122 resultados para ECOLOGICAL CONCENTRATION


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1.Identifying priority areas for marine vertebrate conservation is complex because species of conservation concern are highly mobile, inhabit dynamic habitats and are difficult to monitor. 2.Many marine vertebrates are known to associate with oceanographic fronts – physical interfaces at the transition between water masses – for foraging and migration, making them important candidate sites for conservation. Here, we review associations between marine vertebrates and fronts and how they vary with scale, regional oceanography and foraging ecology. 3.Accessibility, spatiotemporal predictability and relative productivity of front-associated foraging habitats are key aspects of their ecological importance. Predictable mesoscale (10s–100s km) regions of persistent frontal activity (‘frontal zones’) are particularly significant. 4.Frontal zones are hotspots of overlap between critical habitat and spatially explicit anthropogenic threats, such as the concentration of fisheries activity. As such, they represent tractable conservation units, in which to target measures for threat mitigation. 5.Front mapping via Earth observation (EO) remote sensing facilitates identification and monitoring of these hotspots of vulnerability. Seasonal or climatological products can locate biophysical hotspots, while near-real-time front mapping augments the suite of tools supporting spatially dynamic ocean management. 6.Synthesis and applications. Frontal zones are ecologically important for mobile marine vertebrates. We surmise that relative accessibility, predictability and productivity are key biophysical characteristics of ecologically significant frontal zones in contrasting oceanographic regions. Persistent frontal zones are potential priority conservation areas for multiple marine vertebrate taxa and are easily identifiable through front mapping via EO remote sensing. These insights are useful for marine spatial planning and marine biodiversity conservation, both within Exclusive Economic Zones and in the open oceans.

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The Red Sea is a semi-enclosed tropical marine ecosystem that stretches from the Gulf of Suez and Gulf of Aqaba in the north, to the Gulf of Aden in the south. Despite its ecological and economic importance, its biological environment is relatively unexplored. Satellite ocean-colour estimates of chlorophyll concentration (an index of phytoplankton biomass) offer an observational platform to monitor the health of the Red Sea. However, little is known about the optical properties of the region. In this paper, we investigate the optical properties of the Red Sea in the context of satellite ocean-colour estimates of chlorophyll concentration. Making use of a new merged ocean-colour product, from the European Space Agency (ESA) Climate Change Initiative, and in situ data in the region, we test the performance of a series of ocean-colour chlorophyll algorithms. We find that standard algorithms systematically overestimate chlorophyll when compared with the in situ data. To investigate this bias we develop an ocean-colour model for the Red Sea, parameterised to data collected during the Tara Oceans expedition, that estimates remote-sensing reflectance as a function of chlorophyll concentration. We used the Red Sea model to tune the standard chlorophyll algorithms and the overestimation in chlorophyll originally observed was corrected. Results suggest that the overestimation was likely due to an excess of CDOM absorption per unit chlorophyll in the Red Sea when compared with average global conditions. However, we recognise that additional information is required to test the influence of other potential sources of the overestimation, such as aeolian dust, and we discuss uncertainties in the datasets used. We present a series of regional chlorophyll algorithms for the Red Sea, designed for a suite of ocean-colour sensors, that may be used for further testing.

