A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle.

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine spatiotemporal patterns of range expansion. This knowledge gap may stem from two reasons. First, current analytical models fail to integrate the invader's life-history dynamics with heterogeneity in human-mediated dispersal patterns. Second, classical statistical methods often fail to provide reliable estimates of model parameters, such as time and place of species introduction and life-history characteristics, due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads radially along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a hierarchical Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (