Widespread and strong impacts of river fragmentation by human barriers on fishes in the Mekong River Basin

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Here, we provide a comprehensive evaluation of the impacts of river barriers on the distribution of 1,032 fish species in the Mekong Basin. Our analysis revealed that 93% of Mekong fish species suffer from habitat fragmentation, and species with larger habitat range requirements experienced higher river fragmentation impacts. Sub-basins along the main channel in the Lower Mekong had high values of species richness but relatively high barrier impacts. Across all migration types, potamodromous fish had the worst habitat fragmentation status (Fragmentation Index, 42.56 [95% CI, 36.95–46.05]), followed by catadromous fish. Among all IUCN conservation status categories, Critically Endangered species experienced the highest habitat fragmentation index (33.34 [12.53–46.40]). Among all barrier types, small dams and sluice gates contribute more to habitat fragmentation than large dams. Earth and environmental sciences/Ecology/Freshwater ecology Biological sciences/Ecology/Animal migration Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Ecology/Conservation biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Freshwater systems occupy a small fraction of the Earth’s surface but contribute disproportionately to global biodiversity due to the high number of species they host 1 . Such biodiversity is threatened by several interacting pressures (e.g. land use change, climate change, flow alterations, loss of connectivity), resulting in strong declines worldwide 2–5 . The loss of longitudinal connectivity due to the construction of barriers is a particularly relevant pressure 6,7 . Due to their dendritic structure, rivers are particularly sensitive to habitat connectivity losses, as even a single barrier can isolate entire sections of the river network 8–10 . When multiple barriers are placed on a river system, the result is a sequence of isolated sections where the movement of water, sediments, energy and organisms is impeded 11 . Such fragmented rivers are widespread 6,12 , and are expected to further increase in the future due to the global proliferation of dams under construction and planned 13,14 . Riverscape fragmentation impacts the geographic distribution of fish biodiversity. Due to the presence of barriers, potamodromous and diadromous species may be prevented from reaching habitats that are necessary for the completion of their life cycles 15–17 . Even non-migratory fish species are exposed to local extinctions if the isolated river fragments do not offer enough habitat to support viable populations and prevent rescue effects from neighboring populations 14,18 . As a result, river habitat fragmentation impacts the structure of freshwater fish communities 19–22 , with cascading socioeconomic effects, for instance concerning fisheries declines 23,24 . Such impacts are expected to increase in the future, given the projected increases in river fragmentation worldwide 14 . Proper assessments of fish habitat fragmentation are urgently needed in large river basins to facilitate future fish habitat management and protection. The Mekong River lies within the Indo-Burma biodiversity hotspot, supporting the highest fish diversity in Asia, with more than 1,000 freshwater fish species, hundreds of which are endemic 25 . The Mekong basin is also the largest inland fishery in the world, with an estimated mean annual catch of 2.1 million tonnes, about 15% of the world’s total inland fish catch 26 . The river suffers from intensive anthropogenic pressures including river barrier construction, overexploitation, pollution and habitat destruction 25,27–29 . Among all anthropogenic threats barrier construction is the most concerning due to its profound impacts on flow regimes, sediment transportation and freshwater biodiversity and its rapid projected growth in the near future. 29–32 . Existing analyses conducted using global datasets of georeferenced dams highlighted the poor connectivity status of the upper Mekong 30 , and warned about the detrimental effect of connectivity of future hydropower development 33 . However, there is an ongoing debate weather few large dams or many small dams are more detrimental for habitat connectivity and thus, biodiversity 14,18 . All existing studies on habitat fragmentation effects in the world’s largest river basins 14,18,30,33,34 employed global databases or regional databases where barrier data had poor resolution. In particular, these studies used datasets describing barriers located in the main river channel, ignoring barriers located in tributaries. While large dams are more likely to occur in the main channel, other barriers like small dams, weirs, and sluice gates are likely to be located in low-order streams and are more likely to be unreported 12,35 . Estimating the impact of barriers in more distal regions of river networks is also relevant because, while generally poor in species, these sites generally host more unique communities due to their more isolated position in the river network 19,36,37 . Recent developments in AI-assisted identification of barriers in the Mekong Basin has led to the compilation of a comprehensive dataset with more than 13,000 barriers that also include smaller barriers, such as small dams and sluice gates 38 . Although small barriers can have major impacts on fish distribution 39,40 , the impacts of such items on fragmentation of fish populations in the Mekong basin have not been investigated so far. Based on the highlighted knowledge gaps, in this paper, we used a comprehensive barriers dataset to investigate the spatial distribution of fragmentation and its link with fish population connectivity status in the Mekong Basin. We first reconstructed the potential distribution range of all fish species in the Mekong Basin by overlaying fish dispersal abilities on a fish occurrence database 25,41 . We then assessed all species’ habitat fragmentation status using the most complete barrier database and compared the differences in connectivity reduction between migration types and conservation status. Subsequently, we evaluated which types of barriers contributed most to the degradation of river connectivity. With this approach, we identified fish species and regions most threatened by the construction of river barriers in the Mekong Basin, to support future management plans including connectivity restoration and habitat conservation. Results Fish distribution along the Mekong Basin Fish species richness decreased with increasing elevation, from two species in the source region to 410 in sub-basins near the Tonle Sap region (Figure S2). A bivariate choropleth map illustrates that sub-basins near the confluence of the Mun River and the Mekong main channel exhibited the highest fish species richness (157–413 species per sub-basin) as well as the highest river barrier density (8–215 per 100km; Fig. 1 ). Sub-basins along the main channel between the middle and lower Mekong, as well as the Tonle Sap region, also exhibited high species richness and high betweenness centrality, with moderate barrier densities (1–8 per 100km). The upper Mekong exhibited low species richness (2–90 species per sub-basin) and high LCBD, and a few areas in this region had relatively high barrier densities (Fig. 1 ). Potamodromous species (n = 135, see Supplementary Table), undertaking migrations solely in freshwater, were found to mostly inhabit the main channel between the middle and lower Mekong, the Tonle Sap Lake, the Mun River, and sub-basins next to them. Within their distribution ranges, high barrier density mostly occurred in the Mun River, and moderate barrier density was found in sub-basins along the main Mekong channel. Diadromous species (n = 125), which migrate between freshwater and the sea, were mainly found in the Lower Mekong between the Sesan-Mekong confluence and the estuary. Barrier densities were moderate, covering much of the Mekong Delta and reduced further upstream. The listed threatened species (vulnerable [n = 42], endangered [n = 25], and critically endangered [n = 13]) mostly inhabit the main channel between the middle and lower Mekong, the Tonle Sap Lake, and sub-basins next to them (Fig. 2 ). Similarly, high and moderate barrier density along the main channel pose threats to those species. Habitat fragmentation of Mekong fishes Across all 952 fish species, 881 (92.5%) of them suffered from decreased population connectivity. The Population Connectivity Index (PCI) pooled for all the fish species significantly decreased from 4.35 [3.81–5.02] (median and 95% CI, natural status) to 3.70 [3.06–4.21] (moderate passage status). The pooled FI (moderate status) ranged from 0.01 to 76.20, with a median value of 19.37, and was positively related to available distance of fish habitat length (Spearman’s correlation, r = 0.566, P < 0.001; Figure S3). Among all fish families (for circumstances where there were more than five species present), the Pangasiidae (n = 19) experienced most serious habitat fragmentation (Fragmentation Index, FI = 51.91), followed by Notopteridae (n = 5; FI = 51.27) and Channidae (n = 10; FI = 48.84). Among all species, Bagarius suchus (NT species) suffered from the most profound habitat fragmentation level (FI = 76.20), followed by Wallago attu (VU species; FI = 74.43) and Phalacronotus micronemus (FI = 74.37). Significant differences were found in fish habitat fragmentation index between different migration types (Fig. 3 , Kruskal-Wallis H test, χ 2 (5) = 143.9, P < 0.001). Potamodromous fish had the worst FI (42.45 [38.61–46.62]), significantly higher than anadromous fish (14.55 [2.18–27.29]), amphidromous fish 7.30 [5.98–10.45]), and species with unknown migration type (18.13 [16.64–19.92]) (pairwise post hoc, P < 0.05 in all cases). Significant differences in FI^ were also observed (Kruskal-Wallis H test, χ 2 (5) = 19.42, P < 0.001), with potamodromous fish (0.09 [0.06–0.10]) and unknown migration type (0.12 [0.11–0.14]) significantly lower than catadromous fish (0.67 [0.52–1.16]) (pairwise post hoc, P < 0.05 in both cases). Significant differences in FI were also found across all IUCN categories (Fig. 3 , Kruskal-Wallis H test, χ 2 (5) = 30.13, P < 0.001). Critically Endangered species experienced the greatest FI (34.48 [19.46–53.52]). The FI status of Least Concern species was higher than Not Evaluated and Data Deficient (combined) species (pairwise post hoc, P < 0.001). Significant differences in FI^ were observed among conservation status (Kruskal-Wallis H test, χ 2 (5) = 28.1, P < 0.001). CR species had higher FI^ (0.33 [0.18–3.13]) than LC species (0.10 [0.08–0.11]). Contribution of different barrier types to fragmentation Across four major types of barriers, small dams and sluice gates contributed most significantly to habitat fragmentation for all fish species - more so than weirs and large dams (ANOVA, F (3, 3804) = 42.6, P < 0.001; pairwise post hoc, P < 0.001 in all cases; Fig. 4 ) under all passability circumstances (passability ranged between 0.1 