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Phytoplankton, at the base of the marine food web, represent a fundamental food source in coral reef ecosystems. The timing (phenology) and magnitude of the phytoplankton biomass are major determinants of trophic interactions. The Red Sea is one of the warmest and most saline basins in the world, characterized by an arid tropical climate regulated by the monsoon. These extreme conditions are particularly challenging for marine life. Phytoplankton phenological indices provide objective and quantitative metrics to characterize phytoplank- ton seasonality. The indices i.e. timings of initiation, peak, termination and duration are estimated here using 15 years (1997–2012) of remote sensing ocean-color data from the European Space Agency (ESA) Climate Change Initiative project (OC-CCI) in the entire Red Sea basin. The OC-CCI product, comprising merged and bias-corrected observations from three independent ocean-color sensors (SeaWiFS, MODIS and MERIS), and processed using the POLYMER algorithm (MERIS period), shows a significant increase in chlorophyll data cover- age, especially in the southern Red Sea during the months of summer NW monsoon. In open and reef-bound coastal waters, the performance of OC-CCI chlorophyll data is shown to be comparable with the performance of other standard chlorophyll products for the global oceans. These features have permitted us to investigate phytoplankton phenology in the entire Red Sea basin, and during both winter SE monsoon and summer NW monsoon periods. The phenological indices are estimated in the four open water provinces of the basin, and further examined at six coral reef complexes of particular socio-economic importance in the Red Sea, including Siyal Islands, Sharm El Sheikh, Al Wajh bank, Thuwal reefs, Al Lith reefs and Farasan Islands. Most of the open and deeper waters of the basin show an apparent higher chlorophyll concentration and longer duration of phyto- plankton growth during the winter period (relative to the summer phytoplankton growth period). In contrast, most of the reef-bound coastal waters display equal or higher peak chlorophyll concentrations and equal or lon- ger duration of phytoplankton growth during the summer period (relative to the winter phytoplankton growth period). The ecological and biological significance of the phytoplankton seasonal characteristics are discussed in context of ecosystem state assessment, and particularly to support further understanding of the structure and functioning of coral reef ecosystems in the Red Sea.

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Phytoplankton total chlorophyll concentration (TCHLa) and phytoplankton size structure are two important ecological indicators in biological oceanography. Using high performance liquid chromatography (HPLC) pigment data, collected from surface waters along the Atlantic Meridional Transect (AMT), we examine temporal changes in TCHLa and phytoplankton size class (PSC: micro-, nano- and pico-phytoplankton) between 2003 and 2010 (September to November cruises only), in three ecological provinces of the Atlantic Ocean. The HPLC data indicate no significant change in TCHLa in northern and equatorial provinces, and an increase in the southern province. These changes were not significantly different to changes in TCHLa derived using satellite ocean-colour data over the same study period. Despite no change in AMT TCHLa in northern and equatorial provinces, significant differences in PSC were observed, related to changes in key diagnostic pigments (fucoxanthin, peridinin, 19′-hexanoyloxyfucoxanthin and zeaxanthin), with an increase in small cells (nano- and pico-phytoplankton) and a decrease in larger cells (micro-phytoplankton). When fitting a three-component model of phytoplankton size structure — designed to quantify the relationship between PSC and TCHLa to each AMT cruise, model parameters varied over the study period. Changes in the relationship between PSC and TCHLa have wide implications in ecology and marine biogeochemistry, and provide key information for the development and use of empirical ocean-colour algorithms. Results illustrate the importance of maintaining a time-series of in-situ observations in remote regions of the ocean, such as that acquired in the AMT programme.

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Integrated marine planning, which must take into consideration environmental and social impacts, is being introduced widely in Europe, the USA, Australia and elsewhere. Installation of offshore windfarms creates impacts both on local marine ecosystems and the view of the seascape and is one of multiple activities in the marine area that must be addressed by marine planning. The impacts on people's values (and hence welfare) of changes in ecology and amenity that could arise from the installation of a windfarm in the Irish Sea were assessed using a discrete choice experiment administered through an online survey. The ecological changes investigated were: increased species diversity resulting from artificial reef effects, and the effect of electromagnetic fields from subsea cables on marine life; whilst the amenity change was the visibility of offshore turbines from land. Respondents expressed preferences for ecological improvements but had less clear preferences regarding the height and visibility of the turbines. In particular distance decay effects were observed with respondents further away from the coast being less concerned about visual impact created by offshore turbines. Understanding ecological and amenity impacts and how they are valued by people can support the decisions made within marine planning and licensing.

