Fish Ecology and Sustainable Management Strategies in the Yom River Basin, Thailand

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Here, we analyze fish assemblages in the Yom River, one of the last free-flowing tributaries of the Chao Phraya system in Southeast Asia, using standardized surveys from 2000, 2011, and 2023. Across three decades, 223 species representing 43 families were recorded, with Cyprinidae, Nemacheilidae, and Danionidae dominating. Multivariate analyses revealed significant temporal and spatial restructuring of communities: upland zones remained specialized but species-poor, foothill reaches supported intermediate diversity, floodplains harbored tolerant generalists, and the main channel maintained the highest richness, including large migratory taxa. Canonical correspondence analysis identified substrate composition, dissolved oxygen, temperature, and agricultural land cover as key drivers of diversity. Species richness peaked in mid-elevation transitional habitats but declined sharply under elevated ammonia and intensified land use, highlighting nonlinear thresholds. Spawning phenology was tightly coupled to hydrological regimes, with upland species reproducing before monsoon flows and floodplain taxa synchronized with flood pulses. These findings demonstrate that biodiversity in tropical rivers is structured by synergistic environmental stressors and connectivity loss, with clear implications for basin-wide management. The Yom River case emphasizes the global importance of conserving free-flowing tributaries and integrating long-term biodiversity monitoring into adaptive freshwater conservation strategies. freshwater biodiversity fish assemblages long-term monitoring river connectivity environmental drivers tropical rivers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Freshwater habitats, while covering less than one percent of the Earth's surface, harbor a disproportionately high level of global biodiversity and provide vital ecosystem services. In Southeast Asia, large river systems such as the Mekong and its tributaries—including Thailand’s Yom River—are ecological keystones that sustain both biological diversity and human well-being. However, these riverine systems are increasingly threatened by anthropogenic stressors including dam construction, land-use changes, hydrological alterations, pollution, and climate change (Dudgeon, 2020 ). Within the Chao Phraya River system, the Yom River stands out as one of the few remaining large, free-flowing tributaries, offering important seasonal refuge and migration corridors for freshwater fish species (Sukontason et al., 2022 ). Historical studies have reported over 170 fish species within the basin (Champasri, 2000 ; Pila et al., 2012 ), yet ongoing development and agricultural intensification are altering the hydroclimate and fragmenting aquatic habitats (Ngor et al., 2021 ; Intarasu et al., 2023 ). Despite its ecological importance, little is known about how fish communities in the Yom River have changed over time or how assemblage structure responds to multiscale environmental gradients. This lack of integrated, long-term ecological assessment poses a challenge for sustainable management. Fish assemblages, known to be sensitive to habitat quality, hydrological regimes, and landscape connectivity, are valuable bioindicators for assessing riverine ecosystem health (Kang et al., 2020 ; Toussaint et al., 2021 ). This study addresses this knowledge gap by synthesizing three decades of ichthyofaunal data (from 2000, 2011, and 2023) across multiple spatial zones of the Yom River Basin. By examining spatial and temporal variation in species composition in relation to stream velocity, substrate, dissolved oxygen, ammonia, pH, and surrounding land use, the study aims to: (1) detect assemblage shifts along environmental gradients; (2) identify key drivers of biodiversity patterns; and (3) evaluate implications for conservation and sustainable fisheries. The findings contribute to a deeper understanding of longitudinal zonation and offer a foundation for ecosystem-based river basin management in support of Thailand’s biodiversity and climate resilience objectives. 2. Materials and Methods 2.1 Study Area The Yom River Basin in northern Thailand ranges from mountainous headwaters at about 644 m in Phayao Province to lowland floodplains at 26 m above sea level near the confluence with the Nan River in Nakhon Sawan. Four ecological zones are recognised along this continuum: (i) upper‐mountain streams with fast, cool, rocky reaches; (ii) mid‑elevation foothills dominated by gravel and cobble substrates; (iii) floodplain transition zones characterised by sandbars, backwaters and seasonally inundated pools; and (iv) the main river channel with deeper, slower flows. This habitat heterogeneity supports a broad spectrum of freshwater fish assemblages. 2.2 Sampling Design and Effort To capture seasonal and habitat variability, fish were surveyed in three distinct rounds representing the rainy (May–September), dry (October–January) and hot (February–April) seasons. Within each round, sampling sites were stratified by habitat type—lentic waters (reservoirs and oxbow pools), main river channels, tributaries and small streams—to ensure comparable coverage across ecological zones. Each habitat type was sampled at 3–6 sites per round (total 48–60 sites per year), and effort was standardised by sampling each site at similar times of day and for a fixed duration. 2.3 Fish Sampling A multi‑gear approach was employed to minimise gear selectivity. In shallow riffles and headwater streams, scoop nets (1–2 m mouth width, 1 mm mesh) fitted with face masks were used to conduct timed sweeps (two 5‑minute passes per site). In deeper sections and floodplain pools, gill nets (20–50 m length, 2–5 cm stretched mesh) of three different mesh sizes were set for 1–2 hours, and baited funnel traps and lift nets were deployed overnight. Cast nets with 1.5 cm mesh were thrown 20–30 times per site in areas of moderate depth. All captured fishes were enumerated and measured (total length), then most individuals were released; a representative subset was euthanised following ethical protocols and preserved in 10 % formalin (later transferred to 70 % ethanol) as voucher specimens. Identification to species level was based on regional keys (e.g., Rainboth 1996) and updated nomenclature from Froese & Pauly, (2025); uncertain identifications were verified by taxonomic specialists. 2.4 Environmental Data Collection At each sampling event, a suite of environmental variables was recorded to characterise habitat conditions: 1. Physicochemical variables – Water temperature (°C), dissolved oxygen (DO, mg L⁻¹), pH, specific conductivity (µS cm⁻¹) and hardness were measured in situ with a calibrated multiparameter probe (YSI 556). Water samples were collected for laboratory analysis of ammonia‑N, nitrite‑N and total phosphorus following Baird & Bridgewater, (2017). (2017) protocols. Instruments were calibrated daily, and blanks were run with each batch of samples. 2. Physical habitat descriptors – Channel width and depth were measured using a tape and a graduated pole at three transects per site. The current velocity was measured mid‑channel with a portable flow meter (Swoffer 3000). Substrate composition was visually estimated as proportions of boulder (> 256 mm), cobble (64–256 mm), gravel (2–64 mm), sand (0.062–2 mm), silt or mud (< 0.062 mm). Elevation was recorded by GPS (± 5 m). 3. Land‑use variables – The catchment upstream of each site (buffer radius = 1 km) was delineated in a GIS; proportions of forest, agriculture and urban land cover were extracted from Landsat‑derived maps (Land Development Department 2023) and ground‑truthed where possible. 2.5 Biodiversity Indices Species richness (S) and Shannon–Wiener diversity index (H′) were calculated for each site. Differences in diversity among habitats or seasons were tested using one‑way ANOVA; assumptions of normality and homoscedasticity were assessed, and log‑transformation was applied when necessary. Post hoc Tukey tests (α = 0.05) were conducted to identify significant pairwise differences. 2.6 Multivariate Analysis Fish assemblage–environment relationships were examined using several multivariate techniques: 1. Canonical Correspondence Analysis (CCA) – Species abundance data (square‑root transformed and rare species down‑weighted) were ordinated against centred and standardised environmental variables using the vegan package in R. Forward selection and Monte‑Carlo permutation tests (999 permutations) identified significant predictors. 2. Cluster Analysis – Ward’s method with Bray–Curtis dissimilarity was applied to log(x + 1) transformed species abundances to classify sites into ecological zones. The cophenetic correlation coefficient and silhouette width were used to assess cluster robustness. 3. Classification and Regression Trees (CART) – Diversity indices and species richness were modelled as functions of environmental predictors using the rpart package. Trees were pruned based on cross‑validated error rates to avoid overfitting, and variable importance was evaluated. 4. Generalised Additive Models (GAMs) – For key species and overall richness, non‑linear relationships with temperature (Temp), dissolve oxygen (DO), ammonia (NH 3 ) and land‑use intensity were modelled using penalised splines (mgcv package). Models were assessed using AIC and diagnostic residual plots. 5. All data analyses were performed using R version 4.4.0 (R Core Team, 2024). Hierarchical clustering and heatmaps were generated using the heatmap and base stats packages with Ward’s method and Euclidean distance. Boxplots and spider plots were created using ggplot2. The Analysis of Similarities (ANOSIM) was conducted using the vegan package (Oksanen et al., 2020) with 999 permutations to test for significant differences in species abundance patterns among migratory guilds and occurrence-based groups. Principal Coordinates Analysis (PCoA) was also carried out using the ape and ggplot2 packages to visualize inter-group dissimilarities. Species were categorized into migratory guilds and grouped based on their temporal occurrence (i.e., detected in one, two, or all three survey years: 2000, 2011, and 2023). 2.7 Ethical Compliance and Quality Control Fish sampling protocols were approved by the Institutional Animal Care and Use Committee of Maejo University and conducted under permit from the Thai Department of Fisheries (Permit No. XXXX/2023). All handling and euthanasia procedures followed national animal welfare guidelines. Instruments were calibrated before each field trip; water samples were stored on ice and processed within 24 hours. Duplicate measurements and blank samples were used to check for accuracy, and 10 % of specimens were cross‑identified by an independent taxonomist to ensure reliability. 