DOI: 10.1111/ddi.12671

BIODIVERSITY RESEARCH

Seventy years of stream-­fish collections reveal invasions and native range contractions in an Appalachian (USA) watershed

Joseph D. Buckwalter1 | Emmanuel A. Frimpong1 | Paul L. Angermeier1,2 | Jacob N. Barney3

1Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, Abstract USA Aim: Knowledge of expanding and contracting ranges is critical for monitoring inva- 2 Virginia Cooperative Fish and Wildlife sions and assessing conservation status, yet reliable data on distributional trends are Research Unit, U. S. Geological Survey, Virginia Tech, Blacksburg, VA, USA lacking for most freshwater species. We developed a quantitative technique to detect 3Department of Plant Pathology, Physiology, the sign (expansion or contraction) and functional form of range-­size changes for and Weed Science, Virginia Tech, Blacksburg, freshwater species based on collections data, while accounting for possible biases due VA, USA to variable collection effort. We applied this technique to quantify stream-­fish range Correspondence expansions and contractions in a highly invaded river system. Emmanuel A. Frimpong, Department of Fish and Wildlife Conservation, Virginia Tech, Location: Upper and middle New River (UMNR) basin, Appalachian Mountains, USA. Blacksburg, VA, USA. Methods: We compiled a 77-­year stream-­fish collections dataset partitioned into ten Email: [email protected] time periods. To account for variable collection effort among time periods, we aggre- Editor: John Wilson gated the collections into 100 watersheds and expressed a species’ range size as de- tections per watershed (HUC) sampled (DPHS). We regressed DPHS against time by species and used an information-­theoretic approach to compare linear and nonlinear functional forms fitted to the data points and to classify each species as spreader, stable or decliner. Results: We analysed changes in range size for 74 UMNR fishes, including 35 native and 39 established introduced species. We classified the majority (51%) of introduced species as spreaders, compared to 31% of natives. An exponential functional form fits best for 84% of spreaders. Three natives were among the most rapid spreaders. All four decliners were New River natives. Main conclusions: Our DPHS-­based approach facilitated quantitative analyses of dis- tributional trends for stream fishes based on collections data. Partitioning the dataset into multiple time periods allowed us to distinguish long-­term trends from population fluctuations and to examine nonlinear forms of spread. Our framework sets the stage for further study of drivers of stream-­fish invasions and declines in the UMNR and is widely transferable to other freshwater taxa and geographic regions.

KEYWORDS conservation assessment, functional form, introduced species, native invaders, range expansion, species declines

Diversity and Distributions. 2018;24:219–232. wileyonlinelibrary.com/journal/ddi © 2017 John Wiley & Sons Ltd | 219 220 | BUCKWALTER et al.

1 | INTRODUCTION (data points) enables trend detection through regression of range-­size estimates against time. At least one-­quarter of the world’s freshwater fishes (39% in North Range expansion approximates the logistic law (Verhulst, 1838; America) are imperiled (Helfman, 2007; Jelks et al., 2008). Freshwater i.e., initial exponential spread becoming dampened as accessible, fishes had the highest extinction rate among vertebrates in the 20th suitable sites are saturated) if sampling captures the entire scope of century, which was ~877 times greater than the background rate in an invasion, which is often not the case. Depending on the invasion North America (Burkhead, 2012). Biological invasions are among the stage attained and the window of time represented by a collections leading causes of extinction of freshwater fishes globally (Helfman, dataset, range expansion may exhibit alternate functional forms, such 2007) through adverse effects such as hybridization, competition, as exponential or linear, which are nested within the general logistic predation, disease, food web and ecosystem changes, and habitat al- model (Figure 1). Thus, detecting a trend entails comparing multiple teration (Cucherousset & Olden, 2011). Invasive species contributed candidate models of change in range size against the null hypothe- to 68% of the freshwater fish extinctions in North America (Miller, sis (no trend). Furthermore, the observed form of range-­size change, Williams, & Williams, 1989). when interpreted in conjunction with other introduction attributes Knowledge of long-­term trends in species’ range sizes is essen- (e.g., time since introduction, propagule pressure) may have import- tial for assessments of conservation status and invasive spread, but ant implications for the future trajectory of an invasion. For instance, such analyses require long-­term, broadscale species distribution exponential increase in range size implies that a species is likely to data. Since long-­term monitoring datasets collected using consis- continue spreading in the near future and its impacts likely to increase. tent, systematic protocols are scarce, distributional trends have Slow linear growth co-­occurring with relatively small range size or re- yet to be quantitatively assessed for most freshwater fishes, and cent introduction may characterize an invasion in lag phase (Aagaard evidence of changing distributions has been largely subjective and & Lockwood, 2014), whereas slow linear growth with large range size lacking in rigorous quantification (Olden & Poff, 2005). However, may indicate logistic dampening (Figure 1). collections data compiled from museums, universities, agencies, Unless an ecosystem has been greatly altered from its natural state, and individuals can support sound empirical analyses of distribu- native species as a group, given their longer biogeographic history in a tional trends if sources of bias are accounted for (Botts, Erasmus, & basin, are typically assumed to be in equilibrium with the environment Alexander, 2012; Shaffer, Fisher, & Davidson, 1998; Telfer, Preston, and therefore to exhibit relatively stable range sizes, whereas invaders & Rothery, 2002). remain in disequilibrium (i.e., spreading logistically, Figure 1) until suit- When comparing historical and modern collections data, several able areas have been filled (Araújo & Pearson, 2005; Guisan & Thuiller, sources of bias associated with collection effort must be considered: 2005; Peterson, 2003). Thus, comparing the spread rates of introduced (1) collection effort must be sufficient in each time period such that versus native species may provide exploratory insights into the degree absence from a sampling unit is meaningful (Shaffer et al., 1998). to which (1) introduced species have achieved equilibrium in the re- The rate of omission errors (“false absences”; i.e., non-­detection of ceiving environment, with implications for possible future impacts; and a resident species) can be reduced by pooling collections over larger (2) environmental changes have altered native species ranges. spatial (e.g., grid cells, stream reaches or watersheds) or temporal units; (2) variation in sampling intensity among time periods biases Lag phaseExponential phaseStable phase observed trends in range size, but can be accounted for by express- Saturation E ing range size for a given time period as the proportion of all units sampled in which a focal species was found (Botts et al., 2012; Loo, D Keller, & Leung, 2007; Olden & Poff, 2005; Telfer et al., 2002); (3) uneven spatial distribution of collections among time periods may size

