A holistic and comprehensive data approach validates the distribution of the critically endangered flapper skate (Dipturus intermedius).

Morphological similarities between skates of the genus Dipturus in the north-eastern Atlantic and Mediterranean have resulted in longstanding confusion, misidentification and misreporting. Current evidence indicates that the common skate is best explained as two species, the flapper skate (Dipturus intermedius) and the common blue skate (D. batis). However, some management and conservation initiatives developed prior to the separation continue to refer to common skate (as 'D. batis'). This taxonomic uncertainty can lead to errors in estimating population viability, distribution range, and impact on fisheries management and conservation status. Here, we demonstrate how a concerted taxonomic approach, using molecular data and a combination of survey, angler and fisheries data, in addition to expert witness statements, can be used to build a higher resolution picture of the current distribution of D. intermedius. Collated data indicate that flapper skate has a more constrained distribution compared to the perceived distribution of the 'common skate', with most observations recorded from Norway and the western and northern seaboards of Ireland and Scotland, with occasional specimens from Portugal and the Azores. Overall, the revised spatial distribution of D. intermedius has significantly reduced the extant range of the species, indicating a possibly fragmented distribution range.

A baseline for the genetic stock identification of Atlantic herring, Clupea harengus, in ICES Divisions 6.a, 7.b-c.

Atlantic herring in International Council for Exploration of the Sea (ICES) Divisions 6.a, 7.b-c comprises at least three populations, distinguished by temporal and spatial differences in spawning, which have until recently been managed as two stocks defined by geographical delineators. Outside of spawning the populations form mixed aggregations, which are the subject of acoustic surveys. The inability to distinguish the populations has prevented the development of separate survey indices and separate stock assessments. A panel of 45 single-nucleotide polymorphisms, derived from whole-genome sequencing, were used to genotype 3480 baseline spawning samples (2014-2021). A temporally stable baseline comprising 2316 herring from populations known to inhabit Division 6.a was used to develop a genetic assignment method, with a self-assignment accuracy greater than 90%. The long-term temporal stability of the assignment model was validated by assigning archive (2003-2004) baseline samples (270 individuals) with a high level of accuracy. Assignment of non-baseline samples (1514 individuals) from Divisions 6.a, 7.b-c indicated previously unrecognized levels of mixing of populations outside of the spawning season. The genetic markers and assignment models presented constitute a 'toolbox' that can be used for the assignment of herring caught in mixed survey and commercial catches in Division 6.a into their population of origin with a high level of accuracy.

Diving into the vertical dimension of elasmobranch movement ecology.

Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species. Elasmobranchs displayed high intra- and interspecific variability in vertical movement patterns. Substantial vertical overlap was observed for many epipelagic elasmobranchs, indicating an increased likelihood to display spatial overlap, biologically interact, and share similar risk to anthropogenic threats that vary on a vertical gradient. We highlight the critical next steps toward incorporating vertical movement into global management and monitoring strategies for elasmobranchs, emphasizing the need to address geographic and taxonomic biases in deployments and to concurrently consider both horizontal and vertical movements.

Correction: Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning.

[This corrects the article DOI: 10.1371/journal.pone.0188955.].

Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning.

BOOSTED REGRESSION TREES. EXCELLENT FOR DATA-POOR SPATIAL MANAGEMENT BUT HARD TO USE: Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage. BRTS AUTOMATED AND SIMPLIFIED FOR ACCESSIBLE GENERAL USE WITH RICH FEATURE SET: We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement. GBM.AUTO FOR MANAGEMENT IN VARIOUS SETTINGS: By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios.