Title

Population stability and asymmetric migration of caddisfly populations, Stenopsyche marmorata (Stenopsychidae), in aforest-agriculture landscapeAuthor(s)Sueyoshi, Masanao; Tojo, Koji; Ishiyama, Nobuo; Nakamura, Futoshi

CitationAquatic sciences, 85
https://doi.org/10.1007/s00027-023-00992-6

Issue Date

2023-08-23

Doc URL

http://hdl.handle.net/2115/92978

RightsThis version of the article has been accepted for publication, after peer review (when applicable) and is subject toSpringer Nature s AM terms of use, but is not the Version of Record and does not reflect post-acceptance
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article (author version)

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1

Title

2

Population stability and asymmetric migration of caddisfly populations, Stenopsyche marmorata

3(Stenopsychidae), in a forest-agriculture landscape.

4Author names

5

Masanao Sueyoshi1*, Koji Tojo2,3, Nobuo Ishiyama1** and Futoshi Nakamura1

6

The affiliations of the authors

7

1 Graduate School of Agriculture, Hokkaido University, North 9, West 9, Sapporo, 060-0809, Japan.

8

2 Department of Biology, Faculty of Science, Shinshu University, Asahi 3-1-1, Matsumoto, 390-8621,

9

Japan

10

3 Institute of Mountain Science, Shinshu University, Asahi 3-1-1, Matsumoto, 390-8621, Japan

11*Current: National Institute for Environmental Studies 16-2 Onogawa, Tsukuba, 305-8506, Japan

12**Current: Forest Research Institute, Hokkaido Research Organization, Bibai, 079-0198, Japan

13

Corresponding author

14

Masanao Sueyoshi

15sueyoshi.masanao@nies.go.jp

16

ORCID: 0000-0002-5517-0256

17

1

18

Abstract

19

In river systems, frequent floods generally decrease the abundance of riverine species, and20

recent studies have reported that the degree of flood disturbance changes along agricultural gradients.

21

However, the interactive impacts between anthropogenic and flood disturbances on migration dynamics

22

are not well tested. In a forest-agricultural landscape in northern Japan, we observed the population

23

stability (fluctuations in abundances) and migration dynamics (directional gene flow) of a net-spinning24

caddisfly, Stenopsyche marmorata (Stenopsychidae), throughout the snowmelt flood period to test25

whether 1) population stability decreases along agricultural gradients, 2) asymmetric migration occurs

26

from stable populations in forested streams to unstable populations in agriculturally altered streams, and

273) genetic diversity is higher in stable populations. The abundance of S. marmorata was related to28

agricultural development (i.e., pasture cover in catchments); its abundance was higher in agricultural

29

streams before the flood (March), but the quantitative relationship was inverted just after the flood (May)30

and recovered six months post-flood (November). The resistance index (post-flood abundance divided by31

pre-flood abundance) showed a negative correlation with pasture cover in the catchment, indicating that32

population stability decreases along agricultural gradients. The long-term effective number of migrants

33(Nem) and recent migrants (assignment tests) estimated from the genetic analysis showed asymmetric34

colonization from stable populations in forested sites to unstable populations in agricultural sites. In

35contrast, genetic diversity was not related to agricultural development, but private allelic richness2

36(number of unique alleles) was higher in forested streams than in agricultural streams. Our findings

37

suggest that forested streams serve as sources for unstable populations in agriculturally impacted streams

38

and contribute to high genetic diversity at a catchment scale.

39
40

Keywords

41

snowmelt flood, landscape genetics, meta-population, disturbance, anthropogenic impacts

42
43

Statements and Declarations

44

Conflict of interest: The authors declare no competing interests.

45
46
47

3

48

Introduction

49

Immigration and emigration among local populations are key components in meta-population

50

dynamics and contribute to the regional persistence of species (Hanski and Thomas 1994; Hanski 1998).

51

Source–sink relationships are one of the general forms of a meta-population structure, where a source

52

population represents a migration source to other local populations (i.e., net exporter of individuals), and53

a sink population represents a migration sink from the source population (i.e., net importer of individuals)

54(Pulliam 1988; Pulliam and Danielson 1991). Source populations are more likely to occur in stable and55

growing populations than in decreasing populations because the former represents good reproduction and56

survival and can potentially become substantial emigration sources. Generally, most populations are

57

temporally unstable with a large fluctuation of individuals; nevertheless, these populations can persist in

58

focal habitats by continued immigration from source populations (Pulliam 1988, Dias 1996). Therefore,

59

detecting and sustaining source habitats (populations) are essential to maintain the entire meta-population60

structure and are typically considered priority areas for species conservation.