nge Inflection point confound analyses of trends in range size. If necessary, analyses can Ra C be done using only data from units sampled during all time periods, or by subsampling densely sampled units (Botts et al., 2012); (4) sam- pling efficiency or selectivity may also change over time (e.g., due to B a shift in sampling gear or targeted species; Botts et al., 2012; Shaffer A et al., 1998). Time (generations) since introduction

Population fluctuation is a possible source of bias (e.g., mistak- FIGURE 1 The logistic law of population growth (Verhulst, 1838) ing noise for a signal) when measuring changes in range size (Faber-­ adapted to reflect observable functional forms of range expansion. Langendoen et al., 2012; IUCN, 2016). However, prior studies to Depending on which phase of the underlying logistic growth curve detect range changes based on presence-­only collections data (Botts is represented in a collections dataset, an invasion may exhibit alternate forms of spread such as stable or slowly rising linear (A, et al., 2012; Olden & Poff, 2005; Telfer et al., 2002) considered only E), exponential (B), rapidly rising linear (C) or logistic dampening two time periods (historical vs. modern), an approach that provides no (D). “Saturation” indicates that an invading organism occupies all statistical basis to distinguish a long-term­ trend in range size (signal) accessible, suitable sites in the invaded area. [Colour figure can be from short-­term fluctuations (noise). Including multiple time periods viewed at wileyonlinelibrary.com] BUCKWALTER et al. | 221

Our objectives for this study were to (1) develop a quantita- the 1970s, supplemented by unauthorized interbasin transfers of tive framework to detect the sign and functional form of long-­term baitfishes since the mid-­20th century, has added 56 established in- changes in range size of freshwater species based on collections data, troduced fishes (Table S1), such that the New River drainage leads while accounting for possible biases due to variable collection effort; the eastern United States in its ratio of introduced-­to-­native species (2) apply this technique to determine stream-­fish range expansions (1.24:1; Jenkins & Burkhead, 1994). and contractions in a highly invaded Appalachian (USA) basin, specifi- Land cover and hydrology in the UMNR are highly altered. Land cally: (i) classify each species as spreader, decliner or stable; (ii) for each cover comprises 63% forest, 28% agricultural and 7% developed spreader, identify the functional form of spread; (iii) compare average lands (Fry et al., 2011; Figure 2). Five hydroelectric dams on the New spread rates of native versus introduced spreaders; (3) highlight man- River mainstem and one at the mouth of the largest UMNR tributary agement insights revealed through objectives 1–2. (Little River, Virginia) ranging from 4 to 42 m high were constructed from 1902 to 1939 (Figure 2). The largest, Claytor Dam, impounds a 34-­km-­long, 1819-­ha mainstem reservoir (Rosebery, 1951), which, 2 | METHODS along with hundreds of smaller hydroelectric, recreation, flood control and farm impoundments in the UMNR, has been repeatedly stocked 2.1 | Study area and fauna with non-­native game and prey fishes over the past century (Jenkins The scope of our case study included tributaries of the approximately & Burkhead, 1994). 10,000-km­ 2 upper and middle portion of the New River drainage Several UMNR characteristics make this a particularly suitable sys- (hereafter, the UMNR; Figure 2) located in the Appalachian Mountains tem in which to study stream-­fish invasions: (1) the UMNR offers a of North Carolina and Virginia, USA. Since few whole-­community col- sufficient number of invasion cases for statistical analyses and com- lections were reported from the mainstem New River (most sampling parisons; (2) whereas no ecosystem is immune to invasion, originally in the mainstem has exclusively targeted game species), we excluded depauperate ecosystems such as the UMNR are hypothesized to be the mainstem from the study area. The New River drainage has fewer more susceptible to invasion and major community impacts such as native fish species (45) than any other major eastern US drainage, but decline and extirpation of native species (Moyle & Light, 1996); (3) a high proportion (eight of 45 species) of endemic fishes (Jenkins & endemism of the receiving area has been linked to invasion success Burkhead, 1994; Robins, 2005). State-­sanctioned stocking of non-­ (Ruesink, 2005) and also provides impetus from a conservation stand- native game and prey species from the mid-­19th century through point to investigate possible invasion impacts; (4) the UMNR’s two

