundary, and including eDNA data improved
36 model performance at critical range boundary regions without sacrificing broad-scale model
37 performance. Our study highlights how eDNA approaches can supplement large geospatial
38 datasets to result in more accurate SDM predictions, guiding nonnative species management.
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39 Freshwater ecosystems remain extremely vulnerable to the combined threats of multiple
40 stressors (Craig et al. 2017). Climate-induced stream warming is causing widespread changes to
41 species assemblages, promoting the secondary spread of nonnative, and often invasive, species
42 (Comte et al. 2013). Consequently, understanding current and projected future distributions of
43 nonnative species is fundamental to strategic conservation planning for freshwater ecosystems
44 (Bush et al. 2014). Modeling efforts, however, are challenged by the simultaneous need to be
45 both generalizable to capture a species’ broad distribution while also being adequately specific to
46 inform local scale management and conservation practices (Dormann et al. 2012). Although
47 myriad methods to improve model performance exist, data availability remains limited,
48 prompting the continued use of correlative species distribution models that utilize only species
49 occurrence and spatial environmental data. Advancements in the performance of correlative
50 models, however, are possible by integrating diverse datasets that span different spatial scales
51 and sources (Ibáñez et al. 2014). Although this does not necessarily capture processes that set
52 range constraints, spatially diverse datasets may improve model accuracy at critical range
53 boundaries where management relevance is the greatest.
54 The Columbia River Basin (CRB) once supported large populations of ecologically,
55 culturally, and socioeconomically important native salmonids (salmon, trout, and char), but
56 habitat alteration, nonnative species proliferation, and ongoing climate change have contributed
57 to dramatic declines in many parts of their range (Rieman et al. 2015; Hand et al. 2018).
58 Recovery efforts are complicated by the broad expanse and diverse ecology of the CRB, which
59 spans portions of seven states (Washington, Oregon, Idaho, Montana, Nevada, Utah, and
60 Wyoming), two countries (United States and Canada), and countless management agencies that
61 operate at different spatial scales. Although $300 million is spent annually on salmonid recovery
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62 in the CRB, a disproportionate focus on habitat restoration has been highlighted as insufficient to
63 produce meaningful benefits (Naiman et al. 2012; Rieman et al. 2015; Hand et al. 2018). By
64 contrast, landscape scale assessments and factors that impact food webs, such as presence of
65 nonnative species, are cited as critically overlooked components in salmon recovery efforts
66 (Naiman et al. 2012; Rieman et al. 2015).
67 Smallmouth bass (Micropterus dolomieu) is a prevalent nonnative recreational fish in the
68 CRB, and has been highlighted as a critical management priority to achieve salmon recovery
69 goals (Sanderson et al. 2009; Carey et al. 2011). A high trophic-level predator, smallmouth bass
70 is capable of consuming large proportions of salmon runs (up to 35%) when co-occurring with
71 small salmonids in water sufficiently warm for activity (Fritts and Pearsons 2004; Sanderson et
72 al. 2009; Carey et al. 2011). Smallmouth bass was first introduced into the Willamette and
73 Yakima Rivers in the 1920s (Lampman 1946). Secondary introductions, such as the introduction
74 into the John Day River in 1971 (Shrader and Gray 1999), have occurred throughout the region
75 since that time, to include extensive stocking efforts by multiple state agencies (Carey et al.
76 2011). Today, substantial local abundances (up to 2 300 fish per river kilometer) and voracious
77 consumption rates (up to 0.623 salmonids per smallmouth bass per day) have been found in parts
78 of the CRB (Erhardt et al. 2018), making smallmouth bass one of the most frequent predators of
79 juvenile salmonids in the CRB (Carey et al. 2011). These estimates are derived from mainstem
80 rivers or reservoirs where the predominant threat for salmonids occurs during outmigration
81 events. Recent evidence suggests that smallmouth bass is also capable of colonizing some
82 portions of sensitive salmonid spawning and rearing habitat in Columbia River tributaries in
83 spite of having warmer thermal preferences (Lawrence et al. 2014; Rubenson and Olden 2017).
84 This portends a year-round predation and competition threat during a sensitive life-history stage
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85 for salmonids. Despite this, very little information is available on current or predicted future
86 distributions of smallmouth bass and its potential overlap with native salmonid species’
87 spawning and rearing habitat in the CRB.
88 Upstream range boundaries of smallmouth bass constitute the regions of most probable
89 overlap with spawning and rearing salmonids, and thus offer powerful management
90 opportunities. For instance, the high threat to salmon confined to relatively isolated locations
91 may assist in gaining support to prevent further upstream invasions (Rahel 2013) or for localized
92 efforts to control (Loppnow et al. 2013). In addition, upstream range boundaries represent
93 regions where physiological stress, habitat suitability, and/or dispersal barriers present
94 potentially exploitable constraints on expanding populations (Sexton et al. 2009; Lawrence et al.
95 2012). These factors make range boundaries critical battlegrounds for invasive species
96 management. Determining the location of range boundaries, however, often requires extensive
97 sampling or accurate species distribution models developed from large datasets, both of which
98 may be prohibitively costly (Urban et al. 2016). Environmental DNA (eDNA), however, may
99 present a new opportunity to supplement correlative modeling efforts at critical boundary regions
100 by providing fine-scale distributional resolution at relatively low costs. Moreover, eDNA
101 samples may be combined with existing data sources, potentially enhancing model performance
102 both range wide and at range boundaries.
103 In this study we explore the efficacy of developing a correlative species distribution
104 model from existing smallmouth bass records to guide eDNA survey design at range boundaries.
105 By leveraging multiple sources of data, the resulting models can identify management-relevant
106 range boundary regions in individual streams as well as contribute to more accurate basin-wide
107 distribution predictions for smallmouth bass. Our objectives were to 1) develop and validate a
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108 species distribution model for smallmouth bass in the CRB, 2) use eDNA to refine the location
109 of and improve model performance at predicted upstream range boundaries, 3) predict future
110 distributions of smallmouth bass in the CRB under future climate change scenarios, and 4)
111 quantify the degree of spatial overlap of smallmouth bass with critical habitat and designated
112 refugia for CRB salmonid species.