and 0.9). This pattern was evident for potamodromous species, where small dams and sluice gates had a profound impact on habitat fragmentation compared to weirs and large dams (ANOVA, F (3, 536) = 43.1, P < 0.001). For diadromous species, the impact of sluice gates on habitat fragmentation was significantly greater than that of other barrier types (ANOVA, F (3, 496) = 57.4, P 0.05). For species with unknown migratory status, small dams contributed more to habitat fragmentation than the other barrier types under all passability circumstances (ANOVA, F (3, 2720) = 10.6, P < 0.001). In comparison, higher FI^ values contributed by large dams, compared to other barrier types, were observed when considering all fish species (ANOVA, F (3, 3804) = 4.14, P = 0.006) and unknown migratory types (ANOVA, F (3, 2720) = 4.7, P = 0.003) species (Fig. 4 ), suggesting a single large dam can have a large impact on such categories. For potamodromous (ANOVA, P > 0.05), diadromous (ANOVA, F (3, 496) = 1.5, P = 0.004), and non-migratory species (ANOVA, P > 0.05), sluice gates and small dams contributed more to FI^, indicating that these barrier types have a greater localized impact on fragmentation for these groups. Discussion By utilizing the most complete fish occurrence database alongside the most comprehensive basin-scale barrier database, the impacts of various barrier types on habitat fragmentation for all fish species in the Mekong Basin have been assessed for the first time. Our results demonstrate that high species richness predominantly occurs in the middle and lower Mekong Basin along the main channel, which is also the region where a high number of barriers have been constructed 25 . On the other hand, high species uniqueness appeared in the upper Mekong where the Chinese cascade dams are located. Across the entire basin, the most fragmented region appears to be the Chi-Mun sub-basin, characterized by the highest numbers of small dams and sluice gates. In contrast, the Mekong headwaters and most of the upper regions were relatively less fragmented and still provide accessible habitats for certain highland fish species. Nevertheless, among all fish species, 93% were affected by barrier construction, suggesting that the Mekong fish species are seriously threatened by fragmented habitats. Relationships between life history traits and habitat fragmentation Since the start of the Anthropocene, many migratory fish species have experienced reductions in habitat range 42 . According to the Living Planet Index method, among 184 monitored freshwater migratory fish species globally, 81% exhibited a decreasing abundance trend between 1970 and 2020 43 . When migratory fish traverse multiple sub-basins containing various environments, they encounter a broader array of anthropogenic threats including a higher number of constructed barriers than resident species 44 . Habitat loss due to barriers constructed at key locations (e.g., stopover sub-basins, spawning and foraging habitat) may contribute to population decline by limiting migration success 45,46 . On the other hand, when larger areas of habitat are fragmented into smaller, less accessible patches of river channels, fish populations become more isolated, therefore making it more difficult to maintain meta-populations 39 . This results in decreased rates of immigration and emigration, hindering gene flow 44 . In the Mekong Basin, we found that migratory species with larger habitat range and longer dispersal distances suffered more profound habitat fragmentation compared to species with narrower habitat ranges and limited dispersal ability. The greater dispersal distance, the higher the likelihood that species would encounter more barriers during movement 44 , and result in reduced population exchange. The habitat connectivity status of potamodromous species was significantly worse compared to other migration types, particularly for the middle and lower parts of the main Mekong channel and major sub-basins linked to it (e.g., the 3S, Chi-Mun, and Xe Don sub-basins). This suggests that fragmentation impacts might be stronger in the middle part of the basin where potamodromous species were most abundant 47 . Among the taxa exhibiting greatest habitat fragmentation risks, Osteoglossiformes contains many highly threatened species, including the Endangered Asian bony-tongue Scleropages formosus , the Near Threatened royal featherback Chitala blanci , and the now Extinct giant featherback Chitala lopis . In the Mekong, the freshwater habitats of the aforementioned species are evaluated as under pressure due to dam construction 48 , and thus, their conservation status could be further worsened when considering all proposed barriers as well as those omitted from existing databases 49 . As for fish families, the most impacted Pangasiidae includes many flagship and megafauna freshwater species, such as the Critically Endangered Mekong giant catfish Pangasianodon gigas , Giant pangasius Pangasius sanitwongsei and Endangered striped catfish Pangasianodon hypophthalmus , all are ecologically and economically important. Also, this family mostly consists of potamodromous species that undertake long-distance migrations along the main channel of the middle and lower Mekong, which suffer from main channel dams and sluice gates 50 . Further research is urgently needed to re-assess the conservation status of these threatened species, especially those already at stake, using the complete barrier database 49 . Additionally, the passability of majority Mekong fish species at semi-passable barriers remains unclear due to the lack of monitoring and performance standards. Future studies are required to address these gaps and to develop highly efficient fish passes. Large vs. small barrier, which causes greater habitat fragmentation? Our analysis revealed that, across all species, the cumulative effect of small dams and sluice gates nearly doubled the habitat fragmentation compared to large dams at all levels of passability. This finding aligns with previous research suggesting that the cumulative impacts of small-sized barriers is comparable to those of large dams 14,18,51 . Such results underscore the importance of maintaining a comprehensive barrier database to support ecological conservation and management efforts 38 , and highlight the need for standardized comprehensive database across large basin-scale or even global scale. All barriers should be considered both individually, and in terms of cumulative effects, when planning and conducting ecological restoration activities 52 . For example, our analysis suggests that small dams and sluice gates are the main reason of habitat fragmentation for potamodromous fish species, which suffered from the most significant habitat fragmentation among all migratory types. By contrast, large dams, which have generally received more attention in previous studies, contribute least to habitat fragmentation for potamodromous species compared with other barrier types. So, impacts of small-sized barriers should be carefully evaluated during the restoration process 52 . In comparison, the habitat of diadromous species is largely fragmented due to the construction of sluice gates 53,54 . As a large number of tidal gates and flaps have been built in the estuary region in the last 50 years, these have blocked routes between the freshwater environments and the South China Sea 55 , necessitating increased restoration attention in the near future. For non-migratory species (resident fish), all types of barriers contributed equally to the fragmentation. Although the impacts of barriers on fish dispersal may be less severe for these species, their short distances movements between meta-populations, as well as egg and larvae drifting phases, still require protection 56 . However, the impacts of large dams should never be underestimated, and particularly for dams that located in regions with high species uniqueness. For example, a new large hydropower station is set to be constructed at Ganlanba (in the lower reaches of the Lancang River), where the highest LCBD within the basin has been observed. Long-term monitoring will be essential to assess its ongoing impacts. Strategic restoration plan needed to mitigate barrier impacts The decision to prioritize improving the passability of a large dam or removing multiple small barriers in river management must carefully balance ecological benefits, economic costs, and the presence of key species 57,58 . Large dams, while causing significant localized ecological disruptions, are often critical for power generation and regional development. Installing fish passes or bypass channels at such dams can reconnect migratory routes and mitigate some ecological impacts. However, these solutions can be expensive, with installation and maintenance costs running into millions of dollars, and their effectiveness is species-dependent. For example, species with limited swimming ability may still struggle to navigate even well-designed fish passes 59 . Moreover, these measures may not be able to address broader impacts such as modified sediment transport, altered flow regimes, and degraded water quality 58 . In contrast, small barriers often have lower socioeconomical benefits but are numerous and cumulatively cause extensive habitat fragmentation, especially in areas located in remote tributary sections far from the main channel where high fish community differentiation might occur. Removing multiple small barriers could significantly restore connectivity across a broad spatial scale, benefiting a wide range of aquatic species and enhancing ecosystem functions 60 . However, this approach involves substantial logistical and financial challenges, as the costs of identifying, assessing, and dismantling barriers across an entire basin can be high. Moreover, some small barriers serve local needs, such as irrigation or flood control, which complicates their removal due to stakeholder opposition 61 . The presence of species with specific ecological requirements further complicates the decision regarding the optimal management strategy for mitigating impacts of habitat fragmentation from anthropogenic river barriers 58 . For instance, if the region supports a flagship or economically significant migratory species that requires access to upstream habitats, improving the passability of a large dam might be the most effective action when the ^FI value is high. On the other hand, for sub-basins with diverse, localized species (i.e., regions with high LCBD replacement values) that depend on interconnected habitats, removing small barriers may yield greater cumulative benefits (for species with high FI and relative lower ^FI values). A cost-effective and ecologically sound river management strategy often requires an integrated approach 34 . This may involve improving fish passage at key large dams to benefit migratory species while systematically prioritizing and removing small barriers to restore the dendritic structure of the river network 19 . Decisions should be guided by ecological modeling, cost-benefit analyses, and species-specific