Received: 18 December 2017 | Revised: 19 June 2018 | Accepted: 25 June 2018 DOI: 10.1111/faf.12312

ORIGINAL ARTICLE

Fish dispersal in flowing waters: A synthesis of movement-­ and genetic-based­ studies

Lise Comte | Julian D. Olden

School of Aquatic and Fishery Sciences, University of Washington, Seattle, Abstract Washington Enhancing our understanding of fish dispersal is central to the success of modern-­day

Correspondence conservation efforts in freshwater ecosystems. However, methods seeking to estimate Lise Comte, Department of Environmental dispersal are diverse; ranging from direct estimation of individual movements, compu- Science, Policy, and Management, University of California, 309 Mulford Hall, Berkeley, tation of dispersal kernels, to indirect assessment using measures of gene flow across CA. riverscapes. An important question is whether results from these different approaches Email: [email protected] provide a consistent picture of the spatial scales of dispersal. Here, we performed a Funding information review and meta-­analysis of the literature reporting both individual movements and H. Mason Keeler Endowed Professorship (School of Aquatic and Fishery Sciences, genetic data to characterize patterns of dispersal for riverine fishes globally. Across all University of Washington) the studies considered (Ndirect = 206; Nindirect = 205), our results suggest restricted magnitudes of dispersal for riverine fishes, but highlight a large heterogeneity among species, taxonomic orders and geographies. For instance, we found that the maximum parent–offspring dispersal distances varied from 69 m to 1,086 km (median = 12 km;

Nspecies = 107), whereas the dispersal spread derived from isolation-­by-­distance slopes

(σIBD) from genetic data ranged from 19 m to 250 km (median = 1 km; Nspecies = 56). Comparisons of species-­specific values also revealed significant and positive relation- ships between direct and indirect estimates of dispersal distances, indicating that or- ganismal movement ability often translates into effective transmission of genes. Finally, this global overview pointed to important geographic and taxonomic disparities in the study of dispersal for riverine fishes. We thus encourage researchers to broaden the taxonomic and geographical scope of future investigations and identify emerging research frontiers where new scientific efforts are needed.

KEYWORDS dispersal distances, dispersal kernels, isolation by distance, mark–recapture, restricted movement paradigm, telemetry

1 | INTRODUCTION 2013; Bowler & Benton, 2005; Cote et al., 2017; Driscoll et al., 2014). Riverine ecosystems present diverse challenges to aquatic life, includ- Understanding dispersal, in the form of movements of individual or- ing the amount of energy required to move through such a dense me- ganisms and gene flow, remains fundamental to inform modern-­day dium as water, and the directionality and rates of flows (Denny, 1990; conservation efforts. From the determination of long-­term metapop- Olden, Schooley, Monroe, & Poff, 2004; Vogel, 1994). Swimming per- ulation dynamics and persistence in fragmented habitats to predicting formance has long attracted the interest of ecologists, beginning with the spread of invasive species, ecologists have a long history of study- the first experiments measuring fish swimming speed at the end of ing species dispersal (Baguette, Blanchet, Legrand, Stevens, & Turlure, the 19th century (Bainbridge, 1958; Regnard, 1893). Since this time,

