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\begin{document}

\title{Comparative phylogeography of \emph{Himalopsyche} (Trichoptera,
Rhyacophilidae) in the Tibeto-Himalayan Region: An assessment of the
mountain-geobiodiversity hypothesis}



\author[1]{Xiling Deng}%
\author[2]{Sami Domisch}%
\author[3]{Adrien Favre}%
\author[4]{Sonja Jähnig}%
\author[5]{Paul Frandsen}%
\author[4]{Fengzhi He}%
\author[6]{Deep Narayan Shah}%
\author[7]{Ram Devi Tachamo Shah}%
\author[8]{Qinghua Cai}%
\author[1]{Steffen Pauls}%
\affil[1]{Senckenberg Gesellschaft fur Naturforschung}%
\affil[2]{Leibniz Institute of Freshwater Ecology and Inland Fisheries}%
\affil[3]{Regional nature park of the Trient Valley}%
\affil[4]{Leibniz-Institute of Freshwater Ecology and Inland Fisheries in the Forschungsverbund Berlin eV}%
\affil[5]{LOEWE Center for Translational Biodiversity Genomics}%
\affil[6]{Tribhuvan University}%
\affil[7]{Kathmandu University School of Science}%
\affil[8]{Institute of Hydrobiology Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The Tibeto-Himalayan Region is famous for its geography, climatic
influence, and exceptional and immense biodiversity. The
``mountain-geobiodiversity hypothesis (MGH)'' explores the interaction
of topography, climate, and biology in the evolution of mountain
biodiversity. We tested this hypothesis in the Himalayas and the
Hengduan Mountains on a group of caddisflies that are endemic to this
region. We investigated one caddisfly species pair from each mountain
respectively, each pair containing one species inhabiting high elevation
and one inhabiting low elevation. We incorporated genomic and ecological
evidence to reveal population structure, demographic history, and
potential habitat range dating back to the last glacial maximum (LGM) of
each species. The results indicated that in both mountains, the
high-elevation species showed strong local differentiation, while the
low-elevation species were shaped by hydro-morphology indicating greater
regional dispersal activity. Results of demographic history and species
distribution modelling supported demographic expansions for all species
during the LGM linked to an increase in potential habitats. Caddisfly
species in the Himalayas generally exhibited an East-West oriented
dispersal. Species from the Hengduan Mountains showed greater
connectivity on the North-South orientation, suggesting that species
have a higher chance to survive in the Hengduan Mountains by both
in-situ displacement (along the elevational gradients) and long-distance
dispersal (along the latitudinal gradients) during glaciation. Our study
demonstrates that historical geodiversity and climate fluctuations
interact and influence the diversification of caddisflies in the
Tibeto-Himalayan Region, thus supporting the MGH.%
\end{abstract}%



\sloppy


Phylogeography of caddisflies in the THR

Xi-Ling Deng\textsuperscript{1,2,3,4,*}, Sami
Domisch\textsuperscript{4}, Adrien Favre\textsuperscript{5}, Sonja C.
J\selectlanguage{ngerman}ähnig\textsuperscript{4,6}, Paul B. Frandsen\textsuperscript{3,7,8},
Fengzhi He\textsuperscript{4,6,9}, Deep Narayan
Shah\textsuperscript{10}, Ram Devi Tachamo Shah\textsuperscript{11,12},
Qinghua Cai\textsuperscript{13}, Steffen U. Pauls\textsuperscript{1,2,3}

\textsuperscript{1} Senckenberg Research Institute and Natural History
Museum, Frankfurt am Main, Germany

\textsuperscript{2} Institute of Insect Biotechnology,
Justus-Liebig-University Gießen, Gießen, Germany

\textsuperscript{3} LOEWE Centre for Translational Biodiversity Genomics
(LOEWE-TBG), Frankfurt am Main, Germany

\textsuperscript{4} Leibniz Institute of Freshwater Ecology and Inland
Fisheries, Berlin, Germany

\textsuperscript{5} Regional nature park of the Trient Valley, Salvan,
Switzerland

\textsuperscript{6} Geography Department, Humboldt-Universität zu
Berlin, Berlin, Germany

\textsuperscript{7} Department of Plant \& Wildlife Sciences, Brigham
Young University, Provo, USA

\textsuperscript{8} Data Science Lab, Office of the Chief Information
Officer, Smithsonian Institution, Washington, USA

\textsuperscript{9} Center for Biodiversity Dynamics in a Changing World
(BIOCHANGE) and Section for Ecoinformatics and Biodiversity, Department
of Biology, Aarhus University, Aarhus C, Denmark

\textsuperscript{10} Central Department of Environmental Science,
Tribhuvan University, Kirtipur, Nepal

\textsuperscript{11} Aquatic Ecology Centre, School of Science,
Kathmandu University, Dhulikhel, Nepal

\textsuperscript{12} Department of Life Sciences, School of Science,
Kathmandu University, Dhulikhel, Nepal

\textsuperscript{13} State Key Laboratory of Freshwater Ecology and
Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences,
Wuhan, China

* Corresponding author-xiling.deng@outlook.com

\textbf{Abstract}

The Tibeto-Himalayan Region is famous for its geography, climatic
influence, and exceptional and immense biodiversity. The
``mountain-geobiodiversity hypothesis (MGH)'' explores the interaction
of topography, climate, and biology in the evolution of mountain
biodiversity. We tested this hypothesis in the Himalayas and the
Hengduan Mountains on a group of caddisflies that are endemic to this
region. We investigated one caddisfly species pair from each mountain
respectively, each pair containing one species inhabiting high elevation
and one inhabiting low elevation. We incorporated genomic and ecological
evidence to reveal population structure, demographic history, and
potential habitat range dating back to the last glacial maximum (LGM) of
each species. The results indicated that in both mountains, the
high-elevation species showed strong local differentiation, while the
low-elevation species were shaped by hydro-morphology indicating greater
regional dispersal activity. Results of demographic history and species
distribution modelling supported demographic expansions for all species
during the LGM linked to an increase in potential habitats. Caddisfly
species in the Himalayas generally exhibited an East-West oriented
dispersal. Species from the Hengduan Mountains showed greater
connectivity on the North-South orientation, suggesting that species
have a higher chance to survive in the Hengduan Mountains by
both\emph{in-situ} displacement (along the elevational gradients) and
long-distance dispersal (along the latitudinal gradients) during
glaciation. Our study demonstrates that historical geodiversity and
climate fluctuations interact and influence the diversification of
caddisflies in the Tibeto-Himalayan Region, thus supporting the MGH.

\section*{Keywords}

{\label{keywords}}

caddisfly, population genomics, demographic history, species
distribution modelling, Tibeto-Himalayan Region, mountain biodiversity

\section*{1 Introduction}

{\label{introduction}}

Mountains exhibit extraordinarily heterogeneous environments and host a
remarkable diversity of (endemic) terrestrial and aquatic species
(Rahbek et al., 2019; Rahbek et al., 2019; Perrigo et al., 2020). Since
Alexander von Humboldt initiated the principle of \emph{Cosmos} (``unity
of nature'') that combines geology and biology to explain the
distribution patterns of life (Von Humboldt, 1860), researchers have
attempted to explore how geophysical modifications over time, such as
orogeny and climate change, could have influenced biological processes
involved in speciation and diversification (Ding et al., 2020). To
better understand the origin and evolution of biodiversity in mountains,
we investigate four caddisfly species living in the highest and largest
mountain system in the world: the Tibeto-Himalayan Region.

