Brown trout, Salmo trutta were introduced to India in early 19th century for sports and recreation (Singh and Lakra, 2011) serving as favourite fish among anglers. The fish is closely relative to Atlantic salmon, Salmo salar, and both of them have a common origin in Europe. Now-a-days, Salmo trutta is distributed worldwide, as an introduced fish outside its native range of distribution (Klemetsen et. al., 2003). Although brown trout have spread far beyond their initial places of introduction, they are not broadly known as invasive species in Asia (Jacques et al., 2013). Behaviour of non-native brown trout in range of its introductions is increasingly recognised as an important indicator for its success or failure of establishment and spread (Weis and Sol, 2016). Behavioural differences among native and non-native fish species can also influence the ecological implications of invasive species, which have potential to change native fish species populations owing to interactions and predation (Dick, et al., 2017). It is also documented that brown trout can displace native snow trout species competing for food and directly predating on them (Townsend, 1996). The brown trout an introduced fish species has been highly promoted in the Himalaya due to its popularity for recreational angling and food delicacy (Sehegal, 1999; Singh and Lakra, 2011). Brown trout has potential to react to different ecological conditions of rivers, lakes and streams where temperature, stream velocity, substratum and gravels determine the availability and distribution of brown trout (Meredith et al., 2014; Meredith et al., 2016). Brown trout populations are migratory fish and can move over 100 meters per day (Saraniemi et al., 2008).
Understanding relevant spatial scales of environmental and biotic processes and their interaction is highly important to the success of the spatial distribution of brown trout in Himalayan rivers. Species distribution models (SDM) have been applied to determine the occurrence or abundance of an species to ecological habitat. The species-environment relationships have been used to: (i) predict suitable habitats, (ii) identify the most important ecological drivers for the species expansion and (iii) simulating effects of potential habitat changes e.g., related to climate and/or land use change or restoration/protection measures (Pletterbauer et al., 2016; Radinger et al., 2016, 2017; Schmidt et al., 2020). SDMs have been widely used in ecological research and management in various contexts (Guisan and Thuiller, 2005; Elith and Leathwick, 2009) and have also been increasingly used in freshwater ecosystems (Karcher et al., 2019). The Maxent modelling requires only species presence (or occurrence) data and environmental information. Presence-only modelling methods simply require a limited set of known occurrences together with predictor variables such as topography, climate, soil, biogeography etc. (Amanda et al., 2016; Pathak et al. 2021). Therefore, we have used the MaxEnt algorithm for predicting brown trout distribution in the Himalayan region and to assess possible potential distribution expansion to identify the key environmental factors that were highly correlated with the brown trout habitat range. Information generated in this study will be beneficial for management of brown trout in India.