Fish and Fisheries. 2018;19:1063–1077. wileyonlinelibrary.com/journal/faf © 2018 John Wiley & Sons Ltd | 1063 1064 | COMTE and OLDEN considerable effort has focused on relating fish ecomorphology to swimming ability (Blake, 2004; Webb, 1984). For instance, several 1. INTRODUCTION 1063 studies have established the importance of body form and fin shape 2. METHODS 1064 for defining the energetic costs and efficiency of swimming (Fisher & 2.1. Literature review and patterns in publication 1064 Hogan, 2007; Ohlberger, Staaks, & Hölker, 2006; Ojanguren & Braña, activity 2003). These findings have been tempered by evidence, suggesting 2.2. Data analyses 1065 that morphology alone is not the sole mechanism underlying animal 2.2.1. Dispersal metrics 1065 dispersal in nature (Clobert, Le Galliard, Cote, Meylan, & Massot, 2.2.2. Incorporating sources of uncertainty 1066 2009; Duputié & Massol, 2013; Stevens et al., 2013). 2.2.3. Comparing direct and indirect approaches 1066 Almost 50 years ago, Gerking (1959) proposed the restricted move- 3. RESULTS 1067 ment paradigm (sensu Gowan, Young, Fausch, & Riley, 1994), which 3.1. Status and trends in the study of fish dispersal 1067 posited that riverine fishes are largely sedentary and display relatively 3.2. Do direct and indirect estimates of dispersal convey 1068 small home ranges. Since this seminal work, the past two decades have the same information? witnessed a dramatic paradigm shift in movement ecology, facilitated 4. DISCUSSION 1069 by methodological advances that have allowed researchers to quan- 5. CONCLUSIONS 1074 tify movement in the field with increased resolution. It is now recog- nized that both sedentary and mobile behaviours are exhibited within REFERENCES 1074 a given population (Radinger & Wolter, 2014; Rodriguez, 2002; Skalski SUPPORTING INFORMATION 1077 & Gilliam, 2000), related in part to intraspecific variability in the intrin- sic attributes describing life-­history, phenotypic and personality traits (Cote, Clobert, Brodin, Fogarty, & Sih, 2010; Fraser, Gilliam, Daley, Le, timescales, on the order of few generations to over tens of thousands & Skalski, 2001). Additionally, extrinsic environmental factors, such of generations (Crispo & Hendry, 2005; Slatkin, 1993). Another im- as temperature, flow conditions, habitat complexity and food avail- portant distinction is that, contrary to direct methods that measure all ability, also strongly influence movement through different costs and dispersal events, indirect methods measure effective dispersal leading benefits associated with the three successive and interrelated phases to reproduction and thus successful transmission of genes. As such, of dispersal, namely departure, transfer and settlement (Bonte et al., movement and genetic-­based approaches may be difficult to reconcile 2012; Bowler & Benton, 2005; Rasmussen & Belk, 2017). Yet, despite (Broquet & Petit, 2009), yet recent attempts demonstrated that they a growing body of literature, a comprehensive understanding of the can convey similar information under certain circumstances (Kinlan & spatial scales of dispersal in riverine fishes remains elusive. Gaines, 2003; Pinsky et al., 2017; Watts et al., 2007). To the best of our Formulating a general theory of fish dispersal has been chal- knowledge, there have been only few (Shipham, Schmidt, & Hughes, lenged, at least in part, by the multiplicity of methods deployed to 2013; Wilson, Hutchings, & Ferguson, 2004) empirical comparisons of estimate dispersal. Whereas dispersal of animals has been tradition- these dispersal inference methods in riverine environments (but see ally studied through direct movement-­based studies (i.e., mark–re- Bradbury & Bentzen, 2007; Bradbury, Laurel, Snelgrove, Bentzen, & capture, ­radiotracking), additional indirect measures based on genetic Campana, 2008; Selkoe & Toonen, 2011 for meta-­analyses in marine data have become increasingly popular among molecular biologists fishes). (Broquet & Petit, 2009; Hughes, Schmidt, & Finn, 2009). Such meth- Here, we capitalize on the large volume of both genetic and ods measure the degree of genetic differentiation among populations movement data that have accumulated over the last decades to that can subsequently be related to the spatial distances separating provide a global synthesis of the current state of knowledge for riv- them to obtain estimates of gene flow and dispersal distances (i.e., erine fish dispersal. We start by reviewing how dispersal has been isolation by distance; Wright, 1943). Data acquired from direct and measured and assess potential taxonomic and geographic biases in indirect methods differ in many respects, with each approach demon- research. We then investigate what we have learnt about dispersal strating both strengths and weaknesses to inferring dispersal ability. and undertake a systematic comparison between direct and indirect For instance, whereas mark–recapture studies have been criticized for estimates of dispersal. Finally, we provide an overview of current being biased against long-­distance movements (Koenig, Van Vuren, & advances in dispersal research and identify exciting frontiers where Hooge, 1996), estimating dispersal from genetic data often relies on scientific attention is needed. oversimplistic models of gene flow (e.g., island model) whose underly- ing assumptions may be violated in most real populations (Kalinowski, 2 | METHODS 2002; Meirmans, 2012; Whitlock & McCauley, 1999). Beyond methodological considerations, these two approaches 2.1 | Literature review and patterns in publication vary in sensitivity to different facets and mechanisms of dispersal. In activity particular, whereas direct methods seek to estimate movement oc- curring over ecological timescales, on the order of few hours to the We performed a systematic literature review to identify stud- lifetime of an individual, indirect methods are relevant to evolutionary ies that quantified dispersal for riverine fishes using direct (i.e., COMTE and OLDEN | 1065 mark–recapture, radiotracking) or indirect (i.e., gene flow) methods. underlying causes (e.g., feeding, escape, exploration, reproduction). We used Google Scholar and ISI Web of Knowledge and a combina- Although we decided to exclude studies that explicitly focused on tion of keywords: (a) (freshwater OR stream* OR river*) AND fish* migration, we included several studies that reported seasonal vari- (b) (dispersal OR “dispersal distance” OR dispersion OR swim* OR ation in movement patterns (e.g., Honda, Kagiwada, Takahashi, & movement* OR redistribution), (c) (CMR OR MRR OR mark-­release Miyashita, 2014; Trested, Chan, Bridges, & Isely, 2011; Walker, OR mark-­recapture OR radio-­track*) and (d) (genetic* OR DNA OR Adams, & Adams, 2013), although the specific reasons were often allozyme*) AND (structure OR population OR IBD OR “isolation by unclear. We first characterized direct dispersal estimates using the distance” OR FST). values most commonly reported in the articles, that is, mean disper- Movement-­based studies were included only if the following four sal distance between consecutive relocations of all relocalized indi- conditions were met: (a) quantified dispersal distances (i.e., maxi- viduals and maximum dispersal distance of one individual between mum, mean or median dispersal distance, frequency distribution of two consecutive relocations. We also estimated the spread of the individual movement distances) in natural populations, (b) provided dispersal kernel (i.e., standard deviation of the dispersal kernel, σ) by the time between relocations (e.g., number of days between tagging fitting probability density functions to the frequency distributions and subsequent sightings), (c) did not explicitly study migratory be- of individual movement distances (Nathan, Klein, Robledo-­Arnuncio, haviour (e.g., excluding movements between wintering and spawning & Revilla, 2013). As many studies reported the proportion of relo- habitat), and (d) were conducted over more than 24 hr (e.g., excluding calized individuals per distance class without providing the total dial movements). Inclusion criteria for the genetic-­based studies in- number of relocations, we fitted dispersal kernels to these interval-­ cluded the following four conditions: (a) tested isolation by distance censored data by simulating a large number (105) of individual dis-

