Multi-objective optimization of an ecological assembly model

The aim of the present work is to use multi-objective evolutionary algorithms (MOEA) to parameterise an ecological assembly model based on Lotka–Volterra dynamics. In community assembly models, species are introduced from a pool of species according to a sequence of invasion. By manipulating the assembly sequences, we look at the structure of the final communities obtained by a multi-objective process where the goal is to optimize the productivity of the final communities. The MOEA must also meet the constraint that the communities constructed in this fashion have a specified connectance. The Non-dominated Sorting Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2) were employed to optimize sequences according to the multi-objective optimization problem. The results show that the assembly process using optimized sequences generated different community structure than those generated via random sequences. First, the assembled communities are much more productive than those obtained from random sequences. We show that this increase of productivity is due to the degree distribution of the community food web, which was reshaped by the optimization process. In addition, using identical regional species pools the MOEAs were able to generate communities of different expected connectances. These results demonstrate the effectiveness of NSGA-II and SPEA2 for optimizing parameters in ecological models.