data, ensuring that investments align with ecological priorities and stakeholder needs, while minimizing unnecessary expenses 34 . Future direction Beyond connectivity, the natural flow regimes modified by river barriers can potentially disrupt fish spawning, and subsequent hatching and rearing processes. For example, many fish species in the Mekong floodplains spawn at the beginning of the flood season 47 . The flow-regime-adapted spawning behavior can be easily disturbed when dams or sluice gates are operational, potentially delaying migratory or spawning timings 4,62 . Additionally, other species depend on the drifting of eggs (e.g. Bagarius sp. ), larvae, and juvenile stages to downstream nursery and feeding areas to complete their life cycles, but flow velocity is seldom maintained in impounded regions, and the minimum flow speed required to keep larval fish in the water column is rarely met. Furthermore, barrier construction may facilitate the invasion of non-native species 63 , which will also require future investment and management strategies in the Mekong. Conclusion We demonstrate that while the effects of individual large dams on habitat fragmentation and fish distribution in the Mekong Basin are greater than for small barriers, the large number of small barriers causes greater cumulative impacts on fragmentation and resultant risks for effective conservation. We demonstrate the importance of using complete databases with small, as well as large, river barriers for evaluating conservation risks for barrier development and connectivity restoration in large river basins. We encourage the development of thorough barrier and faunal databases in other large, biodiverse river basins and the application of similar analyses of the ecological impacts of existing and projected barrier distributions. Materials and Methods River barrier data processing River barrier data of the Mekong Basin were gathered from the Mekong River Barrier Database 38 . This database comprises 13,054 barriers classified into five types (dam, sluice gate, weir, bridge apron, and others). Ninety seven percent of the barriers in this database were previously unreported in the Mekong Basin compared with other openly accessible databases. To analyse the impact of barrier distribution on fish dispersal across sub-basins (see Section 2.3 ), we first extracted all main channel networks (i.e. the section of a river network that links sub-basins to each other), then manually reviewed all barriers in associated Google Earth satellite (Landsat, NASA-USGS) images, and retained barriers that were located in the main river channel, resulting in 3,066 barriers including 1,648 dams, 488 sluice gates, 840 weirs, 42 bridge aprons, and 48 ‘other’ type barriers (e.g. ramps, bamboo/wooden fences, Figure S1 ). The main channel barrier density for each sub-basin was calculated by dividing barrier numbers by main channel length. Fish species data processing Fish occurrence data for the Mekong Basin were gathered from a published systematic database 25 , that compiles all fish occurrence data by combining long-term field sampling data and data gathered from all existing books, peer-reviewed articles, grey literature, and online databases via literature review (covering the time period from 1970s to 2020s). Non-native fish species, farmed species, and species without occurrence descriptions were excluded from the original database, which resulted in the final database comprising a total of 164,878 occurrence points from 1,032 species (Figure S2). The migration type of each species was gathered from FishBase 64 and published literature. Each species was categorized into one of six migration types: potamodromous, catadromous, anadromous, amphidromous, non-migratory, and unknown (defined according to 65 ; see SupplementaryTable). Each species was categorized into one of eight conservation status categories according to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species ( https://www.iucnredlist.org/ ): Not Evaluated (NE), Data Deficient (DD), Least Concern (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN), Critically Endangered (CR), and Extinct (EX). To analyze the spatial patterns of fish distribution, the cumulative fish species richness of each category was calculated at sub-basin level (HydroBASIN level 8 66 ). To reconstruct the potential distribution range of each species, the dispersal distance of each species was calculated 67 . Total fish length, aspect ratio of the caudal fin, stream order inhabited, and timescale allowed for dispersal were employed in this model 67,68 . Species-specific fish length and aspect ratio of the caudal fin for each species were measured based on figures from published articles or gathered from FishBase. The highest stream order that each species inhabits was gathered using the HydroRIVERS dataset 66 . A time window of 10 years was used in the model, as appropriate to long-term population level dispersal 68,69 . The estimated dispersal distance was used as a radius to create buffer zones for each occurrence point 41,70 . Then, sub-basins (HydroBASIN level 8) that overlapped with buffer zones of all occurrence points were considered as the predicted distribution for that species 28,71 . The predicted distribution of each species was further refined by cropping to its native sub-basins to exclude predictions that were out of range, based on the occurrence points and expert opinion 41 . For example, if one species only moves along the main channel of the Mekong and does not inhabit tributaries according to published literature, when the buffered regions fell within some tributaries, then those overlapped sub-basins were removed during this manual refinement step. Furthermore, if a potential sub-basin was linked with valid habitat only through land rather than river channel, then it was removed during the manual process. To express the beta diversity of each subbasin, we calculated the local contributions to beta diversity (LCBD) as a metric that expresses its compositional uniqueness. Using the ‘BAT’ package, we used Sørensen-based indices of the Podani family to calculate the distance matrix representing beta diversity 72 and its decomposition into the replacement and richness difference components 73 . The replacement component quantifies sites uniqueness based on the local community structure, regardless of the differences in species richness. 2.3 Fish habitat fragmentation level assessment To assess the species’ habitat connectivity, we adopted the Population Connectivity Index (PCI 74 ), which considers structural connectivity (spatial arrangement of the sub-basins and the barriers) and functional connectivity (fish dispersal ability), distribution range (habitat length), and the passability between sub-basins. The PCI considers that a fish population inhabits a single sub-basin: $$\:PCI=\sum\:_{i=1}^{n}\sum\:_{j=1}^{n}{B}_{ij}{c}_{ij}\frac{{l}_{i}}{L}\frac{{l}_{j}}{L}{\delta\:}_{i}{\delta\:}_{j}$$ 1 Where n is the number of sub-basins, B i j measures the dispersal capability of the given fish species between sub-basin i and sub-basin j , c i j is the total cumulative passability of river barriers between sub-basins i and j , and l i and l j represent the total length of the river network in sub-basin i and j, which are inhabited by the specific species; \(\:{\delta\:}_{i}\) and \(\:{\delta\:}_{j}\) represent the presence of a species in subcatchments I and j (0 if species is absent, 1 if present); L represents the total river length of all populations in the basin. Since the PCI is a network-based index, we generated a network based on the spatial distribution of subbasins. We used the 1,130 HydroBASINS level 8 sub-basins as nodes of the network. Based on the spatial distribution of the subbasins, we generated a graph where outlets/entrances between sub-basins are links. We defined the cumulative passability c ij as: $$\:{c}_{ij}=\prod\:_{m=1}^{M}{p}_{m}$$ 2 Where \(\:{p}_{m}\) is the passability of the sub-basin m , defined as: $$\:{p}_{m}=\:{\prod\:}_{k}{p}_{k}^{{n}_{k}}$$ 3 Where \(\:{p}_{k}\) is the passability of each barrier type k, where k = 1 for large dams, k = 2 for small dams, k = 3 for sluice gates, k = 4 for weirs, etc.; nk is the number of k-th fragmentation item in the main channel of sub-basin m. In Eq. 2.1, We set l i equal to the river length if the subbasin is within the species distributional range; and equal to 0 if it is outside its range. The dispersal capacity B ij is defined as follows: $$\:{B}_{ij}={PD}^{{d}_{ij}}$$ 4 The probability of dispersal (PD) is calculated based on fish size and swimming capability 67 . Minimum distance (d) is the main channel length between two populations inhabiting two different sub-basins. Dispersal probability of each species was assigned by creating seven classes of dispersal distance of all species of the entire fish community, by setting the quantiles at 0%, 14.3%, 28.6%, 42.9%, 57.1%, 71.4%, 85.7%, and 100% 74 . Dispersal distance that fell within the quantile ranges was then categorized into one of the seven class boundaries, ranging from 0.3 to 0.9, which represent fish species’ dispersal probability from low to high 74 As this index is applicable to species that occupy two or more sub-basins, species that inhabit only one sub-basin (n = 80) were excluded from further analysis, which retained a total of 952 species. We calculated PCI for each species and scenario (i.e., different barrier passability status). We defined the fragmentation index (FI) as the percentage decrease of PCI when barriers are considered. $$\:FI=\frac{({PCI}_{o}-{PCI}_{F})}{{PCI}_{o}}\times\:100$$ 5 Here, PCI O is the population connectivity index calculated when no barrier is present within the species’ distribution range (i.e. calculating PCI by setting c ij = 1). PCI F represents the fragmented status (i.e. all the barriers are considered) When calculating the PCI F , p m for dams (both large and small) was set to 0 in all circumstances. For semi-passable barriers such as weirs, p m was set to 0.5, representing moderate fish passability. The FI values range from 0 to 100. When fish populations are located in connected (neighboring) sub-basins with no barriers, or barriers are only located at the edge of the fish’s distribution range such that they do not affect the internal dispersal, FI = 0. Conversely, if the habitat is greatly fragmented by barriers, preventing fish from freely moving from one sub-basin to another, the FI value would approach 100. The probability dispersal distance of each species was estimated using the ‘fishmove’ R package 67 . The PCI index was calculated using the ‘riverconn’ package 75 . We also calculated the average fragmentation index (FI^) as the ratio between FI and the number of barriers considered in PCI F . FI^ represents the average fragmentation caused by one barrier and is useful when comparing scenarios where different numbers of barriers are considered. Additionally, as a metric of network centrality, based on the graph used to calculate PCI, we calculated also the betweenness centrality (BC) for each sub-basin (HydroBASIN level 8). BC differentiates between central and isolated sites and was shown to be a strong predictor of fish alpha and beta diversity in undisturbed river systems 19,37,76 . Sites with high BC are located in the main channel, while sites with low BC are located in the tributaries. 