3. Results 3.1 Fish Species Richness and Composition Surveys conducted in 2000, 2011 and 2023 recorded 223 species from 114 genera, 43 families and 15 orders in the Yom River Basin. Eighty‑five species were detected in all three surveys, indicating basin‑wide distributions and ecological tolerance. The 2023 survey documented 209 species—45 of which were new records—reflecting expanded sampling effort and taxonomic updates. Families with the greatest richness were Cyprinidae (carps and minnows), Nemacheilidae (stone loaches) and Danionidae (danios and rasboras). Common species across years and zones included Barbonymus altus , Puntioplites proctozystron , Clarias batrachus and Channa striata . Species turnover was modest: 11 species were unique to the 2000 survey and 43 unique to 2011, suggesting a combination of localised extirpations and improved detection over time. NMDS Ordination of Fish Assemblage Structure To visualize temporal changes in fish assemblage composition, a non-metric multidimensional scaling (NMDS) ordination was performed using Bray–Curtis dissimilarity based on species abundance data from the 2000, 2011, and 2023 surveys. The resulting two-dimensional NMDS plot revealed clear separation among sampling years, with 2000 positioned farther from 2011 and 2023, indicating a marked shift in assemblage structure over time. The proximity of the 2011 and 2023 points suggests relatively similar community composition during the more recent sampling periods, whereas the assemblage in 2000 was compositionally more distinct. This pattern is consistent with the increasing influence of environmental change and anthropogenic disturbance, particularly in the past decade. The stress value of the ordination was low, confirming that the reduced-dimension representation faithfully captured the dissimilarities in fish community structure across the three decades. 3.2 Fish Assemblage Structure by Habitat Zone Cluster analysis and habitat stratification delineated four ecological assemblages that correspond to longitudinal and environmental gradients: 1. Mountain streams – High‑elevation (>500 m) headwaters with steep gradients, cold water and boulder–cobble substrates supported specialised benthic taxa such as Schistura menanensis , Glyptothorax spp. and Devario laoensis . Species diversity was low (mean H′ ≈ 2.3) and correlated with high dissolved oxygen, low conductivity and clear, fast flows. 2. Foothill streams – Mid‑elevation reaches exhibited moderate flows and mixed gravel–sand substrates. Assemblages were dominated by species tolerant of intermediate conditions, including Opsarius pulchellus , Pethia stoliczkana and Homalopteroides smithi . Diversity was higher than in mountain streams (mean H′ ≈ 2.8), reflecting broader ecological niches. 3. Transitional floodplains – Low‑lying channels and oxbow lakes with variable connectivity to the main river harboured species adapted to fluctuating hydrology and low dissolved oxygen, such as Channa striata , Pristolepis fasciatus and Clarias batrachus . These sites showed high species richness during seasonal inundation (mean S ≈ 35; H′ ≈ 3.1). 4. Main river channel – Wide, deep sections with silty substrates supported the most diverse assemblages (mean H′ ≈ 3.5; S ≈ 48). Large migratory and commercially important species ( Pangasianodon hypophthalmus , Hemibagrus filamentosus , Phalacronotus bleekeri ) were characteristic, associating with greater depth, width and turbidity. 3.3 Environmental Drivers of Fish Diversity Canonical Correspondence Analysis (CCA) indicated that substrate type, dissolved oxygen, land use and temperature were the most influential variables shaping fish assemblages (Fig. 1). Axis 1 explained 27.3 % of the variance and separated upland sites (high DO, coarse substrates, forest cover) from lowland sites (warmer water, higher ammonia). Axis 2 (17.6 % variance) contrasted narrow, shallow headwater streams with wide, deep floodplain channels dominated by mud and silt. Sites with mixed gravel–sand substrates and moderate elevation supported the highest diversity, whereas fine sediments, elevated ammonia and low oxygen were associated with depauperate communities dominated by tolerant species. 3.4 Seasonal Reproductive Patterns Gonadal maturity analyses revealed two distinct spawning strategies. Upland taxa (e.g. Schistura sp., Opsarius puchellus , Channa limbata ) spawned in the late dry season (April–May) before the onset of monsoon flows, with partial spawning and multiple ovulation cycles. In contrast, floodplain and riverine species (e.g. Pangasius , Phalacronotus bleekeri , Henicorhynchus ) synchronised reproduction with rising water levels in June–July, coinciding with nutrient pulses and expanded habitats (Fig. 2). These patterns highlight strong coupling between hydrological cues and reproductive phenology. 3.5 Ecological Zonation and Environmental Gradients Ward’s cluster analysis grouped sites into two major zones: an Upper Yom Zone encompassing mountain and foothill tributaries with cold‑water assemblages, and a Lower Yom Zone comprising floodplain and main‑channel habitats with high species overlap and connectivity. Complementary clustering of environmental variables (Fig. 3) showed that temperature, percentage of agricultural land and ammonia formed one group indicative of lowland, nutrient‑enriched conditions; dissolved oxygen and elevation formed a second group representing upland streams; and pH varied independently, likely influenced by local geology. These clusters align with the ecological zonation derived from CCA. Linear regressions (Fig. 4) revealed that Shannon diversity increased with water temperature and agricultural land cover but declined with high dissolved oxygen and ammonia, although some relationships were only marginally significant (e.g. H′ vs. DO, R² ≈ 0.26, p ≈ 0.09). Species richness showed weaker trends, with positive associations with elevation and DO and a negative association with ammonia. Overall, diversity patterns appear to reflect trade‑offs between habitat complexity, productivity and water quality. Nonlinear surface models (Fig. 5) further underscored the complex interactions between temperature and other environmental drivers. Species richness consistently peaked at moderate temperatures (24–26 °C). Richness declined sharply at high ammonia concentrations or high agricultural coverage, indicating nutrient stress. Conversely, richness increased with higher dissolved oxygen but only within a moderate thermal range. Elevation and pH interactions suggested that mid‑elevation reaches and near‑neutral pH maximised diversity. These results highlight the threshold effects and synergies among multiple stressors in regulating fish biodiversity in tropical river systems. Linear regression plots showing the relationships between environmental variables and two diversity indices: Shannon Diversity Index (H′, top row) and species richness (S, bottom row). Environmental variables include dissolved oxygen (DO), temperature (Temp), pH, ammonia (NH₃), forest cover (% Forest), agricultural land use (% Agric), and elevation (m a.s.l.). Shaded areas represent 95% confidence intervals. Notably, Shannon diversity tended to increase with water temperature and percentage of agricultural land use, while it decreased with dissolved oxygen (DO) and ammonia (NH₃) concentrations. Species richness shows more variable patterns, with positive associations with DO and elevation, and negative trends with ammonia. 3.6 Temporal Assemblage Patterns and Migratory Behavior To examine spatiotemporal patterns in species abundance and their relationship to life-history traits, a clustered heatmap was generated using Ward’s method and Euclidean distance based on the top 30 most abundant fish species recorded across the three survey years (2000, 2011, and 2023) (Fig. 6). The heatmap revealed distinct groupings of species with shared abundance trajectories over time. Several non-migratory species showed relatively stable or increasing abundances, while certain local migrants and long-distance migrants displayed marked temporal fluctuations. Species such as Mystus bocourti and Paralaubuca barroni increased steadily from 2000 to 2023, whereas others like Phalacronotus bleekeri declined or remained stable. To test whether migratory guilds explained these abundance trends, an ANOSIM (Analysis of Similarities) was performed based on species' abundance and grouped by migratory behavior. The result yielded a low and nonsignificant R-value (R = -0.0023, p = 0.586), indicating that migratory classification alone did not significantly differentiate abundance patterns across the years. This suggests that other ecological or environmental factors such as habitat degradation, water quality, or trophic flexibility may have stronger effects on abundance trajectories than migratory strategy per se. 4. Discussion 4.1 Fish Diversity and Spatial Patterns The Yom River Basin exhibits considerable ichthyofaunal richness, with 223 species documented across 43 families and 15 orders over three decades of surveys. This richness is comparable to regional patterns observed in other Indo-Burma and Mekong sub-basins (Baran et al., 2005; Dudgeon et al., 2006; Darwall et al., 2018). Dominant families such as Cyprinidae, Nemacheilidae, and Danionidae are typical of tropical Asian streams (Rainboth, 1996; Kottelat, 2013), and their consistent presence across years suggests broad ecological tolerance. The 2023 detection of 45 newly recorded species reflects advancements in survey methodology and taxonomy, as also noted by Deiner et al. (2017) in studies utilizing eDNA and spatial stratification. Species turnover observed between 2000, 2011, and 2023 aligns with documented temporal shifts in fish communities under climate and land use change (Arthington et al., 2010; Matthews, 1998; Olden & Poff, 2003). Such partial continuity and replacement reflect both natural successional processes and anthropogenic disturbances (Nelson et al., 2016; Tejerina-Garro et al., 2005). 4.2 Habitat Zonation and Community Structure Cluster-based analysis delineated four ecological zones i.e., mountain streams, foothill streams, transitional floodplains, and riverine channels representing a longitudinal environmental gradient. This zonation mirrors the predictions of the River Continuum Concept (Vannote et al., 1980) and is consistent with findings in similar tropical river systems (Dudgeon, 2000; Cowx & Welcomme, 1998). 1) Mountain streams, with their steep gradients, coarse substrates, and cool oxygen-rich water, hosted low-diversity but highly specialized species like Schistura menanensis and Glyptothorax spp. (Matthews, 1998; Kottelat, 2013). These environments act as ecological filters favoring benthic specialists adapted to high flow (Pusey et al., 2000). 2) Foothill zones showed greater diversity, supporting ecologically plastic taxa such as Opsarius pulchellus and Pethia stoliczkana , reflecting mid-reach richness peaks described by Benejam et al. (2009) and Baird & Flaherty (2005). 3) Transitional floodplains, characterized by habitat heterogeneity and seasonal connectivity, supported tolerant generalists ( Channa striata , Clarias batrachus ) in dynamic lentic-lotic interfaces, reinforcing the Flood Pulse Concept (Junk et al., 1989). 4) Main river channels, with their large size and slow flow, supported the highest richness and evenness, dominated by large-bodied, migratory species such as Pangasianodon hypophthalmus , in agreement with Mekong studies (Baran et al., 2005; Baird & Flaherty, 2005). 4.3 Environmental Drivers of Fish Communities Canonical Correspondence Analysis (CCA) revealed substrate composition, temperature, land use, and dissolved oxygen as key drivers of fish community structure. These findings are supported by Tejerina-Garro et al. (2005), who emphasized substrate heterogeneity and oxygen availability as dominant determinants in tropical river assemblages. The negative correlation of richness with high temperature and ammonia levels aligns with studies from tropical Asia and Africa showing reduced diversity under thermal and nutrient stress (Daga et al., 2016; Hermoso & Kennard, 2012). Conversely, intermediate elevations (200–250 m) and gravel–sand substrates were associated with higher diversity, reinforcing the importance of transitional habitats in supporting biodiversity (Benejam et al., 2009; Linke et al., 2011). 4.4 Nonlinear and Interaction Effects Multivariate surface modeling demonstrated strong interaction effects between temperature and key environmental factors. For instance, temperature and agriculture (R² = 0.83) had a compounded negative effect on richness, supporting Odum’s (1985) theory of cumulative stress in ecosystems and findings by Leigh et al. (2016) on synergistic environmental impacts in Australian rivers. Richness consistently peaked at 24–26°C, but declined sharply when coupled with elevated ammonia (R² = 0.76) or reduced DO (R² = 0.79). These nonlinear thresholds resemble patterns found by Dudgeon (2000) and McGarvey & Ward (2008), and stress the need for ecological models that capture interaction effects rather than linear correlations alone. 4.5 Reproductive Ecology and Conservation Implications Fish reproductive timing in the Yom Basin mirrors rainfall and flow patterns, as observed in tropical systems globally (Humphries et al., 1999; Pusey et al., 2000). Mountain and foothill species spawn pre-rainy season under clear, stable flows, while floodplain and riverine species synchronize spawning with flood pulses, maximizing larval drift and nutrient availability (Junk et al., 1989; Boonsong, 2001). Protecting these seasonal and habitat-specific spawning strategies is essential, particularly in light of increasing hydrological alteration and dam development in Southeast Asia (Dudgeon et al., 2006; Darwall et al., 2018). 4.6 Longitudinal Zonation and Assemblage Patterns Fish assemblages in tropical rivers are often structured along the longitudinal gradient, reflecting environmental heterogeneity and hydrological connectivity (Vannote et al., 1980). In the Ping–Wang River Basin, Suvarnaraksha et al. (2012) identified four ecological zones i.e., mountainous, piedmont, transitory, and lowland, each dominated by distinct taxa. This spatial pattern is mirrored in the Yom River, where rheophilic species such as Nemacheilidae dominate headwaters, while lowland reaches host more generalist and lentic-adapted species (e.g., Channa spp.). Such zonation underscores the need to maintain longitudinal connectivity for sustaining biodiversity across habitat gradients. 4.7 Environmental Drivers of Diversity Suvarnaraksha et al. (2012) demonstrated that geomorphological variables, especially elevation and river discharge—were stronger predictors of fish richness than water chemistry. This finding aligns with other studies suggesting that landscape-scale drivers play a central role in structuring assemblages (Linke et al., 2011). In the Yom River, elevation, flow velocity, and substrate variation are likely key factors shaping community composition, emphasizing the importance of catchment-scale planning. 4.8 Conservation and Management Implications Hydraulic structures such as dams and weirs in the Ping–Wang system were associated with increased species turnover and reduced habitat continuity, particularly for migratory and headwater-endemic species (Suvarnaraksha et al., 2012). With proposed dams in the Yom River, similar impacts are anticipated. Conservation strategies should therefore integrate zonal protection, barrier mitigation, and participatory monitoring, aligning with integrated river basin management frameworks (Linke et al., 2011; Dudgeon, 2000). 4.9 Management Implications This study provides a robust ecological framework for spatially explicit management. The distinction between Upper Yom (upland, endemic-rich) and Lower Yom (lowland, diverse and productive) zones aligns with conservation planning approaches based on functional zoning and habitat integrity (Cowx & Welcomme, 1998; Hermoso & Kennard, 2012). Environmental clustering of variables (e.g., DO with elevation; temperature with agriculture and ammonia) supports integrative monitoring frameworks. Targeting these environmental gradients for restoration, particularly through riparian buffer zones and seasonal fishery closures can sustain biodiversity under escalating stress (Dudgeon et al., 2006; Arthington et al., 2010). 4.10 Study limitations and future research directions Despite the use of multi-season surveys and multiple gear types, certain limitations remain. Sampling was conducted only in selected seasons, which may miss species with brief spawning periods or whose migrations occur at other times of year (Fischer & Quist, 2014). Although using nets with various mesh sizes reduces gear bias, some species that avoid nets may still be underrepresented due to behavioral or habitat-specific avoidance strategies (Gibson-Reinemer et al., 2016; Zhou et al., 2014). Morphological identification alone may overlook cryptic or morphologically similar species, particularly in regions of high biodiversity. Incorporating techniques such as environmental DNA (eDNA) could improve detection accuracy, especially for elusive or low-density taxa, while genetic studies would clarify population structure and connectivity. Longer-term monitoring that spans all seasons is also needed to capture short- and long-term dynamics and to assess the impacts of climate change (Gibson-Reinemer et al., 2016). 4.11 Environmental, socio‑economic and catchment‑level management implications Beyond the measured variables, human driven degradation such as nutrient and pesticide run off from agriculture, deforestation, and shoreline development, affects fish diversity by increasing sediment, nutrient loads and metal pollution in waterways (Reid et al., 2019; Froese & Pauly, 2025). Regional examples include arsenic contamination from upstream mining in the Kok River, underscoring the importance of source control and multi-agency cooperation. Although the Yom Basin lies entirely within Thailand, mitigating pressures such as agricultural runoff and riparian forest loss requires coordination across provinces and between water authorities. Fish populations also support local livelihoods; declines in economically important species or the imposition of seasonal fishing closures can affect household income (FAO, 2022). Therefore, fisheries management should integrate ecological and social dimensions and provide compensation or alternative livelihoods where necessary (FAO, 2022). In the longer term, restoring riparian corridors, establishing buffer zones, and developing participatory water quality monitoring will help reduce the impacts of climate change and secure food security for riparian communities. 4.12 Implications for management and policy These findings have several implications for management and policy. The observed temporal and spatial shifts in fish assemblages, along with increasing habitat fragmentation, emphasize the urgent need for integrated river basin management approaches. Maintaining longitudinal connectivity, particularly for migratory species, is critical. Policy efforts should focus on restoring degraded habitats, regulating land use along riparian zones, and minimizing anthropogenic stressors such as pollution and dam construction. Furthermore, long-term monitoring programs and adaptive co-management frameworks involving local communities are essential for sustaining fish diversity and ensuring the resilience of freshwater ecosystems under climate and land-use pressures. The ecological zonation and species turnover documented in this study underscore the urgency of implementing integrated river basin management (IRBM) strategies that prioritize both biodiversity and ecosystem functionality. In particular, the observed habitat fragmentation and nutrient-driven stressors align with global freshwater challenges highlighted in Sustainable Development Goal (SDG) Target 6.6, which calls for the protection and restoration of water-related ecosystems, including rivers, wetlands and aquifers by 2030. To meet this target, management plans in the Yom River Basin should be incorporated: 1. Functional zoning based on ecological assemblages and environmental gradients identified through CCA and cluster analyses, enabling targeted habitat protection. 2. Riparian buffer restoration in high-risk lowland zones with elevated ammonia and agricultural runoff, which degrade ecosystem integrity and limit fish reproductive success. 3. Seasonal fishery closures aligned with flow-driven spawning windows, promoting population recovery and hydrological synchrony. 4. Participatory monitoring and co-management frameworks that engage local communities, fostering resilience and inclusive stewardship of aquatic resources. These strategies support not only national biodiversity goals under Thailand’s Master Plan for Water Resources Management but also contribute directly to the implementation of SDG 6.6 by maintaining ecological flows, protecting aquatic corridors, and reducing anthropogenic pressures through cross-sectoral cooperation. Future policies should integrate spatially explicit ecological data such as those provided by this study into decision-support systems and adaptive planning tools to ensure long-term ecosystem health and climate resilience across the basin. 5. Conclusion This study provides the most comprehensive ichthyofaunal assessment to date of the Yom River Basin, underscoring its status as a regional biodiversity hotspot with 223 species documented across 43 families and 15 orders over a three-decade span. Multivariate analyses revealed four ecologically distinct zones i.e., mountain headwaters, foothill streams, transitional floodplains, and the main river channel each characterized by unique assemblages shaped by elevation, substrate, hydrology, and land use. Canonical correspondence analysis identified substrate composition, dissolved oxygen, temperature, and agricultural land cover as key environmental drivers structuring fish communities. Biodiversity was highest in mid-elevation and floodplain habitats and declined under conditions of elevated ammonia and intensified land use, suggesting threshold effects and synergistic stressors. Seasonal reproductive patterns were tightly coupled to hydrological regimes: upland species-initiated spawning prior to monsoon flows, while lowland species synchronized reproduction with flood pulses. These findings highlight the ecological importance of maintaining flow variability and habitat connectivity. Given the increasing anthropogenic pressure, nutrient enrichment, habitat fragmentation, and land-use conversion, spatially explicit conservation strategies are urgently required. Recommended actions include riparian buffer restoration, seasonal fishery closures, and functional zoning aligned with ecological gradients. Incorporating advanced biomonitoring techniques (e.g., environmental DNA) and long-term, multi-seasonal datasets will be essential for refining species detection and assessing ecological change. These results underscore the need for integrated river basin management frameworks that balance biodiversity conservation with socio-economic imperatives. Community-engaged co-management, supported by robust ecological evidence, offers a promising pathway to sustaining tropical freshwater ecosystems under climate and land-use transformation. Declarations Acknowledgements We gratefully acknowledge faculty of Fisheries Technology and Aquatic Resources, Maejo University, for their logistical support, provision of field equipment and the students. We also extend our appreciation to the Department of Fisheries, Thailand for authorizing our research and providing assistance during fieldwork. And thank you to Thongchai Champasri, Kasartsat University for his field survey and data set from 2000. We also appreciate the valuable contributions of local fishers and community members along the Yom River who shared their knowledge and assisted in the sampling efforts. Lastly, we thank our colleagues and research assistants for their help during fieldwork and data processing. Funding This study was financially supported by the Agricultural Research Development Agency (Public Organization), Thailand, under the research project entitled “ A Study of Economic Value, Way of Life, Food Security and Community Participation in the Development of Ecosystems in the Yom River Basin ” (Project No. ARDA 66-007), the Department of Fisheries (Project No. 50-0519-50044-001), and the Biodiversity Research and Training Program (BRT 540057). Additional support was provided by Maejo University, through institutional research grants and field logistics. Author Contributions The authors declare that they have no conflict of interest. A.S. conceived the study, led the field investigations, conducted data analysis, and wrote the original manuscript draft. T.P. contributed to data collection, environmental measurements, statistical analysis, and figure preparation. C.C. provided historical data, assisted in taxonomic verification, and reviewed the manuscript. N.P. coordinated with the Department of Fisheries, supported field logistics, and contributed to policy-relevant discussions. S.S. validated species identification, compiled GIS data, and assisted with manuscript revisions. All authors read and approved the final version of the manuscript. Ethical Approval (if applicable) References Arthington, A.H., et al. (2010). Preserving the biodiversity and ecological services of rivers: new challenges and research opportunities. Freshwater Biology , 55(1), 1–16. Baird, I.G., & Flaherty, M.S. (2005). Mekong River fish conservation zones in southern Laos: Assessing effectiveness using local ecological knowledge. Environmental Management , 36(3), 439–454. Baird, R.B., & Bridgewater, L. (2017). Standard Methods for the Examination of Water and Wastewater (23rd ed.). Washington, D.C.: American Public Health Association Baran, E., Jantunen, T., & Chong, C.K. (2005). Values of inland fisheries in the Mekong River Basin . WorldFish Center. Benejam, L., et al. (2009). Assessing fish metrics and biotic indices in a Mediterranean stream: Effects of sampling protocol and fish community structure. Ecological Indicators , 9(3), 395–403. Boonsong, P. (2001). Seasonal distribution of fish larvae and juveniles in the lower Chao Phraya River, Thailand. Kasetsart Journal: Natural Science , 35, 141–147. Champasri, S. (2000). Fish fauna in the upper Yom Basin . Department of Fisheries, Thailand. Cowx, I.G., & Welcomme, R.L. (1998). Rehabilitation of Rivers for Fish . Fishing News Books. Daga, V.S., et al. (2016). Assessing fish community responses to environmental conditions in subtropical Brazilian streams. Environmental Biology of Fishes , 99, 913–925. Darwall, W., et al. (2018). The diversity, ecology and conservation of freshwater fishes in the Indo-Burma hotspot . Aquatic Conservation , 28(4), 927–941. Deiner, K., et al. (2017). Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Molecular Ecology , 26(21), 5872–5895. Dudgeon, D. (2000). The ecology of tropical Asian rivers and streams in relation to biodiversity conservation. Annual Review of Ecology and Systematics , 31, 239–263. Dudgeon, D., et al. (2006). Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews , 81(2), 163–182. Dudgeon, D. (2020). River conservation in the Anthropocene. Annual Review of Environment and Resources , 45, 29–55. https://doi.org/10.1146/annurev-environ-102017-025849 FAO. (2022). Integrating social dimensions into inland fisheries management: Guidelines and case studies . Food and Agriculture Organization of the United Nations. Fischer, J. R., & Quist, M. C. (2014). Gear and seasonal bias associated with abundance and size structure estimates for lentic freshwater fishes. Journal of Fish and Wildlife Management , 5(2), 394–412. https://doi.org/10.3996/082013-JFWM-054 Froese, R. & Pauly, D. (Eds.). (2025). FishBase . World Wide Web electronic publication. www.fishbase.org, version (04/2025) Gibson-Reinemer, D. K., Stewart, D. R., Fritts, M. W., DeBoer, J. A., & Casper, A. F. (2016). Estimating the effects of environmental variables and gear type on the detection and occupancy of large-river fishes in a standardized sampling program using multiseason Bayesian mixture models. North American Journal of Fisheries Management , 36(6), 1445–1456. https://doi.org/10.1080/02755947.2016.1206642 Hermoso, V., & Kennard, M.J. (2012). Uncertainty in coarse conservation assessments hinders the efficient achievement of conservation goals. Biological Conservation , 147(1), 52–59. Humphries, P., et al. (1999). Fish spawning in response to flood events in the Murray-Darling Basin. Australian Journal of Ecology , 24(5), 608–619. Intarasu, R., Peerapornpisal, Y., & Sricharoendham, B. (2023). Impacts of land use and hydrological alterations on aquatic biodiversity in northern Thailand. Ecological Indicators , 153, 110445. Junk, W.J., Bayley, P.B., & Sparks, R.E. (1989). The flood pulse concept in river-floodplain systems. Canadian Special Publication of Fisheries and Aquatic Sciences , 106, 110–127. Kang, B., Chen, Y., & Liu, H. (2020). Use of fish assemblages as indicators of river health in subtropical Asia. River Research and Applications , 36(4), 550–563. Kottelat, M. (2013). The fishes of the inland waters of Southeast Asia: a catalogue and core bibliography. Raffles Bulletin of Zoology , Supplement No. 27, 1–663. Land Development Department. (2023). Land-use planning tools and frameworks . Retrieved from https://www1.ldd.go.th/ldd_en/en-US/land-use-planning/ Leigh, C., et al. (2016). Ecological effects of extreme climatic events on riverine ecosystems: insights from Australia. Freshwater Biology , 61(3), 300–310. Linke, S., Turak, E., & Nel, J. (2011). Freshwater conservation planning: the case for systematic approaches. Freshwater Biology , 56(1), 6–20. https://doi.org/10.1111/j.1365-2427.2010.02456.x Matthews, W.J. (1998). Patterns in Freshwater Fish Ecology . Springer. McGarvey, D.J., & Ward, G.M. (2008). Scale-dependence of flow and fish assemblages in a large river. Canadian Journal of Fisheries and Aquatic Sciences , 65(4), 731–744. Ngor, P. B., et al. (2021). Climate-driven shifts in freshwater fish phenology in tropical Asia. Nature Climate Change , 11, 781–787. Odum, E.P. (1985). Trends Expected in Stressed Ecosystems . BioScience, 35(7), 419–422. Olden, J.D., & Poff, N.L. (2003). Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Research and Applications , 19(2), 101–121. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., & Wagner, H. (2020). vegan: Community Ecology Package (Version 2.5-7) [R package]. https://CRAN.R-project.org/package=vegan Pila, N., Hanpongkittikul, A., Suksri, S., & Buanak, T. (2012). Fish diversity in the Yom River Basin . Department of Fisheries, Ministry of Agriculture and Cooperatives. Pusey, B.J., et al. (2000). Environmental flow management and fish in Australia. Environmental Biology of Fishes , 59(1), 55–72. R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Rainboth, W.J. (1996). Fishes of the Cambodian Mekong . FAO Species Identification Field Guide. Reid, A. J., Carlson, A. K., Creed, I. F., Eliason, E. J., Gell, P. A., Johnson, P. T. J., et al. (2019). Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews , 94(3), 849–873. https://doi.org/10.1111/brv.12480 Sukontason, N., Wongrat, L., & Phongpaichit, S. (2022). Fish migration and ecological connectivity in Thai tributary rivers: Lessons from the Yom River. Aquatic Conservation , 32(1), 45–60. Suvarnaraksha, A., Lek, S., Lek-Ang, S., & Jutagate, T. (2012). Fish diversity and assemblage patterns along the longitudinal gradient of a tropical river in the Indo-Burma hotspot region (Ping–Wang River Basin, Thailand). Ecology of Freshwater Fish , 21(3), 384–399. https://doi.org/10.1111/j.1600-0633.2012.00564.x Tejerina-Garro, F.L., et al. (2005). Effects of natural and anthropogenic environmental changes on riverine fish assemblages: a framework for ecological assessment of rivers. Brazilian Archives of Biology and Technology , 48(1), 91–100. Toussaint, A., Brosse, S., & Villeger, S. (2021). Global shifts in freshwater fish functional diversity under human influence. Nature Communications , 12(1), 471. Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R., & Cushing, C. E. (1980). The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences , 37(1), 130–137. https://doi.org/10.1139/f80-017 Zhou, S., Klaer, N. L., Daley, R. M., Zhu, Z., Fuller, M., & Smith, A. D. M. (2014). Modelling multiple fishing gear efficiencies and abundance for aggregated populations using fishery or survey data. ICES Journal of Marine Science , 71(9), 2436–2447. https://doi.org/10.1093/icesjms/fsu068 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. 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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-9052336","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607298331,"identity":"1ea1215f-5115-45ca-9e7f-9f8f8f366656","order_by":0,"name":"Apinun Suvarnaraksha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFACxgYgYQOhgCCBWC1pMC0GxGgBg8MwBhFa+Gc3tz3m3XE+mnlGAuOHHwx/8ghqkbhzsN2Y98zt3MYZCcySPQwGxYSddCOxTZq3DayFQRrosMQGQjrkIVrOgW35TZQWA4iWAyAtbMTZYnjnYJvk3Lbk3Maeh22WPQbGhLXI3W5/JvG2zS53Y3vy4Rs/KuQIa2GQgFnXAIpMA4LqkbTIE6N4FIyCUTAKRiYAAHu5Pf7l1GGXAAAAAElFTkSuQmCC","orcid":"","institution":"Maejo University","correspondingAuthor":true,"prefix":"","firstName":"Apinun","middleName":"","lastName":"Suvarnaraksha","suffix":""},{"id":607298332,"identity":"caf242f4-f9a7-4208-a5c5-e6fc4d3b3419","order_by":1,"name":"Thapanee Poldee","email":"","orcid":"","institution":"Maejo University","correspondingAuthor":false,"prefix":"","firstName":"Thapanee","middleName":"","lastName":"Poldee","suffix":""},{"id":607298333,"identity":"9da0bde7-998f-48e9-b20e-6ec736cfef1f","order_by":2,"name":"Chamaiporn Champasri","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Chamaiporn","middleName":"","lastName":"Champasri","suffix":""},{"id":607298334,"identity":"265c686c-f87f-49f7-949b-c3772d6de257","order_by":3,"name":"Nakhorn Pila","email":"","orcid":"","institution":"Ministry of Agriculture and Cooperatives","correspondingAuthor":false,"prefix":"","firstName":"Nakhorn","middleName":"","lastName":"Pila","suffix":""},{"id":607298335,"identity":"5c8748e1-e89c-485a-a8aa-8853af557f66","order_by":4,"name":"Siriwan Suksri","email":"","orcid":"","institution":"Ministry of Agriculture and Cooperatives","correspondingAuthor":false,"prefix":"","firstName":"Siriwan","middleName":"","lastName":"Suksri","suffix":""}],"badges":[],"createdAt":"2026-03-06 15:54:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9052336/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9052336/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104994337,"identity":"ae5108d5-4270-4e92-bdf5-a2f3eb42e331","added_by":"auto","created_at":"2026-03-19 15:59:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":727091,"visible":true,"origin":"","legend":"\u003cp\u003eCanonical Correspondence Analysis (CCA) of fish assemblages in the Yom River Basin, showing ecological groupings (Riverine, Transitory, Stream, and Foothill Ecology) and associated environmental vectors (e.g., DO, temperature, elevation, substrate type, and land use).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9052336/v1/41d57a3d300f67f260b7af18.png"},{"id":104994336,"identity":"9bbe7147-dc7c-4751-83ea-4339e39882b6","added_by":"auto","created_at":"2026-03-19 15:59:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":729588,"visible":true,"origin":"","legend":"\u003cp\u003eSpatio-temporal distribution of representative fish species along the Yom River gradient, categorized by ecological guilds (Mountain, Foothill, Transitory, Riverine) and seasonal occurrence during dry and rainy seasons. Right panel shows the three zones (Upper, Middle, Lower) of the Yom Basin.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9052336/v1/32b3048924fc62bbe33a40c1.png"},{"id":105035686,"identity":"d233a52d-fa3f-4022-ad34-ac837cc6b701","added_by":"auto","created_at":"2026-03-20 07:26:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91033,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of key environmental parameters (DO, elevation, temperature, agriculture percentage, pH, ammonia) based on CCA analysis, clustered using Euclidean distance.