Diversity of Rhyacophila (Trichoptera, Rhyacophilidae) in the Hengduan Mountains

69 Diversity of Rhyacophila (Trichoptera, Rhyacophilidae) in the Hengduan Mountains Sonja Gerwin1,2, Xiling Deng1,3,4, Fengzhi He5,6, Steffen U. Pauls1,3,4 1 Senckenberg Research Institute and Natural History Museum Frankfurt, Senckenberganlage 25, 60325 Frankfurt am Main, Germany 2 Senckenberg Biodiversity and Climate Research Centre, Senckenberganlage 25, 60325 Frankfurt am Main, Germany 3 LOEWE Centre for Translational Biodiversity Genomics, Senckenberganlage 25, 60325 Frankfurt am Main, Germany 4 Institute of Insect Biotechnology, Justus-Liebig-University Gießen, Heinrich-Buff-Ring 26-32, 35392 Gießen, Germany 5 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany 6 Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street 4888, 130102 Changchun, China Corresponding author: Steffen U. Pauls ([email protected]) Copyright: © Sonja Gerwin et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Research Article Abstract Aquatic insects are particularly dependent on environmental conditions because their life cycles are directly linked to physico-chemical conditions in freshwater habitats. Here, we combine DNA barcoding and ecological analysis to determine general distributional patterns for an unknown fauna of Rhyacophila (Trichoptera, Rhyacophilidae) in the Hengduan Mountains in China. In total 415 larval and 109 adult specimens from four major Hengduan Mountain river basins (1022 m – 4381 m a.s.l.) were sequenced and analyzed. Molecular operational taxonomic units (MOTUs) were delimited as putative species analogs. MOTUs were derived from mitochondrial COI (mtCOI) and nuclear wingless (nuWG) data using the tree-based GMYC method. As expected, based on a higher mutation rate, mtCOI delimitation resulted in a much higher number of MOTUs (66) than in WG (27), but not all mtCOI MOTUs were nested within nuWG MOTUs. Many mtCOI MOTUs were geographically restricted and rare, often occurring in only one or two sampling sites. Multivariate GLM analyses confirmed the importance of basins as the primary factor for explaining the diversity of Rhyacophila MOTUs. The high level of geographical restriction of MOTUs among basins and along elevational gradients indicates that Rhyacophila in the Hengduan Mountains are largely range-restricted, dispersal limited and consequently vulnerable to local changes in environmental conditions. Key words: Caddisflies, mountain biodiversity, species delimitation Introduction Due to high levels of habitat heterogeneity, mountains generally host high levels of biological diversity, even over short geographic distances. These patterns are frequently considered to be governed by high gradient topography that leads to clear elevational distribution patterns and topographic dispersal barriers (Favre et al. 2015). In caddisflies, these dispersal barriers can act at either the larval or adult dispersal phases or both. Larval dispersal is limited inside the stream, primarily consists of downstream movement, and should lead to diverAcademic editor: Paul B. Frandsen Received: 16 March 2025 Accepted: 19 August 2025 Published: 10 December 2025 ZooBank: https://zoobank. org/76C30C0D-47F7-4013-BDAE5CB054FC91DB Citation: Gerwin S, Deng X, He F, Pauls SU (2025) Diversity of Rhyacophila (Trichoptera, Rhyacophilidae) in the Hengduan Mountains. In: RíosTouma B, Frandsen PB, Holzenthal RW, Houghton DC, Rázuri-Gonzales E, Pauls SU (Eds) Proceedings of the 18th International Symposium on Trichoptera. ZooKeys 1263: 69–88. https://doi.org/10.3897/ zookeys.1263.153111 ZooKeys 1263: 69–88 (2025) DOI: 10.3897/zookeys.1263.153111 70 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains sification among watersheds (Headwater Pattern, sensu Hughes et al. 2009). However, the upstream movement of flying adults is not restricted to the river valley. While upstream river corridor dispersal is common and important to compensate for downstream larval drift, lateral dispersal between catchments or basins is regularly observed and inferred (e.g., Geismar et al. 2015). In the extremely deep parallel river valleys of the Hengduan Mountains in south-western China, we might expect lateral movement to be restricted due to the extremely high mountains separating neighboring valleys. This restriction would presumably lead to highly structured caddisfly distributions, valley-specific differentiation and even speciation (Malicky 2004; Hoppeler et al. 2016). This, in turn, would lead to a high number of endemic lineages within river valleys in the region. At the same time, the extensive elevational gradients across the mountain range lead to a plethora of environmental conditions across relatively short geographic distances within the river network, potentially driving ecological diversification and