113
114 Methods
115 Smallmouth bass occurrence
116 We collated smallmouth bass distribution data from a diversified set of databases and
117 individual biologists across Washington, Oregon, Montana, Idaho, and Canada. We refer to this
118 as the “initial” dataset, whereas the dataset that also includes the additional eDNA-derived
119 records is referred to as the “final” dataset. Although distribution data was provided for Canada,
120 the Canadian portion of the CRB was not included due to the lack of compatible environmental
121 predictor variables (i.e., current and future mean August water temperature, Variable Infiltration
122 Capacity (VIC) hydrologic models for spring flow, and waterfall distributions). Distribution in
123 Canada, however, is currently limited and concentrated along the border with the United States.
124 Data were provided by Washington Department of Fish and Wildlife (WDFW), Idaho
125 Department of Fish and Game (IDFG), Idaho Department of Environmental Quality (IDEQ),
126 Oregon Department of Fish and Wildlife (ODFW), Yakima Nation Fisheries, Confederated
127 Salish and Kootenai Tribes, and the Nez Perce Tribe. Responses to data requests that contained
128 location, confirmed presence or absence of smallmouth bass, and sampling dates between 1981
129 and 2016 were included in our database. In addition, we included data from the EPA and
130 Regional Environmental Monitoring and Assessment Program (EMAP and REMAP), the EPA
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131 National Rivers and Streams Assessment (NRSA), the USGS National Water Quality
132 Assessment Program (NAWQA), the USGS Nonindigenous Aquatic Species Database
133 (https://nas.er.usgs.gov), Montana Fish, Wildlife, and Parks MFish database
134 (http://fwp.mt.gov/fish), and the published literature (Table S1). Database entries were limited to
135 those that were sampled between 1981 and 2016 and labeled as confirmed, established, accurate,
136 and confined to flowing water environments. Different survey techniques were used by the
137 various agencies and sources (i.e., electrofishing, snorkeling, weir trapping, and seining), thus we
138 recognize issues related to differing sampling efficiency and detection probabilities.
139 Distribution data were assigned to stream segments according to hydrographic flow lines
140 obtained from the National Hydrography Dataset Plus, Version 2 (NHDPlusV2; McKay et al.
141 2012) (http://www.horizon-systems.com/NHDPlus). Stream segments classified as intermittent
142 hydrology and those not containing all climate and habitat data metrics were removed, resulting
143 in 118 708 total segments, equating to approximately 204 700 river kilometers. After excluding
144 distribution records from the same location (river segment), we identified 240 presence records
145 (Fig. 1). Visual investigation revealed no evidence of spatial sampling bias in the presence data,
146 however there was a disproportionate number of absence points in certain regions, including the
147 Willamette River basin. Consequently, to ensure equal representation of all available habitat
148 types across the CRB, absence points were spatially rarefied, resulting in the initial database
149 containing a total of 177 absence records (Fig. 1).
150 We used the predictions from our model based on the initial database to inform the
151 collection location of eDNA samples along the predicted upstream range boundary in 14 major
152 rivers of the CRB (Fig. 1). These rivers encompass a subsample of the diverse environmental
153 contexts and disparate invasion histories of smallmouth bass in the CRB (Carey et al. 2011). All
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154 samples were collected during the late summer (last 2 weeks of July 2016) to ensure congruence
155 with the stream temperature predictor variable used in the model (described below). We defined
156 the predicted range boundary regions as sections of rivers where the probability of presence
157 exhibited a visible decline from > 0.9 towards probabilities < 0.5. In each tributary, 25-75 km
158 stretches of river that bracketed the predicted range boundary of smallmouth bass were targeted
159 for sampling. We attempted to collect samples every 5-7 km, but were often constrained by
160 accessibility to the river. For the Salmon River, sampling locations were informed by
161 conversations with local fishery biologists, guiding us to our sampling location. At each
162 sampling location, 5-L of water was filtered through a 1.5 μm pore-sized fiberglass filter using
163 the equipment and protocol of the Rocky Mountain Research Station (Carim et al. 2016b). When
164 possible, we prioritized sampling locations just downstream of pools where flow increased and
165 the channel-width narrowed. We chose these locations because smallmouth bass are known to
166 prefer slower-water habitat, and sampling at knick-points increased the probability that water
167 from both banks of large rivers would be sampled. Smallmouth bass presence was assessed by
168 extracting and amplifying eDNA from the filters using an assay specifically developed to detect
169 smallmouth bass (Franklin et al. 2018). Marker and assay development as well as extraction and
170 amplification were completed by the Rocky Mountain Research Station (U.S. Forest Service)
171 following the methods in Carim et al. (2016a). Finally, to test the accuracy of eDNA detection in
172 these contexts, we conducted snorkel surveys (when flow and visibility allowed) immediately
173 following eDNA collection. Here, two snorkelers swam 100 m upstream from where water
174 samples were collected on opposite banks of the river. Both snorkelers then floated down the
175 thalweg, noting presence or absence of smallmouth bass.
176 Environmental determinants of smallmouth bass occurrence
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177 Referencing the extensive knowledge of smallmouth bass ecology (e.g., Peterson and
178 Kwak 1999; Sharma and Jackson 2008; Lawrence et al. 2015), we selected seven predictor
179 variables that represent thermal, hydrologic and geomorphic attributes known to influence fish
180 distributions (Table 1, S2). Climate and habitat data were georeferenced at the stream segment
181 scale. We provide summary statistics for the entire network (Table 1) in addition to statistics at
182 presence and absence sites (Table S2).
183 Thermal variables were selected to incorporate conditions that are known to
184 physiologically support smallmouth bass. Specifically, evidence suggests that mean summer
185 daily temperatures near 20-22°C constrain the upstream range boundary of smallmouth bass
186 populations in Columbia River tributaries (Rubenson and Olden 2017) and that winter severity
187 limits the establishment of colonizing populations (Lawrence et al. 2015; Rubenson and Olden
188 2017). As such, we included mean August water temperature (WaterT) and the mean monthly air
189 temperature between October 1 and March 31 (WinterAirT). Historical summer water
190 temperature data were developed by the NorWeST stream temperature project based upon data
191 from 1993-2011, and air temperatures were developed by the University of Washington Climate
192 Impacts Group based upon data from 1916-2006 (Table 1). Because water temperature data was
193 developed for NHDPlusV1, we first spatially joined these data to the NHDPlusV2 layer before
194 conducting our analyses. In addition, reservoir temperatures were not modeled in the baseline
195 water temperature dataset, so river segments in mainstem reservoirs were linearly interpolated
196 between the nearest upstream and downstream riverine segment adjacent to the reservoir.
197 Hydrologic variables were selected based upon smallmouth bass flow affinities.
198 Generally, smallmouth bass thrive in high-order streams and is preferentially found in perennial
199 pools or glides (Dauwalter et al. 2007). We included mean annual flow (MAFlow) to represent
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200 stream size and the slope of each river segment (Slope) to capture localized velocities and habitat
201 structure associated with reach morphology. Slope was extracted from the NHDPlusV2 dataset
202 (Table 1). In addition, smallmouth bass show varied responses to spring hydrologic conditions,
203 specifically related to movement patterns, spawn timing, and spawning success (Rubenson and
204 Olden 2017). We thus included the magnitude of flow during the spring (April-May)
205 standardized by mean annual flow (SpringFlow) to enable comparisons between small headwater
206 streams and large mainstem rivers. All flow metrics were extracted from the Western U.S.
207 Stream Flow Metrics dataset, and were based upon data from 1916-2006 (Table 1).
208 Geomorphic variables were included to represent zones of high-velocity flow or barriers
209 such as waterfalls, cascades, or dams that can deter or prevent smallmouth bass upstream
210 dispersal. Geomorphic variables included slope (described above), the density of dams in the
211 watershed (DamDensity) as published by StreamCat (Hill et al. 2016), and a categorical variable
212 accounting for whether or not the stream segment was located above a waterfall or cascade (as
213 defined in the Fish Passage Barriers dataset from StreamNet.org) (Waterfall) (Table 1).
214 Recognizing that smallmouth bass stocking has occurred above some major barriers in the CRB
215 in the past, we considered barriers in the waterfall dataset functionally absent in the model (i.e.,
216 permeable) in sections of river with a known stocking history. By contrast, although some
217 mainstem dams have fish passage structures, the ability of non-salmonids to utilize these
218 opportunities for upstream movement remains uncertain, so these barriers were retained.
219 Baseline thermal and hydrological metrics (i.e., WaterT, WinterAirT, MAFlow,
220 SpringFlow) were paired with their associated predictions for 2080 according to an ensemble
221 mean of 10 or more global climate models (GCM) for A1B emission scenarios for 2080 (IPCC
222 2007). The A1B emission scenario is considered a moderate estimate for future carbon emission
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223 scenarios, and is similar to the 2010 Representative Concentration Pathway 6.0. Although
224 updated emission scenarios are available from the IPCC 2013, only the A1B scenario was
225 consistently modeled for all our metrics for the same time period (i.e., 2080). For mean August
226 water temperature (WaterT2080), the NorWeST stream temperature scenario 32 was used for the
227 2080 predictions, which incorporates both modeled mean August air temperatures and stream
228 discharge changes for 2080 (specifics in Wenger et al. 2010 and Hamlet et al. 2013). These data
229 also account for differential sensitivity among streams to climate warming (Luce et al. 2014).
230 Predicted air temperatures for winter (WinterAirT2080) were derived by the University of
231 Washington Climate Impacts Group using the ensemble mean from 19 GCMs associated with
232 the A1B emission scenario (specifics in Littell et al. 2011). Predictions for both flow metrics
233 (MAFlow2080 and SpringFlow2080) were developed using the Variable Infiltration Capacity
234 (VIC) macroscale hydrologic model developed by the University of Washington Climate
235 Impacts Group for the stream segments in the western U.S. (specifics in Wenger et al. 2010).
236 Species distribution model
237 We used multivariate adaptive regression spline (MARS) models to estimate smallmouth
238 bass probability of occurrence at the reach scale. We used the initial dataset to predict current
239 distribution for identifying eDNA survey locations and the final (i.e., initial and eDNA records)
240 dataset to predict current and future distribution and overlap with salmon habitat. We selected
241 MARS models because they allow for easy interpretation of complex relationships between the
242 response and predictor variables and have been widely shown to be one of the top performing
243 techniques for species distribution modeling (Muñoz and Felicísimo 2004). MARS models
244 describe non-linear species-environment relationships using a series of piecewise linear segments
245 (Leathwick et al. 2005). MARS models, however, are built assuming normal data, and thus
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246 needed to be adapted to accommodate the binomial error structure of our presence-absence data.
247 As such, we fit generalized linear models (GLM) to the extracted basis functions from the
248 MARS model following the methods of Leathwick et al. (2005) and Elith and Leathwick (2007).
249 We used source code from Elith and Leathwick (2007) to run the models in R (R Foundation for
250 Statistical Computing, Vienna). This code fits the initial models using the ‘mda’ package (Hastie
251 and Tibshirani 1996), extracts the set of linear segments that best describe the nonlinear
252 relationships between the response and explanatory variables (i.e., basis functions), and relates
253 species occurrences to these functions by fitting a GLM with a binomial error distribution to the
254 data. The functions extracted during the MARS/GLM modeling process were applied to each
255 stream segment’s unique set of predictor variables to determine the probability of species
256 occurrence at that location for both current and future (i.e., 2080) conditions. In addition,
257 response curves (i.e., the basis functions used during the MARS modeling process) were
258 examined to evaluate the primary influences of each predictor variable on species occurrence
259 probability in the model. Multicollinearity between variables was examined using Variance
260 Inflation Factor (VIF); high multicollinearity is evident when individual VIFs are ≥ 10 or the
261 mean VIF across variables is ≥ 5. No indications of multicollinearity were discovered between
262 any of the predictor variables (all individual VIF < 2.6; mean VIF = 1.8), thus all variables were
263 included in the model. Although VIF was low, we did reveal moderate levels of correlation (r =
264 0.5-0.6) between WaterT, WinterAirT, and Slope, as well as between WinterAirT and
265 SpringFlow (Table S2).
266 Model validation was conducted using both threshold-independent and threshold-
267 dependent model statistics. For the threshold-independent test, 10-fold cross-validation (i.e., 90%
268 of the data were used to train the model, and the remaining 10% were withheld for testing) was
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269 conducted and model accuracy assessed by calculating the area under the receiver operating
270 characteristics (ROC) curve (AUC) (Olden and Jackson 2002). AUC measures the ability of a
271 model to discriminate between presence and absence sites. A score of 0.5 implies no better than a
272 random guess whereas a score of 1.0 indicates perfect accuracy; intermediate scores are ranked
273 poor (< 0.7), good (0.7-0.9), and excellent (> 0.9) (Olden and Jackson 2002). By contrast,
274 threshold-dependent model statistics were calculated to facilitate comparisons with the
275 distributions of salmonid species. We used the maximum sensitivity plus specificity to determine
276 the threshold value by which species were considered present (Liu et al. 2013). Confusion
277 matrices were then constructed, from which sensitivity (correctly predicted presence), specificity
278 (correctly predicted absence), true skill statistic (TSS) (average of net prediction success),
279 Cohen’s Kappa (extent to which the agreement between observed and expected are better than
280 chance alone), and model accuracy (overall probability that either presence or absence were
281 correctly predicted) were calculated. We then calculated the proportion of total river kilometers
282 predicted to be occupied by smallmouth bass and the predicted percent change by 2080 for each
283 of the 9 HUC-4 sub-basins of the CRB.
284 We used model predictions using the initial dataset to guide our eDNA collection efforts
285 and to determine the effect that combining datasets had on model performance. Predictive
286 performance of the SDMs built using the initial versus final distribution datasets were compared
287 to assess whether model performance was improved with the addition of targeted samples at
288 range boundaries. Model performance was separately evaluated at the range boundaries,
289 comparing initial and final model performance at the eDNA data locations only. To assess the
290 accuracy of our initial model for guiding sampling locations at range boundaries, we calculated
291 the approximate watercourse distance (in river kilometers) between the most upstream presence
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292 point and the location where probability of occurrence dropped below the pre-determined
293 threshold value. Finally, we compared eDNA detection with our detection during paired snorkel
294 surveys.