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9052336/v1/fb8727e53615a78b15d71a63.png"},{"id":104994338,"identity":"e1b13821-c5b8-49f4-bafe-b817d6a9c6dc","added_by":"auto","created_at":"2026-03-19 15:59:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":393867,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression plots showing relationships between species diversity indices (Shannon_H and Richness_S) and selected environmental parameters (DO, temperature, pH, ammonia, forest %, agriculture %, elevation).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9052336/v1/a642e49b059b21a456923d66.png"},{"id":104994340,"identity":"5f8685cf-ea10-4475-9306-2fbfe23565f1","added_by":"auto","created_at":"2026-03-19 15:59:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1908449,"visible":true,"origin":"","legend":"\u003cp\u003e3D surface plots of species richness as a function of environmental variables (e.g., temperature, DO, pH, ammonia, elevation, agriculture %), illustrating interactions and nonlinear trends.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9052336/v1/8cd0060db7d0c6c36f384116.png"},{"id":104994342,"identity":"78b1aa89-0809-414b-b6e5-c11058ca19ce","added_by":"auto","created_at":"2026-03-19 15:59:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":607515,"visible":true,"origin":"","legend":"\u003cp\u003eClustered heatmap of the top 30 fish species by abundance (years 2000, 2011, 2023), color-coded by migratory behavior and arranged using hierarchical clustering of species and sampling years.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-9052336/v1/b14de434e0be16c253543234.png"},{"id":106093775,"identity":"cb1a7c5a-d4d8-4b8e-af49-4cacde22d3ab","added_by":"auto","created_at":"2026-04-03 11:39:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5497712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9052336/v1/b5de2ea3-4ced-4b84-85ec-072dc36ce254.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fish Ecology and Sustainable Management Strategies in the Yom River Basin, Thailand","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFreshwater habitats, while covering less than one percent of the Earth's surface, harbor a disproportionately high level of global biodiversity and provide vital ecosystem services. In Southeast Asia, large river systems such as the Mekong and its tributaries\u0026mdash;including Thailand\u0026rsquo;s Yom River\u0026mdash;are ecological keystones that sustain both biological diversity and human well-being. However, these riverine systems are increasingly threatened by anthropogenic stressors including dam construction, land-use changes, hydrological alterations, pollution, and climate change (Dudgeon, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the Chao Phraya River system, the Yom River stands out as one of the few remaining large, free-flowing tributaries, offering important seasonal refuge and migration corridors for freshwater fish species (Sukontason et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Historical studies have reported over 170 fish species within the basin (Champasri, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Pila et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), yet ongoing development and agricultural intensification are altering the hydroclimate and fragmenting aquatic habitats (Ngor et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Intarasu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its ecological importance, little is known about how fish communities in the Yom River have changed over time or how assemblage structure responds to multiscale environmental gradients. This lack of integrated, long-term ecological assessment poses a challenge for sustainable management. Fish assemblages, known to be sensitive to habitat quality, hydrological regimes, and landscape connectivity, are valuable bioindicators for assessing riverine ecosystem health (Kang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Toussaint et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study addresses this knowledge gap by synthesizing three decades of ichthyofaunal data (from 2000, 2011, and 2023) across multiple spatial zones of the Yom River Basin. By examining spatial and temporal variation in species composition in relation to stream velocity, substrate, dissolved oxygen, ammonia, pH, and surrounding land use, the study aims to: (1) detect assemblage shifts along environmental gradients; (2) identify key drivers of biodiversity patterns; and (3) evaluate implications for conservation and sustainable fisheries. The findings contribute to a deeper understanding of longitudinal zonation and offer a foundation for ecosystem-based river basin management in support of Thailand\u0026rsquo;s biodiversity and climate resilience objectives.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;Study Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Yom River Basin in northern Thailand ranges from mountainous headwaters at about 644 m in Phayao Province to lowland floodplains at 26 m above sea level near the confluence with the Nan River in Nakhon Sawan. Four ecological zones are recognised along this continuum: (i) upper‐mountain streams with fast, cool, rocky reaches; (ii) mid‑elevation foothills dominated by gravel and cobble substrates; (iii) floodplain transition zones characterised by sandbars, backwaters and seasonally inundated pools; and (iv) the main river channel with deeper, slower flows. This habitat heterogeneity supports a broad spectrum of freshwater fish assemblages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;Sampling Design and Effort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo capture seasonal and habitat variability, fish were surveyed in three distinct rounds representing the rainy (May\u0026ndash;September), dry (October\u0026ndash;January) and hot (February\u0026ndash;April) seasons. Within each round, sampling sites were stratified by habitat type\u0026mdash;lentic waters (reservoirs and oxbow pools), main river channels, tributaries and small streams\u0026mdash;to ensure comparable coverage across ecological zones. Each habitat type was sampled at 3\u0026ndash;6 sites per round (total 48\u0026ndash;60 sites per year), and effort was standardised by sampling each site at similar times of day and for a fixed duration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;Fish Sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multi‑gear approach was employed to minimise gear selectivity. In shallow riffles and headwater streams, scoop nets (1\u0026ndash;2 m mouth width, 1 mm mesh) fitted with face masks were used to conduct timed sweeps (two 5‑minute passes per site). In deeper sections and floodplain pools, gill nets (20\u0026ndash;50 m length, 2\u0026ndash;5 cm stretched mesh) of three different mesh sizes were set for 1\u0026ndash;2 hours, and baited funnel traps and lift nets were deployed overnight. Cast nets with 1.5 cm mesh were thrown 20\u0026ndash;30 times per site in areas of moderate depth. All captured fishes were enumerated and measured (total length), then most individuals were released; a representative subset was euthanised following ethical protocols and preserved in 10 % formalin (later transferred to 70 % ethanol) as voucher specimens. Identification to species level was based on regional keys (e.g., Rainboth 1996) and updated nomenclature from Froese \u0026amp; Pauly, (2025); uncertain identifications were verified by taxonomic specialists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp;Environmental Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt each sampling event, a suite of environmental variables was recorded to characterise habitat conditions:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Physicochemical variables\u003c/strong\u003e \u0026ndash; Water temperature (\u0026deg;C), dissolved oxygen (DO, mg L⁻\u0026sup1;), pH, specific conductivity (\u0026micro;S cm⁻\u0026sup1;) and hardness were measured in situ with a calibrated multiparameter probe (YSI 556). Water samples were collected for laboratory analysis of ammonia‑N, nitrite‑N and total phosphorus following Baird \u0026amp; Bridgewater, (2017). \u0026nbsp;(2017) protocols. Instruments were calibrated daily, and blanks were run with each batch of samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Physical habitat descriptors\u003c/strong\u003e \u0026ndash; Channel width and depth were measured using a tape and a graduated pole at three transects per site. The current velocity was measured mid‑channel with a portable flow meter (Swoffer 3000). Substrate composition was visually estimated as proportions of boulder (\u0026gt; 256 mm), cobble (64\u0026ndash;256 mm), gravel (2\u0026ndash;64 mm), sand (0.062\u0026ndash;2 mm), silt or mud (\u0026lt; 0.062 mm). Elevation was recorded by GPS (\u0026plusmn; 5 m).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Land‑use variables\u003c/strong\u003e \u0026ndash; The catchment upstream of each site (buffer radius = 1 km) was delineated in a GIS; proportions of forest, agriculture and urban land cover were extracted from Landsat‑derived maps (Land Development Department 2023) and ground‑truthed where possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5\u0026nbsp;Biodiversity Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecies richness (S) and Shannon\u0026ndash;Wiener diversity index (H\u0026prime;) were calculated for each site. Differences in diversity among habitats or seasons were tested using one‑way ANOVA; assumptions of normality and homoscedasticity were assessed, and log‑transformation was applied when necessary. Post\u0026nbsp;hoc Tukey tests (\u0026alpha;\u0026nbsp;=\u0026nbsp;0.05) were conducted to identify significant pairwise differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6\u0026nbsp;Multivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish assemblage\u0026ndash;environment relationships were examined using several multivariate techniques:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Canonical Correspondence Analysis (CCA)\u003c/strong\u003e \u0026ndash; Species abundance data (square‑root transformed and rare species down‑weighted) were ordinated against centred and standardised environmental variables using the vegan package in R. Forward selection and Monte‑Carlo permutation tests (999 permutations) identified significant predictors.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eCluster Analysis\u003c/strong\u003e \u0026ndash; Ward\u0026rsquo;s method with Bray\u0026ndash;Curtis dissimilarity was applied to log(x + 1) transformed species abundances to classify sites into ecological zones. The cophenetic correlation coefficient and silhouette width were used to assess cluster robustness.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eClassification and Regression Trees (CART)\u003c/strong\u003e \u0026ndash; Diversity indices and species richness were modelled as functions of environmental predictors using the rpart package. Trees were pruned based on cross‑validated error rates to avoid overfitting, and variable importance was evaluated.\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eGeneralised Additive Models (GAMs)\u003c/strong\u003e \u0026ndash; For key species and overall richness, non‑linear relationships with temperature (Temp), dissolve oxygen (DO), ammonia (NH\u003csub\u003e3\u003c/sub\u003e) and land‑use intensity were modelled using penalised splines (mgcv package). Models were assessed using AIC and diagnostic residual plots.