Simulating the spread and establishment of alien species along aquatic and terrestrial transport networks: A multi‐pathway and high‐resolution approach

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 patterns of range expansion. This is partly because current analytical models fail to integrate the invader's life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters 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 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 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 (Dreissena polymorpha) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.

Application of multi-region input-output analysis to examine biosecurity risks associated with the global shipping network

The vast majority of globally traded cargo is transported via maritime shipping. Whilst in port for loading and unloading, these ships can pick up local marine organisms with internal ballast water or as external biofouling assemblages and subsequently move these to destination far beyond their natural ranges. Over the past decades, this mechanism has led to the establishment of hundreds of non-indigenous species (NIS) around global coastlines. Marine NIS cause significant environmental, economic, cultural and human health impacts. Taking effective steps to preventing their dispersal and establishment is an enduring challenge for governments and conservation agencies around the world. Here we use international commodity trade data and a Nobel-Prize-winning economic analysis technique to develop a novel approach for assessing global marine NIS transfer risks. We show that by tracing the origins and destinations of seaborne trade connections, and the nature of the traded commodities, we can predict the strength of shipping vectors and associated marine biosecurity risks. We demonstrate the utility of our approach via a case-study, where we trace the spread of a hypothetical marine NIS from Japan and show the congruence of our model results with documented invasion histories from that region. Our study demonstrates that biosecurity risk can be assessed using established economic modelling frameworks on the basis of monetary transaction data alone, and without the need for detailed itineraries of the many thousand vessels making up the global commercial fleet. Novel, cost-effective tools are needed to mitigate biosecurity risks associated with maritime trade, and to meet conservation goals while enabling economic prosperity. The modelling framework presented here can be expanded to incorporate future risk factors, life-history traits of particular NIS of concern, and even adapted to simulate the dispersal of terrestrial pests or disease agents.