295 Smallmouth bass overlap with critical salmonid habitat and cold-water refugia
296 We assessed the amount of spatial overlap between the predicted current distribution of
297 smallmouth bass and the location of critical (i.e., spawning and rearing) salmonid habitat and
298 cold-water refugia. Critical habitats of salmonids were delineated using publically available
299 species distributions for the 5 anadromous species of Pacific salmon that spawn and rear in the
300 CRB and the 4 resident trout species (StreamNet GIS Data 2003). These salmonid distributions
301 were developed by StreamNet.org using data collected by agencies in Oregon, Washington,
302 Idaho, and Montana and represent the best professional judgment of local fish biologists in the
303 Pacific Northwest region. These distributions contained species locations classified by habitat-
304 type (i.e., year-round residence, migration, rearing, and/or spawning), and Pacific salmon
305 distributions organized by “runs” of individual species (i.e., spring, summer, or fall) when
306 appropriate. To determine critical habitat, we excluded segments marked as migration-only, thus
307 including only rearing, spawning, and year-round habitat. We then used ArcGIS 10.2 to quantify
308 the percentage of total critical habitat (% total river kilometers) of each salmonid species
309 predicted to be sympatric with current smallmouth bass occurrence (i.e., probability greater than
310 the determined occurrence probability threshold). The salmonid species distributions were not
311 projected into the future, so we could not calculate the predicted change in overlap with
312 smallmouth bass under future climate conditions.
313 In addition to the above salmonid distributions, we compared smallmouth bass
314 distributions to cold-water refugia as delineated by the U.S. Forest Service (USFS) Climate
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315 Shield project (Isaak et al. 2015). Isaak et al. (2015) identified specific cold water habitat
316 associated with juvenile bull trout (Salvelinus confluentus) and cutthroat trout (Oncorhynchus
317 clarkii) (www.fs.fed.us/rm/boise/AWAE/projects/ClimateShield.html) – both species of
318 conservation concern that have cold thermal niches relative to other salmonids in the CRB (Isaak
319 et al. 2015). These cold-water refugia represent only a subset of the entire ranges of these
320 species, but are considered priorities for conservation and cold enough to prevent encroachment
321 by nonnative species. Because the probability of smallmouth bass occurrence in these regions
322 was always less than the pre-determined occurrence threshold, we calculated the summary
323 statistics of predicted probabilities in each cold-water refuge under current and future climate
324 conditions instead of number of river kilometers likely inhabited by smallmouth bass.
325
326 Results
327 We show that nonnative smallmouth bass is widely distributed throughout the CRB, with
328 established populations spanning most of the Columbia River mainstem and its major tributaries
329 (Fig. 1). Smallmouth bass is predominantly (79%) located in high-order streams (i.e., stream
330 orders 5-9 which represent all mainstem habitats and major tributaries of the CRB), as well as
331 some low-order streams (i.e., stream orders 1-4); this suggests that smallmouth bass occupy a
332 diverse spectrum of habitats (Table S3). Although smallmouth bass occurrence was predicted
333 across all stream orders, we do note that the low-order streams that had presence points were all
334 located adjacent to high-order streams (Fig. 2a). The eDNA survey included 87 stream locations,
335 resulting in 30 presence records and 57 absence records. At 74 of the sample locations, paired
336 snorkel surveys were also conducted. Smallmouth bass were observed in 18 surveys, and not
337 observed in 56 surveys. We had positive eDNA detection in all 18 sites where smallmouth bass
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338 were also observed as well as 8 positive eDNA detections where no smallmouth bass were
339 observed. There were no smallmouth bass visually detected (n = 48 surveys) at any of the eDNA
340 absence locations. All eDNA data were located in high-order, major CRB tributaries. These 87
341 records were subsequently combined with the initial database, and the model was parameterized
342 again.
343 In concordance with the final distribution dataset, our model predicted a present-day
344 distribution of smallmouth bass that spans a diversity of climates, geomorphology, and
345 hydrography across the CRB (AUC = 0.90, Table 2, Fig. 2a). Geospatial data containing
346 smallmouth bass occurrences and model predictions are available at:
347 https://databasin.org/datasets/eafa4c3d466a41e790843fb73573437e. Although distributed across
348 a large geographic space, smallmouth bass occurrence is predominantly limited to larger, high-
349 order streams or adjacent low-order streams (Fig. 2a). Using the maximum sensitivity plus
350 specificity as a threshold for probability of presence (0.5), we found that approximately 17 660
351 river kilometers of the CRB were predicted as occupied by smallmouth bass under current
352 environmental conditions (Kappa = 0.62, TSS = 0.62; Table 2). The Middle Snake, Middle
353 Columbia, Upper Columbia, and Yakima sub-basins had the highest proportion of total river
354 kilometers predicted to be occupied by smallmouth bass (16%, 13%, 12% and 12% respectively),
355 whereas the Lower Snake, Kootenai-Pend Oreille-Spokane and Lower Columbia had the lowest
356 proportion of occupied riverine habitat (5%, 5%, and 7% respectively; Fig. S1). The model built
357 using the final dataset demonstrated similar performance to a SDM constructed using just the
358 initial database of past survey efforts (Table 2).
359 The primary environmental drivers contributing to model performance included a
360 combination of thermal, hydrological, and geomorphic variables. Mean August water
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361 temperature, spring flow, and slope were the most influential with respect to the amount of total
362 deviance explained (Δ Deviance = 80.3, 29.0, 27.4; respectively), followed by mean annual flow
363 and the presence of barriers (Δ Deviance = 11.3, 3.7; respectively) (Fig. 3). Dam density and
364 winter air temperature did not contribute to model accuracy (Δ Deviance = 0.0). Habitat
365 suitability was generally negatively associated with slope and spring flow and positively
366 associated with water temperatures and large rivers, suggesting that smallmouth bass benefitted
367 from shallow gradients, large rivers, moderate magnitude spring flows, and warm water
368 temperatures (Fig. 3). In addition, the presence of hydrologic barriers was associated with
369 reductions in the probability of smallmouth bass occurrence.
370 Although our model responses generally matched what is known about smallmouth bass
371 habitat suitability, a number of interesting results emerged. First, we expected increasingly steep
372 gradients to have a negative association with smallmouth bass occurrence probability. Instead,
373 we found that shallow gradients had a positive effect on smallmouth bass occurrence probability,
374 whereas steeper gradients had no effect (Fig 3). However, the model response curve shows that
375 smallmouth bass rarely occur in regions where reach slopes exceed 3% (i.e., Slope = 0.03),
376 suggesting that high gradient reaches are not suitable for smallmouth bass. Second, rivers with
377 the most stable flow regimes (i.e., low values of spring flow) were associated with lower
378 probabilities of smallmouth bass occurrence. These more stable flow regimes were
379 predominantly isolated to the coastal regions of the CRB. There was a threshold response to
380 spring flow, however, such that moderate levels of spring flow had a positive impact on
381 smallmouth bass occurrence probability, but as spring flows continued to increase, the
382 relationship changed to a steep, negative association. Third, there was a nonlinear relationship
383 between water temperature and smallmouth bass occurrence probability. A positive association
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384 with smallmouth bass occurrence was only evident when mean August water temperatures
385 exceeded 17°C, and this association slightly weakened when temperatures exceeded 20°C.