The Himalayas and the adjacent Hengduan Mountains (HM) have drawn
increasing interest from biogeographers and ecologists (Favre et al.,
2015; Hoorn et al., 2018; Muellner-Riehl, 2019; Rahbek et al., 2019),
especially after Myers et al. (2000) classified these mountain systems
as two of the global biodiversity hotspots. In recent years,
biogeographic studies on the Tibeto-Himalayan Region have revealed that
the evolution of species was profoundly shaped by changes in
geomorphology and climate over millions of years (e.g., Favre et al.,
2015; Xing \& Ree, 2017; Mosbrugger et al., 2018; Muellner\selectlanguage{english}-Riehl et al.,
2019; Rana et al., 2019; Ding et al., 2020; Rana et al., 2022). For
instance, Ding et al. (2020) revealed that \emph{in situ} speciation,
diversification, and colonization in the alpine flora inhabiting the HM,
the Himalayas, and the Qinghai-Tibetan Plateau were jointly driven by
mountain uplift and intensification of the Asian monsoon system.
Nonetheless, different topographic relief, orogenic activity, and
climate history in the Himalayas and the HM led to distinct biodiversity
patterns (Ding et al., 2020). For example, species richness increases
from west to east in birds (Price et al., 2011), plants (Yan et al.,
2013; Bhattarai et al., 2014; Rana et al., 2019), and mammals
(Srinivasan et al., 2014). This parallels the increase of precipitation
towards the east in the Himalayas. In contrast, a North-South floristic
divide was revealed in the HM, which may be associated with both climate
(separation by the line of regular freezing) and topography (divided by
the Jinsha River, Li et al., 2021). Moreover, patterns of biodiversity
in the HM appear to have been more dynamic through time: diversity
hotspots of montane plants have shifted from the southeastern to the
central and western parts of the HM between the last glacial maximum
(LGM) to today (Liang et al., 2018). The phylogeographic history of the
HM is further complicated by the presence of geographically extensive
and long-lasting barriers to dispersal such as the deeply incised
valleys of the Irrawaddy, the Salween, the Mekong and the Yangtze
rivers, which have been shown to be instrumental in delineating
floristic motifs in the region (Li et al., 2021; Muellner\selectlanguage{english}-Riehl \&
Favre, 2021). Therefore, biogeographers and ecologists have increasingly
viewed evolutionary processes in these two mountain systems as
relatively independent despite their biogeographical interconnection
(e.g., Ding et al., 2020).

The ``mountain-geobiodiversity hypothesis (MGH)'' conceptualizes the
link between geophysical changes and the origin and evolution of
biodiversity based on the Tibeto-Himalayan Region (Mosbrugger et al.
2018). In this hypothesis three boundary conditions are deemed essential
to the accumulation of biodiversity in mountains: (i) full elevational
zonation with lowland, montane, and alpine zones; (ii) the occurrence of
a species-pump driven by climatic fluctuation; and (iii) strong
environmental gradients. Within this conceptual framework, mountain
uplift provides elevational gradients and locally diverse topography.
This condition increases opportunities for local or regional taxa to
adapt to a high variety of niches (i), and fosters a higher resistance
to climate change via vertical displacement (iii). Meanwhile, during
climate fluctuations, for instance in the Quaternary, diversification is
fostered by a species-pump effect (ii) via cyclical range fragmentation
(causing divergence) and secondary contacts (involving hybridization or
reinforcement) (Mosbrugger et al., 2018; Muellner-Riehl, 2019). This
hypothesis was partially verified on a few taxa from the HM (e.g., Fu et
al., 2020, 2022; Wang et al., 2022), while some global-scale
meta-analyses also support it (Muellner\selectlanguage{english}-Riehl et al., 2019). However,
case studies have so far been limited to plant taxa, such that the
validation and refining of the hypothesis for a broader taxonomic
spectrum is missing. Following Favre et al. (2015), which provided a
generalized overview of the origin and evolution of mountain
biodiversity, we investigate the MGH in the context of the
diversification of species of the aquatic insect
genus\emph{Himalopsyche} (Trichoptera, Rhyacophilidae).

\emph{Himalopsyche} is a genus of aquatic caddisflies that inhabit
mountains. Most \emph{Himalopsyche} species are distributed in Central
and East Asia (Hjalmarsson et al., 2019) except for the Nearctic\emph{H.
phryganea} (Ross, 1941). Like all caddisflies, species
of\emph{Himalopsyche} have a merolimnic life cycle and are considered
important bioindicators (Resh \& Unzicker, 1975; Tsuruishi et al., 2006;
Hjalmarsson, 2019; Morse et al. 2019). The larvae of this taxon
generally inhabit turbulent, fast-flowing rivers and streams where they
live as ferocious predators (Hjalmarsson et al., 2018). Currently, there
are 56 described species in the genus (Hjalmarsson et al., 2019), with
23 occurring in the Himalayas and 34 occurring in the HM (some of them
distributed in both). The center of diversity of the
genus\emph{Himalopsyche} is located in these two mountain regions. But
regionally, different species exhibit strongly differentiated niches
usually associated with elevational gradients (Schmid \& Botosaneanu,
1966). For example, some species inhabit lower elevations between
1500--2500 m.a.s.l., as in the case of \emph{H. digitata} (Martynov,
1935, in the Himalayas) or \emph{H. platon} (Malicky, 2011, in the HM),
whereas other species prefer higher elevation ranges between 2000--4500
m.a.s.l., such as \emph{H. tibetana} (Martynov, 1930, in the Himalayas)
and \emph{H. gregoryi} (Ulmer, 1932, in the HM; summarized in
Hjalmarsson 2020). As reported by Lehrian et al. (2009), montane
caddisflies that inhabit different elevation ranges but have similar
geographic distributions may exhibit distinct population structures
putatively associated with varying dispersal capabilities, habitat
specificity or differences in phylogeographic history. Because species
of \emph{Himalopsyche} inhabit different elevations, they are a good
model for investigating how geography has shaped their genetic diversity
over time.

For aquatic species, distribution patterns and dispersal among habitats
are constrained by the dendritic structure of the stream network (Tonkin
et al., 2018). To assess and interpret differing patterns of population
structure in aquatic insects, Finn et al. (2007) and Hughes et al.
(2013) proposed process-based models of population genetic diversity
patterns that account for the structure of drainage systems: the stream
hierarchy model, the death valley model, the headwater model, isolation
by distance and panmixia (or also called as the widespread gene flow
model, Hughes, 2007). These models are assigned to a given species
primarily by their population connectivity, defined by the level of gene
flow among populations throughout the drainage networks. The population
connectivity generally depends on (1) traits that determine dispersal
ability (dispersal ability and behaviour, life cycle, oviposition, and
the spatial distribution of source populations) (Smith \& Smith, 2009;
Parkyn \& Smith, 2011); (2) the distance between populations; (3) the
suitability of the new habitat; and (4) the permeability of the
landscape (Rader et al., 2019). In caddisflies, dispersal is often an
``along-stream'' movement as defined in the context of the
``colonization cycle'' (Muller, 1954; Collier \& Smith, 1997; Petersen
et al., 2004; Winterbourn et al., 2007). Current-induced downstream
movements often occur in the larval stage. Part of this downstream
movement is then compensated by adult females (and also males) flying
upstream prior to mating or egg-laying. However, adults also fly
perpendicular to the stream (lateral dispersal) and thus disperse
overland between catchments allowing gene flow between caddisfly
populations from different catchments (Svensson, 1974; Malicky, 1987;
Collier \& Smith, 1997; Griffith et al., 1998; Bowler \& Benton, 2005;
Wilcock et al., 2007; Smith \& Smith, 2009; Engelhardt et al., 2011;
Muller-Peddinghaus, 2011; Deng et al., 2021b). A genomic study on a
species complex of the \emph{Himalopsyche} revealed that continuous gene
flow can be maintained over millions of years between two basins (Deng
et al., 2021b). Hence, in addition to elevational zonation (i) and
environmental gradients (iii) as proposed in the MGH, and because of the
unique dispersal ability of caddisflies, drainage systems may also be
crucial topographical features that drive distribution ranges of
caddisflies, e.g. through the species-pump effect (ii).

To examine these patterns across both the Himalayas and HM, we studied
four \emph{Himalopsyche} species from the two mountains. For each
mountain range, we chose a species that inhabits high elevation
streams,\emph{H. tibetana} (Himalayas) and \emph{H. gregoryi} (HM), and
another species that inhabits lower elevations, \emph{H. digitata}
(Himalayas) and \emph{H. platon} (HM). We combined population genomics
analysis of 333 individuals across the four species with species
distribution model (SDM) to (1) reveal the genetic pattern of species
inhabiting high-elevation versus low-elevation, and species from the
Himalayas versus the HM, (2) evaluate the role of environmental factors
including climate change, mountain topography and drainage rearrangement
on the evolution of aquatic biodiversity in the Tibeto-Himalayan Region,
and (3) assess the implication of the MGH in different mountain systems
such as the Himalayas and the HM.

\section*{2 Materials and Methods}

{\label{materials-and-methods}}

\subsection*{2.1 Sample collection and DNA
sequencing}

{\label{sample-collection-and-dna-sequencing}}

We analysed 333 individuals across the four \emph{Himalopsyche} species.
Specimens were collected from 2010 to 2020 in the Himalayas (\emph{H.
tibetana} : N\textsubscript{ind} (number of individuals) = 97,
N\textsubscript{pop} (number of populations) = 28; \emph{H. digitata} :
N\textsubscript{ind} = 105; N\textsubscript{pop} = 23) and the HM
(\emph{H. gregoryi} : N\textsubscript{ind} = 71, N\textsubscript{pop} =
18; \emph{H. platon} : N\textsubscript{ind} = 60; N\textsubscript{pop} =
12, Fig. 1). All specimens were preserved in 95\% ethanol and archived
in the collections of the Senckenberg Research Institute and Natural
History Museum (Supplementary Table 1).

We extracted DNA from abdominal and/or thoracic tissue after removing
the intestinal tract following Miller et al. (1988). We purified
low-quality DNA samples with magnetic beads as described by Deng et al.
(2022). After quality check, all DNA samples were sent to Novogene Co.,
Ltd. (Hongkong, China) for DNA library preparation and 2x150 bp whole
genome resequencing with a coverage of 12x on an Illumina HiSeq 2000
platform.

\subsection*{2.2 Data preparation}

{\label{data-preparation}}

We assessed the quality of Illumina reads using FastQC v0.11.8 (Babraham
Institute) and MultiQC v1.7 (Ewels et al., 2016). Following Deng et al.
(2022), we trimmed overrepresented \emph{k} -\emph{mers} using
autotrim.pl v0.6.1 (Waldvogel et al., 2018) with Trimmomatic v0.38
(Bolger et al., 2014) and removed adapters with Cutadapt v2.23 (Martin,
2011). After cleaning, the average sequence coverage was 14x (see Fig.
S1 for details). We mapped the reads to a published \emph{de novo}genome
of \emph{H. tibetana} (Deng et al., 2022, NCBI accession number:
JAHFWH000000000) using Bowtie2 (Fig. S2). Duplicate reads were marked
and indexed using Picard v2.20.8 (Picard Tools -- By Broad Institute)
and SAMtools (v1.1, Danecek et al., 2021).

We used ANGSD (v0.931, Korneliussen et al., 2014) to estimate genotype
likelihoods and GATK (v4.1.7.0, Danecek et al., 2011) for genotype
calling (see Supplementary material 1, section 4 for details and
parameter settings). Since the genotype likelihood algorithm in ANGSD is
designed for low to medium-depth data (Korneliussen et al., 2014), we
used it for all of the population genomic inferences except for the
TreeMix analysis since the program cooperated better with GATK for
calling the SNPs without missing data (Pickrell \& Pritchard, 2012).
With the GATK pipeline, we obtained approximately 4,643,429; 68,261;
221,016; 245,170 SNPs for \emph{H. tibetana} , \emph{H. digitata} ,
\emph{H. gregoryi,} and \emph{H. platon}respectively. The genotype
likelihood estimation with ANGSD produced 1,668,494; 6,756; 88,947:
12,842 high-quality variants for \emph{H. tibetana} , \emph{H. digitata}
, \emph{H. gregoryi,} and \emph{H. platon,}respectively. The quality of
the genotype was shown in Fig. S3, S4, and S5.

\subsection*{2.3 Genetic structure}

{\label{genetic-structure}}

Before performing the principal component analysis (PCA), we pruned
linked sites within our SNP dataset estimated by genotype likelihoods
using ngsLD (Fox et al., 2019). We calculated a covariance matrix for
PCA using PCAngsd (Meisner \& Albrechtsen, 2018) and admixture
proportions using NGSadmix (Skotte et al., 2013) based on the linkage
pruned genotype likelihoods. The optimal value of ancestral components
(K) was determined by CLUMPAK (Kopelman et al., 2015).

We measured the genome-wide nuclear heterozygosity (\selectlanguage{greek}π\selectlanguage{english}) of each sample
based on a site frequency spectrum (SFS) following the workflow
suggested by the authors of ANGSD v0.931 (Korneliussen et al., 2014). To
quantify the alleles inherited from common ancestors in an individual's
lineage, we estimated individual inbreeding coefficients (\emph{F} )
based on the filtered genotype likelihoods (same filter as used for PCA
analyses) using ngsF v1.2.0 (Vieira et al., 2013) following de Jager et
al. (2021).

To better understand the relationships among populations of each
species, we reconstructed a maximum-likelihood tree with migration
events among populations of each species respectively using TreeMix
version 1.13 (Pickrell \& Pritchard, 2012) following Dahms et al. (2022)
and software guidelines. We additionally calculated a median-joining
(MJ) network of each species based on the COI sequences using Network 10
(Fluxus-engineering.com, Bandelt et al., 1999).

To better characterize the genetic structure, we assigned the
populations to subbasins delineated by Lehner and Grill (2013) at level
10 (http://www.hydrosheds.org/page/hydroatlas). The four-digit code of
each subbasin used in this study is replicated from the source layer;
additionally, we use descriptive names for the subbasins (see Table S9).

\subsection*{2.4 Historic effective population
size}

{\label{historic-effective-population-size}}

To gain insights into the history of population-size change, especially
during the LGM, we inferred the demographic history of the four target
species by applying the pairwise sequentially Markovian coalescent
(PSMC) model to each genome (Li \& Durbin, 2011). We prepared the input
files for PSMC and ran the analyses following the software documentation
(more details see Supplementary material 1, section 8). For the final
visualization, we plotted the inverse instantaneous coalescent rate
(IICR) as estimated by PSMC following Humble et al. (2020) and de Jager
et al. (2021) in R. Notably, for the plot we used a generation time of 1
year (Tsuruishi, 2003, 2006) and a per generation mutation rate for
caddisflies of 2.9e-9 as estimated for a Lepidoptera species (Keightley
et al., 2015), though the mutation rate was proposed to be consistent
with most of the insects (Liu et al., 2017).

\subsection*{2.5 Ecological variables statistics and species distribution
modelling
(SDM)}

{\label{ecological-variables-statistics-and-species-distribution-modelling-sdm}}

We collected \emph{in-situ} environmental variables and GIS resources at
each sampling site (all variables see Table S1, S2). We conducted a PCA
for each species pair from the Himalayas and the HM respectively, using
all environmental variables as mentioned above (Fig. S6, S7).

To better understand the influence of different climatic conditions on
\emph{Himalopsyche} , we compared the potential suitable area of each
species between the LGM and the present day by applying SDMs. We used
all known locations of each species from our previous work and the
current study as validated occurrence points in the models
(Supplementary Table 1). According to the current distribution ranges of
the species in references and field observation, we defined the extent
of our study area as the main drainage basins of all four species'
occurrences (Fig. 1, details see Supplementary material 1, section 10).
Considering the high dependence of caddisflies on water generally and
flight ability at the adult stage, we attempted to include hydrological
connectivity in the modelling. Thus, we applied a sub-sampled layer that
was calculated from the DEM with a stream-initialization threshold of
200 upstream 90 m grid cells, resulting in 2771220 sub-catchments and
approximately 10 km\textsuperscript{2} for each sub-catchment.

We downloaded topographic variables from the EarthEnv database and the
bioclimatic variables from the CHELSA database (hereafter 1 km, Table
S3). All variables were chosen based on our supposition of their
importance for the distribution of caddisflies (Graf et al., 2008; Morse
et al., 2019). We used seven bioclimatic datasets (CCSM4, MRI, CNRM,
FGOALS, CM5A, MIROC, and MPI download date: 10.12.2019) provided by
CHELSA for the LGM. All variable layers were cropped to our study area
first and buffered approximately 500 km around the study area to
dissolve and avoid errors caused by the coast. Then we calculated the
univariate statistics of each environmental layer that was assigned to
the sub-sampled layer as described above. Before building the SDMs, we
removed highly correlated variables (correlation coefficient `r' cutoff
= 0.75). The same variables were used for all species at different time
scenarios.

We applied the ensemble method implemented in the R package biomod2
(Thuiller et al., 2009) to construct the SDM for each species at
different time scenarios. This method enables combining the predictive
outputs of different algorithms, thus improving the accuracy of the
modelling (Hao et al., 2019; Stone \& Wolfe, 2021). The details of the
setting were described in Supplementary material 1, section 10. To
compare the distribution range of each species between the LGM and at
present, we obtained a consensus binary map for the LGM prediction by
using the committee averaging method (Araujo \& New, 2007; Gallien et
al., 2012; Gillard et al., 2017).

The final results of the range size change from the LGM to the present
for each species were deposited on the IGB-GeoNode as raster layers
(https://fred.igb-berlin.de/data/package/812). All intermediate results
of the predictions using different models on different time scenarios
were presented in supplementary material 1 (Table S4--S8, Fig. S8--S21).

\section*{3 Results}

{\label{results}}

\subsection*{3.1 Population-level genomic
diversity}

{\label{population-level-genomic-diversity}}

In the Himalayas, the high-elevation \emph{H. tibetana} showed
genome-wide heterozygosity between 0.03 to 0.04 (Fig. 2a) with
populations in the Koshi Basin exhibiting higher heterozygosity compared
with those from the Gandaki Basin. We generally observed increasing
heterozygosity from the western to the eastern Himalayan populations,
while the \emph{F} decreased along the same geography. Populations of
the low-elevation \emph{H. digitata} exhibited a comparatively lower
level of genome-wide heterozygosity and \emph{F} (\selectlanguage{greek}π \selectlanguage{english}=
\textasciitilde{}0.01, \emph{F =} \textasciitilde{}0.13). Moreover,
there was no significant difference among populations from different
basins.

Both HM species, \emph{H. gregoryi} (high-elevation) and \emph{H.
platon} (low-elevation) showed similarly low levels of heterozygosity (\selectlanguage{greek}π
\selectlanguage{english}= \textasciitilde{}0.001). However, populations from some of the HM
basins exhibited an extremely high \emph{F} while the others had a
low\emph{F} . For instance, the \emph{F} of most populations of \emph{H.
platon} were around 0.13, but the populations from the three basins
located in the remote area of the Yangtze basin (ID 1634, 1280, 1205,
Fig. 3) showed \emph{F} approaching 1.0 (Fig. 2h). Moreover, the
heterozygosity of these populations was relatively lower than that of
the remaining populations. Similar to \emph{H. platon} , populations
of\emph{H. gregoryi} also showed fluctuating levels of \emph{F} across
basins: populations from some of the subbasins of Mekong (ID 2531, 2532,
2544) had a high level of \emph{F} (0.5--0.7), as did populations from
one subbasin of the Yangtze (ID 1684). In particular, these basins were
geographically isolated, at least at the level of subbasins.

\subsection*{3.2 Population structure}

{\label{population-structure}}

We found four distinct clusters in the PCA plot for the
high-elevation\emph{H. tibetana} (Fig. 3a, b, c): (i) West and Middle
Gandaki (subbasin ID 4900 and 4754); (ii) East Gandaki (ID 4789) and
West Koshi (ID 4547); (iii) Middle Koshi (ID 4427); (iv) East Koshi (ID
4442). The MJ network of mtCOI sequence data, also supported four
distinct clades, except that several individuals of cluster i shared
mtDNA haplotypes with cluster iv, which may indicate a preservation of
these old haplotypes at Middle Gandaki (ID 4754) or an invasion of
individuals from East Koshi (ID 4442, Fig. S22, Fig. 3c). This same
result was supported by the maximum likelihood estimation of individual
ancestries using NGSadmix with the best K (K = 3), and with the
taxonomic units delineated by TreeMix (Fig. S23). Analyses of PCA,
admixture (with the best K = 4) and TreeMix revealed congruent
structuring patterns for populations of \emph{H. digitata} : populations
were grouped into 5 clusters representing populations from (i) West
Gandaki (ID 4900); (ii) Middle Gandaki (ID 4754); (iii) East Gandaki (ID
4789); (iv) Middle Koshi (ID 4390); (v) East Koshi (ID 4407 and 4442,
Fig. 3d, e, f, Fig. S23). In addition, the MJ network of the mtCOI
sequence data showed evidence of secondary contact between populations
from cluster i and populations from other groups (Fig. S22).

When compared with the two species distributed in the Himalayan region,
populations of \emph{H. gregoryi} and \emph{H. platon} showed more
complex structures. Three distinct groups were recognized within the
samples of \emph{H. gregoryi} from (i) Mekong (including subbasin ID
2531, 2532 and 2544); (ii) West Yangtze (ID 1684); (iii) East Mekong and
West Yangtze (ID 2547, 1668 and 1685, Fig. 3g, h, i). Although the
optimal K could not be determined following the methods outlined in
Evanno et al. (2005), we found congruence between the output of
admixture and PCA: the samples from group iii formed a cluster in the
PCA that simultaneously exhibits a gradient among populations of
different elevations and subbasins. This cluster was assigned to an
admixed group and one clade in the TreeMix but split into multiple
lineages (Fig. S23). It was notable that samples within group iii were
located in the border region of the three subbasins located in the same
mountain area. The population structure of \emph{H. gregoryi} estimated
by the genomic data was partially congruent with the results of the MJ
network: samples of group iii were assigned to the same haplogroup, yet,
samples of groups i and ii were segregated into four haplogroups (Fig.
3i, Fig. S22). The 12 populations of low-elevation species \emph{H.
platon} were grouped into four distinct clusters: (i) Middle and East
Yangtze (ID 1634, 1280 and 1205); (ii) West Yangtze (ID 1625); (iii)
Salween (one population from subbasin ID 2949); (iv) populations from
remaining basins (including Irrawaddy, Salween, Mekong and Yangtze, Fig.
3j, k, l). Similar patterns were also observed in the mtCOI MJ network,
except that one individual from West Yangtze (ID 1625) fused with the
near-panmictic haplogroup (iv), indicating a secondary contact (Fig.
S22).