FIGURE 2 The upper and middle New River (UMNR) study area comprised the Virginia and North Carolina portions of the New River drainage. Left map shows physiographic provinces and major dams. Right map shows 2006 land cover (Fry et al., 2011). Map projection: Universal Transverse Mercator, zone 17N 222 | BUCKWALTER et al. ecoregions provide contrasting abiotic contexts in which to investigate TABLE 1 Partitioning the upper and middle New River (UMNR) invasion outcomes; (5) UMNR watersheds span spatial and temporal fish collection dataset into 10 time periods gradients of human land use practices that provide opportunities to Duration observe human influences on invasion processes. For instance, de- Period Years (year) Collections (%) N x forestation, agriculture and impoundments have altered watershed-­ 1 1938–1953 16 166 (7.1) 56 15 and reach-­scale processes, transforming many Appalachian highland 2 1954–1962 9 174 (7.5) 58 24 streams functionally into lowland streams by increasing water tem- 3 1963–1969 7 186 (8) 54 31 perature, fine sediment and nutrient inputs, and availability of lentic 4 1970–1976 7 196 (8.4) 56 38 habitats (Jones, Helfman, Harper, & Bolstad, 1999). These contextually 5 1977–1983 7 206 (8.8) 65 45 novel conditions often favour lowland generalists, but are less suitable to native highland specialists (Angermeier & Winston, 1998; Hitt & 6 1984–1996 13 224 (9.6) 64 58 Roberts, 2012; Jones et al., 1999; Lapointe, Thorson, & Angermeier, 7 1997–1998 2 196 (8.4) 70 60 2012; Scott & Helfman, 2001). 8 1999–2007 9 232 (9.9) 68 69 9 2008–2011 4 382 (16.4) 71 73 10 2012–2014 3 370 (15.9) 64 76 2.2 | Data compilation and spatial and temporal binning Sum 77 2,332 (100) N represents the number of watersheds sampled during each time period, We compiled a fish collections dataset for the UMNR from records out of 100 watersheds in the UMNR study area. The vector x, representing maintained by federal and state agencies, museums and the authors the number of years from 1938 (year 0) to the end of each time period, was (see Appendix 1 and Fish collections dataset in Supporting Information). used as the independent variable in trend analyses of range size. Each collection record included latitude, longitude, year, species name opposed to treating time as continuous) was to smooth out short-­ and watershed code. The UMNR comprises 100 uniquely numbered, term fluctuations to highlight the longer-­term trend in range size, and hydrologically connected watersheds (i.e., 12-­digit hydrologic units also to address spatial and temporal imbalance of samples from year [hereafter “HUCs”]; mean area = 95 km2), as delineated by the USA to year. If we treated time as continuous, there would be much noise Watershed Boundary Dataset (WBD; Simley & Carswell, 2009). The in the data due to many years with missing data and uneven spatial final 77-­year (1938–2014) UMNR fish collections dataset contained distribution of collections. 19,424 occurrences of 74 species from 2,332 unique sampling events. As mentioned above, to minimize variation in sampling effort Although the appropriate resolution of spatial and temporal sam- among time periods and reduce bias due to spatial imbalance of col- pling units is likely a region-­ and taxon-­specific function of historical lections, we suggest that at least 50% of HUCs (or other spatial units) sampling effort and the environment (e.g., analysis of riverine species be sampled during each time period. Increasing the number of time may require larger units than analysis of headwater species), we offer periods (n) provides more degrees of freedom for regressions, but an the following general guidelines on how to choose spatial and tem- excessive number of time periods will result in insufficient sampling poral sampling units that produced instructive results in our UMNR within a time period. Our information-­theoretic approach to detect case study. trends in range size required at least seven time periods since our We selected HUCs as the spatial sampling unit for the UMNR models had up to K = 5 parameters. To calculate Akaike’s information based on the premise that finer spatial units are preferable to coarser, criterion (Akaike, 1973) corrected for small samples (AICc; Hurvich until sampling becomes too sparse. Sparseness of sampling can be ad- & Tsai, 1989) requires a minimum of n > K + 1 time periods. For the justed by selecting finer or coarser scale spatial and temporal bins. This UMNR, we found that n = 10 provided a sufficient sample size for re- could be an avenue for future research, but at a minimum, to minimize gressions and model comparison while maintaining sampling intensity false absences, we suggest that most spatial units should be sampled >50% HUCs per time period. We used a simple graphical approach to more than once per time period. We started with the finest-­scale screen for potential imbalances in sampling effort among time periods HUCs available then adjusted the temporal bins such that at least 50% (see Addressing biases in collections data). of HUCs were sampled in every time period. We found that this com- bination typically resulted in >1 collection per HUC per time period, with little variability between time periods. We did not attempt to use 2.3 | Classifying spreaders, decliners and stable finer watersheds because: (1) it appeared that finer units would result species in too sparse sampling (i.e., frequently 0–1 collections per HUC per We developed a hierarchical framework to classify UMNR stream time period); and (2) no widely accepted smaller divisions of water- fishes in terms of distributional trends. First, to account for variation sheds were readily available. in sampling intensity between time periods, we defined detections per We partitioned the dataset into ten time periods ranging in du- HUC sampled (DPHS) for each species and time period as: ration from two to 16 (mean = 7.7) years (Table 1). Cut-­offs between n time periods were adjusted to have an approximately equal number DPHS i , i = (1) of collections per time period. Our intent for temporal binning (as Ni BUCKWALTER et al. | 223

where: ni = the number of HUCs the species was detected in during every 2.5 years) were further classified as strong spreaders and those time period i, and Ni = the number of HUCs sampled during time pe- having β1 < 0.004 as weak spreaders. riod i. We then regressed DPHS (y) against vector x representing the We used β1 of the linear model to distinguish strong versus weak number of years from 1938 (year 0 in our dataset) to the end of each spreaders because it represented the average rate of change in DPHS time period. and was straightforward to compare among species. A natural break