(IBD) or provided the genetic differentiation statistics (i.e., FST, GST or persal distances according to the proportions reported for each equivalent) among populations, (b) used watercourse distances or if distance class. To account for the heterogeneity in fish movements, it was possible to recalculate these distances using Inkscape© (i.e., we used a double mixture Gaussian kernel: map representing the sampling locations), (c) more than three pop- 2 2 − x − x 1 2 2 1 2 2 ulations were analysed and (d) only native populations were studied f (x) = p × e stat + 1−p × e mob (1) 2 2   2 2 (i.e., excluding invasive populations with recent colonization history π stat π mob or populations subject to hatchery introgression). Each article and case study was classified according to the relocation methods (i.e., where x is the distance from the source population, σstat and σmob are mark–recapture or radiotracking) or main molecular marker types the dispersal kernel spreads of the stationary and mobile compo- (i.e., allozymes, mitochondrial and microsatellites) used. We also nents, respectively, and p is the proportion of individuals contribut- recorded the ecoregion (Abell et al., 2008) where the studies were ing to the stationary component. As an alternative, we also fitted conducted and assigned each fish species to its taxonomic order (fol- a Laplacian kernel to each frequency distribution of the individual lowing the updated classification of Betancur-­R et al., 2013). movement distances and compared the model fits using the Akaike We extracted all the metrics used to quantify dispersal using di- Information Criterion (AIC). Over the 273 population movement data rect methods and assessed the incidence of isolation by distance by for which both kernel types could be successfully fitted, the double determining the proportion of case studies and species that revealed mixture Gaussian kernel had considerable more support (ΔAIC > 10) statistically significant relationships between genetic differentiation for 85% of the case studies. and watercourse distance. Within a given article, we considered Next, to account for the temporal dimension of animal move- individual case studies if the estimates were reported for different ments, we calibrated a linear mixed-­effect model between each di- methods, species, geographical locations or time periods. When di- rect dispersal estimate and the mean time between relocations using rect estimates were given at several spatial (e.g., river and basin) or species identity as a random factor and the type of methodology temporal (e.g., seasonal and annual) scales, we selected the small- (mark–recapture versus radiotracking) as an interaction. We then est scale at which movement data were presented. Similarly, we se- used a backward variable selection approach based on Wald tests lected the IBD relationships within major population complexes and to identify the best model structure independently for each disper- excluded outliers to minimize the influence of genetic breaks. sal estimate. We found a significant and positive relationship with The data sets supporting this article are available at https:// time as well as a methodological effect for all the direct dispersal figshare.com/s/1e8d8666de57b4028c47. estimates, the only exception being the fraction of the population p contributing to the stationary component. This indicates that long-­ term and radiotelemetry studies tended to record longer distance 2.2 | Data analyses movements (see Supporting Information Table S1 and Supporting Information Figure S1 for additional details). We subsequently used 2.2.1 | Dispersal metrics these models to scale the dispersal estimates to species genera- For direct approaches, we followed Bowler and Benton’s (2005) tion length (estimated using FishBase; Froese & Pauly, 2015) using definition of dispersal as any movement of an individual between ­radiotracking as the methodological reference. This allowed for habitat patches, irrespective of the distance between them or their a more realistic comparison to indirect estimates that represent 1066 | COMTE and OLDEN

dispersal between birth location of parents and that of the offspring, of dispersal kernel spread for both the stationary σstat and mobile while accounting for temporal dispersal dynamics (i.e., without as- σmob components and the fraction of the population p contributing suming a slope = 1) and any potential methodological effect. to the stationary component. Finally, we assessed the sensitivity of Using the isolation-­by-­distance theory in linear (one-dimensional)­ the parent–offspring scaled estimates to the minimum time between environments (Rousset, 1997), we estimated the indirect parent–off- relocations considered in the analyses by repeating these different spring dispersal kernel spread as: methodological steps after excluding studies conducted over a min- imum number of days (from 1 to 400 days; Supporting Information 1 = Figures S3 and S4). IBD 4D m (2) e For the indirect approach, the main sources of uncertainties where m is the slope of the relationship between the linearized ge- were related to the estimation of the slope (m) between genetic netic differentiation (e.g., FST/(1 − FST)) and watercourse distances differentiation and watercourse distances, as well as the speci- and D the effective population density. Due to the variety of meth- e fication of realistic effective population density (De) values. For ods and transformations used to estimate the IBD parameters, we the former, we used a resampling procedure to obtain 100 values recalculated m whenever possible from figures and tables using of m based on a normal distribution using the mean and standard ordinary least squares. We retained only those studies showing a deviation of the slope coefficients estimated using ordinary least positive (R2 Mantel > 0.10) and linear relationship between linear- squares for each case study. For the latter, however, it was not pos- ized F , G or equivalent and watercourse distances as assessed ST ST sible to obtain estimates of De specific to each case study that was using the global test of Peña and Slate (2006). The same test was independent of the data used to estimate m. We therefore used used to identify and remove potential outlier populations to conform a range of 100 values sampled from a uniform distribution vary- to the assumptions of the linear regression (p > 0.05). This procedure ing between 1 and 1,000 individuals per linear kilometre (Chust reduced the likelihood of including studies for which dispersal was et al., 2016; Kinlan & Gaines, 2003; Pinsky, Montes, & Palumbi, not the primary driver of population differentiation. As a result, from 2010). A literature search revealed a range of effective population the 198 potential case studies for which m could be recalculated, densities for riverine fishes (median = 17.86 ind/km, 2.5th–97.5th we retained 96 case studies to calculate indirect dispersal spread percentiles = 0.43–815.79; from 346 estimates based on 30 arti- estimates (mean R2 = 0.51, range = 0.11–0.98). Finally, to account cles and including 21 species from six families) compatible with for the maternal inheritance of mitochondrial DNA, we applied a our assumption. By combining these two sources of uncertainty, twofold correction on m for mitochondrial genetic markers (Kinlan & we obtained 10,000 parent–offspring dispersal kernel spread σIBD Gaines, 2003). It is important to note that after applying this correc- for each case study. tion, no differences were found among the indirect dispersal spread Because the propagation of different sources of uncertainty may estimates obtained using different genetic markers (F = 0.25, df = 37, result in unrealistic combinations of parameters, the credible range p = 0.86 based on a linear mixed model including species as a ran- of values for each dispersal estimate was considered as falling within dom effect on the intercept; see Supporting Information Figure S2). the 2.5th–97.5th percentile interval.