61

Flood disturbance is the major determinant of population fluctuations in instream organisms,

62

and severe floods often cause a catastrophic decrease in local populations (Fisher et al. 1982). The effect63

of flooding is dependent on the availability of instream flow refugia (Townsend and Hildrew 1994;

64

Townsend et al. 1997). Flow refugia, defined as stable habitats with low hydraulic stress during a flood

65(Lancaster and Hildrew 1993), are generally created more in heterogeneous channels (e.g., large boulders,66

woody debris, backwaters and inundated floodplains) (Sedell et al. 1990), and organisms inhabiting4

67

heterogeneous channels show high resistance and resilience to flood disturbance (Palmer et al. 1996;

68

Matthaei et al. 2000; Sueyoshi et al. 2014). These resilient channels or habitats that can be sources of69

migrants for disturbed habitats after flooding are distributed across river networks, thereby shaping the70source–sink dynamics of meta-populations at a catchment scale (Lancaster and Belyea 1997; Lancaster712000).

72

Recently, many studies have reported the negative consequences of instream habitat73

homogenization caused by various types of anthropogenic impacts, such as land use, water degradation74

and fine sediment deposition (Larsen et al. 2009; Maloney et al. 2011; Bini et al. 2014). Such habitat75

homogenization not only reduced biotic and abiotic diversities at base flows but also reduced the76

resistance of riverine organisms to flood disturbances due to the loss of flow refugia (e.g., Parkyn and77

Collier 2004; Mesa 2010). For example, channelization restricted floodplain areas and diminished78

backwaters that function as useful flow refugia during flooding (Negishi et al. 2002). If the source

79

habitats in river networks are degraded by such artificial factors, metapopulations depending on the80source‒sink dynamics cannot be maintained (Vuilleumier and Possingham 2006). However, the impacts

81of flooding on the migration dynamics of riverine organisms along gradients of anthropogenic

82

disturbances are not well known.

83
84

We previously examined aquatic insects’ resistance (i.e., survival rates) to an annual snow-melt
flood disturbance in a forest-agriculture landscape. In a previous study, we revealed that most species

5

85

were significantly decreased by flooding in agriculturally impacted streams compared to forested streams

86

in northern Japan (Sueyoshi et al. 2017). Eventually, approximately 50 % of taxa were eliminated from87

the site with the highest land use intensity. In the study catchments, agriculturally impacted streams were88

characterized by low environmental heterogeneity and much fine sediments, reducing the availability of89

refugia and easily washing out organisms living in the substrate. A net-spinning caddisfly, Stenopsyche

90

marmorata (Stenopsychidae), the dominant species widely distributed across forest-agricultural streams,

91

was affected most severely by the interacting impacts. The species is highly resilient to flood disturbance92

because its nets tightly connect stones and strengthen substrate stability (Takao et al. 2006). Nevertheless,93

the density declined to almost zero in the agricultural streams throughout the flooding season. Because94

the snowmelt flood season precedes the reproductive season (i.e., emergence period) of S. marmorata, the95

recovery of populations that decreased severely by flooding may depend on immigration from96

surrounding stable populations. Therefore, S. marmorata populations are suitable for testing the effects of97

population instability on migration dynamics in forest-agricultural landscapes.

98

In this study, to elucidate the impacts of flooding on migration patterns in human-modified

99

landscapes, we observed the subsequent population dynamics after flood disturbance (i.e., recovery) and100

the genetic structure of S. marmorata. We estimated the migration dynamics among populations using

101

gene flows because genetic data provide useful information to estimate the movement of individuals

102(Slatkin 1987; Storfer et al. 2007), and recent analyses enable the estimation of asymmetric migrations at

6

103

fine spatial scales (Beerli and Felsenstein 1999, 2001; Piry et al. 2004). We hypothesized that (1) gene104

flows between stable (forested sites) and unstable populations (agriculturally impacted sites) are

105

asymmetric and higher from stable to unstable populations than in the opposite direction and (2) the106

stable populations show higher genetic diversity compared to unstable populations because populations

107

experiencing bottlenecks often reduce genetic diversity (e.g., Nei et al. 1975; Leberg 1992; Spencer et al.

1082000).

109
110

Methods

111

Study sites

112

The study sites were in the Kitamihorobetsu River, Hokkaido, a subarctic region of Japan. The

113

catchment area of the Kitamihorobetsu River is 416 km2 and is covered by forests (78 %), natural

114

grasslands (6 %), pastures (15 %) and other land uses (1 %). Many small patches of pastures are located115

along the mainstream and tributaries of the Kitamihorobetsu River (Fig. 1). This river is in a subarctic

116

region with heavy snowfall in winter, which causes large snowmelt floods in the early spring season,

117

usually from early April to late May, for approximately two months (Table 1, Appendix Fig. S1). We

118

selected 27 sites for observations of population dynamics and six sites for observations of the migration

119

dynamics of S. marmorata in three tributaries of the Kitamihorobetsu catchment (Kitamihorobetsu,

120

Penkenai and Ketobetsu streams) (Fig. 1).

7

121

We calculated the percentage of pasture cover within each catchment area as an indicator of

122

agricultural impacts (% pasture) because this index is strongly correlated with the habitat modification of123

stream environments associated with agricultural activity in the studied catchments (e.g., sedimentation of124

fine sediments, water degradation and homogenization of channel hydromorphology; Sueyoshi et al.

