Mapping marine benthic biological traits to facilitate future sustainable development

Escalating societal demands placed on the seabed mean there has never been such a pressing need to align our understanding of the relationship between the physical impact of anthropogenic activities (e.g., installation of wind turbines, demersal fishing) and the structure and function of the seabed assemblages. However, spatial differences in benthic assemblages based on empirical data are currently not adequately incorporated into decision-making processes regarding future licensable activities or wider marine spatial planning frameworks. This study demonstrates that, through harnessing a Big Data approach, large-scale, continuous coverage maps revealing differences in biological traits expressions of benthic assemblages can be produced. We present independent maps based on a suite of response traits (depicting differences in responses to natural or anthropogenically induced change) and effects traits (reflecting different functional potential), although maps derived using single traits or combinations of a range of traits are possible. Models predicting variations in response traits expression provide greater confidence than those predicting effects traits. We discuss how such maps may be used to assist in the decision-making process for the licensing of anthropogenic activities and as part of marine spatial planning approaches. The confidence in such maps to reflect spatial variations in marine benthic trait expression may, in the future, inherently be improved through (1) the inclusion of more empirical macrofaunal assemblage field data; (2) an improved knowledge of marine benthic taxa trait expression; and (3) a greater understanding of the traits responsible for determining a taxon's response to an anthropogenic pressure and a taxon's functional potential.

Modeling demersal fish and benthic invertebrate assemblages in support of marine conservation planning

Marine classification schemes based on abiotic surrogates often inform regional marine conservation planning in lieu of detailed biological data. However, these schemes may poorly represent ecologically relevant biological patterns required for effective design and management strategies. We used a community-level modeling approach to characterize and delineate representative mesoscale (tens to thousands of kilometers) assemblages of demersal fish and benthic invertebrates in the Northwest Atlantic. Hierarchical clustering of species occurrence data from four regional annual multispecies trawl surveys revealed three to six groupings (predominant assemblage types) in each survey region, broadly associated with geomorphic and oceanographic features. Indicator analyses identified 3-34 emblematic taxa of each assemblage type. Random forest classifications accurately predicted assemblage distributions from environmental covariates (AUC > 0.95) and identified thermal limits (annual minimum and maximum bottom temperatures) as important predictors of distribution in each region. Using forecasted oceanographic conditions for the year 2075 and a regional classification model, we projected assemblage distributions in the southernmost bioregion (Scotian Shelf-Bay of Fundy) under a high emissions climate scenario (RCP 8.5). Range expansions to the northeast are projected for assemblages associated with warmer and shallower waters of the Western Scotian Shelf over the 21st century as thermal habitat on the relatively cooler Eastern Scotian Shelf becomes more favorable. Community-level modeling provides a biotic-informed approach for identifying broadscale ecological structure required for the design and management of ecologically coherent, representative, well-connected networks of Marine Protected Areas. When combined with oceanographic forecasts, this modeling approach provides a spatial tool for assessing sensitivity and resilience to climate change, which can improve conservation planning, monitoring, and adaptive management.

What's hot and what's not: Making sense of biodiversity 'hotspots'

Conserving biogeographic regions with especially high biodiversity, known as biodiversity 'hotspots', is intuitive because finite resources can be focussed towards manageable units. Yet, biodiversity, environmental conditions and their relationship are more complex with multidimensional properties. Assessments which ignore this risk failing to detect change, identify its direction or gauge the scale of appropriate intervention. Conflicting concepts which assume assemblages as either sharply delineated communities or loosely collected species have also hampered progress in the way we assess and conserve biodiversity. We focus on the marine benthos where delineating manageable areas for conservation is an attractive prospect because it holds most marine species and constitutes the largest single ecosystem on earth by area. Using two large UK marine benthic faunal datasets, we present a spatially gridded data sampling design to account for survey effects which would otherwise be the principal drivers of diversity estimates. We then assess γ-diversity (regional richness) with diversity partitioned between α (local richness) and β (dissimilarity), and their change in relation to covariates to test whether defining and conserving biodiversity hotspots is an effective conservation strategy in light of the prevailing forces structuring those assemblages. α-, β- and γ-diversity hotspots were largely inconsistent with each metric relating uniquely to the covariates, and loosely collected species generally prevailed with relatively few distinct assemblages. Hotspots could therefore be an unreliable means to direct conservation efforts if based on only a component part of diversity. When assessed alongside environmental gradients, α-, β- and γ-diversity provide a multidimensional but still intuitive perspective of biodiversity change that can direct conservation towards key drivers and the appropriate scale for intervention. Our study also highlights possible temporal declines in species richness over 30 years and thus the need for future integrated monitoring to reveal the causal drivers of biodiversity change.

Limitations of Predicting Substrate Classes on a Sedimentary Complex but Morphologically Simple Seabed

The ocean floor, its species and habitats are under pressure from various human activities. Marine spatial planning and nature conservation aim to address these threats but require sufficiently detailed and accurate maps of the distribution of seabed substrates and habitats. Benthic habitat mapping has markedly evolved as a discipline over the last decade, but important challenges remain. To test the adequacy of current data products and classification approaches, we carried out a comparative study based on a common dataset of multibeam echosounder bathymetry and backscatter data, supplemented with groundtruth observations. The task was to predict the spatial distribution of five substrate classes (coarse sediments, mixed sediments, mud, sand, and rock) in a highly heterogeneous area of the south-western continental shelf of the United Kingdom. Five different supervised classification methods were employed, and their accuracy estimated with a set of samples that were withheld. We found that all methods achieved overall accuracies of around 50%. Errors of commission and omission were acceptable for rocky substrates, but high for all sediment types. We predominantly attribute the low map accuracy regardless of mapping approach to inadequacies of the selected classification system, which is required to fit gradually changing substrate types into a rigid scheme, low discriminatory power of the available predictors, and high spatial complexity of the site relative to the positioning accuracy of the groundtruth equipment. Some of these issues might be alleviated by creating an ensemble map that aggregates the individual outputs into one map showing the modal substrate class and its associated confidence or by adopting a quantitative approach that models the spatial distribution of sediment fractions. We conclude that further incremental improvements to the collection, processing and analysis of remote sensing and sample data are required to improve map accuracy. To assess the progress in benthic habitat mapping we propose the creation of benchmark datasets.

Legacy Data: How Decades of Seabed Sampling can Produce Robust Predictions and Versatile Products

Sediment maps developed from categorical data are widely applied to support marine spatial planning across various fields. However, deriving maps independently of sediment classification potentially improves our understanding of environmental gradients and reduces issues of harmonising data across jurisdictional boundaries. As the groundtruth samples are often measured for the fractions of mud, sand and gravel, this data can be utilised more effectively to produce quantitative maps of sediment composition. Using harmonised data products from a range of sources including the European Marine Observation and Data Network (EMODnet), spatial predictions of these three sediment fractions were generated for the north-west European continental shelf using the random forest algorithm. Once modelled these sediment fraction maps were classified using a range of schemes to show the versatility of such an approach, and spatial accuracy maps were generated to support their interpretation. The maps produced in this study are to date the highest resolution quantitative sediment composition maps that have been produced for a study area of this extent and are likely to be of interest for a wide range of applications such as ecological and biophysical studies.