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Phytoplankton size structure is an important indicator of the state of the pelagic ecosystem. Stimulated by the paucity of in situ observations on size structure, and by the sampling advantages of autonomous remote platforms, new efforts are being made to infer the size-structure of the phytoplankton from oceanographic variables that may be measured at high temporal and spatial resolution, such as total chlorophyll concentration. Large-scale analysis of in situ data has revealed coherent relationships between size-fractionated chlorophyll and total chlorophyll that can be quantified using the three-component model of Brewin et al. (2010). However, there are variations surrounding these general relationships. In this paper, we first revise the three-component model using a global dataset of surface phytoplankton pigment measurements. Then, using estimates of the average irradiance in the mixed-layer, we investigate the influence of ambient light on the parameters of the three-component model. We observe significant relationships between model parameters and the average irradiance in the mixed-layer, consistent with ecological knowledge. These relationships are incorporated explicitly into the three-component model to illustrate variations in the relationship between size-structure and total chlorophyll, ensuing from variations in light availability. The new model may be used as a tool to investigate modifications in size-structure in the context of a changing climate.

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This paper reviews the utility and availability of biological and ecological traits for marine species so as to prioritise the development of a world database on marine species traits. In addition, the ‘status’ of species for conservation, that is, whether they are introduced or invasive, of fishery or aquaculture interest, harmful, or used as an ecological indicator, were reviewed because these attributes are of particular interest to society. Whereas traits are an enduring characteristic of a species and/or population, a species status may vary geographically and over time. Criteria for selecting traits were that they could be applied to most taxa, were easily available, and their inclusion would result in new research and/or management applications. Numerical traits were favoured over categorical. Habitat was excluded as it can be derived from a selection of these traits. Ten traits were prioritized for inclusion in the most comprehensive open access database on marine species (World Register of Marine Species), namely taxonomic classification, environment, geography, depth, substratum, mobility, skeleton, diet, body size and reproduction. These traits and statuses are being added to the database and new use cases may further subdivide and expand upon them.

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Sea ice in the western Antarctic Peninsula (WAP) region is both highly variable and rapidly changing. In the Palmer Station region, the ice season duration has decreased by 92 d since 1978. The sea-ice changes affect ocean stratification and freshwater balance and in turn impact every component of the polar marine ecosystem. Long-term observations from the WAP nearshore and offshore regions show a pattern of chlorophyll (Chl) variability with three to five years of negative Chl anomalies interrupted by one or two years of positive anomalies (high and low Chl regimes). Both field observations and results from an inverse food-web model show that these high and low Chl regimes differed significantly from each other, with high primary productivity and net community production (NCP) and other rates associated with the high Chl years and low rates with low Chl years. Gross primary production rates (GPP) averaged 30 mmolC.m-2.d-1 in the low Chl years and 100 mmolC.m-2.d-1 in the high Chl years. Both large and small phytoplankton were more abundant and more productive in high Chl years than in low Chl years. Similarly, krill were more important as grazers in high Chl years, but did not differ from microzooplankton in high or low Chl years. Microzooplankton did not differ between high and low Chl years. Net community production differed significantly between high and low Chl years, but mobilized a similar proportion of GPP in both high and low Chl years. The composition of the NCP was uniform in high and low Chl years. These results mphasize the importance of microbial components in the WAP plankton system and suggest that food webs dominated by small phytoplankton can have pathways that funnel production into NCP, and likely, export.