To detect multiple functional forms of range expansion or contrac- in the distribution of β1 occurred at 0.004 for UMNR spreaders. In tion in the DPHS time series, we fit six curves to the (up to) 10 data classifying spreaders and decliners, α was relaxed to 0.1 because of points for each species. These curves, hereafter referred to as “mod- the small sample size (five to 10 time periods) in the regressions (spe- els”, represent a null hypothesis (no trend in DPHS) and five alterna- cies introduced into the UMNR after 1953 occurred in less than 10 tive hypotheses describing expected functional forms of range change time periods). To help visualize spatio-­temporal patterns of spread and (spread or decline): decline, we mapped detections of strong spreaders and decliners by Intercept-­only (null) model: time period.

y =β0. (2) We used R statistical software (R Core Team, 2014) for all analyses. Linear model: The lm function was used to fit the null and linear models, and the y x. =β0 +β1 (3) nlsLM function in the “minpack.lm” package (Elzhov, Mullen, Spiess, &

Two-­parameter exponential model (lower asymptote fixed at Bolker, 2013) was used to fit the exponential and logistic models. β1 confidence limits were computed with the confint function. The AICc y = 0): β1x y =β0e . (4) function in the “AICcmodavg” package (Mazerolle, 2015) was used to Three-­parameter exponential model (lower asymptote a compute AICc values. estimated): To test whether introduced species spread faster than natives, β1x y = a+β0e . (5) we regressed average rate of spread (100 × β1 of the linear model, Three-­parameter logistic model (lower asymptote fixed at y = 0, units = HUCs/year) against initial range size (first non-­zero DPHS, upper asymptote b estimated): iDPHS) and tested for equality of slopes between introduced versus b native species. To determine whether introduced species are inher- y = . (6) 1+e−β1(x−xmid) ently more capable of spreading than natives, we repeated the regres- Four-­parameter logistic model (lower and upper asymptotes sion, excluding species from either group whose iDPHS fell outside the estimated): range observed for the other group. b−a y = a+ , (7) 1+e(xmid−x)∕β1 2.4 | Addressing biases in collections data where β0 = y-­intercept or scale; β1 = slope or growth rate, xmid = in- flection point and a and b = lower and upper asymptotes respectively. We took measures to minimize variation in sampling effort among For each species, we defined a Distribution trend response vari- time periods and screen collections for sampling biases that could af- able having four ordinal classes: strong spreader, weak spreader, fect observed range-­size trends. We reduced the prevalence of false stable and decliner. First we computed AICc, Akaike weight and absences by aggregating collections by HUC. By expressing range size the 90% confidence set on the fitted models, by species (Table as a proportion (DPHS), we minimized the effect of temporal variation

S3). Akaike weight (wi) approximates the probability that a candi- in sampling intensity. We adjusted the cut-­offs between time periods date model is the best model in the model set (Anderson, 2008). to distribute collections approximately evenly among time periods

Then, we summed the wi’s from largest to smallest until the sum while maintaining a minimum of 50% of UMNR HUCs sampled during was ≥0.90, and the corresponding subset of models comprised each time period. We plotted collection locations to assess their spa- the 90% confidence set. To evaluate evidence for a trend in DPHS, tial evenness across the UMNR during each time period. To screen for we considered two criteria (1) occurrence of alternative (non-­null) major differences in sampling effort among time periods, we made box models in the 90% confidence set; and (2) estimated β1 confidence plots of the log (x)-­transformed number of fish collections per HUC by intervals (CI) from the regressions. We considered spread or de- time period and the log (x + 1)-­transformed number of non-­game spe- cline to be plausible for a species if a non-­null model whose β1 90% cies reported per collection by time period. The number of non-­game CI did not span zero occurred in the 90% confidence set. Thus, a species indicates the extent to which the whole fish assemblage was species was considered to be stable (no trend in DPHS) if the 90% sampled. If the number of collections per HUC and non-­game species confidence set comprised only the null model, or if the β1 90% CI per collection remain stable across time periods, we expect the prob- spanned zero for all models in the 90% confidence set. Conversely, ability of detection and false-­absence rate for any given species to a species was considered to be a decliner or spreader, respectively, also remain stable. if the 90% confidence set included one or more non-­null models To investigate whether increased detection over time may have having a negative β1 upper 90% confidence limit (UCL) or a positive confounded the trends observed for the study species, we used dy-

