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Aim: The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user-friendly, high-resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive, uniform, high-resolution and readily usable package of global environmental rasters. Location Global, marine. Methods: We compiled global coverage data, e.g. satellite-based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results: We present Bio-ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user-friendly data package for marine species distribution modelling is available for download at http://www.bio-oracle.ugent.be. The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow-water marine organisms. Main conclusions: The availability of this global environmental data package has the potential to stimulatemarine SDM. The high predictive success of the presence-only model of a notorious invasive seaweed shows that the information contained in Bio-ORACLE can be informative about marine distributions and permits building highly accurate species distribution models

Motivation The availability of user‐friendly, high‐resolution global environmental datasets is crucial for bioclimatic modelling. For terrestrial environments, WorldClim has served this purpose since 2005, but equivalent marine data only became available in 2012, with pioneer initiatives like Bio‐ORACLE providing data layers for several ecologically relevant variables. Currently, the available marine data packages have not yet been updated to the most recent Intergovernmental Panel on Climate Change (IPCC) predictions nor to present times, and are mostly restricted to the top surface layer of the oceans, precluding the modelling of a large fraction of the benthic diversity that inhabits deeper habitats. To address this gap, we present a significant update of Bio‐ORACLE for new future climate scenarios, present‐day conditions and benthic layers (near sea bottom). The reliability of data layers was assessed using a cross‐validation framework against in situ quality‐controlled data. This test showed a generally good agreement between our data layers and the global climatic patterns. We also provide a package of functions in the R software environment (sdmpredictors) to facilitate listing, extraction and management of data layers and allow easy integration with the available pipelines for bioclimatic modelling. Main types of variable contained Surface and benthic layers for water temperature, salinity, nutrients, chlorophyll, sea ice, current velocity, phytoplankton, primary productivity, iron and light at bottom. Spatial location and grain Global at 5 arcmin (c. 0.08° or 9.2 km at the equator). Time period and grain Present (2000–2014) and future (2040–2050 and 2090–2100) environmental conditions based on monthly averages. Major taxa and level of measurement Marine biodiversity associated with sea surface and epibenthic habitats. Software format ASCII and TIFF grid formats for geographical information systems and a package of functions developed for R software.

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

Given the problems of species delimitation in algae using morphology or sexual compatibility, molecular data are becoming the standard for delimiting species and testing their traditional boundaries. The idea that species are separately evolving metapopulation lineages, along with theoretical progress in phylogenetic and population genetic analyses, has led to the development of new methods of species delimitation. We review these recent developments in DNA-based species delimitation methods, and discuss how they have changed and continue to change our understanding of algal species boundaries. Although single-locus approaches have proven effective for a first rapid and large-scale assessment of species diversity, species delimitation based on single gene trees falls short due to gene tree-species tree incongruence, caused by confounding processes like incomplete lineage sorting, trans-species polymorphism, hybridization and introgression. Data from unlinked loci and multi-species coalescent methods, which combine principles from phylogenetics and population genetics, may now be able to account for these complicating factors. Several of these methods also provide statistical support regarding species boundaries, which is important because speciation is a process and therefore uncertainty about precise species boundaries is inevitable in recently diverged lineages.

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The Ulvophyceae, one of the four classes of the Chlorophyta, is of particular evolutionary interest because it features an unrivaled morphological and cytological diversity. Morphological types range from unicells and simple multicellular filaments to sheet-like and complex corticated thalli. Cytological layouts range from typical small cells containing a single nucleus and chloroplast to giant cells containing millions of nuclei and chloroplasts. In order to understand the evolution of these morphological and cytological types, the present paper aims to assess whether the Ulvophyceae are monophyletic and elucidate the ancient relationships among its orders. Our approach consists of phylogenetic analyses (maximum likelihood and Bayesian inference) of seven nuclear genes, small subunit nuclear ribosomal DNA and two plastid markers with carefully chosen partitioning strategies, and models of sequence evolution. We introduce a procedure for fast site removal (site stripping) targeted at improving phylogenetic signal in a particular epoch of interest and evaluate the specificity of fast site removal to retain signal about ancient relationships. From our phylogenetic analyses, we conclude that the ancestral ulvophyte likely was a unicellular uninucleate organism and that macroscopic growth was achieved independently in various lineages involving radically different mechanisms: either by evolving multicellularity with coupled mitosis and cytokinesis (Ulvales-Ulotrichales and Trentepohliales), by obtaining a multinucleate siphonocladous organization where every nucleus provides for its own cytoplasmic domain (Cladophorales and Blastophysa), or by developing a siphonous organization characterized by either one macronucleus or millions of small nuclei and cytoplasmic streaming (Bryopsidales and Dasycladales). We compare different evolutionary scenarios giving rise to siphonous and siphonocladous cytologies and argue that these did not necessarily evolve from a multicellular or even multinucleate state but instead could have evolved independently from a unicellular ancestor.

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