2.2.2 | Incorporating sources of uncertainty 2.2.3 | Comparing direct and indirect approaches

Recognizing that every step of our methodological approach ex- We first assessed whether the estimates of the scales at which dis- hibited a certain degree of uncertainty, we used a Monte Carlo persal occurs differ when using direct and indirect approaches by procedure to propagate this uncertainty in the computation of the comparing the distribution of values obtained for the different es- dispersal estimates. timates, irrespective of the studied species or locations. We then For the direct approach, the main source of uncertainty was the tested for the existence of taxonomic and geographic patterns by scaling of the dispersal estimates to the generation length of species. comparing the distribution of the dispersal estimates across taxo- To address this issue, we resampled (100 times) the parameter value nomic orders and latitudes. We used linear mixed models using describing the relationship between the dispersal distances and the species as a random effect on the intercept and taxonomic order mean time between relocations from a normal distribution based on and latitude as fixed categorical and ordered factors, respectively. the slope and associated standard deviation estimated from the lin- For each species, we calculated the latitudinal midpoints where the ear mixed model fitted across all studies (see section 2.2.1). Another studies were conducted that we subsequently aggregated using 10° source of uncertainty specific to the dispersal kernel parameters was latitudinal bands and considered both linear and quadratic relation- the fit of the probability distributions to the sample data. Therefore, ships with the dispersal estimates. prior to the resampling procedure described above, we used a non- Finally, we tested for a systematic relationship between the parametric bootstrap of the frequency distributions of individual direct and indirect estimates by examining only those species that movement distances to obtain 100 estimates of the kernel param- were common to both approaches. We used ordinary least squares eters. This resulted in 100 parent–offspring scaled estimates of regressions based on the species-­specific values obtained by av- mean or maximum dispersal distances and 10,000 scaled estimates eraging the estimates across all Monte Carlo iterations and case COMTE and OLDEN | 1067

FIGURE 1 Temporal trends in the number of articles that quantified dispersal for riverine fishes using (a) direct and (b) indirect methods where others includes amplified fragment length polymorphisms, introns and single nucleotide polymorphisms. Note that as several methodologies might be used in a given article, the number of articles does not equate the total number of articles collected in the study (Ndirect = 206; Nindirect = 205). (c) Spatial coverage of studies that quantified dispersal in riverine fishes expressed as the number of case studies per freshwater ecoregion [Coigure can be viewed at wileyonlinelibrary.com]

studies (for the indirect approach). We specified the indirect dis- We found that direct measures of dispersal according to the persal estimates as response variable and the direct dispersal esti- mark–recapture of individuals across delimitated habitat patches mates as predictor variable. As the mechanisms of dispersal can be remains a common approach; however, over the last decade the fundamentally different for migratory compared to non-­migratory number of radiotelemetry studies has steadily increased (Figure 1a). species (e.g., homing accuracy rather than departure, transfer and The number of genetic-­based studies has also accumulated rapidly settlement; Quinn & Dittman, 1990), we also tested whether these to the point that they nearly exceed the number of studies measur- relationships differ according to species’ migratory behaviour. To ing dispersal using direct methods (Figure 1b). Across all the studies, do so, we introduced an interaction in the model using a categori- direct approaches have most commonly estimated mean (N = 582; cal factor with three classes: non-­migratory, potadromous and di- 139 species) and maximum dispersal distances (N = 264; 108 spe- adromous (following Fishbase, Froese & Pauly, 2015 and various cies) over time frames ranging from 1 day to >4 years (mean time be- literature sources). We then used a backward variable selection ap- tween relocations = 77 days). Indirect-based studies have also relied proach based on Wald tests to select the best model structure inde- on a suite of genetic markers and differentiation metrics (e.g., FST, pendently for each dispersal estimates. Regressions were performed GST, FST’, GST’, Jost’s Dest), encapsulating spatial scales ranging from on log10-­transformed variables to enhance linearity when necessary. about one to 6,000 km (mean maximum riverine distance between populations = 489 km). Interestingly, the majority of these stud- ies documented a significant pattern of isolation by distance when 3 | RESULTS measured either at the case study (53%) or species (51%) level. This suggests that isolation by distance is an important process struc- 3.1 | Status and trends in the study of fish dispersal turing genetic diversity for riverine fishes at the scales considered, Our initial literature search identified 969 articles, including peer-­ although considerable variability in these relationships exists (mean reviewed articles and grey literature sources (e.g., reports, PhD R2 = 0.32, range = 0.01–0.97). dissertations). Among them, 206 articles provided a quantitative es- Both spatial (Figure 1c) and taxonomic (Figure 2) biases are ap- timate of dispersal distances based on direct methods, for a total of parent in the study of fish dispersal, but direct and in direct meth- 622 case studies. Similarly, we revealed 187 articles that provided a ods show comparable overall patterns. Studies have been mainly test of isolation by distance, which when added to 18 additional arti- conducted in freshwater ecoregions located in the Palearctic and cles for which enough information was given to estimate the degree Nearctic realms, and particularly in northern Europe, south-­eastern of isolation by distance among populations, resulted in a total of 397 and north-­western North America. Salmoniformes represents the case studies. most studied taxonomic order according to the number of case 1068 | COMTE and OLDEN

FIGURE 2 Taxonomy-­based tree showing the coverage of studies that quantified dispersal for riverine fishes across orders using direct (black) and indirect (grey) methods. Bars show the total number of case studies per order with darker colours representing the number of studied species. Pies indicate the proportion of studied species relative to the total number of species recognized per order. Orders that are known to comprise freshwater fishes but for which no species has been studied are indicated in grey

studies, whereas Cypriniformes and Perciformes cumulate the stationary component also varied from 0.36 to 0.88 (median = 0.65) highest number of studied species. Despite these general trends, (Figure 3e). In comparison, the parent–offspring dispersal kernel direct approaches are more inclined to study larger-bodied species spread estimated from isolation-­by-­distance slopes σIBD was less (t test: t = 3.68, df = 88, p < 0.001; Supporting Information Figure variable with values ranging from 681 m to 53 km (median = 1 km;

