6533b873fe1ef96bd12d4c23

RESEARCH PRODUCT

Generalist invasion in a complex lake food web

subject

description

Invasive species constitute a threat not only to native populations but also to the structure and functioning of entire food webs. Despite being considered as a global problem, only a small number of studies have quantitatively predicted the food web-level consequences of invasions. Here, we use an allometric trophic network model parameterized using empirical data on species body masses and feeding interactions to predict the effects of a possible invasion of Amur sleeper (Perccottus glenii), on a well-studied lake ecosystem. We show that the modeled establishment of Amur sleeper decreased the biomasses o ftop predator fishes by about 10%–19%. These reductions were largely explained by increased larval competition for food and Amur sleeper predation on fish larvae. In contrast, biomasses of less valued fish of lower trophic positions increased by about 0.4%–9% owing to reduced predation pressure by top piscivores. The predicted impact of Amur sleeper establishment on the biomasses of native fish species vastly exceeded the impacts of current-dayfishing pressures. H2020 European Research Council, Grant/Award Number: COMPLEX-FISH770884; Academy of Finland, Grant/Award Numbers: 317495, 325107,340901; Natural Sciences and Engineering Research Council of Canada; Estonian Research Council, Grant/Award Numbers: PSG32, PRG1167, PRG709, MOBJD29; Estonian University of Life Sciences, Grant/Award Number: P190254PKKH; European Union's Horizon 2020 Research and Innovation Programme, Grant/Award Number: TREICLAKE 951963 H2020 European Research Council, Grant/Award Number: COMPLEX-FISH770884; Academy of Finland, Grant/Award Numbers: 317495, 325107,340901; Natural Sciences and EngineeringResearch Council of Canada; EstonianResearch Council, Grant/Award Numbers: PSG32, PRG1167, PRG709, MOBJD29; Estonian University of Life Sciences, Grant/Award Number: P190254PKKH; European Union's Horizon 2020 Research and Innovation Programme, Grant/AwardNumber: TREICLAKE 951963