\u003c/p\u003e\n\u003cp\u003e5. All data analyses were performed using R version 4.4.0 (R Core Team, 2024). Hierarchical clustering and heatmaps were generated using the heatmap and base stats packages with Ward\u0026rsquo;s method and Euclidean distance. Boxplots and spider plots were created using ggplot2. The Analysis of Similarities (ANOSIM) was conducted using the vegan package (Oksanen et al., 2020) with 999 permutations to test for significant differences in species abundance patterns among migratory guilds and occurrence-based groups. Principal Coordinates Analysis (PCoA) was also carried out using the ape and ggplot2 packages to visualize inter-group dissimilarities. Species were categorized into migratory guilds and grouped based on their temporal occurrence (i.e., detected in one, two, or all three survey years: 2000, 2011, and 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7\u0026nbsp;Ethical Compliance and Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish sampling protocols were approved by the Institutional Animal Care and Use Committee of Maejo University and conducted under permit from the Thai Department of Fisheries (Permit No. XXXX/2023). All handling and euthanasia procedures followed national animal welfare guidelines. Instruments were calibrated before each field trip; water samples were stored on ice and processed within 24 hours. Duplicate measurements and blank samples were used to check for accuracy, and 10 % of specimens were cross‑identified by an independent taxonomist to ensure reliability.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp;Fish Species Richness and Composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurveys conducted in 2000, 2011 and 2023 recorded 223 species from 114 genera, 43 families and 15 orders in the Yom River Basin. Eighty‑five species were detected in all three surveys, indicating basin‑wide distributions and ecological tolerance. The 2023 survey documented 209 species\u0026mdash;45 of which were new records\u0026mdash;reflecting expanded sampling effort and taxonomic updates. Families with the greatest richness were Cyprinidae (carps and minnows), Nemacheilidae (stone loaches) and Danionidae (danios and rasboras). Common species across years and zones included \u003cem\u003eBarbonymus\u0026nbsp;altus\u003c/em\u003e, \u003cem\u003ePuntioplites\u0026nbsp;proctozystron\u003c/em\u003e, \u003cem\u003eClarias\u0026nbsp;batrachus\u003c/em\u003e and \u003cem\u003eChanna\u0026nbsp;striata\u003c/em\u003e. Species turnover was modest: 11 species were unique to the 2000 survey and 43 unique to 2011, suggesting a combination of localised extirpations and improved detection over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNMDS Ordination of Fish Assemblage Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo visualize temporal changes in fish assemblage composition, a non-metric multidimensional scaling (NMDS) ordination was performed using Bray\u0026ndash;Curtis dissimilarity based on species abundance data from the 2000, 2011, and 2023 surveys. The resulting two-dimensional NMDS plot revealed clear separation among sampling years, with 2000 positioned farther from 2011 and 2023, indicating a marked shift in assemblage structure over time. The proximity of the 2011 and 2023 points suggests relatively similar community composition during the more recent sampling periods, whereas the assemblage in 2000 was compositionally more distinct. This pattern is consistent with the increasing influence of environmental change and anthropogenic disturbance, particularly in the past decade. The stress value of the ordination was low, confirming that the reduced-dimension representation faithfully captured the dissimilarities in fish community structure across the three decades.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u0026nbsp;Fish Assemblage Structure by Habitat Zone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster analysis and habitat stratification delineated four ecological assemblages that correspond to longitudinal and environmental gradients:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Mountain streams\u003c/strong\u003e \u0026ndash; High‑elevation (\u0026gt;500 m) headwaters with steep gradients, cold water and boulder\u0026ndash;cobble substrates supported specialised benthic taxa such as \u003cem\u003eSchistura\u0026nbsp;menanensis\u003c/em\u003e, \u003cem\u003eGlyptothorax\u003c/em\u003e spp. and \u003cem\u003eDevario\u0026nbsp;laoensis\u003c/em\u003e. Species diversity was low (mean H\u0026prime;\u0026nbsp;\u0026asymp;\u0026nbsp;2.3) and correlated with high dissolved oxygen, low conductivity and clear, fast flows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Foothill streams\u003c/strong\u003e \u0026ndash; Mid‑elevation reaches exhibited moderate flows and mixed gravel\u0026ndash;sand substrates. Assemblages were dominated by species tolerant of intermediate conditions, including \u003cem\u003eOpsarius\u0026nbsp;pulchellus\u003c/em\u003e, \u003cem\u003ePethia\u0026nbsp;stoliczkana\u003c/em\u003e and \u003cem\u003eHomalopteroides\u0026nbsp;smithi\u003c/em\u003e. Diversity was higher than in mountain streams (mean H\u0026prime;\u0026nbsp;\u0026asymp;\u0026nbsp;2.8), reflecting broader ecological niches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Transitional floodplains\u003c/strong\u003e \u0026ndash; Low‑lying channels and oxbow lakes with variable connectivity to the main river harboured species adapted to fluctuating hydrology and low dissolved oxygen, such as \u003cem\u003eChanna\u0026nbsp;striata\u003c/em\u003e, \u003cem\u003ePristolepis\u0026nbsp;fasciatus\u003c/em\u003e and \u003cem\u003eClarias\u0026nbsp;batrachus\u003c/em\u003e. These sites showed high species richness during seasonal inundation (mean S\u0026nbsp;\u0026asymp;\u0026nbsp;35; H\u0026prime;\u0026nbsp;\u0026asymp;\u0026nbsp;3.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Main river channel\u003c/strong\u003e \u0026ndash; Wide, deep sections with silty substrates supported the most diverse assemblages (mean H\u0026prime; \u0026asymp; 3.5; S \u0026asymp; 48). Large migratory and commercially important species (\u003cem\u003ePangasianodon\u0026nbsp;hypophthalmus\u003c/em\u003e, \u003cem\u003eHemibagrus\u0026nbsp;filamentosus\u003c/em\u003e, \u003cem\u003ePhalacronotus\u0026nbsp;bleekeri\u003c/em\u003e) were characteristic, associating with greater depth, width and turbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u0026nbsp;Environmental Drivers of Fish Diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCanonical Correspondence Analysis (CCA) indicated that substrate type, dissolved oxygen, land use and temperature were the most influential variables shaping fish assemblages (Fig.\u0026nbsp;1). Axis\u0026nbsp;1 explained 27.3\u0026nbsp;% of the variance and separated upland sites (high DO, coarse substrates, forest cover) from lowland sites (warmer water, higher ammonia). Axis\u0026nbsp;2 (17.6\u0026nbsp;% variance) contrasted narrow, shallow headwater streams with wide, deep floodplain channels dominated by mud and silt. Sites with mixed gravel\u0026ndash;sand substrates and moderate elevation supported the highest diversity, whereas fine sediments, elevated ammonia and low oxygen were associated with depauperate communities dominated by tolerant species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4\u0026nbsp;Seasonal Reproductive Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGonadal maturity analyses revealed two distinct spawning strategies. Upland taxa (e.g. \u003cem\u003eSchistura\u0026nbsp;\u003c/em\u003esp., \u003cem\u003eOpsarius puchellus\u003c/em\u003e, \u003cem\u003eChanna\u0026nbsp;limbata\u003c/em\u003e) spawned in the late dry season (April\u0026ndash;May) before the onset of monsoon flows, with partial spawning and multiple ovulation cycles. In contrast, floodplain and riverine species (e.g. \u003cem\u003ePangasius\u003c/em\u003e, \u003cem\u003ePhalacronotus bleekeri\u003c/em\u003e, \u003cem\u003eHenicorhynchus\u003c/em\u003e) synchronised reproduction with rising water levels in June\u0026ndash;July, coinciding with nutrient pulses and expanded habitats (Fig.\u0026nbsp;2). These patterns highlight strong coupling between hydrological cues and reproductive phenology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5\u0026nbsp;Ecological Zonation and Environmental Gradients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWard\u0026rsquo;s cluster analysis grouped sites into two major zones: an Upper Yom Zone encompassing mountain and foothill tributaries with cold‑water assemblages, and a Lower Yom Zone comprising floodplain and main‑channel habitats with high species overlap and connectivity. Complementary clustering of environmental variables (Fig.\u0026nbsp;3) showed that temperature, percentage of agricultural land and ammonia formed one group indicative of lowland, nutrient‑enriched conditions; dissolved oxygen and elevation formed a second group representing upland streams; and pH varied independently, likely influenced by local geology. These clusters align with the ecological zonation derived from CCA.\u003c/p\u003e\n\u003cp\u003eLinear regressions (Fig.\u0026nbsp;4) revealed that Shannon diversity increased with water temperature and agricultural land cover but declined with high dissolved oxygen and ammonia, although some relationships were only marginally significant (e.g. H\u0026prime; vs. DO, R\u0026sup2;\u0026nbsp;\u0026asymp;\u0026nbsp;0.26, p\u0026nbsp;\u0026asymp;\u0026nbsp;0.09). Species richness showed weaker trends, with positive associations with elevation and DO and a negative association with ammonia. Overall, diversity patterns appear to reflect trade‑offs between habitat complexity, productivity and water quality.\u003c/p\u003e\n\u003cp\u003eNonlinear surface models (Fig.\u0026nbsp;5) further underscored the complex interactions between temperature and other environmental drivers. Species richness consistently peaked at moderate temperatures (24\u0026ndash;26\u0026nbsp;\u0026deg;C). Richness declined sharply at high ammonia concentrations or high agricultural coverage, indicating nutrient stress. Conversely, richness increased with higher dissolved oxygen but only within a moderate thermal range. Elevation and pH interactions suggested that mid‑elevation reaches and near‑neutral pH maximised diversity. These results highlight the threshold effects and synergies among multiple stressors in regulating fish biodiversity in tropical river systems.\u003c/p\u003e\n\u003cp\u003eLinear regression plots showing the relationships between environmental variables and two diversity indices: Shannon Diversity Index (H\u0026prime;, top row) and species richness (S, bottom row). Environmental variables include dissolved oxygen (DO), temperature (Temp), pH, ammonia (NH₃), forest cover (% Forest), agricultural land use (% Agric), and elevation (m a.s.l.). Shaded areas represent 95% confidence intervals. Notably, Shannon diversity tended to increase with water temperature and percentage of agricultural land use, while it decreased with dissolved oxygen (DO) and ammonia (NH₃) concentrations. Species richness shows more variable patterns, with positive associations with DO and elevation, and negative trends with ammonia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Temporal Assemblage Patterns and Migratory Behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine spatiotemporal patterns in species abundance and their relationship to life-history traits, a clustered heatmap was generated using Ward\u0026rsquo;s method and Euclidean distance based on the top 30 most abundant fish species recorded across the three survey years (2000, 2011, and 2023) (Fig. 6). The heatmap revealed distinct groupings of species with shared abundance trajectories over time. Several non-migratory species showed relatively stable or increasing abundances, while certain local migrants and long-distance migrants displayed marked temporal fluctuations. Species such as \u003cem\u003eMystus bocourti\u003c/em\u003e and \u003cem\u003eParalaubuca barroni\u003c/em\u003e increased steadily from 2000 to 2023, whereas others like \u003cem\u003ePhalacronotus bleekeri\u003c/em\u003e declined or remained stable.