S5), such as species belonging to Acipenseriformes, Esociformes Nspecies = 56) (Figure 3f). or Siluriformes. Nonetheless, the taxonomic coverage of these Mean and maximum dispersal distances, as well as the dis- studies is still relatively limited (Ndirect = 142 species; Nindirect = 149 persal kernel spread of the mobile component σmob, showed species) and represents only a small fraction of the global riverine a non-­random distribution across taxonomic orders (Figure 4; fish diversity. Table 1a). For instance, species belonging to Acipenseriformes and Siluriformes tended to display greater parent–offspring dis- persal distances, whereas shorter distances were observed for 3.2 | Do direct and indirect estimates of dispersal the species belonging to Galaxiiformes and Cichliformes. No sig- convey the same information? nificant differences were found for the dispersal kernel spread

Our results suggest that the scales at which dispersal occurs in riv- of the stationary component σstat, the fraction of the population erine fishes differ between direct and indirect approaches when p contributing to the stationary component or the indirect esti- comparing estimates across all species and geographical loca- mates σIBD. Although the distribution of the dispersal estimates tions (Figure 3). Parent–offspring dispersal distances estimated varied across latitudinal bands, no clear spatial pattern was dis- using direct methods are multimodal and ranged over several or- cernible along the latitudinal gradient, with the exception of the ders of magnitude: Mean dispersal distance varied from 80 m to maximum dispersal distance for which a significant quadratic

3,377 km (median = 13 km; Nspecies = 134) and maximum dispersal effect was found (Figure 5; Table 1b). This indicated a tendency distance from 69 m to 1,086 km (median = 12 km; Nspecies = 107) for the maximum dispersal distances to be lower at low latitudes, (Figure 3a,b). Dispersal estimates derived from dispersal kernels although small values were similarly observed at high latitudes in also revealed a large range of variation among the 90 fish spe- the Southern Hemisphere. cies included in the analyses (Figure 3c,d). The dispersal kernel Comparisons of species-­specific values revealed significant and spread of the stationary component σstat indicated routine move- positive slopes between the indirect and all direct estimates of dis- ments ranging from 19 m to 250 km (median = 765 m), whereas the persal, except for the fraction of the population p contributing to dispersal kernel spread of the mobile component σmob suggested the stationary component (Figure 6; Table 2). All direct dispersal parent–offspring dispersal distances from 57 m to 2,732 km (me- estimates were also positively correlated, although p showed only dian = 6 km). The fraction of the population p contributing to the weak and/or negative associations with the other estimates (e.g., COMTE and OLDEN | 1069

FIGURE 3 Distribution of parent– offspring dispersal estimates for riverine fishes using (a–e) direct and (f) indirect approaches across all Monte Carlo iterations. Direct estimates are represented by the (a) mean dispersal distance (Mean; Nspecies = 134), (b) maximum dispersal distance (Maximum;

Nspecies = 107), (c) dispersal kernel spread of the stationary component (σstat; Nspecies = 90), (d) dispersal kernel spread of the mobile component (σmob; Nspecies = 90) and (e) fraction of the population contributing to the stationary component

(p; Nspecies = 90). Indirect estimates are represented by (f) the dispersal kernel spread calculated using the isolation-­ by-­distance slopes (σIBD; Nspecies = 56). Dispersal distance estimates in (a–d) and

(f) are expressed in km on a log10 scale r = −0.45 with maximum dispersal distance; Supporting Information However, this global overview also highlighted important knowledge Figure S6). By contrast, no relationship was found between the indi- gaps that stem from spatial and taxonomic disparities in the study of rect estimates and the migratory behaviour of species, using either fish dispersal. an additive or interactive effect with the direct estimates (p > 0.05 Our study revealed strong evidence for limited dispersal of riv- for all Wald tests). This indicated that differences in vagility or in- erine fishes. After carefully accounting for estimate uncertainties, trinsic ability to move can translate directly into differences in con- we found that the median value across all the species considered temporary pattern of spatial genetic structure with R2 varying from for the parent–offspring dispersal kernel spread of the mobile com- 0.32 to 0.46 (Table 2). In addition, a significant and positive associ- ponent was on the order of ~10 km whereas it was on the order of ation was also obtained between the indirect and direct estimates ~1 km for the indirect estimates based on IBD theory. Furthermore, when using a Laplacian kernel (Supporting Information Figure S7), we revealed that, on average, close to two-­third of individuals were demonstrating that our results were robust to the formulation (uni- stationary and displayed movements of only few hundred of metres modal or bimodal) of the dispersal kernel. Nonetheless, it is import- from the source population. This is in stark contrast with marine ant to note that all the slopes were inferior to one (0.23–0.39 on fishes for which a meta-­analysis revealed a median dispersal kernel log scale), indicating that the dispersal distances estimated using spread of ~100 km based on indirect estimates, suggesting differ- genetic-­based methods increased at a much slower rate than the ent mechanisms of dispersal in riverine versus marine environments movement-­based estimates. (Kinlan & Gaines, 2003). In streams and rivers, the branching archi- tecture of the dendritic network imposes a structural constraint on dispersal, which may explain the strong demographic and genetic 4 | DISCUSSION isolation commonly reported in these ecosystems (Campbell Grant, Lowe, & Fagan, 2007; Hughes, Huey, & Schmidt, 2013; Hughes et al., By harnessing an extensive body of existing literature, we demon- 2009). In turn, these constraints are contingent upon the water strated that direct and indirect approaches to estimating riverine dependency of species, such as semiaquatic insects or amphibians fish dispersal convey complementary, if not convergent information. are less influenced by direct hydrologic connectivity than obligate 1070 | COMTE and OLDEN