386 Predicted changes to future (i.e., 2080) flow and temperature resulted in dramatic
387 increases to smallmouth bass distribution throughout most of the CRB (Fig. 2b). Specifically,
388 smallmouth bass are predicted to gain over 12 000 kilometers of river by 2080, representing a
389 69% increase from predicted current occupied habitat (Table 2). In addition, some small
390 headwater tributaries that are currently predicted to be inhospitable to resident smallmouth bass
391 appear vulnerable to invasion under warmer water conditions (Fig. 2b). This was most apparent
392 in the Middle Snake, Middle Columbia, and Yakima sub-basins of the CRB, where smallmouth
393 bass are predicted to spread through an additional 10% of the available riverine habitat in each
394 sub-basin (Fig. S1). All other sub-basins are predicted to see gains of 4-6%, with the exception
395 of the Lower Columbia, where there is no predicted change to the distribution of smallmouth
396 bass (Fig. S1).
397 We found our model based on the initial collation of existing data useful in guiding our
398 localized eDNA sampling efforts. In 10 of 14 sampled rivers, we located a transition from eDNA
399 presence to absence points near the location where the model predicted a range boundary (Fig. 5,
400 S2). According to the initial SDM, the watercourse distance between the most upstream eDNA
401 presence point and the model predicted upstream extent (considered the first location where the
402 model probability of occurrence fell below 0.5) averaged 15 kilometers (range = 0 - 40 km) (Fig.
403 5). In the Yakima, Payette, Kootenai, and Salmon Rivers, no smallmouth bass were detected
404 using eDNA. When comparing model performance at these range boundary regions, we note a
405 marked improvement of the model built using the final database that incorporated eDNA data
406 (Table 2, Fig. S2). The final model demonstrated overall greater performance (as indicated by
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407 Kappa, TSS and Accuracy indices) and was twice as successful in correctly predicting species
408 absence (specificity = 0.61 vs. 0.30) at the range boundaries while demonstrating comparable
409 sensitivity (Table 2).
410 We found various amounts of overlap between the predicted current-day smallmouth bass
411 distributions and critical habitat for all CRB salmonids except Chum salmon (Table 3).
412 Percentages of critical habitat overlap across these species ranged from 3-62% (mean = 20%)
413 (Table 3). Fall Chinook and Sockeye salmon, in particular, had relatively high percentages of
414 overlapping habitat (i.e., 62% and 39% respectively) (Table 3). By contrast, there was little to no
415 predicted overlap between designated cold-water refugia for either bull trout or cutthroat trout.
416 Instead, we see very low (albeit non-zero) probabilities of smallmouth bass presence for both
417 current day (mean = 0.06 for both species) and future climate (mean = 0.06 for both species)
418 predictions (Fig. 4).
419
420 Discussion
421 By combining phenomenological distribution modeling with enhanced species
422 detectability at range edges using eDNA, we provide new insight into the present and forecasted
423 future invasion of smallmouth bass in the Columbia River Basin. Models performed well at
424 predicting smallmouth bass distribution at the basin scale (Table 2), including a strong ability to
425 determine the approximate upstream range boundary locations at the channel segment (Fig. 2a,
426 Fig. 5). Species detection using eDNA sampling proved useful to improve model predictions
427 from correlative models at the range boundaries without sacrificing model performance at the
428 broader spatial extent, and successfully refined the location of the leading-edge invasion to
429 within management-relevant regions of multiple rivers (Fig. 5, Fig. S2).
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430 Empirical data and model predictions depict a broad invasion of smallmouth bass across
431 diverse climates, hydrological conditions, and geomorphologic contexts that define the rivers of
432 the CRB. Combinations of shallow gradients, large rivers that display moderate-magnitude
433 spring flows, and warm water were associated with increased probability of smallmouth bass
434 occurrence. According to these habitat correlates, nearly 18 000 river kilometers are suitable to,
435 or currently, support smallmouth bass populations under current environmental conditions (Fig.
436 2a, Table 2). Although the distribution of smallmouth bass is spread broadly across geographic
437 space, we show that smallmouth bass is still relatively confined to warmer, low-gradient rivers in
438 the CRB (Fig. 2a). This, however, is not unique to cool-water smallmouth bass. Isaak et al.
439 (2017) demonstrate that across 11 fish species, including several cold-water salmonids, only
440 small proportions of total stream habitat (measured only in Idaho) are accessible or suitable to
441 fish. If a majority of the CRB network is not accessible or suitable to fish, the basin-wide
442 occupancy of smallmouth bass may be much more significant than our results depict.
443 Although our model depicts a propensity for smallmouth bass to inhabit large, higher-
444 order streams, it also portrays suitable habitat throughout a number of smaller headwater
445 tributaries (Fig. 2). As a result, we found that there was spatial overlap ranging from 3-62% with
446 critical spawning and rearing habitat for native salmonids across the CRB (Table 3). This
447 observation denotes a potential challenge for salmon conservation where the invasion of
448 smallmouth bass is rarely considered in large-scale conservation and restoration efforts (Carey et
449 al. 2011; Naiman et al. 2012). For anadromous species, in particular, our estimates of habitat
450 overlap provide a conservative estimate of potential impact given that this only depicts the
451 potential threat of year-round co-occurrence, excluding interactions that may occur during
452 outmigration. Furthermore, forecasted changes to flow and temperature regimes predicted a two-
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453 third increase in suitable habitat, equating to over 10 000 more river kilometers of potentially
454 suitable habitat to smallmouth bass in 2080 (Fig. 2b, Table 2). Despite this striking pattern, there
455 remains only scant research evaluating the predator and competitive impacts of smallmouth bass
456 on juvenile salmonids in critical spawning and rearing habitats. Instead, most literature is
457 focused either on predation on outmigrating salmonids (e.g., Fritts and Pearsons 2004) or effects
458 of habitat degradation and loss, dams and impoundments, harvest, and hatcheries (e.g., Naiman
459 et al. 2012). This exposes a potentially important overlooked impact to Pacific salmonids.
460 We show that the critical spawning and rearing habitat of most salmonids, especially fall
461 Chinook and Sockeye salmon, overlap with established year-round smallmouth bass populations
462 (Table 3) and that much of the CRB will see an increase in suitable smallmouth bass conditions
463 in the future (Fig. 2b, Fig. S1). The Middle Snake, Middle Columbia, and Yakima sub-basins, in
464 particular, show high percentages of occupancy by smallmouth bass under current climate
465 conditions, which encompass much of the critical habitat for fall Chinook and Sockeye salmon.
