Understanding how the environment shapes species distribution and affects biodiversity patterns is important in ecology and conservation. Environmental stressors like climate change and anthropogenic impacts may lead to a significant decline in aquatic biodiversity. Therefore, it is imperative to characterise the current community structure and explore environmental drivers that may be important for the future community structure, also in biogeographic areas that are largely outside of the main research focus. We investigated how fish species abundances in the Karun River basin, southwest of Iran, respond to environmental predictors along a longitudinal gradient of 108 sampling sites using Generalised Linear Latent Variable Models (GLLVMs). We evaluated the response of 46 fish species to seven environmental predictors and interpreted the bivariate species co-occurrences in the residual covariance matrix in the light of potential biotic interactions. The latent variable model explained 62% of data variability in fish abundance. We identified temperature as the most important predictor, with alien species responding positively to warmer conditions, suggesting potential distribution shifts due to climate change. In contrast, endemic species showed negative responses to higher temperatures, highlighting their vulnerability. Fish abundance responses to total nitrogen concentration and average precipitation were generally negative, indicating threats from nutrient enrichment and changing rainfall patterns. There were a few systematic negative co-occurrences between alien and native fish species, which may reflect both differing environmental preferences and potential negative interactions. The model showed high predictive accuracy for the occurrence of native species, while accuracy was lower for endemic and alien species, likely due to their more limited geographical distributions. This study contributes to the global understanding of how environmental drivers shape fish communities in semi-arid river systems. By highlighting the contrasting responses of endemic and alien species to selected stressors, it provides valuable insights for predicting and managing biodiversity under climate change, offering a framework applicable to similar ecosystems worldwide.