Interestingly, the PCA using the\emph{in situ} and GIS environmental
variables successfully separated\emph{H. gregoryi} and \emph{H. platon}
in the HM (Fig. S6 and S7), which indicated a different microhabitat of
the two species. However,\emph{H. tibetana} and \emph{H. digitata} in
the Himalayas were not sufficiently separated from each other in the
PCA, suggesting that the two species might inhabit a similar
microhabitat.

\subsection*{3.3 Demographic history and
SDM}

{\label{demographic-history-and-sdm}}

The demographic history of each species was investigated by PSMC
analysis of each genome and as represented by the inverse instantaneous
coalescent rate (IICR; Fig. 4 c, f, i, l). Although the IICR estimates
became less reliable at more recent time scales, which led to a great
variance of the effective population size (\emph{Ne} ) between
individuals, the results revealed a general trend of an
expansion-decline event during or by the end of the LGM (22 ka) for all
of the four target species. In addition, the \emph{Ne} of low-elevation
species were 10,000--100,000 times greater than those of the
high-elevation distributed species. When comparing the mountain ranges,
the \emph{Ne} of the HM and the Himalayan species were more or less at a
similar level at their respective peaks.

Estimation of the effects of climatic versus topographic variables using
ensemble models produced clear results to explain the potential
distribution of species. The highest percentage contribution of the
ensemble model was the seasonal precipitation for the two species in the
Himalayas, and the seasonal temperature, especially the annual range of
temperature for the two species in the HM (Table S5). In addition, for
the ensemble modelling of all four target species, the ROC and TSS
scores are very high (\textgreater{}0.99, Table S4), indicating a very
high model performance.

Coinciding with the maximum \emph{Ne} during the LGM and subsequent
population decline, the predicted climatically suitable area of all
these four target species was greatest during the LGM and contracted to
the present day (Fig. 4 a, d, g, j). For instance, during the LGM, the
predicted suitable area of \emph{H. tibetana} and \emph{H.
digitata}comprised most of the Himalayas, north of Khasi Hills and part
of the HM. However, the present-day suitable area shrunk to a small
region in the middle of the Himalayas, which aligns with the known
present-day distribution range. Interestingly, except for the middle of
the Himalayas, the modelling identified another area on the border of
the Khasi Hills and HM that has been persistent for the existence of the
two species since the LGM. However, no actual presence of the two
species (Fig. 4 a, d) was observed there. Similar to the species
distributed in the Himalayas, species distributed in HM also experienced
a contraction of climatically suitable area from the LGM to the present,
especially for \emph{H. gregoryi} . The suitable area of \emph{H.
gregoryi} during the LGM spread all over the southern and eastern part
of the HM or further, as well as the eastern Himalayas, but dramatically
shrunk to the central part of HM, which still vastly extends the known
present-day distribution range. In addition, the suitable areas for
\emph{H. gregoryi} and \emph{H. platon} shifted northward over time.
Furthermore, the predicted elevational range for all four species during
the LGM is lower than at present, implying a tendency for species to
move uphill in the Holocene (Fig. 4 b, e, h, k).

\section*{4 Discussion}

{\label{discussion}}

To further understand the origin and evolution of mountain biodiversity,
we investigated the genetic diversity and distribution of a caddisfly
group in the Himalayas and the HM. For the first time in aquatic
insects, our results are derived from whole genome re-sequencing of
populations of four congeneric species and fine-grain environmental GIS
data. And, while we interpreted our results carefully, our study
provides evidence that changes in geodiversity and climate affect the
evolutionary history of closely related species occupying varying
ecological niches, thus supporting the MGH.

\subsection*{4.1 Genetic patterns of caddisfly in the Himalayas and the
HM}

{\label{genetic-patterns-of-caddisfly-in-the-himalayas-and-the-hm}}

Present-day genetic diversity patterns offer important insights on the
historic interconnectivity of populations and can help inform the effect
of past environmental change on the studied species. Our results
indicate that in most of the cases, the high-elevation species of both
mountain regions (\emph{H. tibetana} and \emph{H. gregoryi} ) align best
with the headwater model pattern \emph{sensu} Hughes et al. (2007). For
instance, populations of \emph{H. tibetana} (high-elevation species,
Himalayas) in the West Gandaki (subbasin ID 4900) and Middle Gandaki (ID
4754) cluster together and share the same homogeneous ancestry (Fig. 3a,
b, c). Instead of being connected by a stream network, these populations
are connected by a series of local mountain ranges (including Tilitso
Himal, Anna Dakshin, Machhapuchhara and Lamjung Kailas), as defined in
the headwater model pattern. Similarly in \emph{H. gregoryi} (HM):
populations in the Baima Snow Mountains (occupied in North Mekong (ID
2547) and West Yangtze (ID 1668 and 1685)) originated from an admixed
ancestral group and formed one haplogroup based on mtCOI (Fig. S22, Fig.
3g, h, i), in addition to a low level of inbreeding coefficient
(\emph{F} = \textasciitilde{} 0.12), indicating a good connection within
this mountain range. Outside the Baima Snow Mountains, the \emph{H.
gregoryi} populations located in the North and South Mekong River
(containing subbasin ID 2531, 2532, and 2544) were associated with a
stream hierarchy model since they show non-significant genetic variance
among each other. Nevertheless, the ancestral group of these populations
is distinct from others, suggesting limited gene flow between this group
and populations from other mountains. Therefore, the populations of
high-elevation species are generally restricted by local mountain ranges
and show a stronger ``insularity'' among the different mountain ranges,
or a ``Sky Island'' pattern. This pattern implies some degree of
overland dispersal among headwater streams. Caddisflies can disperse
overland during their adult stage, although the associated dispersal
distance seems to be restricted (Geismar et al., 2015). Nonetheless,
high-elevation species likely disperse along local mountain ranges
within or among basins, using suitable habitats (so-called ``montane
archipelagos'') as stepping stones, thus leading to better connectivity
within these mountain ranges. Moreover, due to their strong association
with specific habitats, lowland areas, especially deep and large
valleys, may act as dispersal barriers for these species, thus leading
to limited gene flow among mountains (Myers et al. 2001; Pauls et al.
2006). In fact, freshwater organisms, in particular headwater
specialists, are often endemic to specific mountain ranges due to their
low tolerance to elevated temperature or other habitat factors
characterizing the surrounding lowland (Finn et al., 2007; Hughes et
al., 2009; B\selectlanguage{ngerman}álint et al., 2011; Vitecek et al., 2015, 2017).

In comparison, for the low-elevation species, a significant genetic
diversity was shared among populations across basins, thus providing
support for the stream hierarchy model pattern. It suggests gene flow
mostly occurred among populations from the same basins, indicating that
dispersal within the stream network appears to be the dominant process.
The divergence among dispersal strategies of high-elevation and
low-elevation species is evidenced by the pattern we observed where
populations of \emph{H. digitata} and \emph{H. tibetana} in the West and
Middle Gandaki (ID 4900, 4754): although populations of these two
species partly overlap, populations of the \emph{H. digitata} appeared
as two distinct groups associated with the two subbasins, while
populations of \emph{H. tibetana} grouped together (Fig. 3c, f). In
addition, the population connectivity of \emph{H. digitata} reflects the
dendritic nature of the stream as described by Hughes et al. (2013):
minimal differentiation was observed for populations within the West
Gandaki subbasin (e.g., individuals from subbasin ID 4900, Fig. 3d, e,
f), higher differentiation among populations from different subbasins
(e.g., ID 4900 and 4754) and the highest among populations from
different primary basins (e.g., Gandaki and Koshi). The stream hierarchy
model pattern can also be observed in \emph{H. platon} in the HM.
Several populations located in the Yangtze River (subbasin ID 1625,
1634, 1280, 1205) formed distinct groups, suggesting higher connectivity
among populations within subbasins (Fig. 3j, k, l). In addition, these
populations exhibit low genomic heterozygosity (\selectlanguage{greek}π \selectlanguage{english}\textless{} 0.001) and
an extreme inbreeding coefficient (\emph{F} = \textasciitilde{}0.75 --
\textasciitilde{}0.1, Fig. 2d, h), indicating isolation and future
allopatric speciation. The rest of the populations of \emph{H.
platon}that extend across all four primary basins (Irrawaddy, Salween,
Mekong, and Yangtze) demonstrate panmixia, accompanied by low genetic
diversity and low inbreeding coefficient (Fig. 2d, h). This pattern
suggests that all of these populations are highly connected and gene
flow is strong within all four primary basins until recent times, which
may be due to their very well-developed dispersal abilities as adults or
continuous habitats (Finn et al., 2007; Baggiano et al., 2011).