466 These same sub-basins show the greatest projected increase in habitat suitability under future
467 climate conditions (Fig. S1). Smallmouth bass is predicted to increase by 4-6% in the
468 Willamette, Lower and Upper Snake, Kootenai-Pend O’reille-Spokane, and Upper Columbia.
469 Many of these sub-basins include forested mountain streams that currently support native trout
470 species, including both cutthroat trout and bull trout. A positive implication of our study is that
471 refugia for cutthroat and bull trout (Isaak et al. 2015) appear safe from major encroachment by
472 smallmouth bass both in present day and forecasted futures (Fig. 4). Protecting these refugia and
473 focusing efforts on the early detection and prevention of secondary spread, where possible, may
474 prove a cost-effective strategy to minimize future impacts of smallmouth bass on salmonid
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475 species in these sub-basins. This will require continued investigation of the spatiotemporal
476 patterns of fish movement at the leading-edge of the invasion (Rubenson and Olden 2017).
477 Little change to the projected distribution of smallmouth bass was observed in the Lower
478 Columbia basin. This sub-basin appears relatively buffered to climate-induced temperature
479 changes observed in other sub-basins, with only a small proportion of the sub-basin warming
480 above the model-identified breakpoint of 17°C. Interestingly, further analysis reveals that many
481 of the segments predicted to warm do not see large increases to the probability of smallmouth
482 bass occurrence. Here, non-temperature related drivers, such as hydrologic conditions or slope,
483 are likely important for determining the future of smallmouth bass in this basin. For instance,
484 many of the stream segments predicted to warm also have steep slopes. This suggests that steep
485 slopes may prevent smallmouth bass from moving upstream in response to warming
486 temperatures or that the physical habitat characteristics often associated with steep stream
487 reaches are unsuitable for smallmouth bass colonization. This is supported by our regional
488 database that has very few (n = 4) presence points in stream reaches with slopes > 3% (Table
489 S3), and the model response curve that shows that smallmouth bass rarely occur in stream
490 reaches with slopes > 3% (Fig. 3). Gibson-Reinemer et al. (2017) demonstrated that high channel
491 slopes likely limit the ability of fish to track climate change. Additional research is required to
492 determine what slope constitutes a barrier for movement to smallmouth bass, but our data
493 suggest that the physical habitat characteristics associated with streams that have reach slopes >
494 3% are rarely suitable for smallmouth bass. Knowing where smallmouth bass are unlikely to
495 expand even if climate change increases habitat suitability may help target management funds to
496 regions where secondary spread is more likely.
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497 Correlative associations manifested in the distributional model may facilitate targeted
498 management strategies to prioritize locations for early detection monitoring and prevention of
499 secondary spread of smallmouth bass (Vander Zanden and Olden 2008). Here, we highlight
500 potential mechanisms that may be exploited for management purposes at range boundaries.
501 Warmer water temperatures increased the probability of smallmouth bass occurrence in the CRB
502 (Fig. 3). The positive influence of warmer water temperatures on smallmouth bass corresponds
503 to current understanding of this species’ physiology and aligns with results from previous
504 modeling efforts (e.g., Peterson and Kwak 1999; Sharma and Jackson 2008). Although this clear
505 and strong relationship presents a challenge under likely climate futures, it also highlights a
506 management opportunity that can both benefit native fishes while deterring smallmouth bass
507 (and other cool or warm-water nonnatives). The model-identified breakpoints of 17°C and 20°C
508 may represent water-quality targets and decision points for management action to potentially
509 prevent or dissuade the upstream secondary spread of smallmouth bass.
510 Restoring riparian vegetation remains a powerful approach to offset climate-induced
511 losses in suitable salmonid spawning and rearing habitat while concurrently reducing the
512 upstream expansion of smallmouth bass. By using downscaled regional climate-change forecasts
513 of air temperature and streamflow with a fine-scale stream temperature model, Lawrence et al.
514 (2014) showed that complete riparian restoration was effective at reducing temperatures in a
515 major tributary of the CRB by up to 2.5°C (7-day average-daily mean water temperatures).
516 Smallmouth bass were projected to occupy the entire Middle Fork John Day River (Oregon,
517 USA) in late summer by 2080, but simulations of 50% riparian restoration restricted smallmouth
518 bass from invading most of the upper 30+ km of river where spring Chinook salmon rearing-
519 habitat persisted. Other management efforts, such as the creation of deeper pools, reconnecting
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520 sites to cold water sources, and even the construction of temporary shade structures and selective
521 seasonal releases of colder water from dams (Kurylyk et al. 2015) near or at range boundaries are
522 also potentially viable solutions to reduce water temperature, but require additional investigation.
523 Although efforts to reduce local stream warming reveals encouraging opportunities, the
524 broad-scale increase of habitat suitability throughout the CRB necessitates a diversity of
525 management approaches. Our model suggests that smallmouth bass occurrence is driven by both
526 temperature and flow, and research shows that manipulating releases downstream from dams
527 may prove useful in the deterrence of secondary spread (Carey et al. 2011). Interestingly, we
528 found a positive relationship between smallmouth bass occurrence probability and moderate
529 spring flows. This association may be related to a mismatch in timing of high discharge events
530 and the initiation of smallmouth bass upstream movement and spawning. For instance,
531 smallmouth bass typically commence spawning as water temperatures warm above 15°C
532 (Rubenson and Olden 2017) and large spring discharge events in much of the CRB are a result of
533 snowmelt and are thus associated with cold water temperatures. Careful consideration of the
534 timing of dam releases is required to effectively deter spawning or upstream movement. In
535 addition, tactics such as nest destruction and targeted adult removals (Loppnow et al. 2013) may
536 also prove feasible, especially at range boundaries where population abundances are generally
537 lower. The high-resolution predictions from our study can inform monitoring efforts (potentially
538 using eDNA) in key locations to allow for early detection of smallmouth bass presence. Once
539 present, targeted removal or suppression efforts by state agencies could be instituted sufficiently
540 early in the invasion to reduce or prevent negative impacts. Similarly, outreach to incentivize
541 anglers to actively target and remove smallmouth bass from regions critical to salmonid
542 conservation may be an option.
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543 In addition to the aforementioned management options, intentional fragmentation using
544 natural or artificial barriers to upstream movement may provide opportunities to protect currently
545 uninvaded habitat for native salmonids (Rahel 2013). Here, the assumption would be that
546 selective barriers could be designed to impede movements of smallmouth bass (or other
547 nonnative fishes), but still allow salmonid passage; an undoubtedly large challenge. Intentional
548 fragmentation is successfully used in South Africa, where gabion dams have been constructed to
549 prevent recolonization of nonnative black basses after their removal from streams (Weyl et al.
550 2014). In our model, the probability of occurrence was negatively associated with the location of
551 barrier features such as waterfalls or cascades. Designating areas upstream of these features as
552 refugia and instituting regulations, monitoring, and education programs to restrict the human-
553 aided transport of species above these barriers may provide cost-effective safeguards to ensure
554 these areas remain free from nonnative predators. In short, the notion that barriers may be useful
555 to prevent secondary spread of invasive fishes requires careful consideration of current efforts to
556 reconnect currently fragmented upstream habitat (Swan and Brown 2017). Before restoration
557 projects to defragment streams occur (e.g., fish passage at road culverts), it would be wise to
558 determine the risk of a predator invasion (Rahel 2013). If invasion risk is high, the benefits of
559 increasing habitat connectivity for target native species may be overridden by new predation and
560 competition threats.