β1 lower confidence limit (LCL). Spreaders having β1 for the linear namic occupancy models (MacKenzie, Nichols, Hines, Knutson, & model ≥0.004 (equivalent to a range increase of at least one HUC Franklin, 2003) as a complementary approach to independently verify 224 | BUCKWALTER et al. the range-­change findings of the above DPHS method for a subset of The relationship between rate of spread and iDPHS (with all ten UMNR species (three strong spreaders, two weak spreaders, two species included; Figure 6, left) confirmed our Distribution trend as- stable species and three decliners). The occupancy model accounted signments and demonstrated that introduced species are mostly for time-­varying detection probabilities differently than the DPHS ap- spreading, while most natives are in equilibrium or declining (p-value proach and also used different (26 equal interval, 3-­year) time periods. for the null hypothesis of equal slopes = .024, t = 2.31). The regression Detailed occupancy modelling methods and results are included in for the 40 species occupying the portion of the iDPHS range contain- Supporting Information. ing both introduced and native species (Figure 6, right) suggested that introduced and native species having small ranges did not differ in their inherent ability to spread (p-value for the null hypothesis of equal 3 | RESULTS slopes = .341, t = 0.97). Several patterns emerged from the time series maps for strong 3.1 | Summary of UMNR collections dataset spreaders and decliners (Figs S6–S18): (1) native strong spreaders The screened dataset included 77 species, 42 (55%) of which were in- mountain redbelly dace Chrosomus oreas (Fig. S6), bluehead chub troduced. We excluded three recently detected introductions, eastern Nocomis leptocephalus (Fig. S7), and rosyside dace Clinostomus fundu- mosquitofish Gambusia holbrooki, redline darter Etheostoma rufilinea- loides (Fig. S8) appear to have spread concurrently; (2) three cyprinid tum and highback chub Hybopsis hypsinotus from further analysis be- decliners—tonguetied minnow Exoglossum laurae (Fig. S9), rosyface cause the number of time periods having DPHS data was insufficient shiner Notropis rubellus (Fig. S10) and silver shiner Notropis photoge- for model fitting. Thus, 74 species (39 introduced, 35 native) were nis (Fig. S12)—have maintained a stronghold in the upper New River retained in the final dataset (Table 2). watershed (North Carolina portion); (3) a closer examination of the Collections were distributed evenly across the UMNR for most apparent upstream spread of introduced whitetail shiner Cyprinella ga- time periods (Fig. S5). Box plots showed that the number of collections lactura suggested multiple cryptic introductions upstream of a series per HUC (Fig. S1) and the number of non-­game species per collection of impassible dams (Figure 7). (Fig. S2) were consistent across time periods, indicating that collection For each of the 10 test species to which dynamic occupancy mod- effort per HUC and selectivity for a given species remained reasonably els were applied, a plot of estimated occupancy over time showed a stable. pattern corresponding to the Distribution trend class assigned using the DPHS-­based approach presented herein (even numbered Figs S20–S38). Results of the occupancy models also demonstrated that, 3.2 | Patterns of spread and decline although increased sampling effort affected increased detection over We classified 31 (42%) species as spreaders, four (5%) as decliners time for some species (Tables S4–S13, odd numbered Figs S19–S37), and 39 (53%) as stable. Spreaders comprised 31% of the native spe- the DPHS approach and binning strategy described above success- cies, compared to 51% of the introduced species (Figure 3, Table 2). fully mitigated the potential effect of increased detection. Since the A UMNR native, mountain redbelly dace Chrosomus oreas, showed occupancy model is more data-­intensive and requires considerably the greatest average rate of spread, equivalent to a range increase more work and modelling expertise than the DPHS-­based method in- of 0.7 HUCs per year (Table S3). Salmonids comprised 4% of the spe- troduced in this study, it is unlikely to be applicable to most analyses cies pool, but 22% of the strong spreaders. Otherwise, spreaders were of historical data. proportionally distributed among families (Figure 4). For 26 (84%) of the 31 spreaders, including all nine strong spread- ers, an exponential model (Equations 4 or 5, Figure 6c,d) was most 4 | DISCUSSION plausible (Table 2). For 14 of these exponential spreaders, the linear model (Equation 3, Figure 5b) was also strongly supported (Table S3). Our DPHS-­based approach enabled the use of collections data for The linear model had the greatest support for the remaining five (16%) quantitative analyses of distributional trends while addressing pos- spreaders, three of which also showed strong support for an exponen- sible biases due to variable collection effort. Partitioning the collec- tial model. A logistic model (Equations 6 or 7), which if observed would tions dataset into 10 time periods allowed us to distinguish long-­term suggest that most of the invasion trajectory was represented in our changes in range size from population fluctuations and to analyse study period, was never strongly supported. functional forms of spread. Using this approach, we reconstructed We classified four (11%) of UMNR natives as decliners. No intro- distributional trends for 74 UMNR stream fishes, none of which to duced species qualified as a decliner (Figure 3). Cyprinids comprised our knowledge was previously quantitatively assessed. We found 45% of the species pool, but 75% of the decliners (Figure 4). Silver that, although the rate of new invasions has declined in the UMNR shiner Notropis photogenis showed the greatest average rate of de- since the mid-­20th century (Fig. S4): (1) most fishes (55%) currently cline, equivalent to extirpation from 0.44 HUCs per year since 1938 established in UMNR tributaries were introduced; (2) most introduced (Fig. S3, Table S3). DPHS of sharpnose darter Percina oxyrhynchus species (51%) are spreaders, compared to 31% of natives; (3) most peaked at 10.7% in period 4, but none were reported in period 10 spreaders (84%) have been spreading exponentially, suggesting their (Fig. S3, Table S2). distribution and impacts will continue to increase; (4) four UMNR BUCKWALTER et al. | 225

TABLE 2 Distribution trend and best model of spread for 74 fishes of upper and middle New River tributaries