FIGURE 4 Taxonomy-­based tree showing the distribution of parent– offspring dispersal estimates for riverine fishes at the order level across all Monte Carlo iterations. Direct estimates are represented by the (a) mean dispersal

distance (Mean; Nspecies = 134), (b) maximum dispersal distance (Maximum;

Nspecies = 107), (d) dispersal kernel spread of the stationary component (σstat; Nspecies = 90), (e) dispersal kernel spread of the mobile component (σmob; Nspecies = 90) and (f) fraction of the population contributing to the stationary component

(p; Nspecies = 90). Indirect estimates are represented by (c) the dispersal kernel spread calculated using the isolation-­

by-­distance slopes (σIBD; Nspecies = 56). Dispersal estimates in (a–e) are expressed

in km on a log10 scale. ***p < 0.001; **0.001 ≤ p < 0.01; *0.01 ≤ p < 0.05 for the effect of taxonomic order specified as a categorical variable using linear mixed models with species as a random effect riverine organisms such as fishes (Alp, Keller, Westram, & Robinson, several of the dispersal estimates, suggesting that these differences 2012; Mims et al., 2015; Phillipsen et al., 2015). Maximum dispersal may reflect to some extent biological and ecological differences be- distances, on the other hand, largely exceed the previous estimates. tween the species pools included in the analyses. Indeed, previous This suggests that dispersal is highly leptokurtic, characterized by studies demonstrated that both genetic-­ and movement-­based disper- heavy local dispersal together with rare long-­distance dispersal sal estimates could be successfully correlated to fish morphological events. Extended movements in the search for food, to reduce the and life-­history characteristics (Bradbury & Bentzen, 2007; Bradbury risk of predation or escape from periodically adverse environmental et al., 2008; Comte & Olden, 2018; Radinger & Wolter, 2014). For in- conditions, have been commonly reported in riverine fishes (Crook, stance, whereas periodic strategists (i.e., large body size, late matu- 2004; Lucas & Batley, 1996; Rubenson & Olden, 2017), which may ration, high fecundity and low juvenile survivorship) are expected to account for the long-­distance movements observed from the source cope with extended periods of adverse conditions by migrating large populations where parent–offspring dispersal events are normally distances, species with opportunistic strategists (i.e., small-­bodied short. species with early maturation and low juvenile survivorship) are Our results validated the long-­standing perception regarding thought to be better able to capitalize on these conditions by persist- the restricted dispersal of riverine fishes (Gerking, 1959; Rodriguez, ing in situ (Winemiller & Rose, 1992). In turn, indirect estimates may be 2002), but also highlighted considerable heterogeneity in dispersal lower and less variable because overall genetic-­based studies tend to scale among species. In particular, direct estimates varied over several focus on smaller-­bodied species. Nonetheless, the comparison of the orders of magnitude and exhibited distinct modes, whereas the indi- species-­specific dispersal estimates confirmed that indirect estimates rect estimates showed considerable less variability. However, we also are lower for most species and metrics considered, although this re- demonstrated non-­random taxonomic and geographical patterns for sult is based on a somewhat limited sample size. Alternatively, these COMTE and OLDEN | 1071

TABLE 1 Results of the linear mixed models between the gene flow originates from small-­distance movements. In addition, parent–offspring dispersal estimates and (a) the taxonomic order the association with the dispersal kernel spread of the stationary and (b) latitudinal midpoint for riverine fishes. Direct estimates are component suggests that routine movements (as opposed to genu- represented by the mean dispersal distance (Mean), maximum ine dispersal) can translate into effective dispersal (Burgess, Baskett, dispersal distance (Maximum), dispersal kernel spread of the Grosberg, Morgan, & Strathmann, 2016; Van Dyck & Baguette, 2005). stationary component (σstat), dispersal kernel spread of the mobile Given that tagging is mainly targeting adults, these relationships also component (σmob) and fraction of the population contributing to the stationary component (p). Indirect estimates are represented by the demonstrate the importance of adult compared to larval and juvenile dispersal kernel spread calculated using the isolation-­by-­distance dispersal to gene flow. Nonetheless, genetic dispersal estimates may slopes (σ ). Dispersal estimates were log -­transformed (except p) IBD 10 not directly reflect the complex movement patterns observed at the

Nspecies df F p population level (Lancaster & Downes, 2017). Successful reproduc- (a) Taxonomic order tion may represent relatively rare events, because of the costs of dis- persal or poor adaptation of immigrants to local conditions (Peterson, Mean 134 16 6.68 <0.001 Hilborn, & Hauser, 2014). Riverine impoundment and dam-­induced Maximum 107 13 4.08 <0.001 habitat fragmentation may also greatly reduce gene flow between σ 90 13 1.54 0.123 stat neighbouring populations, even for species with putatively high σ 90 13 2.59 0.005 mob dispersal ability (Blanchet, Rey, Etienne, Lek, & Loot, 2010; Fluker, p 90 13 0.64 0.812 Kuhajda, & Harris, 2014). As a result, effective dispersal may be