specialization (Favre et al. 2015). Here we assess the elevational and network-associated diversity patterns of Rhyacophila species in streams and rivers in the Hengduan Mountains. We predict that valley system endemics will dominate the overall diversity patterns. Considering the high elevational gradients in these rivers from ~1000 m a.s.l. to >4300 m a.s.l., we further expect to see elevational diversification in response to varying environmental conditions from subtropical to alpine conditions. Materials and methods Study sites Larval and adult specimens were collected in the Yangtze (33 sites), Salween (7 sites), Mekong (6 sites) and Irrawaddy (3 sites) basins (see Fig. 1) by Xiling Deng and Fengzhi He in 2018. Sampling methods were described in detail in Hjalmarsson et al. (2018). In short, larval specimens were collected by hand picking from rocky substrates or collected by kick-sampling and hand nets. Adults were collected using aerial hand nets and light trapping with passive pan traps. Environmental variables At each site, elevation [m], conductivity [µs], salinity [ppt], total amount of dissolved solids (TDS) [g/L], water temperature [°C], air temperature [°C], dissolved oxygen [mg/L], dissolved oxygen [%], pH and the oxidation reduction potential [mV] were measured using a multiparameter water quality meter (YSI professional plus, US). Altitude and the exact geographical position were determined with a GPS device (Garmin Etrex, Schaffhausen). Identification and preparation of specimens The inspection of the larvae and sorting into morphospecies was done using an Olympus SZX7 stereoscope with a mounted DP25 5MP camera. To identify the adults to species/genus level, one or two male individuals per morphospecies were selected and the genitals were cleared with lactic acid (Blahnik et al. 2007). Identifications were based on Morse et al. (1994) 71 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains for larvae and unpublished species pages from H. Malicky (Lunz am See, Austria) and Schmid 1970 for adults. One or two hind legs of each individual were removed for DNA extraction. DNA extraction, amplification, and sequencing We extracted DNA from all Rhyacophila specimens collected. In total 524 specimens of Rhyacophila were included in this study (415 larvae, 109 adults; 1–43 specimens per site). Genomic DNA of each specimen was extracted using hindlegs and/or abdominal tissue with the HotShot protocol (Montero-Pau et al. 2008). We amplified and sequenced two gene fragments to complement morphospecies assignment by means of integrative species delimitation: the COI barcode region (658bp) and the nuclear gene wingless (472bp). PCR and sequencing primers are listed in Table 1. PCR amplification of the mtDNA COI was performed with 2.0 µl genomic DNA in 11µl reactions, using the following recipe: 2.0 µl of reaction Buffer (5XMy Taq Red Reaction Buffer, meridian bioscience), 0.4 µl of each primer, 0.1 µl Taq DNA Polymerase (My Taq DNA Polymerase, meridian bioscience) and 6.1 µl of sterile water. PCR of the nDNA wingless was performed using 3.0 µl genomic DNA of each individual in 12 µl reactions. Otherwise, the recipe was as for mtCOI. The PCR settings for COI amplification were as follows: an initial denaturation step at 95 °C for 1 min; 35 cycles of 20 s denaturation at 95 °C, 30 s annealing at 43 °C, product extension at 72 °C for 30 s; a final extension at 72 °C for 5 min. The PCR settings for the wingless amplification were identical except for the annealing temperature which was set to 50 °C. Sichuan Province Yunnan Province Tibetan Autonomous Region Myanmar Sampling Sites Polical boundaries Rivers Irrawaddy Mekong Salween Yangtze No Rhyacophila foundd Irrawaddy Yangtze Mekong Salween Figure 1. Distribution of sampling sites in the four major drainage basins of the Hengduan Mountains. 72 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains To verify the success of the amplification and to detect possible contamination, the PCR products and negative controls were visualized on a 1.5% agarose gel. Before sequencing, the PCR products were purified using an ExoSap purification. PCR products were then sequenced bidirectionally using the PCR primers on an ABI 3730XL sequencer at the Senckenberg Biodiversity and Climate Research Centre Laboratory Centre using standard reaction parameters. Sequence editing Sequences of all gene fragments were edited and aligned in Geneious Prime v. 11.1.5. Primer regions were deleted from the final consensus sequences. Ambiguities in nuWG were coded following standard IUPAC codes. The sequences were aligned using default settings of the Geneious alignment tool and short sequences under 400bp were excluded from the alignment. Bayesian phylogenetic reconstruction Two data sets were used for the Bayesian phylogenetic reconstruction: a 658 bp long alignment of 301 specimens sequenced for mtCOI, and a 472 bp long alignment of 196 specimen sequences for nuWG. The data sets were collapsed to unique haplotypes using the program Collapse 1.2 with default settings (Posada 2004). The best fitting evolution model of each data set was calculated using JModeltest v. 2.1.10 (Darriba et al. 2012). According to the AICc = AIC with correction for smaller sample sizes, BIC = Bayesian Information Criterion and DT = decision-theoretic performance-based criteria HKY+I+G was the best-fitting model for the COI data set, while K80+I+G was the best for the wingless data set. The ultrametric tree was computed with BEAUti (Bayesian Evolutionary Analysis Utility) v. 1.10.4 and BEAST (Bayesian Evolutionary Analysis Sampling Trees) v. 1.10.4 (Drummond and Rambaut 2007). In BEAUti, the uncorrelated relaxed clock was chosen with a lognormal relaxed distribution for both data sets. The tree prior was set to “Speciation:Yule Process” with a random starting tree. For both data sets the priors kappa, pInv, and the substitution model, were adjusted as suggested in JModeltest. The COI dataset was run for 500,000,000 generations, sampling every 50,000th tree and wingless for 100,000,000 generations, sampling every 10,000th tree. To visualize and analyze the MCMC log files from BEAST, the MCMC Trace Analysis Tool (Tracer) v. 1.7.1 was used (Rambaut et al. 2018). Trees were combined in LogCombiner v. 1.7.1, the first 30% were thereby discarded as burn-in. With TreeAnnotator v. 1.10.4 a maximum clade credibility tree was generated and visualized in FigTree v. 1.4. Table 1. Information on the primers used to amplify COI mtDNA and Wingless nucDNA. Primer Direction Sequence (5’ – 3’) Gene Reference COI-F forward GGTCAACAAATCATAAAGATATTGG COI Folmer et al. 1994 COI-R reverse TAAACTTCAGGGTGACCAAAAAATCA COI Folmer et al. 1994 Wingnut1a forward GAAATGCGNCARGARTGYAA wingless Vitecek et al. 2015 Wingnut3 reverse ACYTCRCARCACCARTGRAA wingless Vitecek et al. 2015 73 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains Molecular species delimitation using GMYC The General Mixed Yule Coalescent (GMYC) method was developed to delimit independently evolving species, using single locus data (Fujisawa and Barraclough 2013). This method defines species boundaries by identifying the transition point in the branching pattern of a haplotype ultrametric tree. This transition separates population-level processes, such as coalescence, from species-level processes, such as diversification (Pons et al. 2006). The GMYC analysis was conducted in R, v. 3.6.3 (R Core Team 2024), using the splits (Species Limits by Threshold Statistics) package (Ezard et al. 2017). The ultrametric trees were analyzed using the single-threshold method, which uses a single threshold to specify the transition from betweento within-species branching (Monaghan et al. 2009). This approach is considered more conservative than the multiple threshold method that allows different transition thresholds across the phylogeny. We hereafter refer to the GMYC derived species surrogates as molecular operational taxonomic units (MOTUs). Species abundances and environmental variables We tested which environmental variables were associated with the abundance of the delimited MOTUs using Generalized Linear Models (GLMs). Prior to the analysis all environmental variables were tested for correlation using the Pearson correlation test. Every variable that had a significant correlation coefficient above 0.7 or below -0.7, was removed from the statistical analysis. Sampling sites for which measurements were not available were also removed. The GLM was implemented in the R package mvabund (Wang et al. 2012b) using the function manyglm. In this approach, a separate GLM is fit to each MOTU. The approach allows the use of non-normal data distributions, incorporating several explanatory variables simultaneously and takes correlation between species into account (Wang et al. 2012b). Because we collected many more specimens and often more species at some sites than others, we included the number of Rhyacophila individuals collected at each site as “individuals per site” to control for sampling biases. The analysis was run on raw MOTU count data, using the negative binomial distribution, which has been shown to be appropriate for count data because the mean-variance function tends to be quadratic rather than linear (O’Hara and Kotze 2010). To check if the assumptions (e.g., the distribution) of the model were correct, normal Q-Q, residuals vs fitted and Scale-Location plots were used. The final formula for the generalized linear model was: manyglm (species abundance matrix ~ Individuals per site + basin + elevation + conductivity + dissolved oxygen + Air temperature + ORP, family = negative binomial), all the continuous variables were scaled and centered, and basin was included as a factor. To test for significant effects of the explanatory variables on species abundance ANOVA was computed using the anova.manyglm function. We used a bootstrap method based on probability integral transform residuals, the “PIT-trap” (Warton et al. 2017), for estimating p-values were obtained based on 1000 resamples. In addition to these multivariate test statistics, univariate species-by-species results were calculated with p-values adjusted for multiple testing, using a step-down resampling procedure (Wang et al. 2012b). Level of significance was p < 0.05. 