1252016). The range of pasture cover in our study catchments was relatively small, less than 18 %; however,126

land development was concentrated along the stream channels, and clear-cutting of riparian forests

127

increased the inputs of fine sediments in agricultural streams (Appendix Fig. S1). The % pasture was128

calculated based on an available vegetation/land-use map (scale of 1:50,000, Ministry of the Environment

129

of Japan, 1998) using a function of Spatial Analyst in ArcGIS 10.0 (ESRI, USA) and ranged from 0-13017.9 % in our study sites. Six sites included in the genetic analysis exhibited three levels of agricultural131

development in the range of our study catchments: two upstream sites in the Kitamihorobetsu and132

Penkenai streams were characterized by low (0 % and 1.0 %) agricultural development; two downstream

133

sites in the Penkenai and Ketobetsu streams were characterized by moderate (11.7 % and 10.2 %,134

respectively) agricultural development; and one downstream site in the Kitamihorobetsu stream and one

135

upstream site in the Ketobetsu stream were characterized by high (both 17.9 %) agricultural development

136(Fig. 1).

137

Ecological characteristics of S. marmorata

138

The target species of this study is a net-spinning caddisfly, S. marmorata (Stenopsychidae,

8

139

Trichoptera). We chose the following selection criteria: 1) general species distributed across forest-

140

agricultural landscapes; 2) high fluctuations in abundance throughout the snowmelt flooding period

141(Sueyoshi et al. 2017); 3) well-understood phylogeography (Saito et al. 2018); 4) microsatellite markers

142

that can estimate fine-scale genetic structures and gene flows (Yaegashi et al. 2014); and 5) well-143

established detailed ecological traits and life cycles (Gose 1970). This species prefers rocky substrates

144

and shows high resistance to flood disturbance because the net connects the stones and increases bed

145

stability (Takao et al. 2006; Nunokawa et al. 2008). Movements of larvae (especially for later instars) are

146

limited because of their coherent nets, whereas the dispersal distance of adult flies is very long147

(estimation based on observed data, 2.5-12 km in Nishimura 1981; genetic data, 12-18 km in Yaegashi et148

al. 2014). Therefore, most of the movement of individuals between local populations would occur in the149

emergence period. The emergence period is once from May to July after the snowmelt flood season in a

150

subarctic region (S. griseipennis, a synonym of S. marmorata, in Gose 1970).

151

Sampling design

152

At each of the 27 sites, we first collected quantitative samples of larvae of S. marmorata using

153

twelve quadrats (25*25 cm) with a Surber net (0.3 mm mesh) before (March) and after the snowmelt

154

flood (May) in 2011 to evaluate the effects of flood disturbance on S. marmorata populations. We set two

155

quadrats on each of the six transects across the reach during the study period. These two samplings (Mar156

and May) were conducted to collect individuals from various mesohabitats to examine the effects of

9

157

flooding on all insect species (see details in Sueyoshi et al. 2017). S. marmorata, the study species in this158

study, was mainly found in riffle habitats; therefore, we focused on riffle habitats to examine the re-159

establishment of populations of S. marmorata after the flood. Three kick samples (1 kick sample collected160

over 2 min in a 1 m2 area) were collected using a D-frame net (1 mm mesh) after the emergence period

161(November 2011) (Gose 1970; Table 1). Kick sampling in riffles could more efficiently collect162

individuals of the species than quadrat sampling across the reach.

163

Data analysis

164

To clarify the distribution (i.e., population density) of S. marmorata in agricultural gradients

165

and the impacts of a flood on each population, we constructed generalized linear models (GLMs) with

166

Poisson error distributions for each of the three observation periods (March, May and November). We did

167

not use density per area (ind. m-2), and instead used the total number of individuals collected at twelve

168

quadrats or three kick samples as a response variable because the count data are generally better fitted

169

using Poisson than Gaussian distributions used for continuous variables in GLM (Qian 2017). Thus, the170

response variable was the total abundance at each site, and the explanatory variable was % pasture.

171

Additionally, the resistance index to flood disturbances was calculated using the first two samples (March

172

and May) collected by the same sampling methods (Sueyoshi et al. 2017). The index was calculated by173

the post-flood abundance divided by the pre-flood abundance. Higher resistance represents higher

174

stability of the population density between pre- and post-flood. To examine the relationships between the

10

175

resistance index and % pasture, a GLM with a Gaussian error distribution was performed. The

176

significance of the coefficients of an explanatory variable was judged by the Akaike information criterion

177(AIC). If the ΔAIC (difference between the full and null models) was larger than 2, the explanatory

178

variable was influential for the response variable (Burnham and Anderson 2002). All statistical analyses

179

were conducted using the R environment for statistical computing (R Core Team 2022) with the package

180

MuMIn (Barton 2022).

181

Sampling and genetic microsatellite genotyping

182

For the genetic analysis, we collected 23-32 individual S. marmorata larvae from each of six

183

sites (KiU, KiD, KeU, KeD, PeU and PeD) in November 2011 (Table 1; Fig. 1). These sites were selected

184

based on the results of the first two sampling periods (March and May in Fig. 2), with different

185

combinations of population stability, agricultural impacts, and longitudinal location (i.e., up- and186

downstream). The specimens were preserved in 100 % EtOH. A total of 163 individuals were genotyped

187(Table 3). Total genomic DNA was extracted from the ethanol-preserved tissue of the specimens and188

purified using the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA). Seven microsatellite loci

189

(Steno01,03, 04, 07, 08, 09, and 10) developed for S. marmorata (Molecular Ecology Resources Primer

190

Development Consortium 2009) were amplified by polymerase chain reaction (PCR). These markers can191

be used to assess fine-scale gene flows within a geographic distance of 40 km (Yaegashi et al. 2014), and192

previous studies have shown asymmetric migrations in stream networks using fewer than ten markers

11

193

(Yamazaki et al. 2011; Kanno et al. 2014). Each 10-μL reaction volume contained 10 ng template DNA,

194

0.25 U rTaq polymerase (Toyobo, Osaka), varying concentrations of primers, 1.2 μL of 25 mM MgCl2,

195

2.0 μL of 2 mM dNTP mix, and 2.0 μL of 10× rTaq polymerase buffer. The PCR protocol was as follows:

196

95°C for 2 min; 35 × (95°C for 30 s, 47 or 50°C for 30 s, 72°C for 1 min); and a final extension step of

197

72°C for 5 min. We analysed the obtained fragment sizes on an ABI 3130xl automated sequencer

198(Applied Biosystems) with a GeneScan 500 LIZ size standard (Applied Biosystems), and the size of each199

peak was determined using Peak Scanner 1.0 (Applied Biosystems).

200

Genetic diversity

201

We calculated the expected heterozygosity (He) and fixation index (Fis) for each population

202

using GENALEX 6.4 (Peakall and Smouse 2006), and rarefied allelic richness and rarefied private allelic203

richness (unique alleles that appeared only in one population) were calculated using HP-Rare v. June-6-

2042006 (Kalinowski 2005). We checked the evidence of null alleles using MICRO-CHECKER 2.2.3 (Van205

Oosterhout et al. 2004) and tested the linkage disequilibrium and deviation from Hardy–Weinberg

206

equilibrium for each population and locus using GENEPOP 4.2 (Raymond and Rousset 1995). Significant

207levels were adjusted by Bonferroni correction. To test the relationships between agricultural development

208

and each genetic diversity, we checked their correlations by using GLMs with Gaussian error

209

distributions. The significance of the coefficients of an explanatory variable was judged by the Akaike

210

information criterion (AIC).

12

211
212

Genetic clustering, estimation of migration rates, and assignment tests
First, we estimated the average migration rates among local populations using a coalescent-213

based approach implemented in MIGRATE-N3.6.4 (Beerli and Felsenstein 1999, 2001). The method uses

214

Bayesian inference to estimate the pairwise mutation-scaled migration rate, M (M = m/μ, where m is the215

migration rate per generation and μ is the mutation rate per generation), and mutation-scaled effective

216

population size, θ (θ = 4Neμ, where Ne is the effective population size) (Beerli 2006). This method allows

217

the estimation of asymmetrical gene flow among populations and is therefore appropriate for clarifying

218

migrations among populations with differences in instability. Although MIGRATE-N was often used to219

estimate long-term gene flows (generally across 4Ne generations), a recent study reported that the220

estimation was influenced by the current migration change and provided better estimates of the recent

221

migration rate than the other disequilibrium-based method (Samarasin et al., 2017). Therefore, we used

222

this estimation for migration dynamics in our study regions. We clustered all populations based on genetic

223

distance using POPTREE2 (Takezaki et al. 2010) prior to the estimation of gene flow. The Dsw distance

224(Shriver et al. 1995), which can be used for microsatellite repeats, was used for clustering via the225

unweighted pair group method with arithmetic mean (UPGMA). Then, we estimated the migration rates

226

among clustered groups by using MIGRATE-N. Additionally, we estimated the migration rates between

227

upstream and downstream areas in each of the three streams because these two areas of each stream

228

belonged to different genetic clusters (see Results) and showed different combinations of agricultural

13

229

development: low vs. high in the Kitamihorobetsu stream, low vs. moderate in the Penkenai stream and230

high vs. moderate in the Ketobetsu stream (Fig. 1, Table 3).

231

We ran Markov chain Monte Carlo runs with 100 sampling increments, 350,000 recorded steps

232

and 30,000 burn-in steps using the Brownian motion model with static heating of 4 temperatures (1.0, 1.5,

233

3.0, and 1000000.0). The prior distributions of M and θ were set to include the upper limit of the posterior

234

distributions as an exponential prior distribution. The average effective number of migrants between

235

populations per generation (Nem) was calculated as M ×θ/4. By referring to Ishiyama et al. (2015), we

236

also calculated net immigration (the total number of effective immigrants minus the total number of237

effective emigrants) for each of the six populations and genetic clusters. A negative value of Nem indicates

238

that the number of emigrants exceeds the number of immigrants.