σIBD 56 13 0.78 0.680 smaller than the potential for dispersal, with the number of effective 2 (b) Latitude + Latitude migrants declining with increasing spatial scales. Mean 134 1 1.64 0.201 From a conservation perspective, our study indicates that in- 1 0.19 0.665 ferring dispersal ability from genetic data is possible with R2 vary- Maximum 107 1 5.67 0.019 ing from 0.3 to 0.5 between the indirect and direct estimates (e.g., 2 1 5.79 0.018 R = 0.4 between σIBD and maximum dispersal distance). However, it may be misleading to only consider the indirect dispersal distances. σstat 90 1 1.99 0.162 Given rapid rates of global change, the short-­distance movements 1 0.37 0.546 estimated from effective dispersal may not be sufficient for the re- σmob 90 1 2.44 0.122 alization of rapid migration (Isaak & Rieman, 2013). In those cases, 1 0.16 0.692 the potential for long-­distance movements as estimated using the p 90 1 7.59 0.007 maximum dispersal distance would be crucial for the continued 1 0.54 0.465 persistence of these species. Similarly, given the importance of 56 1 0.96 0.331 σIBD long-­distance dispersal for metapopulation dynamics and structure 1 0.11 0.747 (Trakhtenbrot, Nathan, Perry, & Richardson, 2005), greater con- sideration should be given to ways of mitigating effects of existing barriers to movement of riverine fishes (Olden, 2016). Nonetheless, differences may be attributed to the uncertainties surrounding our es- prioritizing and making effective conservation plans require com- timates. Using erroneous effective population sizes can result in con- plementary information on both demographic and genetic aspects siderable biases when estimating dispersal from isolation-­by-­distance of population status (Paz-­Vinas et al., 2013). As such, genetic-­based slopes (Pinsky et al., 2017). Decreasing the effective densities would approaches provide a powerful way to identify fine-­scale popula- predictably increase the dispersal kernel spreads estimated using the tion structure and to shed light on the processes shaping patterns method of Rousset (1997). Other methodological factors related to the of genetic diversity and functional connectivity, both of which are study design and conditions (e.g., study scale, number of genotyped central concern to conservation and management (McRae & Beier, individuals or sampled populations) together with the potential inclu- 2007; Schmidt & Schaefer, 2018). sion of IBD patterns influenced by other factors than dispersal (e.g., Beyond providing an overview of the dispersal scales in riverine hierarchical population structure, ecological preferences or postgla- fishes, our global synthesis showed a marked evolution in the study cial recolonization) may also have influenced the estimates, although of dispersal: first from mark–recapture to radiotelemetry methods and these effects might be difficult to tease apart (Crispo & Hendry, 2005; more recently through the use of indirect genetic-­based approaches. Meirmans, Goudet, & Gaggiotti, 2011; Selkoe & Toonen, 2011). This is not surprising as genetic-­based methods have been facili- Despite the discrepancies observed between the direct and indi- tated by the development of highly polymorphic marker types (e.g., rect estimates, we revealed positive correlations across fish species. microsatellites), such that they are now often less expensive than This provides empirical evidence that variation in the intrinsic ability direct approaches (Broquet & Petit, 2009). However, the promise of to move is an important determinant of the spatial genetic structure easy estimates of dispersal by inference from genetic data must be of populations. Noteworthily, all the slopes were inferior to one, in- viewed with caution (Meirmans, 2012; Whitlock & McCauley, 1999). dicating that in spite of the leptokurtic distribution of dispersal, most Isolation by distance may fail to capture nonlinear effects of complex 1072 | COMTE and OLDEN

FIGURE 5 Spatial distribution of parent–offspring dispersal estimates for riverine fishes per 10° latitudinal band across all Monte Carlo iterations. Direct estimates are represented by the (a) mean dispersal distance (Mean;

Nspecies = 134), (b) maximum dispersal distance (Maximum; Nspecies = 107), (d) dispersal kernel spread of the stationary

component (σstat; Nspecies = 90), (e) dispersal kernel spread of the mobile

component (σmob; Nspecies = 90) and (f) fraction of the population contributing to

the stationary component (p; Nspecies = 90). Indirect estimates are represented by (c) the dispersal kernel spread calculated using the isolation-­by-­distance slopes

(σIBD; Nspecies = 56). Dispersal estimates in (a–e) are expressed in km on a log10 scale. ***p < 0.001; ** 0.001 ≤ p < 0.01; *0.01 ≤ p < 0.05 for the quadratic effect of latitude using linear mixed models with species as a random effect

landscapes and historical or environmental processes on the genetic more precise estimates of parent–offspring dispersal parameters structure of populations (Manel & Holderegger, 2013; McRae & Beier, (Aguillon et al., 2017; Nathan, Perry, Cronin, Strand, & Cain, 2003). 2007; Wang & Bradburd, 2014). The directionality of water flows is For instance, as most tracking studies tend to be biased towards also likely to promote asymmetric migration through the riverine net- small spatiotemporal scales, extrapolating movements observed work (Paz-­Vinas, Loot, Stevens, & Blanchet, 2015), which may violate over relatively short time spans to species generation length as the assumption underlying these analyses. Similarly, the distribution done in this study can represent an important source of uncer- ranges of most temperate riverine fishes are affected by postglacial tainty. This is concerning as several of the scaled parent–offspring recolonization events that have left complex genetic imprints such as estimates showed a non-­negligible sensitivity to the minimum time the assumption of equilibrium between genetic drift and gene flow between relocations included in the analyses. In addition, whereas may not be achieved in many high-­latitude populations (see Crispo current-­mediated passive dispersal of early life-­history stages is & Hendry, 2005). Isolation-­by-­distance models should thus serve as thought to represent a fundamental process to range dynamic and baseline for the evaluation of more complex genetic structure mod- gene flow of riverine organisms (Lechner, Keckeis, & Humphries, els, whose statistical toolbox is constantly expanding (Broquet & Petit, 2016), direct approaches are primarily designed to monitor active 2009; Hughes et al., 2009; Manel & Holderegger, 2013; Pauls et al., dispersal. Indeed, despite the constant miniaturization of individ- 2014). ual marking and tracking devices (Roussel, Haro, & Cunjak, 2000), Alternatively, direct estimates through genetic-­based par- practical limitations remain regarding the implementation of these entage assignment constitute a promising approach to provide techniques on the youngest stages of fishes. As such, coupling COMTE and OLDEN | 1073