2.4 Contribution of different types of barriers to habitat fragmentation To assess how different types of barriers affect species with different migration types, and which type of barrier contributed most to habitat fragmentation, four major type barriers: large dam (> 10m), small dam (< 10m), weir and sluice gate were selected. Then, the FI for each species was calculated at nine different passability status values (from 0.1, 0.2, 0.3 to 0.9), assuming only one type of barrier is present in each occasion. After that, the difference between FI large−dam , FI small−dam , FI weir and FI sluice among all migratory type of fish was evaluated. 2.5 Data analysis Spearman’s correlation was used to identify the relationship between fish habitat range and its associated fragmentation index. Kruskal-Wallis H tests were used to identify if there were significant differences in fragmentation index between different migration types and IUCN conservation status 77 ( https://www.iucnredlist.org/ ), followed by a Bonferroni post-hoc test to identify the significantly different groups. ANOVA followed by a Turkey post-hoc test were used to assess the difference in fragmentation status (FI and ^FI) contributed by different types of barriers across migratory types. Declarations Data availability The river barrier data used in this study are available at Zenodo [10.5281/zenodo.10141668]. The fish occurrence data, shapefiles and associated R code used in this study have been deposited in Zenodo [10.5281/zenodo.14730557]. ACKNOWLEDGEMENTS The study was funded by the National Natural Science Foundation of China (42301064), and the Yunnan Scientist Workstation on International River Research of Daming He (K264202011220). 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Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol Process 27 , 2171–2186 (2013). Radinger, J. & Wolter, C. Patterns and predictors of fish dispersal in rivers. Fish and Fisheries 15 , 456–473 (2014). Radinger, J. et al. The future distribution of river fish: The complex interplay of climate and land use changes, species dispersal and movement barriers. Glob Chang Biol 23 , 4970–4986 (2017). Herrera-R, G. A. et al. The combined effects of climate change and river fragmentation on the distribution of Andean Amazon fishes. Glob Chang Biol 26 , 5509–5523 (2020). Cassemiro, F. A. S. et al. Landscape dynamics and diversification of the megadiverse South American freshwater fish fauna. Proceedings of the National Academy of Sciences 120 , (2023). Wang, J. et al. Analysing spatio-temporal patterns of non‐native fish in a biodiversity hotspot across decades. Divers Distrib 29 , 1492–1507 (2023). 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Additional Declarations There is NO Competing Interest. Supplementary Files Appendix.docx fishoverall.xlsx Dataset 1 Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5894851","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411247560,"identity":"01bb7b3e-0799-41f0-a135-33fbb1663785","order_by":0,"name":"Jingrui Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACxmYwJQFmP4CIJRCvhdmAKC3IgE2CKC3M7byHX1e2WTDIz8g9Vs274zADP3uOAcPPHfgcxpdmebZNgsHgRl7abd4zhxkke94YMPaewaeFx8ywEaRFIsfsNm/bYaDeHANmxjYitMjPyDErBmmxJ0KL8UOQFoYbOWbMYFskiLCFseGcBI/BmTfGknPb0nkkzjwrONiLR4th/xnjjw1ldXLy7TmGH962WcvxtydvfPATn5YGSHTwgDhMPFDGAdwaGBjkgVHzAe7KH/iUjoJRMApGwYgFAIwqR1RX1ftDAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-9046-448X","institution":"Yunnan University","correspondingAuthor":true,"prefix":"","firstName":"Jingrui","middleName":"","lastName":"Sun","suffix":""},{"id":411247561,"identity":"1f9d1813-b7b4-4a18-bc16-609d28399430","order_by":1,"name":"Damiano Baldan","email":"","orcid":"","institution":"National Institute of Oceanography and Applied Geophysics","correspondingAuthor":false,"prefix":"","firstName":"Damiano","middleName":"","lastName":"Baldan","suffix":""},{"id":411247562,"identity":"b160341e-01d7-4bf4-8d5c-0bc622371144","order_by":2,"name":"Martyn Lucas","email":"","orcid":"","institution":"Durham University","correspondingAuthor":false,"prefix":"","firstName":"Martyn","middleName":"","lastName":"Lucas","suffix":""},{"id":411247563,"identity":"f5af4668-ad37-448c-ab4b-c6e3d678c80c","order_by":3,"name":"Jie Wang","email":"","orcid":"","institution":"Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wang","suffix":""},{"id":411247564,"identity":"c0e93c1c-a84e-41d6-a260-fe6a151d4ba5","order_by":4,"name":"Amaia Rodeles","email":"","orcid":"https://orcid.org/0000-0003-4109-6777","institution":"Universidade de Vigo","correspondingAuthor":false,"prefix":"","firstName":"Amaia","middleName":"","lastName":"Rodeles","suffix":""},{"id":411247565,"identity":"3ecc944d-9c18-487d-b0cb-b0303a7c5e0e","order_by":5,"name":"Shams Galib","email":"","orcid":"","institution":"University of Rajshahi","correspondingAuthor":false,"prefix":"","firstName":"Shams","middleName":"","lastName":"Galib","suffix":""},{"id":411247566,"identity":"debd4de6-a7eb-41ae-bbff-49ea8f837da2","order_by":6,"name":"Juan Tao","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Tao","suffix":""},{"id":411247567,"identity":"e904e0e9-01d2-4629-87f0-0766f69805aa","order_by":7,"name":"Mingbo Li","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Mingbo","middleName":"","lastName":"Li","suffix":""},{"id":411247568,"identity":"58e98606-a63d-4ddb-a6b7-f273f7498555","order_by":8,"name":"Daming He","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Daming","middleName":"","lastName":"He","suffix":""},{"id":411247569,"identity":"28ea831d-8715-400f-a331-307aeefb7964","order_by":9,"name":"Chengzhi Ding","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Chengzhi","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-01-24 10:46:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5894851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5894851/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-02467-y","type":"published","date":"2025-07-07T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77747877,"identity":"29456ae0-01c8-45b3-83fd-b0d15d1480e6","added_by":"auto","created_at":"2025-03-05 07:05:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171071,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between barrier density (ranged from 0 - 2.2 per km) and total species richness (ranged from 0 - 952 species; [a]), local contributions to beta diversity (LCBD) [b].\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5894851/v1/521b9e5cf58e8b4481908afa.png"},{"id":77749019,"identity":"b06998d8-a95d-45d1-8e07-c82ea106afe4","added_by":"auto","created_at":"2025-03-05 07:13:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159368,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between barrier density and potamodromous species richness (ranged from 0-135 species; [a]), diadromous migrant fish species richness (ranged from 0-125 species; [b]) threatened fish species richness (IUCN status: Vulnerable (low threat), Endangered (moderate threat), and Critically endangered (highest threat) [c], in each sub-basin of the Mekong River.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5894851/v1/771576334983394de9f0c7ae.png"},{"id":77747875,"identity":"8e23ceb8-df0d-495d-a2ec-c8d0a7e06a8b","added_by":"auto","created_at":"2025-03-05 07:05:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28347,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the fragmentation index (FI) and mean fragmentation index (FI^) across different migration types (potamodromous, catadromous, anadromous, amphidromous, non-migratory, and unknown [NA]) and conservation status (Critically Endangered[CR], Endangered [EN], Vulnerable [VU], Near Threatened [NT], Least Concern [LC], Not Evaluated [NE], and Data Deficient [DD]), with statistically significant differences denoted by letters.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5894851/v1/2316a9cecafd3d03f1af278b.png"},{"id":77747882,"identity":"ed7ff17a-b559-4139-9248-2bbfca1916d1","added_by":"auto","created_at":"2025-03-05 07:05:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41314,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of habitat fragmentation by barrier type across different fish migration categories (potamodromous, diadromous, non-migratory, and unknown [NA]) and passability scenarios. This set of graphs displays the Fragmentation Index (FI)and average fragmentation index (FI^) for four barrier types (large dam, small dam, weir, and sluice gate) across various passability values, stratified by migratory behavior of fish species. Each point represents the mean FI, and the error bars indicate the 95% confidence interval (CI).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5894851/v1/b61bf6691ea0b59a0d513ac3.png"},{"id":86220712,"identity":"4486d8ce-2efb-444f-b65a-36edfaae694a","added_by":"auto","created_at":"2025-07-08 07:05:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1507902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5894851/v1/448a4783-127b-4843-aa92-0488c689ebcd.pdf"},{"id":77747881,"identity":"d0d4b052-ee7e-47f9-8722-0938304016eb","added_by":"auto","created_at":"2025-03-05 07:05:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5800047,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5894851/v1/59d4fdad12058a1cb19a84b6.docx"},{"id":77749017,"identity":"01755d4c-a10e-4090-bed4-2b8665a0597f","added_by":"auto","created_at":"2025-03-05 07:13:12","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":404019,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"fishoverall.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5894851/v1/72f46a22128b2c8da6811460.