Our results highlight that identifying the prevalent model of genetic
structure for aquatic organisms may not always be clear-cut, especially
for taxa with a broad distribution (Hughes et al., 2013; Sproul et al.,
2014). In our case some species even appear to follow multiple patterns:
Stream hierarchy model patterns observed in the low-elevation species
could also be interpreted as isolation by distance since the genetic
diversity also increased with geographic distance (or ``stream
distance'', the same as geographic distance in this case).

In line with previous studies that suggested that a general pattern of
population structure is challenging to identify for caddisflies (Lehrian
et al., 2009; Pauls et al., 2009; Engelhardt et al., 2011; Previ\selectlanguage{polish}š\selectlanguage{english}i\selectlanguage{polish}ć \selectlanguage{english}et
al., 2014), our study also reveals that the patterns
in\emph{Himalopsyche} are likely to be species-specific: high-elevation
species are associated with a headwater model pattern, whereas the
low-elevation species tend to be more similar to stream hierarchy model
or panmixia patterns, indicating that habitat specialization is a
crucial factor for shaping the population structure. Further, species in
the HM show a better-connected gene flow within the mountain range than
species distributed in the Himalayas (panmixia appears in the HM),
implying that the effects of topography are strong for the dispersal of
species with an aquatic life history in this region. Overall, these
complex patterns may result from either the complexity of the dendritic
stream network embedded in a high-gradient landscape or simply the
geographic scale of the studies examined (Hughes et al., 2013).

\subsection*{4.2 Key abiotic factors in shaping the montane biodiversity
of
caddisflies}

{\label{key-abiotic-factors-in-shaping-the-montane-biodiversity-of-caddisflies}}

Ecological preferences and dispersal abilities cannot explain
present-day distribution patterns without interpreting the data in the
context of a species' history (Avise 2000). In particular, topography
and historical climate changes may have a strong impact on the current
genetic diversity pattern of caddisflies. Our study, combined with the
current literature (e.g., Favre et al., 2015; Antonelli et al., 2018;
Mosbrugger et al., 2018; Muellner-Riehl et al., 2019; Muellner-Riehl,
2019), suggests that the evolution of climate, mountain topography, as
well as historical drainage rearrangement, are key factors for the
evolution of aquatic biodiversity in the Tibeto-Himalayan Region. These
factors have interacted with each other through time and vastly
contributed to shaping current diversity patterns in these aquatic
insects.

\subsubsection*{4.2.1 Genetic patterns versus climate and topographical
features}

{\label{genetic-patterns-versus-climate-and-topographical-features}}

The influence of climate on population diversity of caddisflies is
significant, especially in the Himalayan region. In line with other
organisms in previous studies (e.g., Wikramanayake et al., 2002; Price
et al., 2011; Yan et al., 2013; Rana et al., 2019), we find an increased
genetic diversity of our target caddisfly species from west to east in
the Himalayan region at the population level, especially for the
high-elevation species. There may be a link between this climatic
gradient and the genetic diversity of caddisflies across the whole
mountain region, leading to patterns associated with topography and
longitude (Bookhagen \& Burbank, 2006, 2010): species richness increased
approximately threefold from the northwest to the east of the Himalayas,
especially at higher elevations, which is in accordance with the fact
that the lowland receives about three times more precipitation in the
subtropical East than the Northwest (Anders et al., 2006; Kreft \& Jetz,
2007; Bookhagen \& Burbank, 2010; Rana et al. 2019, 2022). This pattern
is strongly supported by studies on regional plant diversity (Yan et
al., 2013; Muellner-Riehl, 2019; Rana et al., 2022). Precipitation may
be the direct or indirect factor resulting in the current distribution
of caddisfly species (Collier \& Smith, 1997). Furthermore, since both
larval and adult stages of caddisflies are reliant on flowing waters,
surface water availability from rainfall and snowmelt in the east of the
Himalayas may significantly benefit them (Bookhagen \& Burbank, 2010).

Compared to the straightforward genetic diversity pattern of caddisflies
in the Himalayas, a common distribution pattern of genetic diversity is
difficult to identify for caddisflies in the HM. The complex terrain
characterizing the HM region offers highly heterogeneous microclimates,
making it difficult to evaluate the impacts of climate change in general
(Yin et al., 2020). Yet, a variety of genetic diversity patterns of
caddisflies were observed in the HM, which is strongly correlated with
topographic complexity, in particular those associated with elevation
and hydromorphology. Generally, species in the HM show better
connectivity all over the region, but there are plenty of allopatric
populations observed for both species. For the high-elevation species,
the main distribution pattern is the ``Sky Island'', thus the geographic
barrier made by certain lower elevations. However, for the low-elevation
species, the mechanism of formation of the allopatric populations is
different. Unlike the alpine species, the low-elevation species
generally show better connectivity within basins, especially in \emph{H.
platon} which shows near-panmixia across four primary basins.
Furthermore, the fact that both strongly connected (the upper Yangtze:
Jinsha River) and allopatric populations (the middle Yangtze: Yalong
River and Dadu River) occurred in the Yangtze Basin, may derive from
ancient drainage re-organizations (e.g., via a so-called ``river
capture'', Fig. 3m). Firstly, the upper Yangtze River used to flow
southward as a tributary of the paleo-Red River until the late Eocene
(Clark et al., 2004; Zheng, 2015; He et al., 2021; Zheng et al., 2021),
which was not the case of its middle part (see Zhang et al., 2017).
Fuelled by regional orogeny and erosion (Zheng, 2015; Wang et al.,
2018), a so-called river capture event occurred, changing the course of
the upper Yangtze away from the paleo-Red River and into its current
course (Clark et al., 2004; Zheng, 2015; Deng et al., 2021a). Our
results reveal patterns consistent with an ancient disconnection:
populations located in the Yalong (ID 1634) and Dadu River (ID 1280 and
1205) showed a distinct genetic ancestry with any other populations with
any K values, including those located at the upper Yangtze River (ID
1625 and 1659). Secondly, although the populations located in the
Yanglong and Dadu River represented the same ancestral, they are
distinct clusters in population structure, indicating a recent
divergence. It is in line with the interpretation that the paleo-Yalong
and paleo-Dadu Rivers drained together with the middle Yangtze to the
paleo Xigeda lake before ca. 1.3 Ma, but segregate after ca. 1.3 Ma
since the paleo-Dadu River was integrated into the lower Yangtze
directly (Deng et al., 2021a). Until ca. 1.3 Ma, the middle Yangtze
captured the upper Yangtze and drained into the lower Yangtze drainage
basin (Kong et al., 2009; Deng et al., 2021a). Thus, populations
of\emph{H. platon} potentially experience some gene flow along the
connected river networks, as shown in our TreeMix analysis: gene flow
has occurred among populations from the upper Yangtze (ID 1625) and
middle Yangtze (ID 1634). However, the genetic connection seems to be
very limited, since the populations located in the middle Yangtze showed
extremely high inbreeding coefficient.

