Abstract
Biological invasions are a major stressor on ecosystems worldwide, but tools to predict their predatory impact remain limited. Here, we quantified invader impacts using two complementary approaches: functional responses (to reveal per capita and multiple predator interaction strengths) and ecomorphology (to reveal trophic profiles and competitive overlap). We compared Mozambique tilapia Oreochromis mossambicus, a native southern African cichlid, and a near-trophically analogous invasive congener, the Nile tilapia Oreochromis niloticus . Both Nile tilapia and Mozambique tilapia exhibited a potentially prey population destabilizing Type II functional response. In both single and multiple predator pairings, invasive Nile tilapia had significantly greater prey consumption rates than native Mozambique tilapia, and thereby a greater predatory impact than its native congeneric. Attack rates were greater for Nile tilapia than Mozambique tilapia, with both species showing more similar handling times and maximum feeding rates. No evidence for multiple predator effects was detected within or between these species, and therefore impacts of both species increased additively in the presence of conspecific or heterospecific competitors. Morphological trait analyses found general differences between these two species, with the invasive Nile tilapia having distinctively larger lower jaw closing force, gill resistance and gill raker length, which facilitated greater feeding capacities over the native species. Trophic profiles predicted using morphological trait differences showed high dietary overlap and served as evidence for potential exploitative competition between the two species. These results reveal superior interaction strengths and ecomorphological trait profiles of an invasive over native species which could influence impact and native species replacement dynamics. Novel applications of functional response and ecomorphology provide complementary insights into predatory and competitive impacts from invasive species, aiding impact prediction across environmental contexts.