2020 ESA Annual Meeting (August 3 - 6)

COS 216 Abstract - Environmental DNA port surveys identify higher order network global shipping model as best for predicting ship-borne biological invasions

David Lodge, Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, Erin K. Grey, Division of Chemistry and Biological Sciences, Governors State University, University Park, IL, Jose A. Andres, Ecology and Evolutionary Biology, Cornell University, Mandana Saebi, Department of Computer Science, University of Notre Dame, Notre Dame, IN, Paul Czechowski, Department of Anatomy, University of Otaga, Otago, New Zealand, Nitesh V. Chawla, Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, James Corbett, Marine Science and Policy, University of Delaware, Zhaojun Wang, School of Marine Science and Policy, University of Delaware, Newark, DE, Kristy Deiner, Department of Environmental Systems Science, ETH Zurich, Zurich, IN, Switzerland and Kara Andres, Department of Ecology and Evolutionary Biology, Cornell University
Background/Question/Methods

The global shipping network has spread species beyond their native ranges. However, predicting the spread of species through complex shipping networks remains challenging. We constructed competing global models to predict species invasions caused by ships’ ballast water, and tested these models using environmental DNA (eDNA) metabarcoding from water samples taken in each of 22 ports distributed across the globe. Harnessing the availability of multiple large datasets with global coverage, we parameterized our global shipping network models with ship movements, ship characteristics related to introduction of species, coastal environments (temperature and salinity), and marine ecoregions. Alternative model networks incorporated either first order ship movements only, i.e., only the origin and destination port of each ship movement, or included the most important higher order ship movements, i.e., considering the dependence of any destination on multiple past voyages, consistent with patterns in the shipping data. Both First Order Networks (FON) and Higher Order Networks (HON) were parameterized for known characteristics of ballast water vectored introductions. We tested whether the FON or HON best explained patterns of differences among ports in a genetic biodiversity metric (UNIFRAC distance) based on eDNA metabarcoding from water samples taken in each port in our study.

Results/Conclusions

As expected, ports with more similar environments were more similar in biodiversity. Ecoregions explained little variation in the UNIFRAC distance, probably because of the coarse resolution of global marine ecoregion delineations. More importantly, FON and HON models revealed that shipping homogenizes biodiversity, i.e., increased shipping connections among ports were significantly and negatively related to UNIFRAC distance. Also as predicted, the HON model explained more variation in biodiversity than the FON model. Thus we have provided a more accurate global model to predict ship-borne invasions, and have demonstrated the feasibility of efficient, standardized, global, eDNA-based coastal biodiversity surveys. Our results illustrate that the risk of species introduction differs substantially among voyages. Risk based approaches to ship management would allow management resources to be prioritized where risk could be lowered most efficiently. Surveillance and monitoring based on eDNA could dramatically enhance baseline knowledge of coastal biodiversity globally, improve the delineation of global coastal biogeographic regions, and provide near real-time early detection for incipient invasions, making rapid management responses possible.