561 Environmental DNA enabled rapid local-scale assessments of multiple rivers across a
562 large geographical area, confirming the location of management-relevant range boundary regions
563 in individual streams. We found it encouraging that basin-wide models were sufficiently accurate
564 to identify upstream invasion extents to within 15 km of the field-based boundary (Fig. 5).
565 Furthermore, including the eDNA presence and absence data in the final model improved model
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566 performance at critical range boundary regions (Table 2, Fig. 5, Fig. S2). We show that including
567 supplemental eDNA from targeted regions can improve the fine-scale distributional resolution of
568 correlative models at relatively low costs without sacrificing broad-scale model performance.
569 There were some exceptions to identifying approximate range boundaries using eDNA
570 technology. In the Salmon, Kootenai, and Payette Rivers, sampling occurred upstream of where
571 the initial model predicted the range boundary (Fig. S2), therefore the series of absences was
572 expected. In the Yakima River, presence data from our initial distribution database overlaps with
573 the lower 3 eDNA samples that showed absences, suggesting the range boundary was sampled,
574 but that smallmouth bass was not detected by eDNA. Similarly, eDNA results suggest that the
575 range boundary was located in the Clark Fork, but the initial model depicts that smallmouth bass
576 have a high probability of occurrence hundreds of kilometers farther upstream (Fig. S2); calling
577 into question either eDNA detection accuracy or model performance. Little research currently
578 exists on eDNA detection in large rivers such as the Yakima and Clark Fork, and conditions such
579 as low visibility, swift currents, and deep and wide channels make testing the accuracy of eDNA
580 sampling difficult. Both eDNA detection and factors that influence invasion expansion rates in
581 large rivers are ripe areas for additional research. We do note, however, that the final model
582 improved upon the initial model in all of the aforementioned examples (Fig. 5).
583 One key limitation to our study is that eDNA research in waterbodies caution that spatial
584 inferences made on eDNA could vary dramatically between systems based upon local transport
585 processes (Jane et al. 2014; Perez et al. 2017). We specifically targeted rivers with diverse
586 habitat conditions, to include different sizes, sediment load, and velocities. These factors alter
587 DNA concentrations, the detection probability of eDNA, and the distance from an eDNA source
588 at which detection occurs (Pilliod et al. 2013; Goldberg et al. 2016). Although we concede that
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589 there is certain to be variability in the precise river kilometer relative to a positive detection from
590 eDNA that defines the range boundary across these systems, we argue that the precision and
591 insight gained relative to the effort exerted far exceeds other capabilities currently available to
592 managers. In addition, we visually located smallmouth bass at 18 of the 30 eDNA detections and
593 had an absence point upstream of the most upstream presence point in all rivers, further
594 narrowing the potential location of the range boundary (Fig. 5). Thus, although there are
595 uncertainties in the range at which a positive eDNA detection might occur, our study illustrates
596 the ability to rapidly narrow our knowledge of an invasion extent to within kilometers in multiple
597 rivers across the diverse and vast CRB in a matter of days using only a correlative species
598 distribution model and eDNA.
599
600 Conclusion
601 Conservation and recovery of endangered salmonid species in the CRB are inextricably
602 intertwined with the management of nonnative species. Successful management, however, is
603 contingent on accurate information about species’ distributions – especially at range boundary
604 regions. Here, we show how combining new eDNA technology with broad-scale
605 phenomenological modeling was effective in elucidating the current and potential future
606 distribution of invasive smallmouth bass in the CRB and identified range boundary regions to a
607 management-relevant scale. Furthermore, the flexibility, accuracy, and rapidity of these methods
608 are not unique to smallmouth bass, but can also be applied to nearly two dozen other nonnative
609 predators currently established in the CRB, thus providing essential information to managers
610 tasked with ensuring the long-term future sustainability of salmonids.
611
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612 Acknowledgments
613 We thank Washington and Oregon Departments of Fish and Wildlife, Idaho Department of Fish
614 and Game, Idaho Department of Environmental Quality, Montana Fish, Wildlife, and Parks, the
615 Rocky Mountain Research Station, USGS, the Confederated Salish and Kootenai Tribes, and the
616 Nez Perce Tribe Department of Fisheries Resource Management for assisting with this project.
617 We specifically thank Bruce Baker, Andrew Weiss, Dale Gombert, Anthony Fritts, Jennifer
618 Nelson, Rebecca Wassell, Craig Barfoot, Devin Olson, Brett Hodgson, Rod French, Kyle
619 Bratcher, Scott Favrot, Brian Jonasson, Stan Gregory, Evan Brown, Brent Smith, Ken Tiffan,
620 Ladd Knotek, Ryan Kreiner, Jason Fales, and Jason Pappani for providing smallmouth bass
621 distribution data. We also thank two anonymous reviews for helpful comments that improved the
622 manuscript and Charlie Luce for providing supplementary flow data. Funding support was
623 provided by the National Science Foundation Graduate Research Fellowship Program to ESR
624 and the University of Washington H. Mason Keeler Endowed Professorship awarded to JDO.
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669 Gibson-Reinemer, D.K., Rahel, F.J., and Albeke, S.E. 2017. Natural and anthropogenic barriers
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691 Ibáñez, I., Diez, J.M., Miller, L.P, Olden, J.D., Sorte, C.J.B., Blumenthal, D.M., Bradley, B.A.,
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714 Lampman, B.H. 1946. The coming of the pond fishes: an account of the introduction of certain
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736 Liu, C., White, M., and Newell, G. 2013. Selecting thresholds for the prediction of species
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758 Perez, C.R., Bonar, S.A., Amberg, J.J., Ladell, B., Rees, C., Stewart, W.T., Gill, C.J., Cantrell,
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781 Sexton, J.P., McIntyre, P.J., Angert, A.L., and Rice, K.J. 2009. Evolution and ecology of species
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803 Weyl, O.L.F., Finlayson, B., Impson, N.D., Woodford, D.J., and Steinkjer, J. 2014. Threatened