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The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent sublittoral rock habitats in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. They can be used to identify critical aspects of an ecosystem that may be studied further, or serve as the basis for the selection of indicators for environmental monitoring purposes. The models produced by this project are control diagrams, representing the unimpacted state of the environment free from anthropogenic pressures. It is intended that the models produced by this project will be used to guide indicator selection for the monitoring of this habitat in UK waters. CEMs may eventually be produced for a range of habitat types defined under the UK Marine Biodiversity Monitoring R&D Programme (UKMBMP), which, along with stressor models, are designed to show the interactions within impacted habitats, would form the basis of a robust method for indicator selection. This project builds on the work to develop CEMs for shallow sublittoral coarse sediment habitats (Alexander et al 2014). The project scope included those habitats defined as ‘sublittoral rock’. This definition includes those habitats that fall into the EUNIS Level 3 classifications A3.1 Atlantic and Mediterranean high energy infralittoral rock, A3.2 Atlantic and Mediterranean moderate energy infralittoral rock, A3.3 Atlantic and Mediterranean low energy infralittoral rock, A4.1 Atlantic and Mediterranean high energy circalittoral rock, A4.2 Atlantic and Mediterranean moderate energy circalittoral rock, and A4.3 Atlantic and Mediterranean low energy circalittoral rock as well as the constituent Level 4 and 5 biotopes that are relevant to UK waters. A species list of characterising fauna to be included within the scope of the models was identified using an iterative process to refine the full list of species found within the relevant Level 5 biotopes. A literature review was conducted using a pragmatic and iterative approach to gather evidence regarding species traits and information that would be used to inform the models and characterise the interactions that occur within the sublittoral rock habitat. All information gathered during the literature review was entered into a data logging pro-forma spreadsheet that accompanies this report. Wherever possible, attempts were made to collect information from UK-specific peer-reviewed studies, although other sources were used where necessary. All data gathered was subject to a detailed confidence assessment. Expert judgement by the project team was utilised to provide information for aspects of the models for which references could not be sourced within the project timeframe. A multivariate analysis approach was adopted to assess ecologically similar groups (based on ecological and life history traits) of fauna from the identified species to form the basis of the models. A model hierarchy was developed based on these ecological groups. One general control model was produced that indicated the high-level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in sublittoral rock habitats. In addition to this, seven detailed sub-models were produced, which each focussed on a particular ecological group of fauna within the habitat: ‘macroalgae’, ‘temporarily or permanently attached active filter feeders’, ‘temporarily or permanently attached passive filter feeders’, ‘bivalves, brachiopods and other encrusting filter feeders’, ‘tube building fauna’, ‘scavengers and predatory fauna’, and ‘non-predatory mobile fauna’. Each sub-model is accompanied by an associated confidence model that presents confidence in the links between each model component. The models are split into seven levels and take spatial and temporal scale into account through their design, as well as magnitude and direction of influence. The seven levels include regional to global drivers, water column processes, local inputs/processes at the seabed, habitat and biological assemblage, output processes, local ecosystem functions, and regional to global ecosystem functions. The models indicate that whilst the high level drivers that affect each ecological group are largely similar, the output processes performed by the biota and the resulting ecosystem functions vary both in number and importance between groups. Confidence within the models as a whole is generally high, reflecting the level of information gathered during the literature review. Physical drivers which influence the ecosystem were found to be of high importance for the sublittoral rock habitat, with factors such as wave exposure, water depth and water currents noted to be crucial in defining the biological assemblages. Other important factors such as recruitment/propagule supply, and those which affect primary production, such as suspended sediments, light attenuation and water chemistry and temperature, were also noted to be key and act to influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. Output processes performed by the biological assemblages are variable between ecological groups depending on the specific flora and fauna present and the role they perform within the ecosystem. Of particular importance are the outputs performed by the macroalgae group, which are diverse in nature and exert influence over other ecological groups in the habitat. Important output processes from the habitat as a whole include primary and secondary production, bioengineering, biodeposition (in mixed sediment habitats) and the supply of propagules; these in turn influence ecosystem functions at the local scale such as nutrient and biogeochemical cycling, supply of food resources, sediment stability (in mixed sediment habitats), habitat provision and population and algae control. The export of biodiversity and organic matter, biodiversity enhancement and biotope stability are the resulting ecosystem functions that occur at the regional to global scale. Features within the models that are most useful for monitoring habitat status and change due to natural variation have been identified, as have those that may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Biological, physical and chemical features of the ecosystem have been identified as potential indicators to monitor natural variation, whereas biological factors and those physical /chemical factors most likely to affect primary production have predominantly been identified as most likely to indicate change due to anthropogenic pressures.

