Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion
Sharma, Aashna et al. (2021), Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion, Dryad, Dataset, https://doi.org/10.5061/dryad.f1vhhmgxh
With the concept of ‘riverscapes’ long pending to be acknowledged in the ‘landscape-centric’ legislative framework of Himalayan nations, conservation of native riverine species stays practically unheeded. This necessitates urgent prioritization of stream networks to conserve the lotic taxa under invasion pressures. Himalayan riverscapes are pervaded with the invasive-exotic brown trout Salmo trutta, posing serious threats to the co-occurring native, the snow trout Schizothorax richardsonii. Using intensive surveys (218.7km) and geostatistical stream network models (n=537), we contrasted snow trout in two stream networks with and without invasives, for assessing differences in their spatial distribution. Our models indicate invasion-induced relegations of natives from the river mainstem into headwaters, with large sections of mainstem occupied by invasives. Furthermore, a concerningly small percentage of potential habitat left for natives to occupy in the mainstem is threatened, where a 100% overlap of native and invasive trout distributions is predicted. With a higher presence probability for the natives in headwaters of invaded watershed as compared to the non-invaded watershed, we highlight the headwater streams as potential refugia for the natives under invasion.
Synthesis and Applications: Our approach of basin-scale dendritic prioritization provides immediate management solutions to tackle brown trout invasion threats in Himalaya. We inform decisions on delineation of headwaters as invasion refugia for native fish, with assisted recovery of their fragmented populations in the river mainstems through targeted management of invasives
The dataset is collected through field surveys and yearly samplings across two watersheds. The data has been processed using ArcGIS and R. All the data has been made available in spatial format for easy reproducibility.
The data is provided as Landscape Network (lsn.ssn) objects, which contain the spatial files for the predictions as well as the occurrences.
Department of Science and Technology, Ministry of Science and Technology, India, Award: DST/SPLICE/ CCP/NMSHE/TF-2/ WII/2014[G]