239

We also employed the assignment test based on the Bayesian method by Rannala and240

Mountaion (1997) implemented in Geneclass 2.0 (Piry et al. 2004) to assess some recently observed

241

migrations and determine the most likely populations of origin for each case. This test computed the242

probability of a particular multilocus genotype of each individual to be encountered in a given population

243

within a few generations. The probability was calculated using the method of Paetkau et al. (2004) based244

on 10,000 Monte Carlo simulations and a type I error rate of 0.01. Assignment tests were conducted245

among three genetic clusters and six original populations. We determined the source of each individual as

246

being the population having the highest probability.

14

247

Results

248

Local population dynamics of S. marmorata along agricultural gradients249

The population density of S. marmorata ranged from 0 to 92 ind. m-2. S. marmorata was never250

found at three sites in the upstream region of the Ketobetsu stream on all sampling occasions (Fig. 1).

251

These three sites were excluded from the statistical analyses. In GLM analyses, ΔAIC between the full252

and null models was larger than 2 for all response variables (Table 2), suggesting that % pasture was

253

influential for all response variables. The abundance of S. marmorata exhibited a positive correlation with254

pasture cover before the flood (Mar) but a negative correlation after the flood (May). The relationship at

255

six months after the flood (Nov) was positive (Fig. 2). The populations at both KiD and KeU, which

256

represented the highest pasture cover, decreased from 10.7 ind. m-2 (Mar) to 0 (May) but recovered to257

12.7 and 6.0 ind. m-2 (Nov), respectively. The resistance index showed a positive correlation with pasture258

cover. Among the six sites included in the genetic analysis, the populations at sites with low and moderate259

pasture cover (KiU, PeU, PeD and KeD) were relatively stable during the year, whereas those at sites with

260

high pasture cover (KiD and KeU) were unstable and showed high fluctuations in population densities.

261

Genetic diversity

262

There was no evidence of null alleles among all loci in all populations. Significant linkage263

disequilibrium was detected only in the BD population at the combined Steno02 and 06 loci (corrected P

264< 0.05). Significant deviation from Hardy–Weinberg equilibrium was detected only in the BD population

15

265

at Steno 07 (corrected P < 0.05). However, other populations showed nonsignificant linkage

266

disequilibrium and deviation from Hardy–Weinberg equilibrium at all loci. We therefore did not exclude267

these loci from any analysis. Seven microsatellite loci were polymorphic (the number of alleles ranged

268

from 6-11). Similar values of genetic diversity (Ar and He), except for Pr, were obtained among the six

269

populations (Table 3), and genetic diversity presented a nonsignificant correlation with agricultural

270

development (ΔAIC between the full and null models < 2; Table 2, Appendix Fig. S2). In contrast, private

271

allelic richness (Pr) showed a negative correlation with agricultural development (ΔAIC between the full272

and null models = 8.46; Table 2, Appendix Fig. S2).

273

Genetic clustering

274

The six populations were clustered into three groups: cluster 1 (KiU, PeU), cluster 2 (KiD, PeD,

275

KeD) and cluster 3 (KeU), which were supported by high bootstrap values (Fig. 3). This clustering reflected276

agricultural development and positions in the stream network. Cluster 1 (KiU, PeU) consisted of upstream

277

sites with low agricultural development, cluster 2 (KiD, PeD, KeD) consisted of downstream sites with

278

moderate or high agricultural development, and cluster 3 (KeU) consisted of an upstream site with high279

agricultural development (Figs. 1, 4).

280

Asymmetric migration

281
282

Recent migration among the six populations estimated by the assignment test showed that selfassignment (where the assigned population was the sampled population) was relatively high in an

16

283

upstream site of the Kitamihorobetsu stream (KiU), up- and downstream sites of the Penkenai stream

284(PeU, PeD), more than 30 %, whereas low in the other three sites (KiD, KeD, KeU), less than 20 %. The

285

origin of all sampled individuals was biased towards the Penkenai River (PeU, PeD) (Table 4). Recent

286

migration of the three genetic clusters also showed similar patterns; the percentage of assigned

287

individuals was the highest in cluster 1 (KiU, PeU) or cluster 2 (KiD, KeD, PeD) and the lowest in cluster

288

3 (KeU) for all sampled clusters (Table 4).

289

We estimated long-term migration rates among the three genetic clusters and between the up-

290

and downstream sites within each stream. The up- and downstream sites within each stream belonged to291

different genetic clusters and showed different combinations of agricultural development. The cluster292

setting enabled us to separately examine the effects of agricultural development (i.e., population stability)293

and flow direction (i.e., up- and downstream gradients) on migration.

294

All estimated θ and M values for the three clusters significantly differed from zero (95 %295

confidence intervals did not overlap zero). Estimated effective migration sizes (Nem) among three genetic296

clusters showed asymmetric migration, and the values were higher for migrations from cluster 1 (KiU,

297

PeU) to clusters 2 (KiD, PeD, KeD) and 3 (KeU) and from cluster 2 (KiD, PeD, KeD) to cluster 3 (KeU)

298(Fig. 4; Appendix Table S1). Specifically, Nem from cluster 3 (KeU) to the other clusters was less than299

half of Nem from clusters 1 and 2. The results regarding net immigration showed that cluster 1 and cluster

300

2 served as net migration sources (negative net immigration), whereas cluster 3 served as a net migration

17

301

sink (positive net immigration) (lower part of Appendix Table S1).

302

Significant asymmetric migration was found between the upstream and downstream parts of303

each of the three streams (Fig. 5; Appendix Table S2). In the Kitamihorobetsu stream, a higher Nem of304

downstream migration and negative net immigration in an upstream population indicated that an upstream

305

population served as a source for a downstream population. In contrast, in the Penkenai and Ketobetsu

306

streams, the higher Nem of upstream migration and negative net immigration in downstream populations

307

indicated that downstream populations served as sources for upstream populations. The results for the308

Kitamihorobetsu and Ketobetsu streams showed that sites with low and moderate agricultural309

development (i.e., stable populations) provided individuals to sites with high agricultural development

310(i.e., unstable populations), whereas the Penkenai stream showed asymmetric migration from sites with

311

moderate agricultural development to sites with low agricultural development.

312
313

Discussion

314

Flow directions or environmental gradients along river networks often shape the asymmetric315

migration dynamics of stream organisms (e.g., Kanno et al. 2011; Terui et al. 2014; Nakajima et al. 2021).

316

In this study, recent and long-term asymmetric migrations of S. marmorata populations were found across317

the catchment; however, the migratory directions observed were different within and between each of the318

three tributaries. The decreasing population stability along agricultural gradients and different land uses in

18

319

each tributary could be the reason why migratory directions were biased. Previous studies have reported

320that flooding frequently induces asymmetric migration through the creation of population stability

321

heterogeneity (e.g., Johnson 2004; Shama et al. 2011; Chiu et al. 2023). Our findings indicate that, beyond322

the impacts of flooding, artificial land use within catchments can further modify the migration dynamics

323

by increasing the vulnerability of instream organisms to flood disturbances. Although we focused on only

324

S. marmorata, the interactive impacts between natural and anthropogenic disturbances on migration

325

dynamics are also highly likely to be observable in other species and ecosystems because environmental

326

stochasticity is a proxy of the migration dynamics of metapopulations (Hanski 1998).

327

S. marmorata was more abundant in agriculturally impacted streams before the flood (Mar) and328

in forested streams just after the flood (May). These patterns and the negative relationship between the329

resistance index and % pasture indicated that the populations of S. marmorata were more stable in forested

330

streams. While the species can show high resistance to flood disturbances in rocky substrates because of331

their strong silky nets (Takao et al. 2006), clearcutting along the channels and agricultural land use in the332

catchments can enhance sediment inputs into streams (Sweeney & Newbold 2014; Appendix Fig. S1) and333

may increase the mobility of substrate materials and living individuals during flood events. The population

334

densities decreased by flood disturbance partially recovered and showed a positive relationship with335

agricultural gradients in November. Although the sampling method employed in the period differed from

336

that in previous periods (see details in Methods), the shift from a negative correlation in May to a positive

19

337

correlation in November suggests the recovery of local populations in agricultural streams throughout the338

emergence and oviposition periods. Thus, stability in populations of S. marmorata was higher in forested

339

streams than in agricultural streams.

340

We hypothesized that S. marmorata showed asymmetric migration in the river networks and that341

directional migration occurred from stable populations at forested sites to unstable populations at

342

agriculturally impacted sites. Both recent (assignment tests) and long-term migrations (Nem) showed343

similar migratory dynamics; more individuals colonized from stable to unstable populations than in the344

opposite direction. Specifically, both estimations of recent migrations among all six populations and three345

clusters resulted in zero assignments to cluster 3, suggesting that the most unstable population upstream of346

the Ketobetsu River was dependent on colonization from surrounding populations and became the almost

347

complete sink of individuals. Long-term and recent migration dynamics often show different patterns in348

river networks (e.g., Yamazaki et al. 2011; Ishiyama et al. 2015). Similar recent and long-term migration349

dynamics in our study river could be due to the predictable and annual disturbances by the interaction

350

between snowmelt flooding and long-term press disturbance over 100 years by agricultural development.

351

The interactive impacts have gradually degraded the populations in agriculturally impacted streams from a352

long-term perspective, formed asymmetric migrations along the gradient of population stability, and353

resulted in the current strong source‒sink populations.