Species Code Best model Distribution trend

Chrosomus oreas (mountain redbelly dace) ChOrea exp2 Strong spreader Clinostomus funduloides (rosyside dace) ClFund exp2 Nocomis leptocephalus (bluehead chub) NoLept exp3 Notropis rubricroceus (saffron shiner)a NoRubr exp2 Salmo trutta (brown trout)a SaTrut exp2 Oncorhynchus mykiss (rainbow trout)a OnMyki exp2 Ambloplites rupestris (rock bass)a AmRupe exp2 Lepomis auritus (redbreast sunfish)a LeAuri exp2 Etheostoma caeruleum (rainbow darter)a EtCaer exp2 Campostoma anomalum (central stoneroller) CaAnom exp2 Weak spreader Exoglossum maxillingua (cutlips minnow)a ExMaxi exp2 Cyprinella galactura (whitetail shiner)a CyGala exp2 Luxilus coccogenis (warpaint shiner)a LuCocc exp3 L. cerasinus (crescent shiner)a LuCera exp2 Notropis leuciodus (Tennessee shiner)a NoLeuc exp2 N. chiliticus (redlip shiner)a NoChil lin N. telescopus (telescope shiner)a NoTele lin Thoburnia rhothoeca (torrent sucker)a ThRhot exp2 Ameiurus nebulosus (brown bullhead)a AmNebu exp2 Noturus insignis (margined madtom) NoInsi exp2 Cottus bairdii (mottled sculpin) CoBair exp2 Micropterus punctulatus (spotted bass)a MiPunc exp2 M. salmoides (largemouth bass)a MiSalm exp2 Lepomis cyanellus (green sunfish) LeCyan exp2 Percina gymnocephala (Appalachia darter) PeGymn exp2 P. roanoka (Roanoke darter)a PeRoan lin Etheostoma kanawhae (Kanawha darter) EtKana exp3 E. simoterum (snubnose darter)a EtSimo lin E. nigrum (Johnny darter) EtNigr lin E. olmstedi (tessellated darter)a EtOlms exp2 E. flabellare (fantail darter) EtFlab exp2 Exoglossum laurae (tonguetied minnow) ExLaur exp2 Decliner Notropis rubellus (rosyface shiner) NoRube exp2 N. photogenis (silver shiner) NoPhot exp2 Percina oxyrhynchus (sharpnose darter) PeOxyr lin Amia calva (bowfin)a AmCalv Null Stable Esox niger (chain pickerel)a EsNige Null Cyprinus carpio (common carp)a CyCarp Null Carassius auratus (goldfish)a CaAura Null Notemigonus crysoleucas (golden shiner)a NoCrys Null Rhinichthys cataractae (longnose dace) RhCata Null R. atratulus (blacknose dace) RhAtra Null Semotilus atromaculatus (creek chub) SeAtro Null Nocomis platyrhynchus (bigmouth chub) NoPlat Null Phenacobius teretulus (Kanawha minnow) PhTere Null

(Continues) 226 | BUCKWALTER et al.

TABLE 2 (Continued)

Species Code Best model Distribution trend

Cyprinella spiloptera (spotfin shiner) CySpil Null Luxilus albeolus (white shiner) LuAlbe Null L. chrysocephalus (striped shiner) LuChry Null Lythrurus ardens (rosefin shiner) LyArde Null Notropis hudsonius (spottail shiner)a NoHuds Null N. scabriceps (New River shiner) NoScab Null N. volucellus (mimic shiner) NoVolu Null N. procne (swallowtail shiner)a NoProc Null Pimephales promelas (fathead minnow)a PiProm Null P. notatus (bluntnose minnow) PiNota Null Hypentelium nigricans (Northern hogsucker) HyNigr Null Moxostoma cervinum (blacktip jumprock)a MoCerv Null Catostomus commersonii (white sucker) CaComm Null Ictalurus punctatus (channel catfish) IcPunc Null Ameiurus natalis (yellow bullhead)a AmNata Null A. melas (black bullhead)a AmMela Null Salvelinus fontinalis (brook trout) SaFont Null Cottus kanawhae (Kanawha sculpin) CoKana Null Pomoxis nigromaculatus (black crappie)a PoNigr Null P. annularis (white crappie)a PoAnnu Null Micropterus dolomieu (smallmouth bass)a MiDolo Null Lepomis megalotis (longear sunfish)a LeMega Null L. macrochirus (bluegill)a LeMacr Null L. gibbosus (pumpkinseed)a LeGibb Null L. microlophus (redear sunfish)a LeMicr Null Perca flavescens (yellow perch)a PeFlav Null Percina caprodes (logperch) PeCapr Null Etheostoma osburni (candy darter) EtOsbu Null E. blennioides (greenside darter) EtBlen Null Six models (null; linear = lin; 2-­parameter exponential = exp2; 3-­parameter exponential = exp3; 3-­parameter logistic = log3; 4-­parameter logistic = log4) were fitted to the detections per HUC sampled time series by species (Table S2). Each species was classified as spreader, decliner or stable based on Akaike’s information criterion corrected for small samples (AICc) and 90% confidence limits on the slope/growth-­rate term (β1) of the fitted models (Methods, Table S3). Spreaders were split into weak and strong classes based on β1 of the linear model. aIntroduced species.

natives, including two previously considered stable by state conserva- & Williams, 1999; M. Pinder, VDGIF, personal communication; B. tion agencies, show evidence of range contraction; and (5) the upper Tracy, North Carolina Department of Environmental Quality, per- portion of the UMNR has served as a stronghold for three cyprinid sonal communication). species in decline elsewhere. Although the spreaders were disproportionately represented by introduced species, three native minnows, mountain redbelly dace (Chrosomus oreas), bluehead chub (Nocomis leptocephalus) and rosyside 4.1 | Spreaders dace (Clinostomus funduloides), were among the strongest spreaders The rapid spread of introduced salmonids is undoubtedly due and have spread concomitantly in the UMNR (Figs S6–S8). We hy- in large part to high propagule pressure (Lockwood, Cassey, & pothesize that nest association with bluehead chub hosts has facili- Blackburn, 2005). Due to their status as highly preferred game- tated the establishment, and therefore spread, of mountain redbelly fishes, rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo and rosyside dace, as well as an introduced nest associate, saffron trutta) have been widely and abundantly stocked for many decades shiner (Notropis rubricroceus), into degraded highland streams (Hitt throughout the UMNR (Jenkins & Burkhead, 1994; Fuller, Nico, & Roberts, 2012; Peoples, Tainer, & Frimpong, 2011). All three are BUCKWALTER et al. | 227