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Widespread and strong impacts of river fragmentation by human barriers on fishes in the Mekong River Basin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFreshwater systems occupy a small fraction of the Earth\u0026rsquo;s surface but contribute disproportionately to global biodiversity due to the high number of species they host \u003csup\u003e1\u003c/sup\u003e. Such biodiversity is threatened by several interacting pressures (e.g. land use change, climate change, flow alterations, loss of connectivity), resulting in strong declines worldwide \u003csup\u003e2\u0026ndash;5\u003c/sup\u003e. The loss of longitudinal connectivity due to the construction of barriers is a particularly relevant pressure \u003csup\u003e6,7\u003c/sup\u003e. Due to their dendritic structure, rivers are particularly sensitive to habitat connectivity losses, as even a single barrier can isolate entire sections of the river network \u003csup\u003e8\u0026ndash;10\u003c/sup\u003e. When multiple barriers are placed on a river system, the result is a sequence of isolated sections where the movement of water, sediments, energy and organisms is impeded \u003csup\u003e11\u003c/sup\u003e. Such fragmented rivers are widespread \u003csup\u003e6,12\u003c/sup\u003e, and are expected to further increase in the future due to the global proliferation of dams under construction and planned \u003csup\u003e13,14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRiverscape fragmentation impacts the geographic distribution of fish biodiversity. Due to the presence of barriers, potamodromous and diadromous species may be prevented from reaching habitats that are necessary for the completion of their life cycles \u003csup\u003e15\u0026ndash;17\u003c/sup\u003e. Even non-migratory fish species are exposed to local extinctions if the isolated river fragments do not offer enough habitat to support viable populations and prevent rescue effects from neighboring populations \u003csup\u003e14,18\u003c/sup\u003e. As a result, river habitat fragmentation impacts the structure of freshwater fish communities \u003csup\u003e19\u0026ndash;22\u003c/sup\u003e, with cascading socioeconomic effects, for instance concerning fisheries declines \u003csup\u003e23,24\u003c/sup\u003e. Such impacts are expected to increase in the future, given the projected increases in river fragmentation worldwide \u003csup\u003e14\u003c/sup\u003e. Proper assessments of fish habitat fragmentation are urgently needed in large river basins to facilitate future fish habitat management and protection.\u003c/p\u003e \u003cp\u003eThe Mekong River lies within the Indo-Burma biodiversity hotspot, supporting the highest fish diversity in Asia, with more than 1,000 freshwater fish species, hundreds of which are endemic \u003csup\u003e25\u003c/sup\u003e. The Mekong basin is also the largest inland fishery in the world, with an estimated mean annual catch of 2.1\u0026nbsp;million tonnes, about 15% of the world\u0026rsquo;s total inland fish catch \u003csup\u003e26\u003c/sup\u003e. The river suffers from intensive anthropogenic pressures including river barrier construction, overexploitation, pollution and habitat destruction \u003csup\u003e25,27\u0026ndash;29\u003c/sup\u003e. Among all anthropogenic threats barrier construction is the most concerning due to its profound impacts on flow regimes, sediment transportation and freshwater biodiversity and its rapid projected growth in the near future. \u003csup\u003e29\u0026ndash;32\u003c/sup\u003e. Existing analyses conducted using global datasets of georeferenced dams highlighted the poor connectivity status of the upper Mekong\u003csup\u003e30\u003c/sup\u003e, and warned about the detrimental effect of connectivity of future hydropower development \u003csup\u003e33\u003c/sup\u003e. However, there is an ongoing debate weather few large dams or many small dams are more detrimental for habitat connectivity and thus, biodiversity \u003csup\u003e14,18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll existing studies on habitat fragmentation effects in the world\u0026rsquo;s largest river basins \u003csup\u003e14,18,30,33,34\u003c/sup\u003e employed global databases or regional databases where barrier data had poor resolution. In particular, these studies used datasets describing barriers located in the main river channel, ignoring barriers located in tributaries. While large dams are more likely to occur in the main channel, other barriers like small dams, weirs, and sluice gates are likely to be located in low-order streams and are more likely to be unreported \u003csup\u003e12,35\u003c/sup\u003e. Estimating the impact of barriers in more distal regions of river networks is also relevant because, while generally poor in species, these sites generally host more unique communities due to their more isolated position in the river network \u003csup\u003e19,36,37\u003c/sup\u003e. Recent developments in AI-assisted identification of barriers in the Mekong Basin has led to the compilation of a comprehensive dataset with more than 13,000 barriers that also include smaller barriers, such as small dams and sluice gates \u003csup\u003e38\u003c/sup\u003e. Although small barriers can have major impacts on fish distribution \u003csup\u003e39,40\u003c/sup\u003e, the impacts of such items on fragmentation of fish populations in the Mekong basin have not been investigated so far.\u003c/p\u003e \u003cp\u003eBased on the highlighted knowledge gaps, in this paper, we used a comprehensive barriers dataset to investigate the spatial distribution of fragmentation and its link with fish population connectivity status in the Mekong Basin. We first reconstructed the potential distribution range of all fish species in the Mekong Basin by overlaying fish dispersal abilities on a fish occurrence database \u003csup\u003e25,41\u003c/sup\u003e. We then assessed all species\u0026rsquo; habitat fragmentation status using the most complete barrier database and compared the differences in connectivity reduction between migration types and conservation status. Subsequently, we evaluated which types of barriers contributed most to the degradation of river connectivity. With this approach, we identified fish species and regions most threatened by the construction of river barriers in the Mekong Basin, to support future management plans including connectivity restoration and habitat conservation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFish distribution along the Mekong Basin\u003c/h2\u003e \u003cp\u003eFish species richness decreased with increasing elevation, from two species in the source region to 410 in sub-basins near the Tonle Sap region (Figure S2). A bivariate choropleth map illustrates that sub-basins near the confluence of the Mun River and the Mekong main channel exhibited the highest fish species richness (157\u0026ndash;413 species per sub-basin) as well as the highest river barrier density (8\u0026ndash;215 per 100km; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sub-basins along the main channel between the middle and lower Mekong, as well as the Tonle Sap region, also exhibited high species richness and high betweenness centrality, with moderate barrier densities (1\u0026ndash;8 per 100km). The upper Mekong exhibited low species richness (2\u0026ndash;90 species per sub-basin) and high LCBD, and a few areas in this region had relatively high barrier densities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePotamodromous species (n\u0026thinsp;=\u0026thinsp;135, see Supplementary Table), undertaking migrations solely in freshwater, were found to mostly inhabit the main channel between the middle and lower Mekong, the Tonle Sap Lake, the Mun River, and sub-basins next to them. Within their distribution ranges, high barrier density mostly occurred in the Mun River, and moderate barrier density was found in sub-basins along the main Mekong channel. Diadromous species (n\u0026thinsp;=\u0026thinsp;125), which migrate between freshwater and the sea, were mainly found in the Lower Mekong between the Sesan-Mekong confluence and the estuary. Barrier densities were moderate, covering much of the Mekong Delta and reduced further upstream. The listed threatened species (vulnerable [n\u0026thinsp;=\u0026thinsp;42], endangered [n\u0026thinsp;=\u0026thinsp;25], and critically endangered [n\u0026thinsp;=\u0026thinsp;13]) mostly inhabit the main channel between the middle and lower Mekong, the Tonle Sap Lake, and sub-basins next to them (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, high and moderate barrier density along the main channel pose threats to those species.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHabitat fragmentation of Mekong fishes\u003c/h3\u003e\n\u003cp\u003eAcross all 952 fish species, 881 (92.5%) of them suffered from decreased population connectivity. The Population Connectivity Index (PCI) pooled for all the fish species significantly decreased from 4.35 [3.81\u0026ndash;5.02] (median and 95% CI, natural status) to 3.70 [3.06\u0026ndash;4.21] (moderate passage status). The pooled FI (moderate status) ranged from 0.01 to 76.20, with a median value of 19.37, and was positively related to available distance of fish habitat length (Spearman\u0026rsquo;s correlation, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.566, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S3). Among all fish families (for circumstances where there were more than five species present), the Pangasiidae (n\u0026thinsp;=\u0026thinsp;19) experienced most serious habitat fragmentation (Fragmentation Index, FI\u0026thinsp;=\u0026thinsp;51.91), followed by Notopteridae (n\u0026thinsp;=\u0026thinsp;5; FI\u0026thinsp;=\u0026thinsp;51.27) and Channidae (n\u0026thinsp;=\u0026thinsp;10; FI\u0026thinsp;=\u0026thinsp;48.84). Among all species, \u003cem\u003eBagarius suchus\u003c/em\u003e (NT species) suffered from the most profound habitat fragmentation level (FI\u0026thinsp;=\u0026thinsp;76.20), followed by \u003cem\u003eWallago attu\u003c/em\u003e (VU species; FI\u0026thinsp;=\u0026thinsp;74.43) and \u003cem\u003ePhalacronotus micronemus\u003c/em\u003e (FI\u0026thinsp;=\u0026thinsp;74.37).\u003c/p\u003e \u003cp\u003eSignificant differences were found in fish habitat fragmentation index between different migration types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test, χ\u003csup\u003e2\u003c/sup\u003e (5)\u0026thinsp;=\u0026thinsp;143.9, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Potamodromous fish had the worst FI (42.45 [38.61\u0026ndash;46.62]), significantly higher than anadromous fish (14.55 [2.18\u0026ndash;27.29]), amphidromous fish 7.30 [5.98\u0026ndash;10.45]), and species with unknown migration type (18.13 [16.64\u0026ndash;19.92]) (pairwise post hoc, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in all cases). Significant differences in FI^ were also observed (Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test, χ\u003csup\u003e2\u003c/sup\u003e (5)\u0026thinsp;=\u0026thinsp;19.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with potamodromous fish (0.09 [0.06\u0026ndash;0.10]) and unknown migration type (0.12 [0.11\u0026ndash;0.14]) significantly lower than catadromous fish (0.67 [0.52\u0026ndash;1.16]) (pairwise post hoc, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in both cases).\u003c/p\u003e \u003cp\u003eSignificant differences in FI were also found across all IUCN categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test, χ\u003csup\u003e2\u003c/sup\u003e (5)\u0026thinsp;=\u0026thinsp;30.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Critically Endangered species experienced the greatest FI (34.48 [19.46\u0026ndash;53.52]). The FI status of Least Concern species was higher than Not Evaluated and Data Deficient (combined) species (pairwise post hoc, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences in FI^ were observed among conservation status (Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test, χ\u003csup\u003e2\u003c/sup\u003e (5)\u0026thinsp;=\u0026thinsp;28.1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). CR species had higher FI^ (0.33 [0.18\u0026ndash;3.13]) than LC species (0.10 [0.08\u0026ndash;0.11]).