FIGURE 6 Comparison of direct and indirect parent–offspring dispersal estimates for riverine fishes averaged at the species level. Direct estimates are represented by the (a) mean dispersal distance (Mean; Nspecies = 19), (b) maximum dispersal distance (Maximum;

Nspecies = 16), (c) dispersal kernel spread of the stationary component (σstat; Nspecies = 15), (d) dispersal kernel spread of the mobile component (σmob; Nspecies = 15) and (e) fraction of the population contributing to the stationary component

(p; Nspecies = 15). Indirect estimates are represented by the dispersal kernel spread calculated using the isolation-­ by-­distance slopes (σIBD). Dispersal estimates are expressed in km on a log10 scale (except p). Lines are the fits from linear regressions where ***p < 0.001; ** 0.001 ≤ p < 0.01; *0.01 ≤ p < 0.05

genetic tools with tracking methods (i.e., individual genetic tag- TABLE 2 Results of the linear regressions between the direct ging) may substantially improve our understanding of the dispersal and indirect parent–offspring dispersal estimates for riverine fishes scales in many rare, endangered and small-­bodied riverine species averaged at the species level. Direct estimates are represented by the mean dispersal distance (Mean), maximum dispersal distance (Andreou et al., 2012). (Maximum), dispersal kernel spread of the stationary component Our study showed that over the last decades, a considerable (σstat), dispersal kernel spread of the mobile component (σmob) and amount of research has been devoted to the study of dispersal in fraction of the population contributing to the stationary component riverine fishes, yet our knowledge is still highly incomplete. Study bi- (p). Indirect estimates are represented by the dispersal kernel ases observed towards the Northern Hemisphere, especially in the spread calculated using the isolation-­by-­distance slopes (σIBD). Palearctic realm may impede our ability to detect macroecological pat- Dispersal estimates for both the response and predictor variables were log -­transformed (except p) terns in dispersal ability, or conversely understand the contribution 10 2 of dispersal dynamics to large-­scale diversity patterns (e.g., climate-­ Nspecies Slope p R mediated dispersal–ecological specialization trade-­off; Jocque, Field, Mean 19 0.23 0.001 0.46 Brendonck, & De Meester, 2010). In addition, these biases may imply Maximum 16 0.26 0.011 0.38 that current estimates of dispersal are overrepresented by studies σstat 15 0.39 0.008 0.43 on highly dispersal taxa whose biogeographical histories have been σmob 15 0.29 0.029 0.32 strongly imprinted by the last glaciations, whereby the genetic data p 15 −0.86 0.501 0.04 may not represent contemporary processes (Griffiths, 2015). As such, the dispersal scales characterized in this review may not be broadly applicable to other low-­latitude taxa or geographical regions. Similarly, Broadening the spatial and taxonomic scope of dispersal studies progress in our understanding of the ecological and evolutionary thus undeniably represents urgent issues in the freshwater realm. mechanisms underlying dispersal may be dampened by the fact that a More importantly, understanding the ecological and evolutionary mere 1% of known species have been considered. mechanisms underlying dispersal will be paramount in determining 1074 | COMTE and OLDEN whether, and if so how, species will respond to rapid environmental population genetic study of two stream invertebrates with differ- change (Bush & Hoskins, 2017; Urban, Zarnetske, & Skelly, 2013). ing dispersal abilities. Freshwater Biology, 57, 969–981. https://doi. org/10.1111/j.1365-2427.2012.02758.x The identification of the environmental and biological determi- Andreou, D., Vacquie-Garcia, J., Cucherousset, J., Blanchet, S., Gozlan, R. nants of dispersal (Comte & Olden, 2018; Radinger & Wolter, 2014) E., & Loot, G. (2012). Individual genetic tagging for teleosts: An em- holds considerable promise to map and predict the movements pirical validation and a guideline for ecologists. Journal of Fish Biology, of individuals and gene flow in riverine fishes (Radinger, Kail, & 80, 181–194. https://doi.org/10.1111/j.1095-8649.2011.03165.x Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M., & Turlure, C. Wolter, 2014). Integrating this knowledge to guide conservation (2013). Individual dispersal, landscape connectivity and ecological actions, however, will necessitate the concomitant development networks. Biological Reviews, 88, 310–326. https://doi.org/10.1111/ of analytical methods accounting for the unique characteristics brv.12000 of dendritic networks (Hughes et al., 2009; Paz-­Vinas & Blanchet, Bainbridge, B. Y. R. (1958). The speed of swimming of fish as related to size and the frequency and amplitude of the tail beat. Journal of 2015; Peterson et al., 2013). Experimental Biology, 35, 109–133. 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Evolutionary Applications, 3, 291–304. https:// dependency of dispersal (Clobert et al., 2009; Stevens, Pavoine, & doi.org/10.1111/j.1752-4571.2009.00110.x Baguette, 2010), case studies are an important tool in the quest for Bonte, D., & Dahirel, M. (2017). Dispersal: A central and independent a better understanding of the causes, mechanisms, and spatiotempo- trait in life history. Oikos, 126, 472–479. https://doi.org/10.1111/ ral patterns of dispersal and their role in various ecological and evo- oik.03801 Bonte, D., Van Dyck, H., Bullock, J. M., Coulon, A., Delgado, M., Gibbs, lutionary processes. We thus hope that our overview will encourage M., … Travis, J. M. J. (2012). Costs of dispersal. Biological Reviews, 87, researchers to broaden the taxonomic and geographical scope of dis- 290–312. https://doi.org/10.1111/j.1469-185X.2011.00201.x persal studies and will further stimulate the search for the processes Bowler, D. E., & Benton, T. G. (2005). 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