Environment and shipping drive environmental DNA beta‐diversity among commercial ports - Jose Andrés; Paul Czechowski; Erin Grey; Mandana Saebi; Kara Andres; Christopher Brown; Nitesh Chawla; James J. Corbett; Rein Brys; Phillip Cassey; Nancy Correa; Marty R. Deveney; et al. (Molecular Ecology, 2023)
The spread of nonindigenous species by shipping is a large and growing global problem that harms coastal ecosystems and economies and may blur coastal biogeographical patterns. This study coupled eukaryotic environmental DNA (eDNA) metabarcoding with dissimilarity regression to test the hypothesis that ship‐borne species spread homogenizes port communities. We first collected and metabarcoded water samples from ports in Europe, Asia, Australia and the Americas. We then calculated community dissimilarities between port pairs and tested for effects of environmental dissimilarity, biogeographical region and four alternative measures of ship‐borne species transport risk. We predicted that higher shipping between ports would decrease community dissimilarity, that the effect of shipping would be small compared to that of environment dissimilarity and shared biogeography, and that more complex shipping risk metrics (which account for ballast water and stepping‐stone spread) would perform better. Consistent with our hypotheses, community dissimilarities increased significantly with environmental dissimilarity and, to a lesser extent, decreased with ship‐borne species transport risks, particularly if the ports had similar environments and stepping‐stone risks were considered. Unexpectedly, we found no clear effect of shared biogeography, and that risk metrics incorporating estimates of ballast discharge did not offer more explanatory power than simpler traffic‐based risks. Overall, we found that shipping homogenizes eukaryotic communities between ports in predictable ways, which could inform improvements in invasive species policy and management. We demonstrated the usefulness of eDNA metabarcoding and dissimilarity regression for disentangling the drivers of large‐scale biodiversity patterns. We conclude by outlining logistical considerations and recommendations for future studies using this approach.