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38
806 Table 1: List of predictor variables used in the smallmouth bass distribution model, as well as summary statistics.
Variable name
Description
Time
Period
Source
Unit
Mean (Min, Max)
WaterT
Mean August water temperature
1993-2011
NorWeST stream
temperature 1
°C
13.2 (0, 29.8)
WaterT2080
Future mean August water
temperature
2070-2099
NorWeST stream
temperature1
°C
15.4 (0, 30.4)
WinterAirT
Mean monthly air temperature
October 1-March 31
1916-2006
Climate Impacts
Group2
°C
0.6 (-8.1, 8.3)
WinterAirT2080
Future mean monthly air
temperature October 1-March 31
2070-2099
Climate Impacts
Group2
°C
3.2 (-4.6, 10.3)
SpringFlow
Mean daily flow between April 1
and May 31
1915-2006
Western U.S. Stream
Flow Metrics3
cms·cms-1
1.0 (0.2, 3.1)
SpringFlow2080
Future mean daily flow between
April 1 and May 31
2070-2099
Western U.S. Stream
Flow Metrics3
cms·cms-1
0.7 (0.1, 3.4)
MAFlow
Mean of the yearly cumulative
discharge
1915-2006
Western U.S. Stream
Flow Metrics3
cms
35.3 (1.6 x 10-6,
7 627)
MAFlow2080
Future mean of the yearly
cumulative discharge
2070-2099
Western U.S. Stream
Flow Metrics3
cms
39.0 (1.7 x 10-6,
8 282)
Slope
Maximum-minimum elevation /
length of the NHD Flowline
NA
NHDPlusV24
km·km-1
0.06 (1.0 x 10-5,
0.7)
DamDensity
Density of georeferenced dams
within the upstream watershed
NA
StreamCat5
dams·km-2
0.003 (0, 23)
Waterfall
Categorical. River segments
upstream of a waterfall or
cascade in the Fish Passage
Barriers dataset by StreamNet
assigned value of 1.
NA
StreamNet6
NA
NA
807 1. www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html
808 2. http://regclim.coas.oregonstate.edu/index.html
809 3. https://www.fs.fed.us/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml
810 4. http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php
811 5. https://www.epa.gov/national-aquatic-resource-surveys/streamcat
812 6. http://www.streamnet.org/online-data/GISData.html
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39
813 Table 2: Comparison of basin-wide and range boundary test statistics using 10-fold cross-
814 validation (AUC) and confusion matrices for the model with (“Final”) and without (“Initial”)
815 supplemental eDNA data. The range boundary statistics measure model performance at the
816 eDNA sample locations only.
817
Basin-wide
Range Boundary
Test Statistic
Final
Initial
Final
Initial
AUC
0.90
0.93
0.91
0.78
Sensitivity
0.83
0.88
0.93
1.00
Specificity
0.80
0.81
0.61
0.30
TSS
0.62
0.69
0.55
0.30
Kappa
0.62
0.69
0.47
0.23
Accuracy
0.81
0.85
0.72
0.54
Current distribution (km)
17 660
22 209
NA
NA
Future distribution (km)
29 818
33 068
NA
NA
818
819 Note: Threshold value (0.5) was determined by maximizing the sum of sensitivity and
820 specificity. Estimated distribution is the total river kilometers occupied by smallmouth bass
821 based upon predicted probabilities of occurrence exceeding the threshold value for both current
822 (1981-2016) and projected future (2080).
823
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40
824 Table 3: Percentage of the spawning, rearing, and/or year-round habitat of each Columbia River
825 Basin salmonid species predicted to overlap with smallmouth bass and total river kilometers
826 (RKM) of predicted overlap.
Species
Run
% of Habitat
RKM
Chinook salmon
Fall
62
2 125
Spring
22
3 359
Summer
9
140
Chum salmon
NA
0
0
Coho salmon
NA
19
1 174
Sockeye salmon
NA
39
116
Steelhead
Summer
11
2 606
Steelhead
Winter
15
1 272
Bull Trout
NA
6
1 160
Redband Trout
NA
14
2 810
Westslope Cutthroat Trout
NA
3
1 361
Yellowstone Cutthroat Trout
NA
4
172
827
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Figure Captions
Fig. 1. The Columbia River Basin and the presence (filled circles) and absence (open circles) of
smallmouth bass from the assembled regional database and eDNA presence (filled diamonds)
and absence (open diamonds) data. The Canadian portion of the Columbia River Basin was not
included in the model although smallmouth bass occurs along the United States border. Stream
order 1 not depicted. Map data sources: Esri, DeLorme, HERE, MapmyIndia.
Fig. 2. Modeled distribution of smallmouth bass for a) current day (1981-2016) and b) projected
future (2080). Stream order 1 not depicted. Map data sources: Esri, DeLorme, HERE,
MapmyIndia.
Fig. 3. Response curves for environmental variables included in the species distribution model
(only those with significant contributions to the model are shown). The x-axes represent the
range of predicted values for each environmental variable. The y-axes represent each variable’s
standardized effect on the model response.
Fig. 4. The probability of smallmouth bass occurrences in designated climate refugia for bull
trout and cutthroat trout under current (1981-2016) and future (2080) climate scenarios. The lines
at the top, middle and bottom of each box represent the 75th percentile, median and 25th
percentile of the values, respectively, vertical bars (whiskers) represent 95% confidence interval,
and points represent all observations outside the confidence interval.
Fig. 5. The predicted probability of smallmouth bass occurrence (color gradient) in 14 major
tributaries of the Columbia River Basin, as well as observed presence (closed circles) and
absence (open circles) locations according to eDNA sampling. The x-axis is a measure of
relative distance, where 0 indicates the most downstream and 200 the most upstream portions of
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the sampled rivers. Known waterfalls or cascades thought to be barriers to upstream dispersal are
indicated with an ‘X’. SF, MF and NF refer to South, Middle and North Fork, respectively.
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Figure 1
The Columbia River Basin and the presence (filled circles) and absence (open circles) of
smallmouth bass from the assembled regional database and eDNA presence (filled diamonds)
and absence (open diamonds) data. The Canadian portion of the Columbia River Basin was not
included in the model although smallmouth bass occurs along the United States border. Stream
order 1 not depicted. Map data sources: Esri, DeLorme, HERE, MapmyIndia.
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Figure 2
Modeled distribution of smallmouth bass for a) current day (1981-2016) and b) projected future
(2080). Stream order 1 not depicted. Map data sources: Esri, DeLorme, HERE, MapmyIndia.
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Figure 3
Response curves for environmental variables included in the species distribution model (only
those with significant contributions to the model are shown). The x-axes represent the range of
predicted values for each environmental variable. The y-axes represent each variable’s
standardized effect on the model response.
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Figure 4
The probability of smallmouth bass occurrences in designated climate refugia for bull trout and
cutthroat trout under current (1981-2016) and future (2080) climate scenarios. The lines at the
top, middle and bottom of each box represent the 75th percentile, median and 25th percentile of
the values, respectively, vertical bars (whiskers) represent 95% confidence interval, and points
represent all observations outside the confidence interval.
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Figure 5
The predicted probability of smallmouth bass occurrence (color gradient) in 14 major tributaries
of the Columbia River Basin, as well as observed presence (closed circles) and absence (open
circles) locations according to eDNA sampling. The x-axis is a measure of relative distance,
where 0 indicates the most downstream and 200 the most upstream portions of the sampled
rivers. Known waterfalls or cascades thought to be barriers to upstream dispersal are indicated
with an ‘X’. SF, MF and NF refer to South, Middle and North Fork, respectively.
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