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Interspecific hybridization is an important evolutionary phenomenon that generates genetic variability in a population and fosters species diversity in nature. The availability of large genome scale data sets has revolutionized hybridization studies to shift from the observation of the presence or absence of hybrids to the investigation of the genomic constitution of hybrids and their genome-specific evolutionary dynamics. Although a handful of methods have been proposed in an attempt to identify hybrids, accurate detection of hybridization from genomic data remains a challenging task. In addition to methods that infer phylogenetic networks or that utilize pairwise divergence, site pattern frequency based and population genetic clustering approaches are popularly used in practice, though the performance of these methods under different hybridization scenarios has not been extensively examined. Here, we use simulated data to comparatively evaluate the performance of four tools that are commonly used to infer hybridization events: the site pattern frequency based methods HyDe and the $D$-statistic (i.e., the ABBA-BABA test) and the population clustering approaches structure and ADMIXTURE. We consider single hybridization scenarios that vary in the time of hybridization and the amount of incomplete lineage sorting (ILS) for different proportions of parental contributions ($\gamma$); introgressive hybridization; multiple hybridization scenarios; and a mixture of ancestral and recent hybridization scenarios. We focus on the statistical power to detect hybridization and the false discovery rate (FDR) for comparisons of the $D$-statistic and HyDe, and the accuracy of the estimates of $\gamma$ as measured by the mean squared error for HyDe, structure, and ADMIXTURE. Both HyDe and the $D$-statistic are powerful for detecting hybridization in all scenarios except those with high ILS, although the $D$-statistic often has an unacceptably high FDR. The estimates of $\gamma$ in HyDe are impressively robust and accurate whereas structure and ADMIXTURE sometimes fail to identify hybrids, particularly when the proportional parental contributions are asymmetric (i.e., when $\gamma$ is close to 0). Moreover, the posterior distribution estimated using structure exhibits multimodality in many scenarios, making interpretation difficult. Our results provide guidance in selecting appropriate methods for identifying hybrid populations from genomic data. [ABBA-BABA test; ADMIXTURE; hybridization; HyDe; introgression; Patterson’s $D$-statistic; Structure.]

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Median‐joining (MJ) was proposed as a method for phylogeographical analysis and is enjoying increasing popularity. Herein, we evaluate the efficacy of the approach as originally intended. We show that median‐joining networks (MJNs) are theoretically untenable for evolutionary inference, and that confusion has afflicted their use for over 15 years. The approach has two obvious shortcomings: its reliance on distance‐based phenetics (overall similarity instead of character transformations) and the lack of rooting (no direction or history). Given that evolution involves both change and time, and the absence of rooting removes time (ancestor–descendant relationships) from the equation, the approach cannot yield defensible evolutionary interpretations. We also examine the impact of MJ analyses on evolutionary biology via an analysis of citations and conclude that the spread of MJNs through the literature is difficult to explain, especially given the availability of character‐based analyses.

Ballast water is a leading vector for the introduction of aquatic invasive species worldwide and, once a novel species is established, regional ballast water exchange between ports can accelerate secondary spread. The importance of shipping induced invasions in the Laurentian Great Lakes has resulted in policies that require more stringent ballast water treatment standards for transoceanic shipping than is required of ships operating regionally within the Great Lakes. As a result, ballast water discharges within the Great Lakes are not well regulated, primarily because of the challenge of treating the high volumes of water carried by vessels that are confined to the waters of the Great Lakes. We used a discrete-time Markov chain model on a network with annual time-steps to simulate ballast water management scenarios at high-priority ports in the Great Lakes shipping network for two potential invaders, golden mussel (Limnoperna fortunei) and monkey goby (Neogobius fluviatilis). We chose high-priority ports by using graph-theoretic network analysis techniques to calculate six network centrality metrics for 151 ports in the network. Ports scoring high in network centrality scores have more ties with other ports or are positioned within the network such that they potentially have greater influence over the secondary spread of aquatic invasive species than other ports. We simulated secondary spread scenarios where hypothetical ballast water treatment was implemented at the top twenty ranked ports in each network centrality metric, as well as the top twenty busiest ports by ship arrivals. The results of each scenario were compared to a scenario where no management action was taken. Simulated secondary spread for both golden mussel and monkey goby resulted in significantly reduced infestation probabilities (p < 0.001) under all management scenarios when compared to unmanaged spread scenarios. Management at ports with inwardly directed ties to other ports reduced infestations by the greatest amount compared to other management scenarios; 65.4% for golden mussel and 74.6% for monkey goby. The indegree centrality of ports in the Great Lakes was found to be an important factor in governing secondary spread. Here we show that prioritized management, like high volume shore based treatment systems based on network centrality, is a potentially effective strategy for impeding the secondary spread of new or localized invasive species in the Great Lakes.