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Areas that contain ecologically distinct biological content, called bioregions, are a central component to spatial and ecosystem‐based management. We review and describe a variety of commonly used and newly developed statistical approaches for quantitatively determining bioregions. Statistical approaches to bioregionalization can broadly be classified as two‐stage approaches that either ‘Group First, then Predict’ or ‘Predict First, then Group’, or a newer class of one‐stage approaches that simultaneously analyse biological data with reference to environmental data to generate bioregions. We demonstrate these approaches using a selection of methods applied to simulated data and real data on demersal fish. The methods are assessed against their ability to answer several common scientific or management questions. The true number of simulated bioregions was only identified by both of the one‐stage methods and one two‐stage method. When the number of bioregions was known, many of the methods, but not all, could adequately infer the species, environmental and spatial characteristics of bioregions. One‐stage approaches, however, do so directly via a single model without the need for separate post‐hoc analyses and additionally provide an appropriate characterization of uncertainty. One‐stage approaches provide a comprehensive and consistent method for objectively identifying and characterizing bioregions using both biological and environmental data. Potential avenues of future development in one‐stage methods include incorporating presence‐only and multiple data types as well as considering functional aspects of bioregions.

Marine protected areas (MPAs) are the preferred tool for preventing marine biodiversity loss, as reflected in international protected area targets. Although the area covered by MPAs is expanding, there is a concern that opposition from resource users is driving them into already low-use locations, whereas high-pressure areas remain unprotected, which has serious implications for biodiversity conservation. We tested the spatial relationships between different human-induced pressures on marine biodiversity and global MPAs. We used global, modeled pressure data and the World Database on Protected Areas to calculate the levels of 15 different human-induced pressures inside and outside the world's MPAs. We fitted binomial generalized linear models to the data to determine whether each pressure had a positive or negative effect on the likelihood of an area being protected and whether this effect changed with different categories of protection. Pelagic and artisanal fishing, shipping, and introductions of invasive species by ships had a negative relationship with protection, and this relationship persisted under even the least restrictive categories of protection (e.g., protected areas classified as category VI under the International Union for Conservation of Nature, a category that permits sustainable use). In contrast, pressures from dispersed, diffusive sources (e.g., pollution and ocean acidification) had positive relationships with protection. Our results showed that MPAs are systematically established in areas where there is low political opposition, limiting the capacity of existing MPAs to manage key drivers of biodiversity loss. We suggest that conservation efforts focus on biodiversity outcomes and effective reduction of pressures rather than prescribing area-based targets, and that alternative approaches to conservation are needed in areas where protection is not feasible.Evaluación de la Presencia de Áreas Marinas Protegidas contra sus Capacidades de Reducir las Presiones sobre la Biodiversidad Resumen Las áreas marinas protegidas (AMPs) son la herramienta preferida para prevenir la pérdida de biodiversidad marina, como se ve reflejado en los objetivos internacionales para las áreas protegidas. Mientras que el área que ocupan las MPAs está expandiéndose, existe una preocupación de que la oposición de los usuarios de recursos los esté llevando hacia localidades que ya son de bajo uso mientras que las áreas de alta presión permanecen sin protección, lo que tiene implicaciones serias para la conservación de la biodiversidad. Analizamos las relaciones espaciales entre diferentes presiones inducidas por humanos sobre la biodiversidad marina y las áreas marinas protegidas del mundo. Utilizamos datos mundiales de presiones modeladas y la Base de Datos de Áreas Protegidas para calcular los niveles de 15 diferentes presiones inducidas por humanos dentro y fuera de las MPAs del mundo. Ajustamos los modelos lineales binomiales y generalizados a los datos para determinar si cada una de las presiones tenía un efecto positivo o negativo sobre la probabilidad de que un área estuviera protegida y si este efecto cambió con diferentes categorías de protección. La pesca pelágica y artesanal, las embarcaciones, y la introducción de especies invasoras por parte de los barcos tuvieron una relación negativa con la protección y esta relación persistió incluso bajo las categorías más restrictivas de protección (es decir, áreas protegidas clasificadas bajo la categoría VI de la Unión Internacional para la Conservación de la Naturaleza, una categoría que permite el uso sostenible). Como contraste, las presiones surgidas de fuentes dispersadas y difusivas (por ejemplo, la contaminación y la acidificación del océano) tuvieron relaciones positivas con la protección. Nuestros resultados muestran que las MPAs están establecidas sistemáticamente en áreas en donde hay una baja oposición política, lo que limita la capacidad de las MPAs existentes para manejar los causantes más importantes de la pérdida de la biodiversidad. Sugerimos que los esfuerzos de conservación se enfoquen en los resultados de biodiversidad y en la reducción efectiva de las presiones en lugar de ordenar objetivos basados en el área y que se necesitan estrategias alternativas a la conservación en áreas en donde la protección no es viable.