\u003c/p\u003e\n\u003cp\u003eTo test whether migratory guilds explained these abundance trends, an ANOSIM (Analysis of Similarities) was performed based on species\u0026apos; abundance and grouped by migratory behavior. The result yielded a low and nonsignificant R-value (R = -0.0023, p = 0.586), indicating that migratory classification alone did not significantly differentiate abundance patterns across the years. This suggests that other ecological or environmental factors such as habitat degradation, water quality, or trophic flexibility may have stronger effects on abundance trajectories than migratory strategy per se.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Fish Diversity and Spatial Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Yom River Basin exhibits considerable ichthyofaunal richness, with 223 species documented across 43 families and 15 orders over three decades of surveys. This richness is comparable to regional patterns observed in other Indo-Burma and Mekong sub-basins (Baran et al., 2005; Dudgeon et al., 2006; Darwall et al., 2018). Dominant families such as Cyprinidae, Nemacheilidae, and Danionidae are typical of tropical Asian streams (Rainboth, 1996; Kottelat, 2013), and their consistent presence across years suggests broad ecological tolerance. The 2023 detection of 45 newly recorded species reflects advancements in survey methodology and taxonomy, as also noted by Deiner et al. (2017) in studies utilizing eDNA and spatial stratification.\u003c/p\u003e\n\u003cp\u003eSpecies turnover observed between 2000, 2011, and 2023 aligns with documented temporal shifts in fish communities under climate and land use change (Arthington et al., 2010; Matthews, 1998; Olden \u0026amp; Poff, 2003). Such partial continuity and replacement reflect both natural successional processes and anthropogenic disturbances (Nelson et al., 2016; Tejerina-Garro et al., 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Habitat Zonation and Community Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster-based analysis delineated four ecological zones i.e., mountain streams, foothill streams, transitional floodplains, and riverine channels representing a longitudinal environmental gradient. This zonation mirrors the predictions of the River Continuum Concept (Vannote et al., 1980) and is consistent with findings in similar tropical river systems (Dudgeon, 2000; Cowx \u0026amp; Welcomme, 1998).\u003c/p\u003e\n\u003cp\u003e1) Mountain streams, with their steep gradients, coarse substrates, and cool oxygen-rich water, hosted low-diversity but highly specialized species like \u003cem\u003eSchistura menanensis\u003c/em\u003e and \u003cem\u003eGlyptothorax spp.\u003c/em\u003e (Matthews, 1998; Kottelat, 2013). These environments act as ecological filters favoring benthic specialists adapted to high flow (Pusey et al., 2000).\u003c/p\u003e\n\u003cp\u003e2) Foothill zones showed greater diversity, supporting ecologically plastic taxa such as \u003cem\u003eOpsarius pulchellus\u003c/em\u003e and \u003cem\u003ePethia stoliczkana\u003c/em\u003e, reflecting mid-reach richness peaks described by Benejam et al. (2009) and Baird \u0026amp; Flaherty (2005).\u003c/p\u003e\n\u003cp\u003e3) Transitional floodplains, characterized by habitat heterogeneity and seasonal connectivity, supported tolerant generalists (\u003cem\u003eChanna striata\u003c/em\u003e, \u003cem\u003eClarias batrachus\u003c/em\u003e) in dynamic lentic-lotic interfaces, reinforcing the Flood Pulse Concept (Junk et al., 1989).\u003c/p\u003e\n\u003cp\u003e4) Main river channels, with their large size and slow flow, supported the highest richness and evenness, dominated by large-bodied, migratory species such as \u003cem\u003ePangasianodon hypophthalmus\u003c/em\u003e, in agreement with Mekong studies (Baran et al., 2005; Baird \u0026amp; Flaherty, 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Environmental Drivers of Fish Communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCanonical Correspondence Analysis (CCA) revealed substrate composition, temperature, land use, and dissolved oxygen as key drivers of fish community structure. These findings are supported by Tejerina-Garro et al. (2005), who emphasized substrate heterogeneity and oxygen availability as dominant determinants in tropical river assemblages.\u003c/p\u003e\n\u003cp\u003eThe negative correlation of richness with high temperature and ammonia levels aligns with studies from tropical Asia and Africa showing reduced diversity under thermal and nutrient stress (Daga et al., 2016; Hermoso \u0026amp; Kennard, 2012). Conversely, intermediate elevations (200\u0026ndash;250 m) and gravel\u0026ndash;sand substrates were associated with higher diversity, reinforcing the importance of transitional habitats in supporting biodiversity (Benejam et al., 2009; Linke et al., 2011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Nonlinear and Interaction Effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate surface modeling demonstrated strong interaction effects between temperature and key environmental factors. For instance, temperature and agriculture (R\u0026sup2; = 0.83) had a compounded negative effect on richness, supporting Odum\u0026rsquo;s (1985) theory of cumulative stress in ecosystems and findings by Leigh et al. (2016) on synergistic environmental impacts in Australian rivers.\u003c/p\u003e\n\u003cp\u003eRichness consistently peaked at 24\u0026ndash;26\u0026deg;C, but declined sharply when coupled with elevated ammonia (R\u0026sup2; = 0.76) or reduced DO (R\u0026sup2; = 0.79). These nonlinear thresholds resemble patterns found by Dudgeon (2000) and McGarvey \u0026amp; Ward (2008), and stress the need for ecological models that capture interaction effects rather than linear correlations alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Reproductive Ecology and Conservation Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish reproductive timing in the Yom Basin mirrors rainfall and flow patterns, as observed in tropical systems globally (Humphries et al., 1999; Pusey et al., 2000). Mountain and foothill species spawn pre-rainy season under clear, stable flows, while floodplain and riverine species synchronize spawning with flood pulses, maximizing larval drift and nutrient availability (Junk et al., 1989; Boonsong, 2001).\u003c/p\u003e\n\u003cp\u003eProtecting these seasonal and habitat-specific spawning strategies is essential, particularly in light of increasing hydrological alteration and dam development in Southeast Asia (Dudgeon et al., 2006; Darwall et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Longitudinal Zonation and Assemblage Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish assemblages in tropical rivers are often structured along the longitudinal gradient, reflecting environmental heterogeneity and hydrological connectivity (Vannote et al., 1980). In the Ping\u0026ndash;Wang River Basin, Suvarnaraksha et al. (2012) identified four ecological zones i.e., mountainous, piedmont, transitory, and lowland, each dominated by distinct taxa. This spatial pattern is mirrored in the Yom River, where rheophilic species such as Nemacheilidae dominate headwaters, while lowland reaches host more generalist and lentic-adapted species (e.g., \u003cem\u003eChanna\u003c/em\u003e spp.). Such zonation underscores the need to maintain longitudinal connectivity for sustaining biodiversity across habitat gradients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7 Environmental Drivers of Diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuvarnaraksha et al. (2012) demonstrated that geomorphological variables, especially elevation and river discharge\u0026mdash;were stronger predictors of fish richness than water chemistry. This finding aligns with other studies suggesting that landscape-scale drivers play a central role in structuring assemblages (Linke et al., 2011). In the Yom River, elevation, flow velocity, and substrate variation are likely key factors shaping community composition, emphasizing the importance of catchment-scale planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.8 Conservation and Management Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHydraulic structures such as dams and weirs in the Ping\u0026ndash;Wang system were associated with increased species turnover and reduced habitat continuity, particularly for migratory and headwater-endemic species (Suvarnaraksha et al., 2012). With proposed dams in the Yom River, similar impacts are anticipated. Conservation strategies should therefore integrate zonal protection, barrier mitigation, and participatory monitoring, aligning with integrated river basin management frameworks (Linke et al., 2011; Dudgeon, 2000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.9 Management Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides a robust ecological framework for spatially explicit management. The distinction between Upper Yom (upland, endemic-rich) and Lower Yom (lowland, diverse and productive) zones aligns with conservation planning approaches based on functional zoning and habitat integrity (Cowx \u0026amp; Welcomme, 1998; Hermoso \u0026amp; Kennard, 2012).\u003c/p\u003e\n\u003cp\u003eEnvironmental clustering of variables (e.g., DO with elevation; temperature with agriculture and ammonia) supports integrative monitoring frameworks. Targeting these environmental gradients for restoration, particularly through riparian buffer zones and seasonal fishery closures can sustain biodiversity under escalating stress (Dudgeon et al., 2006; Arthington et al., 2010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.10\u0026nbsp;Study limitations and future research directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the use of multi-season surveys and multiple gear types, certain limitations remain. Sampling was conducted only in selected seasons, which may miss species with brief spawning periods or whose migrations occur at other times of year (Fischer \u0026amp; Quist, 2014). Although using nets with various mesh sizes reduces gear bias, some species that avoid nets may still be underrepresented due to behavioral or habitat-specific avoidance strategies (Gibson-Reinemer et al., 2016; Zhou et al., 2014). Morphological identification alone may overlook cryptic or morphologically similar species, particularly in regions of high biodiversity. Incorporating techniques such as environmental DNA (eDNA) could improve detection accuracy, especially for elusive or low-density taxa, while genetic studies would clarify population structure and connectivity. Longer-term monitoring that spans all seasons is also needed to capture short- and long-term dynamics and to assess the impacts of climate change (Gibson-Reinemer et al., 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.11\u0026nbsp;Environmental, socio‑economic and catchment‑level management implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond the measured variables, human driven degradation such as nutrient and pesticide run off from agriculture, deforestation, and shoreline development, affects fish diversity by increasing sediment, nutrient loads and metal pollution in waterways (Reid et al., 2019; Froese \u0026amp; Pauly, 2025). Regional examples include arsenic contamination from upstream mining in the Kok River, underscoring the importance of source control and multi-agency cooperation. Although the Yom Basin lies entirely within Thailand, mitigating pressures such as agricultural runoff and riparian forest loss requires coordination across provinces and between water authorities. Fish populations also support local livelihoods; declines in economically important species or the imposition of seasonal fishing closures can affect household income (FAO, 2022). Therefore, fisheries management should integrate ecological and social dimensions and provide compensation or alternative livelihoods where necessary (FAO, 2022). In the longer term, restoring riparian corridors, establishing buffer zones, and developing participatory water quality monitoring will help reduce the impacts of climate change and secure food security for riparian communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.12 Implications for management and policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings have several implications for management and policy. The observed temporal and spatial shifts in fish assemblages, along with increasing habitat fragmentation, emphasize the urgent need for integrated river basin management approaches. Maintaining longitudinal connectivity, particularly for migratory species, is critical. Policy efforts should focus on restoring degraded habitats, regulating land use along riparian zones, and minimizing anthropogenic stressors such as pollution and dam construction. Furthermore, long-term monitoring programs and adaptive co-management frameworks involving local communities are essential for sustaining fish diversity and ensuring the resilience of freshwater ecosystems under climate and land-use pressures.\u003c/p\u003e\n\u003cp\u003eThe ecological zonation and species turnover documented in this study underscore the urgency of implementing integrated river basin management (IRBM) strategies that prioritize both biodiversity and ecosystem functionality. In particular, the observed habitat fragmentation and nutrient-driven stressors align with global freshwater challenges highlighted in Sustainable Development Goal (SDG) Target 6.6, which calls for the protection and restoration of water-related ecosystems, including rivers, wetlands and aquifers by 2030.\u003c/p\u003e\n\u003cp\u003eTo meet this target, management plans in the Yom River Basin should be incorporated:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp;Functional zoning based on ecological assemblages and environmental gradients identified through CCA and cluster analyses, enabling targeted habitat protection.\u003c/p\u003e\n\u003cp\u003e2. Riparian buffer restoration in high-risk lowland zones with elevated ammonia and agricultural runoff, which degrade ecosystem integrity and limit fish reproductive success.\u003c/p\u003e\n\u003cp\u003e3. Seasonal fishery closures aligned with flow-driven spawning windows, promoting population recovery and hydrological synchrony.\u003c/p\u003e\n\u003cp\u003e4. Participatory monitoring and co-management frameworks that engage local communities, fostering resilience and inclusive stewardship of aquatic resources.\u003c/p\u003e\n\u003cp\u003eThese strategies support not only national biodiversity goals under Thailand\u0026rsquo;s Master Plan for Water Resources Management but also contribute directly to the implementation of SDG 6.6 by maintaining ecological flows, protecting aquatic corridors, and reducing anthropogenic pressures through cross-sectoral cooperation. Future policies should integrate spatially explicit ecological data such as those provided by this study into decision-support systems and adaptive planning tools to ensure long-term ecosystem health and climate resilience across the basin.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides the most comprehensive ichthyofaunal assessment to date of the Yom River Basin, underscoring its status as a regional biodiversity hotspot with 223 species documented across 43 families and 15 orders over a three-decade span. Multivariate analyses revealed four ecologically distinct zones i.e., mountain headwaters, foothill streams, transitional floodplains, and the main river channel each characterized by unique assemblages shaped by elevation, substrate, hydrology, and land use.\u003c/p\u003e \u003cp\u003eCanonical correspondence analysis identified substrate composition, dissolved oxygen, temperature, and agricultural land cover as key environmental drivers structuring fish communities. Biodiversity was highest in mid-elevation and floodplain habitats and declined under conditions of elevated ammonia and intensified land use, suggesting threshold effects and synergistic stressors.\u003c/p\u003e \u003cp\u003eSeasonal reproductive patterns were tightly coupled to hydrological regimes: upland species-initiated spawning prior to monsoon flows, while lowland species synchronized reproduction with flood pulses. These findings highlight the ecological importance of maintaining flow variability and habitat connectivity.\u003c/p\u003e \u003cp\u003eGiven the increasing anthropogenic pressure, nutrient enrichment, habitat fragmentation, and land-use conversion, spatially explicit conservation strategies are urgently required. Recommended actions include riparian buffer restoration, seasonal fishery closures, and functional zoning aligned with ecological gradients. Incorporating advanced biomonitoring techniques (e.g., environmental DNA) and long-term, multi-seasonal datasets will be essential for refining species detection and assessing ecological change.\u003c/p\u003e \u003cp\u003eThese results underscore the need for integrated river basin management frameworks that balance biodiversity conservation with socio-economic imperatives. Community-engaged co-management, supported by robust ecological evidence, offers a promising pathway to sustaining tropical freshwater ecosystems under climate and land-use transformation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe gratefully acknowledge faculty of Fisheries Technology and Aquatic Resources, Maejo University, for their logistical support, provision of field equipment and the students. We also extend our appreciation to the Department of Fisheries, Thailand for authorizing our research and providing assistance during fieldwork. And thank you to Thongchai Champasri, Kasartsat University for his field survey and data set from 2000.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also appreciate the valuable contributions of local fishers and community members along the Yom River who shared their knowledge and assisted in the sampling efforts. Lastly, we thank our colleagues and research assistants for their help during fieldwork and data processing.\u003c/p\u003e\n\u003ch2\u003eFunding \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study was financially supported by the Agricultural Research Development Agency (Public Organization), Thailand, under the research project entitled \u003cem\u003e\u0026ldquo;\u003c/em\u003eA Study of Economic Value, Way of Life, Food Security and Community Participation in the Development of Ecosystems in the Yom River Basin\u003cem\u003e\u0026rdquo;\u003c/em\u003e (Project No. ARDA 66-007), the Department of Fisheries (Project No. 50-0519-50044-001), and the Biodiversity Research and Training Program (BRT 540057). Additional support was provided by Maejo University, through institutional research grants and field logistics.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;A.S. conceived the study, led the field investigations, conducted data analysis, and wrote the original manuscript draft. T.P. contributed to data collection, environmental measurements, statistical analysis, and figure preparation. C.C. provided historical data, assisted in taxonomic verification, and reviewed the manuscript. N.P. coordinated with the Department of Fisheries, supported field logistics, and contributed to policy-relevant discussions. S.S. validated species identification, compiled GIS data, and assisted with manuscript revisions. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eEthical Approval (if applicable)\u003c/h2\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArthington, A.H., et al. (2010). Preserving the biodiversity and ecological services of rivers: new challenges and research opportunities. \u003cem\u003eFreshwater Biology\u003c/em\u003e, 55(1), 1\u0026ndash;16.\u003c/li\u003e\n \u003cli\u003eBaird, I.G., \u0026amp; Flaherty, M.S. (2005). Mekong River fish conservation zones in southern Laos: Assessing effectiveness using local ecological knowledge. \u003cem\u003eEnvironmental Management\u003c/em\u003e, 36(3), 439\u0026ndash;454.\u003c/li\u003e\n \u003cli\u003eBaird, R.B., \u0026amp; Bridgewater, L. (2017). \u003cem\u003eStandard Methods for the Examination of Water and Wastewater\u003c/em\u003e (23rd ed.). 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Modelling multiple fishing gear efficiencies and abundance for aggregated populations using fishery or survey data. \u003cem\u003eICES Journal of Marine Science\u003c/em\u003e, 71(9), 2436\u0026ndash;2447. https://doi.org/10.1093/icesjms/fsu068\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"freshwater biodiversity, fish assemblages, long-term monitoring, river connectivity, environmental drivers, tropical rivers","lastPublishedDoi":"10.21203/rs.3.rs-9052336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9052336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFreshwater biodiversity faces unprecedented pressures from habitat fragmentation, land-use intensification, and climate change, yet few tropical basins have long-term records to assess ecological trajectories. Here, we analyze fish assemblages in the Yom River, one of the last free-flowing tributaries of the Chao Phraya system in Southeast Asia, using standardized surveys from 2000, 2011, and 2023. Across three decades, 223 species representing 43 families were recorded, with Cyprinidae, Nemacheilidae, and Danionidae dominating. Multivariate analyses revealed significant temporal and spatial restructuring of communities: upland zones remained specialized but species-poor, foothill reaches supported intermediate diversity, floodplains harbored tolerant generalists, and the main channel maintained the highest richness, including large migratory taxa. Canonical correspondence analysis identified substrate composition, dissolved oxygen, temperature, and agricultural land cover as key drivers of diversity. Species richness peaked in mid-elevation transitional habitats but declined sharply under elevated ammonia and intensified land use, highlighting nonlinear thresholds. Spawning phenology was tightly coupled to hydrological regimes, with upland species reproducing before monsoon flows and floodplain taxa synchronized with flood pulses. These findings demonstrate that biodiversity in tropical rivers is structured by synergistic environmental stressors and connectivity loss, with clear implications for basin-wide management. The Yom River case emphasizes the global importance of conserving free-flowing tributaries and integrating long-term biodiversity monitoring into adaptive freshwater conservation strategies.\u003c/p\u003e","manuscriptTitle":"Fish Ecology and Sustainable Management Strategies in the Yom River Basin, Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 15:59:43","doi":"10.21203/rs.3.rs-9052336/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d4cc7ab-6f27-414c-9288-11a4993b3c72","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T13:12:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 15:59:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9052336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9052336","identity":"rs-9052336","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}