Impacts of non-native fishes: from global patterns to Indian contexts

Impacts of non-native fish species (NNF) on ecosystems and human communities can be substantial and in principle be either detrimental or beneficial for native species or people. Studies on NNF vary geographically, with little research comparing NNF impacts in wealthier countries to those in other parts of the world. We assessed impacts of the 20 most widely distributed fish species that are non-native to India and compared their documented impacts in India with those elsewhere in the world. We considered detrimental and beneficial environmental and socio-economic impacts, applying all four Impact Classification for Alien Taxa (ICAT) tools to assess impacts of non-native species: the IUCN Environmental Impact Classification for Alien Taxa (EICAT), EICAT+, Socio-Economic Impact Classification for Alien Taxa (SEICAT), and SEICAT+. Five NNF were classified as Data Deficient due to a lack of relevant impact records. Among the 442 relevant impact records for the 15 remaining NNF at the global scale, detrimental environmental impacts dominated (384), with fewer records pertaining to beneficial environmental (34), detrimental socio-economic (14) and beneficial socio-economic (10) impacts. A predominance of detrimental environmental impacts (35) over all other impact types (10) was also detected for India. Our results show that NNF can cause reductions in the population size of native species, particularly in macrophytes. Severity of detrimental environmental impacts (at the individual or population level of native taxa) did neither vary with transfer type (inter- or intra-realm), nor impact location (India or rest of the world), nor detection type (observed, field experiment, in-situ mesocosm, exclosure/enclosure experiment, or intervention). Based on our findings, we suggest prioritizing macrophytes for threat evaluation—while emphasizing that other taxa are not free from risk from NNF—and conducting targeted studies in India to detect impacts of NNF and fill critical impact data gaps.