57.1 80 60 Native Introduced (a) 51.3 70 (n = 35) (n = 39) 48.7 50 60

) 50 40 (% 31.4 40 30 30 20 equency 20 10 Fr 11.4 0 10 80 0.0 (b) 0 70 Decliner Spreader Stable 60 50 FIGURE 3 Relative frequencies of species in Distribution trend 40 classes for 39 introduced and 35 native fishes of upper and middle 30 20 New River drainage basin Frequency (% ) 10 0 80 (c) “strong” (nearly obligate) Nocomis nest associates (Pendleton, Pritt, 70 60 Peoples, & Frimpong, 2012) whose reproductive success relies heavily 50 on nests and/or nest protection provided by Nocomis hosts (Peoples 40 & Frimpong, 2013). Nest association is thought to be mutually bene- 30 20 ficial, as Nocomis hosts benefit from reduced predation on their own 10 eggs through the dilution effect (Johnston, 1994; Peoples & Frimpong, 0 e e 2013). Nest association with bluehead chub was previously implicated e in facilitating rapid expansion of an introduced minnow, rough shiner Amiidae Codae Percida Esocidae Ictaluridae Cyprinidae

(Notropis baileyi), in the Chattahoochee River drainage of Alabama and Salmonida Catostomidae Georgia, USA (Herrington & Popp, 2004; Walser, Falterman, & Bart, Centrarchida 2000). FIGURE 4 Relative frequencies of upper and middle New River The introduced non-­game spreader whitetail shiner (Cyprinella stream-­fish species by family. Bold column borders indicate families that were over-­represented among the strong spreaders (b, n = 9) galactura) initially appeared to have invaded the UMNR by upstream or decliners (c, n = 4) compared to the total distribution of species dispersal from founding colonies located in the lower portion of the among families (a, n = 74) drainage. However, the presence of five impassible mainstem dams belies this possibility (Figure 7). We interpret the apparent upstream dispersal of whitetail shiner to be the result of multiple cryptic intro- four are associated with clear water and seem to avoid heavy silt, ductions, most likely bait-­bucket releases (Fuller et al., 1999), during and the tonguetied minnow prefers cool water. Two of the decliners, the latter half of the 20th century. At least three independent intro- tonguetied minnow and sharpnose darter, possess three of the four ductions are implicated (1) the original introduction in Wolf Creek ecological attributes associated with extinction-­prone freshwater prior to 1954; (2) at least one introduction upstream of Fries Dam fishes in Virginia, including small range, ecological specialization, and prior to 1998; and (3) at least one introduction upstream of the Little limited range of waterbody sizes used by the species (Angermeier, River Dam prior to 1998. Telescope shiner (Notropis telescopus) ex- 1995). hibited a similar pattern of upstream invasion. These invaders’ ability The silver shiner possesses no extinction-­prone attribute iden- to readily bypass multiple barrier dams highlights the role of humans tified by Angermeier (1995), yet it was identified as the strongest in facilitating UMNR invasions and illustrates the need to curtail un- decliner in our study. The silver shiner ranges from the Lake Erie authorized introductions before embarking on a conservation strat- drainage south through most of the Ohio River basin (Page & Burr, egy relying on barriers to isolate upstream natives from downstream 2011). It is considered to be highly sensitive to silt and pollution and invaders (Fausch, Rieman, Dunham, Young, & Peterson, 2008; Rahel, was extirpated from rapidly urbanizing streams near Columbus, Ohio 2013). (Miltner, White, & Yoder, 2004). It appears to be in decline throughout the southern Appalachians of the United States (Johnston, Ramsey, Sobaski, & Swing, 1995) and is listed as threatened in Canada, where 4.2 | Decliners threats include elevated suspended sediment and other anthropo- Our study identified four native decliners in UMNR tributaries: genic impacts (COSEWIC, 2011). In Virginia, Jenkins and Burkhead tonguetied minnow (Exoglossum laurae), rosyface shiner (Notropis (1994) described the silver shiner as widespread in the New River and rubellus), silver shiner (Notropis photogenis) and sharpnose darter Tennessee River drainages, but verging on extirpation in the upper (Percina oxyrhynchus). Jenkins and Burkhead (1994) offered possible Big Sandy drainage due to pervasive siltation from coal mining and clues as to why these four species have apparently decreased: all hillslope agriculture. 228 | BUCKWALTER et al.