Species population dynamics are driven by spatial and temporal changes in the environment, anthropogenic activities and conservation management actions. Understanding how populations will change in response to these drivers is fundamental to a wide range of ecological applications, but there are few open-source software options accessible to researchers and managers that allow them to predict these changes in a flexible and transparent way. We introduce an open-source, multi-platform r package, steps, that models spatial changes in species populations as a function of drivers of distribution and abundance, such as climate, disturbance, landscape dynamics and species ecological and physiological requirements. To illustrate the functionality of steps, we model the population dynamics of the greater glider Petauroides volans, an arboreal Australian mammal. We demonstrate how steps can be used to simulate population responses of the glider to forest dynamics and management with the types of data commonly used in ecological analyses. steps expands on the features found in existing software packages, can easily incorporate a range of spatial layers (e.g. habitat suitability, vegetation dynamics and disturbances), facilitates integrated and transparent analyses within a single platform and produces interpretable outputs of changes in species' populations through space and time. Further, steps offers both ready-to-use, built-in functionality, as well as the ability for advanced users to define their own modules for custom analyses. Thus, we anticipate that steps will be of significant value to environment and wildlife managers and researchers from a broad range of disciplines.

Bioregions are important tools for understanding and managing natural resources. Bioregions should describe locations of relatively homogenous assemblages of species occur, enabling managers to better regulate activities that might affect these assemblages. Many existing bioregionalization approaches, which rely on expert-derived, Delphic comparisons or environmental surrogates, do not explicitly include observed biological data in such analyses. We highlight that, for bioregionalizations to be useful and reliable for systems scientists and managers, the bioregionalizations need to be based on biological data; to include an easily understood assessment of uncertainty, preferably in a spatial format matching the bioregions; and to be scientifically transparent and reproducible. Statistical models provide a scientifically robust, transparent, and interpretable approach for ensuring that bioregions are formed on the basis of observed biological and physical data. Using statistically derived bioregions provides a repeatable framework for the spatial representation of biodiversity at multiple spatial scales. This results in better-informed management decisions and biodiversity conservation outcomes.

Addressing the challenge of protecting biodiversity in the global ocean requires a sound knowledge and understanding of the complex marine environment. Since 2008 the Global Ocean Biodiversity Initiative (GOBI) has been established as a voluntary dedicated group of marine institutions and scientists working to support conservation and protection of marine biodiversity. A focus has been work to support the Convention on Biological Diversity's Ecologically or Biologically Significant Marine Area (EBSA) process. GOBI partners have provided expert interpretation of evidence-based information and sought to compile and collate available information. An effective and coherent global network of marine protected areas must include bioregional representative replicates of features; once described, EBSAs can help focus attention on where and what kind of protective measures may be needed. GOBI is currently undertaking a 5-year programme of research funded by the German International Climate Initiative, working to strengthen baselines and contribute new data to the EBSA and other processes. This involves developing detailed biogeographies for the Pacific and Indian Oceans, assessing the movement of migratory species, advancing understanding of biodiversity at vents and seeps, developing a model governance system for the Costa Rica Thermal Dome, and incorporating Important Bird and Biodiversity Areas and Important Marine Mammal Areas. GOBI has taken initiatives to build on the results of the Census of Marine Life and ensure best available marine biodiversity information is considered by states and intergovernmental organizations. GOBI support for ocean governance, including data development and expert consultation, will also contribute to the United Nations Decade of Ocean Science for Sustainable Development (2021–2030). Future challenges include capacity building and new approaches to incorporate traditional knowledge.

1.Generalised Dissimilarity Modelling (GDM) is a statistical method for analysing and predicting patterns of turnover in species composition, usually in response to environmental gradients that vary in space and time. GDM is becoming widely applied in ecology and conservation science to interpret macro-ecological and biogeographical patterns, to support conservation assessment, predict changes in species distributions under climate change and prioritise biological surveys. 2.Inferential and predictive uncertainty is difficult to characterise using current implementations of GDM, reducing the utility of GDM in ecological risk assessment and conservation decision making. Current practice is to undertake permutation tests to assess the importance of variables in GDM. Permutation testing overcomes the issue of data-dependence (because dissimilarities are calculated on a smaller number of observations) but it does not give a quantification of uncertainty in predictions. Here, we address this issue by utilising the Bayesian bootstrap, so that the uncertainty in the observations is carried through the entire analysis (including into the predictions). 3.We tested our Bayesian Bootstrap GDM (BBGDM) approach on simulated datasets and two benthic species datasets. We fitted BBGDMs and GDMs to compare the differences in inference and prediction of compositional turnover that resulted from a coherent treatment of model uncertainty. We showed that our BBGDM approach correctly identified the signal within the data, resulting in an improved characterisation of uncertainty and enhanced model based inference. 4.We show that our approach gives appropriate parameter estimates while better representing the underlying uncertainty that arises when conducting inference and making predictions with GDMs. Our approach to fitting GDMs will provide more realistic insights into parameter and prediction uncertainty. This article is protected by copyright. All rights reserved.

The deep ocean is the largest and least-explored ecosystem on Earth, and a uniquely energy-poor environment. The distribution, drivers and origins of deep-sea biodiversity remain unknown at global scales. Here we analyse a database of more than 165,000 distribution records of Ophiuroidea (brittle stars), a dominant component of sea-floor fauna, and find patterns of biodiversity unlike known terrestrial or coastal marine realms. Both patterns and environmental predictors of deep-sea (2,000-6,500 m) species richness fundamentally differ from those found in coastal (0-20 m), continental shelf (20-200 m), and upper-slope (200-2,000 m) waters. Continental shelf to upper-slope richness consistently peaks in tropical Indo-west Pacific and Caribbean (0-30°) latitudes, and is well explained by variations in water temperature. In contrast, deep-sea species show maximum richness at higher latitudes (30-50°), concentrated in areas of high carbon export flux and regions close to continental margins. We reconcile this structuring of oceanic biodiversity using a species-energy framework, with kinetic energy predicting shallow-water richness, while chemical energy (export productivity) and proximity to slope habitats drive deep-sea diversity. Our findings provide a global baseline for conservation efforts across the sea floor, and demonstrate that deep-sea ecosystems show a biodiversity pattern consistent with ecological theory, despite being different from other planetary-scale habitats.

The biogeography of the deep-sea benthic fauna is uncertain due to the vast size and incomplete exploration of these environments. While shallow water assemblages are differentiated into tropical, temperate and polar faunas, it is unknown whether these units extend to lower depths. Here we use model-based and multivariate statistics to analyse megafaunal benthic samples along a 2300 km transect off the eastern Australian continental margin. We show that a temperate-tropical transition between 33-30°S occurs at both lower bathyal (~2500 m) and abyssal (~4000 m) depths. This transition occurs despite almost uniform temperature, salinity and dissolved oxygen concentrations occurring across latitudes at these depths. Conversely, the patterns are consistent with the flux of organic matter to the seafloor, which varies from being relatively high in the productive temperate off SE Australia to low levels in more-oligotrophic tropical waters. Biodiversity is not uniform across the deep-sea and regional-scale heterogeneity needs to be incorporated into marine park designs.

Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes.

Our knowledge of the distribution and evolution of deep-sea life is limited, impeding our ability to identify priority areas for conservation1. Here we analyse large integrated phylogenomic and distributional datasets of seafloor fauna from the sea surface to the abyss and from equator to pole of the Southern Hemisphere for an entire class of invertebrates (Ophiuroidea). We find that latitudinal diversity gradients are assembled through contrasting evolutionary processes for shallow (0-200 m) and deep (>200 m) seas. The shallow-water tropical-temperate realm broadly reflects a tropical diversification-driven process that shows exchange of lineages in both directions. Diversification rates are reversed for the realm that contains the deep sea and Antarctica; the diversification rates are highest at polar and lowest at tropical latitudes, and net exchange occurs from high to low latitudes. The tropical upper bathyal (200-700 m deep), with its rich ancient phylodiversity, is characterized by relatively low diversification and moderate immigration rates. Conversely, the young, specialized Antarctic fauna is inferred to be rebounding from regional extinctions that are associated with the rapid cooling of polar waters during the mid-Cenozoic era.

Aims Marine soft sediments cover much of the deep ocean and are one of the largest habitats in the world, yet much of our understanding about their diversity is based on sampling in the North Atlantic. The deep-sea benthos provides a simplified environment in which to explore the processes which maintain species richness. Here we investigate the influence of energy and habitat complexity on benthic species richness along an oligotrophic continental margin within the Indian Ocean. Location The upper continental margin of western Australia (c. 13–35° S, 100–1000 m depth). Methods We examined the species richness of selected polychaetes (Annelida) and crustaceans in sediment grab samples. We used generalized linear models and hierarchical partitioning to examine the relationship and relative importance of temperature, productivity (particulate organic carbon flux, net primary productivity and depth) and habitat complexity (sediment particle size diversity and grain size) on species richness at 51 sites. Results In contrast to benthic studies in the North Atlantic, we found that species richness was higher on the shelf than on the slope. Species richness was positively correlated with net primary productivity; this relationship was influenced by high species richness in two areas where oceanic mixing is known to enhance primary productivity. Habitat heterogeneity and temperature were less influential. Main conclusion This study represents one of the first extensive quantitative studies of deep-water benthos in the Indo-West Pacific, and provides further evidence that bathymetric gradients of species richness are variable between regions, probably due to variation in local oceanography and productivity regimes. Our findings provide support for the overriding influence of productivity on species richness, even over relatively small ranges in depth and productivity. As climate change is expected to modify biogeochemical fluxes to the deep seafloor, this is likely to affect the communities of deep-sea fauna.

However derived, predictions of global marine species diversity rely on existing real data. All methods, whether based on past rates of species descriptions, on expert opinion, on the fraction of undescribed species in samples collected, or on ratios between taxa in the taxonomic hierarchy, suffer the same limitation. Here we show that infaunal macrofauna (crustaceans and polychaetes) of the lower bathyal depth range are underrepresented among available data and documented results from Australia. The crustacean and polychaete fauna (only partially identified) of the bathyal continental margin of Western Australia comprised 805 species, representing a largely novel and endemic fauna. Overall, 94.6% of crustacean species were undescribed, while 72% of polychaete species were new to the Australian fauna, including all tanaidaceans, amphipods, and cumaceans, as well as most isopods. Most species were rare, and the species accumulation rate showed no sign of reaching an asymptote with increasing area sampled. Similar data are likely for the largely unexplored bathyal regions. This leads us to conclude that the numbers upon which extrapolations to larger areas are based are too low to provide confidence. The Southern Australian and Indo-West Pacific deep-sea regions contribute significantly to global species diversity. These regions and bathyal and abyssal habitats generally are extensive, but are so-far poorly sampled. They appear to be dominated by taxonomically poorly worked and species-rich taxa with limited distributions. The combination of high species richness among infaunal taxa—compared to better known taxa with larger individuals, higher endemism than presently acknowledged because of the presence of cryptic species, the low proportion of described species in these taxa, and the vast extent of unexplored bathyal and abyssal environments—will lead to further accumulation of new species as more and more deep sea regions are explored. It remains to be tested whether ratios of 10 or more undescribed to described species, found in this study for the dominant taxa and for the deep Southern Ocean and the Indo-West Pacific, are replicable in other areas. Our data and similar figures from other remote regions, and the lack of faunal overlap, suggest that Appeltans et al.’s (Current Biology 22:1–14, 2012) estimate that between one-third and two-thirds of the world’s marine fauna is undescribed is low, and that Mora et al.’s (PLoS Biol 9(8):e1001127. doi:10.1371/journal.pbio.1001127, 2011) of 91% is more probable. We conclude that estimates of global species, however made, are based on limited data.

Aim Biogeographical regions are often used as a basis for management strategies, yet a challenge for biodiversity management across broad scales is establishing biogeographical regions that are robust across taxonomic groups. Methods Finite mixture models were developed to predict multiple species assemblages termed archetypes. Modelled species archetypes were developed using Decapoda, Ophiuroidea and Polychaeta species, which were grouped based on their similar responses to oceanographic and geographical gradients. Location Outer-shelf and slope (50–1200 m) of the continental margin of Western Australia (~11° S–36° S). Results Four faunal regions were defined based on cross-taxa surrogates grouped as archetypes. These faunal regions were defined by oxygen, salinity, carbon and temperature gradients across latitude and bathymetry. Two broad latitudinal bands and two bathyal regions were described. Adjacent faunal groups were not defined by abrupt geographical breaks but rather transitions. Main conclusions These results suggest that faunal distributions were less finely resolved than existing marine bioregions on the Western Australian continental margin and that environmental gradients are correlated with distributions of benthic marine invertebrates. Identifying biogeographical regions based on these methods has the potential to inform management across a broad range of environments.

Exploration of poorly known regions of the Australian continental margin has resulted in the discovery of two new species in the scale worm family Eulepethidae. Grubeulepis kurnai sp. nov. occurs in southeastern Australia while Proeulepethus payungu sp. nov. was collected at one site in the Indian Ocean on the continental margin of Western Australia. Pareulepis malayana (Horst, 1913), also collected from the continental margin of Western Australia, is newly recorded from Australia, representing a range extension of that species previously known from Madagascar, Malaysia and the South China Sea. Four species, and four of the six known genera of Eulepethidae are now known from Australian waters. The family Eulepethidae remains species-poor compared with most polychaete families, and now comprises 21 species world wide.