\u003c/p\u003e\n\u003ch3\u003eContribution of different barrier types to fragmentation\u003c/h3\u003e\n\u003cp\u003eAcross four major types of barriers, small dams and sluice gates contributed most significantly to habitat fragmentation for all fish species - more so than weirs and large dams (ANOVA, F (3, 3804)\u0026thinsp;=\u0026thinsp;42.6, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; pairwise post hoc, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in all cases; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) under all passability circumstances (passability ranged between 0.1 and 0.9). This pattern was evident for potamodromous species, where small dams and sluice gates had a profound impact on habitat fragmentation compared to weirs and large dams (ANOVA, F (3, 536)\u0026thinsp;=\u0026thinsp;43.1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For diadromous species, the impact of sluice gates on habitat fragmentation was significantly greater than that of other barrier types (ANOVA, F (3, 496)\u0026thinsp;=\u0026thinsp;57.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For non-migratory species, no significant differences were found across the four types of barriers (ANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For species with unknown migratory status, small dams contributed more to habitat fragmentation than the other barrier types under all passability circumstances (ANOVA, F (3, 2720)\u0026thinsp;=\u0026thinsp;10.6, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn comparison, higher FI^ values contributed by large dams, compared to other barrier types, were observed when considering all fish species (ANOVA, F (3, 3804)\u0026thinsp;=\u0026thinsp;4.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and unknown migratory types (ANOVA, F (3, 2720)\u0026thinsp;=\u0026thinsp;4.7, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting a single large dam can have a large impact on such categories. For potamodromous (ANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), diadromous (ANOVA, F (3, 496)\u0026thinsp;=\u0026thinsp;1.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and non-migratory species (ANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), sluice gates and small dams contributed more to FI^, indicating that these barrier types have a greater localized impact on fragmentation for these groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy utilizing the most complete fish occurrence database alongside the most comprehensive basin-scale barrier database, the impacts of various barrier types on habitat fragmentation for all fish species in the Mekong Basin have been assessed for the first time. Our results demonstrate that high species richness predominantly occurs in the middle and lower Mekong Basin along the main channel, which is also the region where a high number of barriers have been constructed \u003csup\u003e25\u003c/sup\u003e. On the other hand, high species uniqueness appeared in the upper Mekong where the Chinese cascade dams are located. Across the entire basin, the most fragmented region appears to be the Chi-Mun sub-basin, characterized by the highest numbers of small dams and sluice gates. In contrast, the Mekong headwaters and most of the upper regions were relatively less fragmented and still provide accessible habitats for certain highland fish species. Nevertheless, among all fish species, 93% were affected by barrier construction, suggesting that the Mekong fish species are seriously threatened by fragmented habitats.\u003c/p\u003e\n\u003ch3\u003eRelationships between life history traits and habitat fragmentation\u003c/h3\u003e\n\u003cp\u003eSince the start of the Anthropocene, many migratory fish species have experienced reductions in habitat range\u003csup\u003e42\u003c/sup\u003e. According to the Living Planet Index method, among 184 monitored freshwater migratory fish species globally, 81% exhibited a decreasing abundance trend between 1970 and 2020 \u003csup\u003e43\u003c/sup\u003e. When migratory fish traverse multiple sub-basins containing various environments, they encounter a broader array of anthropogenic threats including a higher number of constructed barriers than resident species \u003csup\u003e44\u003c/sup\u003e. Habitat loss due to barriers constructed at key locations (e.g., stopover sub-basins, spawning and foraging habitat) may contribute to population decline by limiting migration success \u003csup\u003e45,46\u003c/sup\u003e. On the other hand, when larger areas of habitat are fragmented into smaller, less accessible patches of river channels, fish populations become more isolated, therefore making it more difficult to maintain meta-populations\u003csup\u003e39\u003c/sup\u003e. This results in decreased rates of immigration and emigration, hindering gene flow \u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the Mekong Basin, we found that migratory species with larger habitat range and longer dispersal distances suffered more profound habitat fragmentation compared to species with narrower habitat ranges and limited dispersal ability. The greater dispersal distance, the higher the likelihood that species would encounter more barriers during movement \u003csup\u003e44\u003c/sup\u003e, and result in reduced population exchange. The habitat connectivity status of potamodromous species was significantly worse compared to other migration types, particularly for the middle and lower parts of the main Mekong channel and major sub-basins linked to it (e.g., the 3S, Chi-Mun, and Xe Don sub-basins). This suggests that fragmentation impacts might be stronger in the middle part of the basin where potamodromous species were most abundant \u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong the taxa exhibiting greatest habitat fragmentation risks, Osteoglossiformes contains many highly threatened species, including the Endangered Asian bony-tongue \u003cem\u003eScleropages formosus\u003c/em\u003e, the Near Threatened royal featherback \u003cem\u003eChitala blanci\u003c/em\u003e, and the now Extinct giant featherback \u003cem\u003eChitala lopis\u003c/em\u003e. In the Mekong, the freshwater habitats of the aforementioned species are evaluated as under pressure due to dam construction \u003csup\u003e48\u003c/sup\u003e, and thus, their conservation status could be further worsened when considering all proposed barriers as well as those omitted from existing databases \u003csup\u003e49\u003c/sup\u003e. As for fish families, the most impacted Pangasiidae includes many flagship and megafauna freshwater species, such as the Critically Endangered Mekong giant catfish \u003cem\u003ePangasianodon gigas\u003c/em\u003e, Giant pangasius \u003cem\u003ePangasius sanitwongsei\u003c/em\u003e and Endangered striped catfish \u003cem\u003ePangasianodon hypophthalmus\u003c/em\u003e, all are ecologically and economically important. Also, this family mostly consists of potamodromous species that undertake long-distance migrations along the main channel of the middle and lower Mekong, which suffer from main channel dams and sluice gates \u003csup\u003e50\u003c/sup\u003e. Further research is urgently needed to re-assess the conservation status of these threatened species, especially those already at stake, using the complete barrier database \u003csup\u003e49\u003c/sup\u003e. Additionally, the passability of majority Mekong fish species at semi-passable barriers remains unclear due to the lack of monitoring and performance standards. Future studies are required to address these gaps and to develop highly efficient fish passes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLarge vs. small barrier, which causes greater habitat fragmentation?\u003c/h2\u003e \u003cp\u003eOur analysis revealed that, across all species, the cumulative effect of small dams and sluice gates nearly doubled the habitat fragmentation compared to large dams at all levels of passability. This finding aligns with previous research suggesting that the cumulative impacts of small-sized barriers is comparable to those of large dams \u003csup\u003e14,18,51\u003c/sup\u003e. Such results underscore the importance of maintaining a comprehensive barrier database to support ecological conservation and management efforts \u003csup\u003e38\u003c/sup\u003e, and highlight the need for standardized comprehensive database across large basin-scale or even global scale.\u003c/p\u003e \u003cp\u003eAll barriers should be considered both individually, and in terms of cumulative effects, when planning and conducting ecological restoration activities \u003csup\u003e52\u003c/sup\u003e. For example, our analysis suggests that small dams and sluice gates are the main reason of habitat fragmentation for potamodromous fish species, which suffered from the most significant habitat fragmentation among all migratory types. By contrast, large dams, which have generally received more attention in previous studies, contribute least to habitat fragmentation for potamodromous species compared with other barrier types. So, impacts of small-sized barriers should be carefully evaluated during the restoration process \u003csup\u003e52\u003c/sup\u003e. In comparison, the habitat of diadromous species is largely fragmented due to the construction of sluice gates \u003csup\u003e53,54\u003c/sup\u003e. As a large number of tidal gates and flaps have been built in the estuary region in the last 50 years, these have blocked routes between the freshwater environments and the South China Sea \u003csup\u003e55\u003c/sup\u003e, necessitating increased restoration attention in the near future. For non-migratory species (resident fish), all types of barriers contributed equally to the fragmentation. Although the impacts of barriers on fish dispersal may be less severe for these species, their short distances movements between meta-populations, as well as egg and larvae drifting phases, still require protection \u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the impacts of large dams should never be underestimated, and particularly for dams that located in regions with high species uniqueness. For example, a new large hydropower station is set to be constructed at Ganlanba (in the lower reaches of the Lancang River), where the highest LCBD within the basin has been observed. Long-term monitoring will be essential to assess its ongoing impacts.