74 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains Results Dataset In this study, in total 524 Rhyacophila individuals (415 larvae and 109 adults) were included. For the Rhyacophila larvae samples, 301 COI sequences (658 bp, 75% success rate) and 196 wingless sequences (472 bp, 50% success rate) were generated. Furthermore, 109 sequences of adult Rhyacophila, which were collected from the same basins, were generated. The COI alignment contained 207 unique haplotypes, whereas the wingless alignment contained 150 unique haplotypes. The data is available in the Barcode of Life BOLD Systems database as record set Hengduan Shan Rhyacophila “HSRHY”. Species delimitation and life stage association GMYC Analysis The threshold time (T) infers the transitioning from speciation level events to coalescent level events. As expected T varied between the two gene fragments, transitioning earlier with the nuclear marker (T = -0.01), compared to the mitochondrial (T = -0.03) (Suppl. material 1). Transition points are indicated by red lines in the lineage-through-time plots and specimens belonging to the same GMYC species are marked red in the phylogenetic tree (Suppl. material 1). The tip nodes separating GMYC clusters and nodes within clusters were well supported (BBP > 0.95) in phylogenetic trees from both gene fragments. Deeper nodes, however, were found to have only weak support (BPP < 0.95) (Suppl. material 1). The single threshold method delimited 66 putative species (hereafter referred to as MOTUs) composed of 42 distinct clusters and 24 singletons (represented by a single haplotype) in the COI dataset. GMYC analysis of the wingless gene fragment delimited 27 putative species, of which five MOTUs were singletons (Suppl. material 1). Several specimens associated with COI MOTUs clustered within different WG GMYC MOTUs, showing incongruence for species delimitation patterns among the gene fragments using GMYC. Since we successfully sequenced many more specimens for mtCOI, we performed all downstream analyses based on the COI-derived MOTUs. Larval abundance and distributional patterns Overall, larval Rhyacophila were collected at 49 of 63 sampling sites, covering the four main river basins and an elevational range of 1022 m – 4381 m a.s.l. COI MOTUs were distributed as follows: 279 individuals (134 haplotypes, 47 MOTUs) in Yangtze, 67 individuals (39 haplotypes, 18 MOTUs) in Mekong, 39 individuals (20 haplotypes, 13 MOTUs) in Salween and 20 individuals (8 haplotypes, 7 MOTUs) in Irrawaddy. On average we found four COI MOTUs per sampling site, with values ranging from one to twelve. The highest numbers of MOTUs occurred between 2000 m a.s.l. and 3500 m a.s.l., with site 61 at 2840 m a.s.l. in the Yangtze river basin exhibiting the greatest MOTU richness (n = 12). The Yangtze river basin was dominated by MOTU 20 (23 individuals, 12 different haplotypes). This MOTU contains adults that were identified as belonging to the hingstoni species group. 75 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains The Mekong river basin was characterized by the MOTUs 13 (6 haplotypes) and 15 (7 haplotypes). In the Salween and Irrawaddy river systems, no single MOTU dominated the river basin. Many MOTUs (43 of 66) were rare and only occurred at one or two sampling sites. With the remaining MOTUs mostly occurring at three or four sites (max 8 sites), and the majority of MOTUs restricted to a single basin (46 of 66), few MOTUs can be considered widespread (Fig. 2). The distribution of COI-based MOTUs varied greatly between Yangtze and the three other river basins. In the Yangtze 74% of MOTUs were private, and were only collected in this catchment. The Mekong, Salween and Irrawaddy contained mostly abundant MOTUs that occurred in at least two basins (Figs 2, 3). Four MOTUs were especially abundant (sampled in three basins at up to eight sampling sites) and covered a wide range of environmental variables: MOTU 28 (Yangtze, Mekong, Salween), MOTU 31 (Yangtze, Mekong, Salween), MOTU 27 (Yangtze, Mekong, Irrawaddy) and MOTU 13 (Yangtze, Mekong, Salween). Variability of environmental parameters is presented in Table 2. Because of the effect of elevation on other