Interestingly, the population of \emph{H. platon} located in subbasin ID
1625 shares the same ancestral source with the near-panmictic
populations when K = 3. However, when increasing the value of K, it is
assigned to a unique ancestry and the near-panmictic populations are
inferred to have a certain level of ancestry from this unique population
with relatively high inbreeding (Fig. 3, Fig. 2). Notably, this
population is located at the middle of the First Bend of the Yangtze
River (as shown in Fig. 3m). The First Bend is the place where upper
Yangtze makes a dramatic turn to the North and starts flowing eastward
to the East China Sea (Zhang et al., 2017). It is regarded as the
capture point of the upper Yangtze away from the Red River, thus
resulting in a key period for geo(hydro)morphology in this area (Clark
et al., 2004; Li et al., 2022). The processes leading to the present-day
structure and diversity patterns remain unclear, however. Very limited
information is available on the genetic diversity patterns of plants or
animals here. It would be worthwhile to investigate species of this
specific region in future studies.

\subsubsection*{4.2.2 Distributional and demographic history of the four
caddisfly
species}

{\label{distributional-and-demographic-history-of-the-four-caddisfly-species}}

Repeated climatic fluctuations in the Pleistocene promoted range
contractions and expansions of numerous plant and animal species, which
is believed to result in high diversification rates in the
Tibeto-Himalayan Region (Xing \& Ree, 2017; Mosbrugger et al., 2018;
Ding et al., 2020). However, the effects of Pleistocene glacial cycles
on the distribution and demography of species may vary across regions
(Hewitt, 2000). For example, in addition to ecological preferences,
life-history traits, and dispersal ability (Tonzo \& Ortego, 2021),
local topography and latitude likely have strong effects on rates of
dispersal, colonization, and extinction of a species during climate
fluctuations.

Our ensemble model results show that in comparison to the present,
suitable habitats for all four target species were more extensive during
the LGM but shifted from their current core distribution (Fig. 4).
Nevertheless, SDMs depict similar suitable habitats for species from the
same mountain region, but divergent patterns for species from the
Himalayas and the HM. In the Himalayas, the core potential habitat of
both species (at intermediate elevations), seems to have only shifted a
little between the LGM and today, indicating minor
\emph{in-situ}vertical displacement along the elevational gradient (Fig.
4). Dispersal between the East and the West of the range seems also to
have been possible, as indicated by the results of the ancestral
components. However, suitable habitats at the eastern and westernmost
part of the Himalayan range were likely only available during the LGM,
indicating potential local extinction or long-distance migration if
species occurred there before the LGM. In comparison, for both species
in the HM, a latitudinal displacement (from South at warm times to North
at colder times) and a vertical displacement (move up to a higher
elevation) occurred after the LGM, which was not the case in the
Himalayas. Latitudinal displacement was aided by the mountain ranges
long North-South extent (Fig. 5b), whereas the Himalayas are
latitudinally strongly constrained by the high plateau to the North and
the tropical lowland to the South (Fig. 5a).

In line with the SDM results, evidence of the population size history
based on genomic data showed that all species experienced an abrupt
demographic expansion during the LGM, followed by a population size
decline from the end of the LGM until recent times (Fig. 4). Previous
studies combining genetic data and ecological niche modelling have also
detected such demographic expansions during the LGM, particularly in
cold-tolerant/adapted species, for example in grasshoppers (Tonzo \&
Ortego, 2021), Vipers (Yousefi et al., 2015), caribou (Taylor et al.,
2021), birds (Garg et al., 2020), and plants (Manthey et al., 2017).
The\emph{Himalopsyche} species we investigated are cold-tolerant, and
their ability to disperse overland (to some extent) may have allowed
them to persist \emph{in situ} regionally throughout cold snaps. In
fact, this was the case for many mountain caddisfly species in Central
Europe (Pauls et al., 2006; Kubow et al., 2010; Lehrian et al., 2010;
Previ\selectlanguage{polish}š\selectlanguage{english}i\selectlanguage{polish}ć \selectlanguage{english}et al., 2014), despite a much stronger impact of glaciation on
this continent (Vi\selectlanguage{polish}š\selectlanguage{english}njevi\selectlanguage{polish}ć \selectlanguage{english}et al., 2020) than in the Tibeto-Himalayan
Region (Mao et al., 2021). The limited formation of inland ice during
the LGM in the Tibeto-Himalayan Region may have represented an
opportunity for cold-adapted species to expand their range rather than
being moved far away by massive ice sheets.

\subsection*{4.3 Implication of MGH in different mountain
systems}

{\label{implication-of-mgh-in-different-mountain-systems}}

In line with the MGH, we have shown that the historical evolution of
abiotic conditions such as changing topography (including paleo-drainage
reorganization) and climate are likely to have played dominant roles in
shaping the genetic diversity pattern of caddisflies in the Himalayas
and the HM. The three MGH boundary conditions (i.e. extensive
elevational zonation, response to climatic fluctuations, and a
high-relief terrain with environmental gradients; Mosbrugger et al.,
2018) are met in both the Himalayas and the HM. Yet, we observe distinct
genetic diversity patterns of species in each mountainous region,
indicating that although the conditions of the MGH are generally
realized opportunities and constraints for species vary across different
mountain systems. To explain the formation of different genetic
diversity patterns in each mountain region, it seems crucial to compare
the geological and climatic conditions in each region and their effects
on the evolution process of species under the concept of MGH.

The uplift of the Himalayas and HM are both caused by the India-Eurasia
collision since the Paleocene (65--60 Ma). The ranges reached their
modern height before the Pliocene (as reviewed by Ding et al., 2022).
Although Muellner-Riehl (2019) indicates that the major uplift of the
Qinghai-Tibet plateau proper would not have had an impact at the level
of intraspecific divergence (as it is vastly older), the distinctive
topographic features of these two mountain systems that resulted from
this orogenic process has influenced the interconnectivity of habitats
among and within each mountainous region (Craw et al., 2016; Rahbek et
al., 2019). Compared to the Himalayas, the HM provide a more extensive
area in the North-South orientation, thus implying better geographic
connectivity for species to shift their distribution towards the south
during the cooling period (Xing \& Ree, 2017). Our results have verified
that caddisfly populations in the HM show better connectivity and a
broader potential habitat during the LGM. For this particular scenario
that is associated with Pleistocene climatic fluctuations, Flantua et
al. (2018) recommended a concept called ``flickering connectivity system
(FCS)'' to explain the rapid diversification and high species richness
in mountain ecosystems. Unlike the MGH, which attempts to explain the
origination and evolution of mountain biodiversity throughout the whole
uplift history, the FCS emphasizes the connectivity dynamics resulting
from repetitive climatic changes during the Pleistocene. As reviewed by
(Muellner-Riehl, 2019), many previous phylogeographic studies have
indicated that the late Pleistocene is a crucial time period for
intraspecific diversification in the Tibeto-Himalayan Region. Therefore,
the FCS concept is applicable to explain the formation of intraspecific
diversification of caddisflies that occurred in a more recent time
period (the LGM).