FIGURE 5 Illustrations of functional forms of spread exhibited by fishes of upper and middle New River tributaries, with lines of fit for the six models being compared (Equations 2–7). The null model (Equation 2) fit best for the 39 stable species exemplified in (a). Of the 31 spreaders, the linear model (Equation 3) fit best for five species exemplified in (b), the two-­parameter exponential model (Equation 4) for 23 species (c) and the three-­parameter exponential model (Equation 5) for three species (d). The logistic model (Equations 6 and 7) never fit best (Table 2). The scales of the y-axes­ in (a)–(d) vary due to differing ranges of detections per HUC sampled (DPHS defined in Equation 1) observed among species. [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 6 Comparison of spread rates of introduced (I) and native (N) fishes in upper and middle New River tributaries from 1938 to 2014, controlling for initial range size (iDPHS, first nonzero detections per HUC sampled [DPHS] in the time series). “HUCs” in the y-­axis label stands for sixth-­level hydrologic units (subwatersheds). The regression with all 74 species included (left) shows that introduced species, on average, are spreading, while natives are stable (p-value for equal slopes = .024). The regression for the 40 species occupying the portion of the iDPHS range containing both introduced and native species (right) shows that introduced and native species do not differ in their inherent ability to spread (p-value for equal slopes = .341)

The sharpnose darter is a riverine species found in southern in crevices between coarse substrates (Jenkins & Burkhead, 1994), tributaries of the upper and middle Ohio River, including the New which likely makes it vulnerable to silt deposition and embedded- and Big Sandy drainages in Virginia. It probes for macroinvertebrates ness. Along with the tonguetied minnow, the sharpnose darter BUCKWALTER et al. | 229

FIGURE 7 Whitetail shiner Cyprinella galactura distribution map in the upper and middle New River drainage. This invader was first detected in 1954 in the Wolf Creek headwaters (Jenkins & Burkhead, 1994). Subsequent detections of whitetail shiner upstream of a series of impassible dams on the New and Little rivers suggest at least three unauthorized introductions occurred: (1) Wolf Creek prior to 1954; (2) upstream of Fries Dam prior to 1998; (3) upstream of Little River Dam prior to 1998. Map projection: Universal Transverse Mercator, zone 17N

has been recognized as a species of moderate conservation need higher in elevation than the Virginia HUCs (Jarvis, Reuter, Nelson, & in Virginia (VDGIF, 2015). Its status as a decliner in the UMNR is Guevara, 2008). The higher elevation and greater forest cover of the tentative pending more thorough community sampling of the New North Carolina watersheds likely maintain cooler water preferred by River mainstem, where prior collections have mainly targeted game tonguetied minnow. species. Environmental resistance of the North Carolina HUCs to inva- Declining DPHSs of New River natives, especially silver shiner and sions offers an alternate explanation. The abundance and repro- rosyface shiner, which were considered secure from a conservation ductive success of Nocomis and their strong associates in the New standpoint by the Virginia Department of Game and Inland Fisheries River basin are positively related to agricultural land use, suggesting (VDGIF, 2015), may warrant further investigation. Our results empha- that the importance of nest association increases along a gradient of size the importance of fish community monitoring to detect changes physical stress (Peoples, Blanc, & Frimpong, 2015). Watersheds with in fish distribution and abundance. Community sampling in the Virginia lower agricultural land use, such as the North Carolina portion of the portion of the New River mainstem has been especially sparse. Such UMNR, may therefore be more resistant to invasion by Nocomis and monitoring could also provide early detection of new introductions associates. Where established, these species are the most abundant and increase our understanding of stream communities, invasion im- UMNR tributary fishes and likely impact the receiving community pacts and effects of other environmental changes. through competition for limited resources. Trautman (1981) noted The North Carolina portion of the UMNR has provided refuge that the tonguetied minnow rarely co-­occurs with Nocomis. And Hitt to the three cyprinid decliners (rosyface shiner, silver shiner and and Roberts (2012) documented the extirpation of two nest spawn- tonguetied minnow). Since agriculture is associated with instream silt- ers, creek chub Semotilus atromaculatus and bigmouth chub Nocomis ation (Berkman & Rabeni, 1987; Walser & Bart, 1999; Waters, 1995), platyrhynchus, from three UMNR streams originally surveyed by and all three cyprinid decliners are silt sensitive, this pattern may be Burton and Odum (1945), while the bluehead chub and its nest asso- at least partially explained by the lower percentage of agricultural land ciates dramatically increased their occupancy. None of the decliners use in the North Carolina portion than in the Virginia portion of the is a strong Nocomis nest associate and therefore would not be ex- UMNR. Agricultural land use (Fry et al., 2011) averaged 30% for the pected to directly benefit from the spread of bluehead chub. The role 79 HUCs in the Virginia portion of the UMNR and 20% for the 21 of bluehead chub and associates in declines of non-­associate cyprin- North Carolina HUCs. The North Carolina HUCs also average 219 m ids warrants further study. 230 | BUCKWALTER et al.

Unit are jointly sponsored by the U.S. Geological Survey, Virginia 4.3 | Functional forms of spread Tech, VDGIF and Wildlife Management Institute. Use of trade names Although functional form of spread has long been a central concept or commercial products does not imply endorsement by the U.S. in invasion biology, few (if any) other studies of stream-­fish invasions government. have addressed it empirically. Our finding 31 UMNR spreaders, 26 spreading exponentially, suggests that UMNR fish distributions are DATA ACCESSIBILITY highly dynamic, that most UMNR fish communities are readily invaded, and that impacts of expanding invaders are yet to be fully realized. UMNR fish detection histories and covariates for occupancy models That no spreader showed substantial evidence of logistic dampen- are available as csv files from the Harvard Dataverse: https://doi. ing (Figure 1d) over a 77-­year study period suggests that stream-­fish org/10.7910/DVN/KS4HIR. invasions are long-­term processes that are commonly observed only partially via available data. ORCID

Emmanuel A. Frimpong http://orcid.org/0000-0003-2043-8627 4.4 | Conclusion

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