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStrategic restoration plan needed to mitigate barrier impacts\u003c/h3\u003e\n\u003cp\u003eThe decision to prioritize improving the passability of a large dam or removing multiple small barriers in river management must carefully balance ecological benefits, economic costs, and the presence of key species \u003csup\u003e57,58\u003c/sup\u003e. Large dams, while causing significant localized ecological disruptions, are often critical for power generation and regional development. Installing fish passes or bypass channels at such dams can reconnect migratory routes and mitigate some ecological impacts. However, these solutions can be expensive, with installation and maintenance costs running into millions of dollars, and their effectiveness is species-dependent. For example, species with limited swimming ability may still struggle to navigate even well-designed fish passes \u003csup\u003e59\u003c/sup\u003e. Moreover, these measures may not be able to address broader impacts such as modified sediment transport, altered flow regimes, and degraded water quality \u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, small barriers often have lower socioeconomical benefits but are numerous and cumulatively cause extensive habitat fragmentation, especially in areas located in remote tributary sections far from the main channel where high fish community differentiation might occur. Removing multiple small barriers could significantly restore connectivity across a broad spatial scale, benefiting a wide range of aquatic species and enhancing ecosystem functions \u003csup\u003e60\u003c/sup\u003e. However, this approach involves substantial logistical and financial challenges, as the costs of identifying, assessing, and dismantling barriers across an entire basin can be high. Moreover, some small barriers serve local needs, such as irrigation or flood control, which complicates their removal due to stakeholder opposition \u003csup\u003e61\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe presence of species with specific ecological requirements further complicates the decision regarding the optimal management strategy for mitigating impacts of habitat fragmentation from anthropogenic river barriers \u003csup\u003e58\u003c/sup\u003e. For instance, if the region supports a flagship or economically significant migratory species that requires access to upstream habitats, improving the passability of a large dam might be the most effective action when the ^FI value is high. On the other hand, for sub-basins with diverse, localized species (i.e., regions with high LCBD replacement values) that depend on interconnected habitats, removing small barriers may yield greater cumulative benefits (for species with high FI and relative lower ^FI values).\u003c/p\u003e \u003cp\u003eA cost-effective and ecologically sound river management strategy often requires an integrated approach \u003csup\u003e34\u003c/sup\u003e. This may involve improving fish passage at key large dams to benefit migratory species while systematically prioritizing and removing small barriers to restore the dendritic structure of the river network \u003csup\u003e19\u003c/sup\u003e. Decisions should be guided by ecological modeling, cost-benefit analyses, and species-specific data, ensuring that investments align with ecological priorities and stakeholder needs, while minimizing unnecessary expenses \u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eFuture direction\u003c/h3\u003e\n\u003cp\u003eBeyond connectivity, the natural flow regimes modified by river barriers can potentially disrupt fish spawning, and subsequent hatching and rearing processes. For example, many fish species in the Mekong floodplains spawn at the beginning of the flood season \u003csup\u003e47\u003c/sup\u003e. The flow-regime-adapted spawning behavior can be easily disturbed when dams or sluice gates are operational, potentially delaying migratory or spawning timings \u003csup\u003e4,62\u003c/sup\u003e. Additionally, other species depend on the drifting of eggs (e.g. \u003cem\u003eBagarius sp.\u003c/em\u003e), larvae, and juvenile stages to downstream nursery and feeding areas to complete their life cycles, but flow velocity is seldom maintained in impounded regions, and the minimum flow speed required to keep larval fish in the water column is rarely met. Furthermore, barrier construction may facilitate the invasion of non-native species\u003csup\u003e63\u003c/sup\u003e, which will also require future investment and management strategies in the Mekong.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe demonstrate that while the effects of individual large dams on habitat fragmentation and fish distribution in the Mekong Basin are greater than for small barriers, the large number of small barriers causes greater cumulative impacts on fragmentation and resultant risks for effective conservation. We demonstrate the importance of using complete databases with small, as well as large, river barriers for evaluating conservation risks for barrier development and connectivity restoration in large river basins. We encourage the development of thorough barrier and faunal databases in other large, biodiverse river basins and the application of similar analyses of the ecological impacts of existing and projected barrier distributions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRiver barrier data processing\u003c/h2\u003e \u003cp\u003eRiver barrier data of the Mekong Basin were gathered from the Mekong River Barrier Database \u003csup\u003e38\u003c/sup\u003e. This database comprises 13,054 barriers classified into five types (dam, sluice gate, weir, bridge apron, and others). Ninety seven percent of the barriers in this database were previously unreported in the Mekong Basin compared with other openly accessible databases. To analyse the impact of barrier distribution on fish dispersal across sub-basins (see Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e), we first extracted all main channel networks (i.e. the section of a river network that links sub-basins to each other), then manually reviewed all barriers in associated Google Earth satellite (Landsat, NASA-USGS) images, and retained barriers that were located in the main river channel, resulting in 3,066 barriers including 1,648 dams, 488 sluice gates, 840 weirs, 42 bridge aprons, and 48 \u0026lsquo;other\u0026rsquo; type barriers (e.g. ramps, bamboo/wooden fences, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The main channel barrier density for each sub-basin was calculated by dividing barrier numbers by main channel length.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFish species data processing\u003c/h2\u003e \u003cp\u003eFish occurrence data for the Mekong Basin were gathered from a published systematic database \u003csup\u003e25\u003c/sup\u003e, that compiles all fish occurrence data by combining long-term field sampling data and data gathered from all existing books, peer-reviewed articles, grey literature, and online databases via literature review (covering the time period from 1970s to 2020s). Non-native fish species, farmed species, and species without occurrence descriptions were excluded from the original database, which resulted in the final database comprising a total of 164,878 occurrence points from 1,032 species (Figure S2). The migration type of each species was gathered from FishBase \u003csup\u003e64\u003c/sup\u003e and published literature. Each species was categorized into one of six migration types: potamodromous, catadromous, anadromous, amphidromous, non-migratory, and unknown (defined according to \u003csup\u003e65\u003c/sup\u003e; see SupplementaryTable). Each species was categorized into one of eight conservation status categories according to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): Not Evaluated (NE), Data Deficient (DD), Least Concern (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN), Critically Endangered (CR), and Extinct (EX). To analyze the spatial patterns of fish distribution, the cumulative fish species richness of each category was calculated at sub-basin level (HydroBASIN level 8 \u003csup\u003e66\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eTo reconstruct the potential distribution range of each species, the dispersal distance of each species was calculated \u003csup\u003e67\u003c/sup\u003e. Total fish length, aspect ratio of the caudal fin, stream order inhabited, and timescale allowed for dispersal were employed in this model \u003csup\u003e67,68\u003c/sup\u003e. Species-specific fish length and aspect ratio of the caudal fin for each species were measured based on figures from published articles or gathered from FishBase. The highest stream order that each species inhabits was gathered using the HydroRIVERS dataset \u003csup\u003e66\u003c/sup\u003e. A time window of 10 years was used in the model, as appropriate to long-term population level dispersal \u003csup\u003e68,69\u003c/sup\u003e. The estimated dispersal distance was used as a radius to create buffer zones for each occurrence point \u003csup\u003e41,70\u003c/sup\u003e. Then, sub-basins (HydroBASIN level 8) that overlapped with buffer zones of all occurrence points were considered as the predicted distribution for that species \u003csup\u003e28,71\u003c/sup\u003e. The predicted distribution of each species was further refined by cropping to its native sub-basins to exclude predictions that were out of range, based on the occurrence points and expert opinion \u003csup\u003e41\u003c/sup\u003e. For example, if one species only moves along the main channel of the Mekong and does not inhabit tributaries according to published literature, when the buffered regions fell within some tributaries, then those overlapped sub-basins were removed during this manual refinement step. Furthermore, if a potential sub-basin was linked with valid habitat only through land rather than river channel, then it was removed during the manual process.\u003c/p\u003e \u003cp\u003eTo express the beta diversity of each subbasin, we calculated the local contributions to beta diversity (LCBD) as a metric that expresses its compositional uniqueness. Using the \u0026lsquo;BAT\u0026rsquo; package, we used S\u0026oslash;rensen-based indices of the Podani family to calculate the distance matrix representing beta diversity \u003csup\u003e72\u003c/sup\u003e and its decomposition into the replacement and richness difference components \u003csup\u003e73\u003c/sup\u003e. The replacement component quantifies sites uniqueness based on the local community structure, regardless of the differences in species richness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Fish habitat fragmentation level assessment\u003c/h2\u003e \u003cp\u003eTo assess the species\u0026rsquo; habitat connectivity, we adopted the Population Connectivity Index (PCI \u003csup\u003e74\u003c/sup\u003e), which considers structural connectivity (spatial arrangement of the sub-basins and the barriers) and functional connectivity (fish dispersal ability), distribution range (habitat length), and the passability between sub-basins. The PCI considers that a fish population inhabits a single sub-basin:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:PCI=\\sum\\:_{i=1}^{n}\\sum\\:_{j=1}^{n}{B}_{ij}{c}_{ij}\\frac{{l}_{i}}{L}\\frac{{l}_{j}}{L}{\\delta\\:}_{i}{\\delta\\:}_{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003en\u003c/em\u003e is the number of sub-basins, \u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003ej\u003c/sub\u003e measures the dispersal capability of the given fish species between sub-basin \u003cem\u003ei\u003c/em\u003e and sub-basin \u003cem\u003ej\u003c/em\u003e, \u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003ej\u003c/sub\u003e is the total cumulative passability of river barriers between sub-basins \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, and \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represent the total length of the river network in sub-basin i and j, which are inhabited by the specific species; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e represent the presence of a species in subcatchments I and j (0 if species is absent, 1 if present); L represents the total river length of all populations in the basin. Since the PCI is a network-based index, we generated a network based on the spatial distribution of subbasins. We used the 1,130 HydroBASINS level 8 sub-basins as nodes of the network. Based on the spatial distribution of the subbasins, we generated a graph where outlets/entrances between sub-basins are links.