environmental variables, e.g. temperature, the wide altitudinal range may explain the variability of the other measured environmental variables in the Yangtze river basin. Multivariate regression analysis using Generalized Linear Models (GLMs) were applied to test whether environmental variables were significantly associated with the occurrence and abundance of delimited MOTUs. Prior to GLM analysis, Pearson correlation tests revealed strong and significant correlations of elevation with water temperature as well as of conductivity with salinity, total dissolved solids and the pH. Hence, water temperature, total dissolved solids, salinity, and pH were not included in the GLM analysis. Explanatory variables used in the GLM analysis all exhibited significant effects on mean MOTU abundance (Table 3). Table 2. Environmental parameters presented as ranges for each river basin. River Basin Altitude (m a.s.l.) Conductivity (µs cm-1)Air Temp. (°C) DO (mg/L) ORP (mV) Yangtze 1022–4381 7.3–286.5 6.7–23.7 6.88–8.85 62.5–303.2 Mekong 1966–3475 28.3–205.1 10.2–19.8 7.65–8.02 98.2–152.1 Salween 1439–3190 9.5–60.4 15.6–21.1 7.36–7.69 98.6–152.3 Irrawaddy 1457–2721 15.6–32.7 14–21.3 7.51–7.99 111.8–162.3 Figure 2. Distribution of shared (pale grey) and private (dark grey) mtCOI MOTUs at the river basin level. The size of the circle is relative to the number of mtCOI MOTUs found per basin. 76 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains Figure 3. Elevational distribution of all recovered MOTUs. Shown are the elevation (y-axis) of the sites where each MOTU (x-axis) was found. The plot is segmented by basin to highlight the distribution of wide spread MOTUs vs those MOTUs that were private to an individual basin. Circle size reflects the number of individuals. Point color indicates distribution of MOTUs (shared among basin: blue; private: purple). 77 ZooKeys 1263: 69–88 (2025), DOI: 10.3897/zookeys.1263.153111 Sonja Gerwin et al.: Elevational diversity of Rhyacophila in the Hengduan Mountains Besides individuals per site (Deviance = 144.8, p = 0.001), which reveals potential sampling biases, river basin (Deviance = 176.4, p = 0.003) and oxidation reduction potential (Deviance = 108, p = 0.001) were the most important variables. In addition to multivariate tests, which examine the influence of each environmental variable used in the GLM on mean MOTU abundance, univariate tests were conducted. These examine the effect of each variable on each MOTU individually. Univariate species analysis revealed a large range of responses to the different environmental variables used in the GLM analysis. Statistically significant responses were found in the COI dataset with the following GMYC MOTUs: MOTU 31 exhibited a significant response towards conductivity (Deviance = 16.57, p = 0.037). This MOTU occurred at middle altitudinal sites (2322 m – 3190 m), with conductivities ranging from 20 µs per cm to 79.7 µs per cm indicating the importance of a relatively low conductivity for the abundance of this MOTU (Suppl. material 2). For MOTU 20, a statistically significant response towards the oxidation reduction potential was found (Deviance = 15.81, p = 0.05). This MOTU dominated the Yangtze with 23 individuals in 12 haplotypes at two sampling sites (2918 m with an ORP of 78.9 mV and 3827 m with an ORP of 162.1 mV; Fig. 3). MOTU composition varied along the environmental gradients within and between basins (Figs 3, 4, Suppl. material 2). Some haplotypes of the same MOTU seem to share environmental preferences, indicating distinct environmental niches. Especially the oxidation reduction potential (Fig. 4, center bars in the heat map) and elevation (Figs 3, 4, left bars of the heat map) showed patterns along the phylogenetic tree. Discussion Rhyacophila from the Hengduan Mountains were sequenced to investigate the species diversity of this region. We linked the delimited MOTU diversity to habitat characteristics and environmental gradients. This study revealed primary structuring of species occurrences aligns with river basins, with numerous unique MOTUs identified in the Mekong and Yangtze basins. Additionally, MOTU variation was associated with ORP followed by conductivity. Conductivity is indicative of geological and geomorphological conditions and also appears to have a structuring effect. Furthermore, elevation and the correlated parameters, air temperature, and DO also showed a significant structuring effect. 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Pauls Data type: pdf Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/zookeys.1263.153111.suppl1 Supplementary material 2 Distribution of MOTUs in relation to conductivity Authors: Sonja Gerwin, Xiling Deng, Fengzhi He, Steffen U. Pauls Data type: pdf Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/zookeys.1263.153111.suppl2