The Scientific Paper of the Future (SPF) concept, initiated by the EarthCube OntoSoft Funded Project, encourages scientists to publish not only peer-reviewed journal articles, but also all associated data, software (data processing scripts), and computational workflows, in order to enable full science reproducibility. While the SPF concept was originally aimed at geoscientists, it can also be applied to interdisciplinary projects such as between ecology, economics, and maritime shipping. Multi-region input-output (MRIO) analysis is a method from economics for analyzing economic interdependencies between different regional entities. Entities can be countries, regions within a country, or groups of countries. MRIO can also be used to analyze other types of interdependencies, such as the environmental impact of one region’s activities on another. For this project, we use MRIO to analyze the global spread of marine non-indigenous species via cargo ships. Over 90% of global trade occurs by maritime shipping. Along with intended cargo, ships provide a means for marine organisms to move to locations beyond their natural ranges, mainly via hull fouling or in ballast tanks. These species can have harmful ecological and economic impacts at their destinations. By using MRIO to follow the imports and exports of commodities between countries, we can deduce the magnitude of seaborne trade connections based on physical volume of commodity traded, and therefore the magnitude and geographic distribution of marine biosecurity risk. MRIO model construction involved incorporating a diversity of data types from ecology, economics, and shipping, and has turned out to be a surprisingly complex endeavor. My poster will demonstrate the principles of an SPF by providing a diagram of the computational workflow involved in the model’s construction, including an explanation for each dataset incorporated into the model’s input parameters and each piece of software written to process the data and assemble and run the model.

Environmental DNA (eDNA) analyses are powerful for describing marine biodiversity but must be optimized for their effective use in routine monitoring. To maximize eDNA detection probabilities of sparsely distributed populations, water samples are usually concentrated from larger volumes and filtered using fine-pore membranes, often a significant cost-time bottleneck in the workflow. This study aimed to streamline eDNA sampling by investigating plankton net versus bucket sampling, direct versus sequential filtration including self-preserving filters. Biodiversity was assessed using metabarcoding of the small ribosomal subunit (18S rRNA) and mitochondrial cytochrome c oxidase I (COI) genes. Multi-species detection probabilities were estimated for each workflow using a probabilistic occupancy modelling approach. Significant workflow-related differences in biodiversity metrics were reported. Highest amplicon sequence variant (ASV) richness was attained by the bucket sampling combined with self-preserving filters, comprising a large portion of micro-plankton. Less diversity but more metazoan taxa were captured in the net samples combined with 5 µm pore size filters. Pre-filtered 1.2 µm samples yielded few or no unique ASVs. The highest average (~32%) metazoan detection probabilities in the 5 µm pore size net samples confirmed the effectiveness of pre-concentrating plankton for biodiversity screening. These results contribute to streamlining eDNA sampling protocols for uptake and implementation in marine biodiversity research and surveillance.