354

However, some combinations of local populations showed the reverse direction of migration,

20

355

from unstable to stable populations (higher assignments to the downstream population of the Penkenai

356

River and upstream migration in the Penkenai River in long-term migration). There are two possible causes

357

for this pattern in the studied rivers. First, the general dispersion of S. marmorata is highly biased in the358

upstream direction. Nishimura (1981) reported that the upstream dispersal of adult flies was approximately

359

3 times higher than their downstream dispersal; therefore, migrants from downstream to upstream

360

populations should be more abundant in natural rivers. Second, moderately stable populations (KeD and

361

PeD) at downstream sites showed a comparable individual density to stable populations at forested

362

upstream sites (KiU and PeU) after the flood. The surviving individuals can contribute to reproduction and363

dispersal to surrounding populations. Thus, asymmetric migration occurred from moderately unstable364

populations to stable populations. In contrast, the observation of downstream-biased migration in the365

Kitamihorobetsu River and overland migration from cluster 1 to cluster 3 supported our hypothesis. The

366

migration patterns recorded in the Kitamihorobetsu River provide particularly strong supportive evidence

367

because migrants from upstream to downstream sites were more abundant, although the general dispersal

368

pattern of S. marmorata is upstream biased. Migrants across different streams were also identified. The

369

geographical distances between genetic clusters or up- and downstream sites within each stream ranged

370

from approximately 4 to 12 km. S. marmorata can fly up to 12 km overland and 18 km along the riverline

371(estimates based on observed data, 2.5-12 km in Nishimura 1981; and genetic data, 12-18 km in Yaegashi

372

et al. 2014). Therefore, individuals could be supplied to unstable populations across different streams. These

21

373

results suggested that upstream-biased asymmetric migration in natural systems can be altered by habitat

374

changes resulting from agricultural land use, but stable populations in forested streams can supply

375

individuals and sustain subpopulations in agriculturally impacted streams.

376

We hypothesized that genetic diversity (heterozygosity and allelic richness) would be higher in377

more stable populations. However, genetic diversity (He and allelic richness) showed no correlation with378

the level of agricultural development and presented relatively high values at all sites. Genetic diversity in379

unstable populations depends on the dispersal ability, number of populations and reproductive system of

380

the species involved (Berendonk et al. 2009; Ray 2001). Berendonk et al. (2009) compared stable381

populations of three midge species to unstable populations of three different midge species and reported382

low genetic diversity in unstable populations. In this case, the unstable populations were not near source

383

populations; therefore, recovery depended on self-recruitment (i.e., high inbreeding coefficients were384

observed), and the opportunity for genetic drift increased. In our study catchment, source populations

385

were distributed along the river, and individuals frequently migrated among populations. Therefore, sink386

populations could receive immigrants from various sources and maintain high genetic diversity despite

387

annual bottlenecks caused by snowmelt floods. In contrast, private allelic richness (i.e., the number of388

unique alleles) showed a negative relationship with agricultural development, which indicated that gene389

alleles present in unstable populations with high agricultural development became subsets of the alleles of390

stable populations. To maintain regional genetic diversity, population sources with more private alleles

22

391
392

should be preferentially preserved.
Instream organisms adapt to seasonal natural disturbance regimes and maintain populations

393(Lytle and Poff 2004); however, human impacts have drastically altered disturbance regimes and changed394

metapopulation dynamics (Zeigler et al. 2017). If the altered disturbance regime exceeds the recovery

395

capacity of the metapopulation, the species will be extirpated from the system. Under the current land use,

396

having a low proportion of artificially altered areas in the studied catchment, the metapopulations can be

397

maintained despite annual snowmelt floods. However, future global climate change may increase the398

intensity and frequency of hydrological disturbances (Hirabayashi et al. 2013; Milly et al. 2002).

399

Therefore, we should carefully observe the population dynamics in forest-agricultural landscapes and400

need to predict metapopulation dynamics under changing climate scenarios.

401
402
403

Acknowledgments
We thank members of Ecosystem Management Group in Hokkaido University for observation404

and discussion. Members of Hokkaido Aquatic Insects Workshops helped species identification. This

405

work was funded by Grants in Aid for Scientific Research (No. 23248021) from the Ministry of406

Education, Science & Culture, Japan.

407

Author Contributions

408

All authors discussed the study plan. M.S. and N.I. conducted the observation and collected

23

409

samples. M.S. and K.T. did the genetic analyses. All authors contributed critically to the draft and gave

410

final approval for publication.

411

Data accessibility

412

Data available from the figshare repository https://doi.org/10.6084/m9.figshare.14333204.v

413

1

414

Declaration of competing interest

415

All authors have no conflict in interest.

416
417
418

Supporting Information
Supporting Information can be found online in the Supporting Information section.

419

24

420

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32

592

Tables593

Table 1594

Sampling times, snowmelt flooding period and emergence period of Stenopsyche marmorata in 2011.

595

Detailed sampling information is provided in the Methods section. The emergence period in Hokkaido

596

refers to Gose (1970).
2011
Sampling for
population
dynamics

Jan

Feb

Mar
12
quad
rats

Apr

May
12
quad
rats

Sampling for
genetic
analysis

Jun

Jul

Aug

Sep

Oct

Nov
3
kick
sam
ples
2332
ind.

Snowmeltflood
Emergence
period of S.
marmorata

597
598

33

Dec

599

Table 2. Summary of results of GLM for abundances in each sampling period, resistance index, and600

genetic diversities (allelic richness, expected heterozygosity and private allelic richness). The full model601

with ΔAIC > 2 compared to the null model is represented in bold.
Variable

Model

Abundance
(Mar)

full

2.55

null

2.85

full

2.56

null

2.03

Abundance(May)

Intercept

Abundance
(Nov)

full

3.23

null

3.40

Resistance

full

1.24

null

0.63

full

6.24

null

5.82

full

0.69

null

0.67

full

0.51

null

0.33

Ar
He
Pr

coefficientsof %pasture
0.034-0.0780.020-0.074-0.043-0.003-0.018

602
603

34

ΔAIC

logLikAIC-158.96

321.9

0.0

-167.85

337.7

15.8

-83.59171.2

0.0-103.24

208.5

37.3

-101.18

206.4

0.0

-106.68

215.4

9.0

-17.3741.9

0.0-24.57

53.7

11.8

-3.98

14.0

0.0

-5.04

14.1

0.1

13.50

-21.0

0.0

12.05

-20.1

0.9

8.28

-10.6

0.0

3.05

-2.1

8.5

604

Table 3605

Agricultural development and genotype data of six populations. % Pasture, pasture cover within a606

catchment area; N, number of samples subjected to genotyping; He, expected heterozygosity; Fis, fixation607

index; Ar, allelic richness; Pr, private allelic richness.
Site% Pasture

N

He

Fis

Ar

Pr

KiU

0

32

0.68

-0.10

5.97

0.44

PeU

1.0

30

0.69

-0.02

6.17

0.53

KeD

10.2

25

0.67

-0.01

5.74

0.32

PeD

11.7

29

0.70

0.00

6.64

0.41

KeU

17.9

24

0.60

-0.15

4.82

0.14

KiD

17.9

23

0.66

-0.03

5.57

0.15

608
609

35

610

Table 4611

Assignment tests of each individual for six populations (up) and three genetic clusters (bottom) using

612

GeneClass2. Values represent the percentage of individuals from the sampled population (row) to the613

assigned population (column). Bold indicates the percentage of self-assignment.
Assigned
SampledCluster1

Cluster2
Cluster3

Cluster1

Cluster2

Cluster3

KiU

PeU

KiD

PeD

KeD

KeU

KiU

31.3

21.9

15.6

25.0

6.3

0.0

PeU

20.0

33.3

0

40.0

6.7

0.0

KiD

17.4

21.7

13.0

39.1

8.7

0.0

PeD

13.8

20.7

10.4

51.7

3.5

0.0

KeD

16.0

24.0

8.0

32.0

20.0

0.0

KeU

12.5

50.0

20.8

12.5

4.2

0.0

18.5

28.6

11.3

33.4

8.2

0.0

Average
Assigned

Cluster1

Cluster2

Cluster3

Cluster1

61.9

38.1

0.0

Cluster2

48.1

52.0

1.1

Cluster3

47.8

52.2

0.0

Average

52.6

47.4

0.0

Sampled

614
615

36

616

Figure Captions

617

Fig. 1 Study sites for the examination of population dynamics (black and white triangles) and genetic

618

analysis (white triangles). The six sites included in the genetic analysis were in the upstream and619

downstream regions of three streams (sites KeU, KeD, KiU, KiD, PeU and PeD). The letters (L, I and H)

620

beside the site names indicate three levels of agricultural development: L: low % pasture (0-1 %), I:

621

moderate (10-12 %) and H: high (approximately 18 %), which corresponds to population stability (see622

Methods). Sites enclosed by the dotted line in the upstream region of the Ketobetsu stream showed an

623

absence of S. marmorata during all sampling occasions.

624

Fig. 2 Relationships between pasture cover within a catchment area and the abundance and resistance of625

the S. marmorata population. White circles indicate sites where genetic samples were collected (KiU,

626

KiD, PeU, PeD, KeU, KeD). Solid lines are the lines predicted by the GLM. The resistance index is627

calculated by post-flood abundance/preflood abundance. (see Methods). Mar; March, Nov; November.

628

Fig. 3 Genetic clustering of six populations by UPGMA based on Dsw distance. Numbers indicate629

bootstrap support.

630

Fig. 4 Effective number of migrants (Nem) among genetic clusters. The thickness of grey arrows

631

represents Nem.

632

Fig. 5 Effective number of migrants (Nem) between up- and downstream sites within each stream. The

633

thickness of the grey arrows represents Nem.

37

634

Figures635
636

Fig. 1

637

38

638
639

Fig. 2

39

640
641

Fig. 3

642

40

643
644

Fig. 4

645

41

646

647
648

Fig. 542