Environment and shipping drive environmental DNA beta-diversity among commercial ports.

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.

Economic and environmental impacts of ballast water management on Small Island Developing States and Least Developed Countries.

The Ballast Water Management Convention can decrease the introduction risk of harmful aquatic organisms and pathogens, yet the Convention increases shipping costs and causes subsequent economic impacts. This paper examines whether the Convention generates disproportionate invasion risk reduction results and economic impacts on Small Island Developing States (SIDS) and Least Developed Countries (LDCs). Risk reduction is estimated with an invasion risk assessment model based on a higher-order network, and the effects of the regulation on national economies and trade are estimated with an integrated shipping cost and computable general equilibrium modeling framework. Then we use the Lorenz curve to examine if the regulation generates risk or economic inequality among regions. Risk reduction ratios of all regions (except Singapore) are above 99%, which proves the effectiveness of the Convention. The Gini coefficient of 0.66 shows the inequality in risk changes relative to income levels among regions, but risk reductions across all nations vary without particularly high risks for SIDS and LDCs than for large economies. Similarly, we reveal inequality in economic impacts relative to income levels (the Gini coefficient is 0.58), but there is no evidence that SIDS and LDCs are disproportionately impacted compared to more developed regions. Most changes in GDP, real exports, and real imports of studied regions are minor (smaller than 0.1%). However, there are more noteworthy changes for select sectors and trade partners including Togo, Bangladesh, and Dominican Republic, whose exports may decrease for textiles and metal and chemicals. We conclude the Convention decreases biological invasion risk and does not generate disproportionate negative impacts on SIDS and LDCs.

Integrated Biological Risk and Cost Model Analysis Supports a Geopolitical Shift in Ballast Water Management.

This work evaluates efficacies of plausible ballast water management strategies and standards by integrating a global species spread risk assessment with a policy cost-effectiveness analysis. Specifically, we consider species spread risks and costs of port- and vessel-based strategies under both current organism concentration standards and stricter standards proposed by California. For each scenario, we estimate species spread risks and patterns using a higher-order analysis of a global ship-borne species spread model and estimate fleet costs for vessel- and barge-based ballast water treatment systems for each standard. We find that stricter standards may reduce species spread risk by a factor of 17 globally and would greatly simplify the complex network of ship-borne species spread. The current policy of IMO standards is most cost-effectively achieved through ship-based treatment, and that any additional risk reduction will be most cost-effectively achieved by port-based (or barge-based) technologies, particularly if these are strategically implemented at the top ports within the largest clusters. Barge-based ballast water management would require a shift in governance, and we suggest that this next level of policymaking could be feasible for special areas designated by the IMO, by State or multistate authorities, or by voluntary port applications.

On the use of real-world datasets for reaction yield prediction.

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.

Ballast water-mediated species spread risk dynamics and policy implications to reduce the invasion risk to the Mediterranean Sea.

This study helps understand the ballast water-mediated species spread risk dynamics in the Mediterranean and examine potential policy options for ballast water management to further reduce species spread risk in the region. Results show that Gibraltar, Suez, and Istanbul remained high-risk ports from 2012 to 2018, and they are hub ports connecting several clusters. We reveal ballast water management implications for both the Mediterranean region and individual hub ports respectively. To further reduce the risks of individual Mediterranean hub ports beyond the IMO standards, the most effective (cost-effective) regulatory method is to set more stringent regulation towards such hub ports besides the IMO regulation. To further reduce the risks of the Mediterranean as a whole, the most effective (cost-effective) regulatory scenario is to set more stringent regulation towards all Mediterranean ports besides the IMO regulation. The barge-based method is the most cost-effective technology to achieve stricter regulations.

HONEM: Learning Embedding for Higher Order Networks.

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network, that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher order dependencies in the network. Thus, the embeddings that are generated may not accurately represent the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this study presents higher order network embedding (HONEM), a higher order network (HON) embedding method that captures the non-Markovian higher order dependencies in a network. HONEM is specifically designed for the HON structure and outperforms other state-of-the-art methods in node classification, network reconstruction, link prediction, and visualization for networks that contain non-Markovian higher order dependencies.

Higher-order patterns of aquatic species spread through the global shipping network.

The introduction and establishment of nonindigenous species (NIS) through global ship movements poses a significant threat to marine ecosystems and economies. While ballast-vectored invasions have been partly addressed by some national policies and an international agreement regulating the concentrations of organisms in ballast water, biofouling-vectored invasions remain largely unaddressed. Development of additional efficient and cost-effective ship-borne NIS policies requires an accurate estimation of NIS spread risk from both ballast water and biofouling. We demonstrate that the first-order Markovian assumption limits accurate modeling of NIS spread risks through the global shipping network. In contrast, we show that higher-order patterns provide more accurate NIS spread risk estimates by revealing indirect pathways of NIS transfer using Species Flow Higher-Order Networks (SF-HON). Using the largest available datasets of non-indigenous species for Europe and the United States, we then compare SF-HON model predictions against those from networks that consider only first-order connections and those that consider all possible indirect connections without consideration of their significance. We show that not only SF-HONs yield more accurate NIS spread risk predictions, but there are important differences in NIS spread via the ballast and biofouling vectors. Our work provides information that policymakers can use to develop more efficient and targeted prevention strategies for ship-borne NIS spread management, especially as management of biofouling is of increasing concern.

Network analysis of ballast-mediated species transfer reveals important introduction and dispersal patterns in the Arctic.

Rapid climate change has wide-ranging implications for the Arctic region, including sea ice loss, increased geopolitical attention, and expanding economic activity resulting in a dramatic increase in shipping activity. As a result, the risk of harmful non-native marine species being introduced into this critical region will increase unless policy and management steps are implemented in response. Using data about shipping, ecoregions, and environmental conditions, we leverage network analysis and data mining techniques to assess, visualize, and project ballast water-mediated species introductions into the Arctic and dispersal of non-native species within the Arctic. We first identify high-risk connections between the Arctic and non-Arctic ports that could be sources of non-native species over 15 years (1997-2012) and observe the emergence of shipping hubs in the Arctic where the cumulative risk of non-native species introduction is increasing. We then consider how environmental conditions can constrain this Arctic introduction network for species with different physiological limits, thus providing a tool that will allow decision-makers to evaluate the relative risk of different shipping routes. Next, we focus on within-Arctic ballast-mediated species dispersal where we use higher-order network analysis to identify critical shipping routes that may facilitate species dispersal within the Arctic. The risk assessment and projection framework we propose could inform risk-based assessment and management of ship-borne invasive species in the Arctic.