\u003c/p\u003e \u003cp\u003eWe defined the cumulative passability c\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{c}_{ij}=\\prod\\:_{m=1}^{M}{p}_{m}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{m}\\)\u003c/span\u003e\u003c/span\u003e is the passability of the sub-basin \u003cem\u003em\u003c/em\u003e, defined as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{p}_{m}=\\:{\\prod\\:}_{k}{p}_{k}^{{n}_{k}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the passability of each barrier type k, where k\u0026thinsp;=\u0026thinsp;1 for large dams, k\u0026thinsp;=\u0026thinsp;2 for small dams, k\u0026thinsp;=\u0026thinsp;3 for sluice gates, k\u0026thinsp;=\u0026thinsp;4 for weirs, etc.; nk is the number of k-th fragmentation item in the main channel of sub-basin m. In Eq.\u0026nbsp;2.1, We set \u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e equal to the river length if the subbasin is within the species distributional range; and equal to 0 if it is outside its range.\u003c/p\u003e \u003cp\u003eThe dispersal capacity B\u003cem\u003eij\u003c/em\u003e is defined as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{B}_{ij}={PD}^{{d}_{ij}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe probability of dispersal (PD) is calculated based on fish size and swimming capability \u003csup\u003e67\u003c/sup\u003e. Minimum distance (d) is the main channel length between two populations inhabiting two different sub-basins. Dispersal probability of each species was assigned by creating seven classes of dispersal distance of all species of the entire fish community, by setting the quantiles at 0%, 14.3%, 28.6%, 42.9%, 57.1%, 71.4%, 85.7%, and 100% \u003csup\u003e74\u003c/sup\u003e. Dispersal distance that fell within the quantile ranges was then categorized into one of the seven class boundaries, ranging from 0.3 to 0.9, which represent fish species\u0026rsquo; dispersal probability from low to high \u003csup\u003e74\u003c/sup\u003e As this index is applicable to species that occupy two or more sub-basins, species that inhabit only one sub-basin (n\u0026thinsp;=\u0026thinsp;80) were excluded from further analysis, which retained a total of 952 species.\u003c/p\u003e \u003cp\u003eWe calculated PCI for each species and scenario (i.e., different barrier passability status). We defined the fragmentation index (FI) as the percentage decrease of PCI when barriers are considered.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:FI=\\frac{({PCI}_{o}-{PCI}_{F})}{{PCI}_{o}}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, PCI\u003csub\u003eO\u003c/sub\u003e is the population connectivity index calculated when no barrier is present within the species\u0026rsquo; distribution range (i.e. calculating PCI by setting c\u003csub\u003eij\u003c/sub\u003e = 1). PCI\u003csub\u003eF\u003c/sub\u003e represents the fragmented status (i.e. all the barriers are considered) When calculating the PCI\u003csub\u003eF\u003c/sub\u003e, p\u003csub\u003em\u003c/sub\u003e for dams (both large and small) was set to 0 in all circumstances. For semi-passable barriers such as weirs, p\u003csub\u003em\u003c/sub\u003e was set to 0.5, representing moderate fish passability. The FI values range from 0 to 100. When fish populations are located in connected (neighboring) sub-basins with no barriers, or barriers are only located at the edge of the fish\u0026rsquo;s distribution range such that they do not affect the internal dispersal, FI\u0026thinsp;=\u0026thinsp;0. Conversely, if the habitat is greatly fragmented by barriers, preventing fish from freely moving from one sub-basin to another, the FI value would approach 100. The probability dispersal distance of each species was estimated using the \u0026lsquo;fishmove\u0026rsquo; R package \u003csup\u003e67\u003c/sup\u003e. The PCI index was calculated using the \u0026lsquo;riverconn\u0026rsquo; package \u003csup\u003e75\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe also calculated the average fragmentation index (FI^) as the ratio between FI and the number of barriers considered in PCI\u003csub\u003eF\u003c/sub\u003e. FI^ represents the average fragmentation caused by one barrier and is useful when comparing scenarios where different numbers of barriers are considered. Additionally, as a metric of network centrality, based on the graph used to calculate PCI, we calculated also the betweenness centrality (BC) for each sub-basin (HydroBASIN level 8). BC differentiates between central and isolated sites and was shown to be a strong predictor of fish alpha and beta diversity in undisturbed river systems \u003csup\u003e19,37,76\u003c/sup\u003e. Sites with high BC are located in the main channel, while sites with low BC are located in the tributaries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Contribution of different types of barriers to habitat fragmentation\u003c/h2\u003e \u003cp\u003eTo assess how different types of barriers affect species with different migration types, and which type of barrier contributed most to habitat fragmentation, four major type barriers: large dam (\u0026gt;\u0026thinsp;10m), small dam (\u0026lt;\u0026thinsp;10m), weir and sluice gate were selected. Then, the FI for each species was calculated at nine different passability status values (from 0.1, 0.2, 0.3 to 0.9), assuming only one type of barrier is present in each occasion. After that, the difference between FI\u003csub\u003elarge\u0026minus;dam\u003c/sub\u003e, FI\u003csub\u003esmall\u0026minus;dam\u003c/sub\u003e, FI\u003csub\u003eweir\u003c/sub\u003e and FI\u003csub\u003esluice\u003c/sub\u003e among all migratory type of fish was evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation was used to identify the relationship between fish habitat range and its associated fragmentation index. Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e tests were used to identify if there were significant differences in fragmentation index between different migration types and IUCN conservation status \u003csup\u003e77\u003c/sup\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), followed by a Bonferroni post-hoc test to identify the significantly different groups. ANOVA followed by a Turkey post-hoc test were used to assess the difference in fragmentation status (FI and ^FI) contributed by different types of barriers across migratory types.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe river barrier data used in this study are available at Zenodo [10.5281/zenodo.10141668]. The fish occurrence data, shapefiles and associated R code used in this study have been deposited in Zenodo [10.5281/zenodo.14730557].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the National Natural Science Foundation of China (42301064), and the Yunnan Scientist Workstation on International River Research of Daming He (K264202011220).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJS, DB, and MC contributed to the conceptualization and discussion of the content. JS led the writing and all authors contributed substantially to the drafts of the manuscript. All authors reviewed and edited the manuscript before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial and Non-Financial Competing Interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDudgeon, D. \u003cem\u003eet al.\u003c/em\u003e Freshwater biodiversity: importance, threats, status and conservation challenges. \u003cem\u003eBiological Reviews\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e, 163\u0026ndash;182 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, F. \u003cem\u003eet al.\u003c/em\u003e The global decline of freshwater megafauna. \u003cem\u003eGlob Chang Biol\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 3883\u0026ndash;3892 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReid, A. 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(2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5894851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5894851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Mekong River, a global freshwater biodiversity hotspot, has suffered from intensive barrier construction, resulting in major challenges in safeguarding its fauna. Here, we provide a comprehensive evaluation of the impacts of river barriers on the distribution of 1,032 fish species in the Mekong Basin. Our analysis revealed that 93% of Mekong fish species suffer from habitat fragmentation, and species with larger habitat range requirements experienced higher river fragmentation impacts. Sub-basins along the main channel in the Lower Mekong had high values of species richness but relatively high barrier impacts. Across all migration types, potamodromous fish had the worst habitat fragmentation status (Fragmentation Index, 42.56 [95% CI, 36.95\u0026ndash;46.05]), followed by catadromous fish. Among all IUCN conservation status categories, Critically Endangered species experienced the highest habitat fragmentation index (33.34 [12.53\u0026ndash;46.40]). Among all barrier types, small dams and sluice gates contribute more to habitat fragmentation than large dams.\u003c/p\u003e","manuscriptTitle":"Widespread and strong impacts of river fragmentation by human barriers on fishes in the Mekong River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-05 07:05:07","doi":"10.21203/rs.3.rs-5894851/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"79afb597-0352-4cbb-9e46-31e3454ffb8e","owner":[],"postedDate":"March 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43849638,"name":"Earth and environmental sciences/Ecology/Freshwater ecology"},{"id":43849639,"name":"Biological sciences/Ecology/Animal migration"},{"id":43849640,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":43849641,"name":"Earth and environmental sciences/Ecology/Conservation biology"}],"tags":[],"updatedAt":"2025-07-08T07:05:34+00:00","versionOfRecord":{"articleIdentity":"rs-5894851","link":"https://doi.org/10.1038/s43247-025-02467-y","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-07-07 04:00:00","publishedOnDateReadable":"July 7th, 2025"},"versionCreatedAt":"2025-03-05 07:05:07","video":"","vorDoi":"10.1038/s43247-025-02467-y","vorDoiUrl":"https://doi.org/10.1038/s43247-025-02467-y","workflowStages":[]},"version":"v1","identity":"rs-5894851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5894851","identity":"rs-5894851","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}