Caddisflies of the HM and the Himalayas may have reacted to the climatic
fluctuations of the Pleistocene by shifting their geographic range
within mountain areas. Consequently, the dynamically changing
connectivity levels may lead to fragmentation, colonization (dispersal),
intermixing, and hybridization, thus promoting diversification in
mountain ecosystems (Flantua et al., 2018; Muellner-Riehl, 2019).
Moreover, since topography and climate conditions are distinct in
different mountain systems, the diversification of species shaped by
these four processes under the concept of the FCS is unique in each
mountain. The resulting interaction between topography and climate is
considered the ``fingerprint'' of a mountain (Flantua et al., 2018).
Although the ``fingerprints'' are not yet specified for all the mountain
systems of the world, our study revealed that the ``fingerprints''
differ between the Himalayas and the HM. Hence, in agreement with
Muellner-Riehl (2019), we think that together with climate fluctuations
as one driver of diversification in mountain systems \emph{sensu} MGH,
the FCS is relevant for explaining the montane biodiversity patterns of
caddisflies. However, given the fact that species react differently to
changes in their environment, more studies are needed in the future to
explore how the entire biota reacts when facing climatic challenges.

\section*{Acknowledgements}

{\label{acknowledgements}}

We thank Subodh Sharma for obtaining the fieldwork permit from Kathmandu
University. We are thankful to Xianfu Li and Lu Tan for their help with
the fieldwork permit. We thank Jaime R. Garcia Marquez for providing
valuable advice in species distribution modelling. We thank Oskar
Schr\selectlanguage{ngerman}öder, Jacqueline Heckenhauer, and Shenglin Liu for giving
professional suggestions on data analyses, results interpretation and
comments on the manuscript. We thank Angelika Losey Kurthen, Jiatang Li,
and a number of Nepalese students for supporting our fieldwork.

This research was funded by the Deutsche Forschungsgemeinschaft (DFG),
grant number PA 1617/2\selectlanguage{english}-1 and PA 1617/2\selectlanguage{english}-2 awarded to SUP, JA 1827/6-2
awarded to SCJ, DO 1880/3-2 to SD, and AF 1117/1-2 awarded to AF.
Reference genome sequencing was supported through the
LOEWE--``Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer
Exzellenz'' of Hesse's Ministry of Higher Education, Research and the
Arts. We acknowledge funding through German Federal Ministry of
Education and Research (BMBF grant 033W034A). SD acknowledges funding by
the Leibniz-Competition (J45/2018). SCJ acknowledges funding through the
Leibniz Competition project ``Freshwater Megafauna Futures''.

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\textbf{Data Accessibility}

All the COI and CAD data for this research are available in the GenBank
of National Center for Biotechnology Information (NCBI), and the
accession codes of each individual are provided in Supplementary Table
1. The raw data of populations have been deposited at NCBI under the
Bioproject ID PRJNA749154. The reference genome of H. tibetana was
previously published by Deng et al. (2022) and has been deposited at
NCBI under the BioProject ID PRJNA728835. Scripts used to generate the
analyses presented in the paper were deposited in GitLab (Project ID:
31757987).

\section*{Author Contributions}

{\label{author-contributions}}

SUP, SD and SJ designed the study. RDTS, DNS, QC and FH identified study
areas and designed fieldwork. SUP, RDTS, DNS, XD, FH, and PBF performed
the fieldwork. XD conducted the laboratory work. XD, SD performed the
data analysis. XD and AF drafted the manuscript. All authors contributed
to the writing of the manuscript. All authors approved the final draft
of the manuscript.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1 Study area and sampling sites in this study. The study area of
this research was limited to the main river basins of the species
distribution based on previous knowledge, more details are described in
section 2.11. A: overview of the study area; B: primary basins in the
study area. C: sampling sites of the four target species, for more
detail see Fig. 3.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2 Population genetic diversity (genome-wide heterozygosity (\selectlanguage{greek}π,
\selectlanguage{english}a--d) and inbreeding coefficient (\emph{F} , e--h)) of the four target
species. The x-axes show the value of each index that is associated with
the primary basins (GAN: Gandaki Basin; KOS: Koshi Basin; MEK: Mekong
Basin; YAN: Yangtze Basin; IRR: Irrawaddy Basin; SAL: Salween Basin) and
subbasins (Lehner and Grill 2013,
http://www.hydrosheds.org/page/hydroatlas). The colours of the box plots
are consistent with the colours shown in the map of Fig. 3.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 3 Population structure of \emph{H. tibetana} (a, b, c), \emph{H.
digitata} (d, e, f), \emph{H. gregoryi} (g, h, i) and \emph{H.
platon}(j, k, l, m), including results of PCA (a, d, g, j) and admixture
(b, e, h, k) analyses based on genomic data. Best fit K in the admixture
estimates was indicated with black boxes except for \emph{H. gregoryi} ,
for which the methods outlined in Evanno et al. (2005) did not lead to
conclusive results. The colour and the code represented the minimal
basins that the sample was assigned to. (c, f, i, l) Sampling sites of
the four target species in this study. The inner pie chart indicates the
distribution of inferred admixture clusters with best fit K (except
for\emph{H. gregoryi} , for which K3 was selected) of each species at
the given sites; Outer ring pie charts indicate the distribution of
haplogroups calculated by the MJ network based on mtCOI (more details
see Fig. S22). Colourful patches on the map show the minimal basins as
categorized in this study. (m) Paleo-drainage rearrangement of the
Yangtze Basin and sampling location of \emph{H. platon} in this area.
The inner pie chart showed the distribution of inferred admixture
clusters with K4. Outer ring pie charts indicated the distribution of
haplogroups calculated by the MJ network based on mtCOI. The
Paleo-drainage maps were reproduced from (Deng et al. 2021a).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 4 Changes in distribution range from the LGM (minority prediction)
to present day (a, d, g, j), predicted elevational range during the LGM
and at the present day (b, e, h, k) and historic effective population
size (c, f, i, l) of \emph{H. tibetana} (a, b, c), \emph{H. digitata}(d,
e, f), \emph{H. gregoryi} (g, h, i), \emph{H. platon} (j, k, l). The
white dots in the distribution range size map show the actual
observation of each population. In the predicted elevational range plots
(b,e,h,k), the grey line presents the background elevation range of the
whole study area, and the dashed line indicates the mean elevation of
the suitable area of species at a time scenario (purple: during the LGM;
cyan: at present). The maps can be viewed online at the original spatial
resolution at https://fred.igb-berlin.de/data/package/812. The y-axis of
the PSMC plots for \emph{H. gregoryi} (i) was re-scaled to filter the
noise closer to the present time (\textasciitilde{}5,000 years before
the present).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Fig. 5 Diagrammatic sketch of the topographic landscape in the Hengduan
Mountains (a) and the Himalayas (b) and the potential
\emph{in-situ}displacement (short arrows) and long-distance dispersal
(long arrows) of caddisflies during the LGM. Different colours of the
caddisflies indicate high-elevation species (yellow) and low-elevation
species (green). During the glacial period, which means a cooler
climate, a vertical displacement along the elevational gradient
(\emph{in-situ}displacement) may occur on both high- and low-elevation
caddisfly species. However, compared to the Himalayas, a wider extent of
mountain ranges along the North-South orientation in the Hengduan
Mountains gives the possibility for species to disperse further south
(long-distance dispersal). During the interglacial period, the same
dispersal or replacement may happen but in the opposite direction.
Therefore, geographic connection and isolation may repeatedly occur
during climate fluctuation, thus forming the so-called ``species pump''
effect and promoting speciation or diversification, according to the MGH
(Mosbrugger et al. 2018).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1SampleSites/Fig1SampleSites}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2-diversity-violin/Fig2-diversity-violin}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3-Pop-Str-1/Fig3-Pop-Str-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3-Pop-Str-2/Fig3-Pop-Str-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig5-MGH/Fig5-MGH}

\end{center}
\end{figure}

\selectlanguage{english}
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