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The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent sublittoral rock habitats in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. They can be used to identify critical aspects of an ecosystem that may be studied further, or serve as the basis for the selection of indicators for environmental monitoring purposes. The models produced by this project are control diagrams, representing the unimpacted state of the environment free from anthropogenic pressures. It is intended that the models produced by this project will be used to guide indicator selection for the monitoring of this habitat in UK waters. CEMs may eventually be produced for a range of habitat types defined under the UK Marine Biodiversity Monitoring R&D Programme (UKMBMP), which, along with stressor models, are designed to show the interactions within impacted habitats, would form the basis of a robust method for indicator selection. This project builds on the work to develop CEMs for shallow sublittoral coarse sediment habitats (Alexander et al 2014). The project scope included those habitats defined as ‘sublittoral rock’. This definition includes those habitats that fall into the EUNIS Level 3 classifications A3.1 Atlantic and Mediterranean high energy infralittoral rock, A3.2 Atlantic and Mediterranean moderate energy infralittoral rock, A3.3 Atlantic and Mediterranean low energy infralittoral rock, A4.1 Atlantic and Mediterranean high energy circalittoral rock, A4.2 Atlantic and Mediterranean moderate energy circalittoral rock, and A4.3 Atlantic and Mediterranean low energy circalittoral rock as well as the constituent Level 4 and 5 biotopes that are relevant to UK waters. A species list of characterising fauna to be included within the scope of the models was identified using an iterative process to refine the full list of species found within the relevant Level 5 biotopes. A literature review was conducted using a pragmatic and iterative approach to gather evidence regarding species traits and information that would be used to inform the models and characterise the interactions that occur within the sublittoral rock habitat. All information gathered during the literature review was entered into a data logging pro-forma spreadsheet that accompanies this report. Wherever possible, attempts were made to collect information from UK-specific peer-reviewed studies, although other sources were used where necessary. All data gathered was subject to a detailed confidence assessment. Expert judgement by the project team was utilised to provide information for aspects of the models for which references could not be sourced within the project timeframe. A multivariate analysis approach was adopted to assess ecologically similar groups (based on ecological and life history traits) of fauna from the identified species to form the basis of the models. A model hierarchy was developed based on these ecological groups. One general control model was produced that indicated the high-level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in sublittoral rock habitats. In addition to this, seven detailed sub-models were produced, which each focussed on a particular ecological group of fauna within the habitat: ‘macroalgae’, ‘temporarily or permanently attached active filter feeders’, ‘temporarily or permanently attached passive filter feeders’, ‘bivalves, brachiopods and other encrusting filter feeders’, ‘tube building fauna’, ‘scavengers and predatory fauna’, and ‘non-predatory mobile fauna’. Each sub-model is accompanied by an associated confidence model that presents confidence in the links between each model component. The models are split into seven levels and take spatial and temporal scale into account through their design, as well as magnitude and direction of influence. The seven levels include regional to global drivers, water column processes, local inputs/processes at the seabed, habitat and biological assemblage, output processes, local ecosystem functions, and regional to global ecosystem functions. The models indicate that whilst the high level drivers that affect each ecological group are largely similar, the output processes performed by the biota and the resulting ecosystem functions vary both in number and importance between groups. Confidence within the models as a whole is generally high, reflecting the level of information gathered during the literature review. Physical drivers which influence the ecosystem were found to be of high importance for the sublittoral rock habitat, with factors such as wave exposure, water depth and water currents noted to be crucial in defining the biological assemblages. Other important factors such as recruitment/propagule supply, and those which affect primary production, such as suspended sediments, light attenuation and water chemistry and temperature, were also noted to be key and act to influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. Output processes performed by the biological assemblages are variable between ecological groups depending on the specific flora and fauna present and the role they perform within the ecosystem. Of particular importance are the outputs performed by the macroalgae group, which are diverse in nature and exert influence over other ecological groups in the habitat. Important output processes from the habitat as a whole include primary and secondary production, bioengineering, biodeposition (in mixed sediment habitats) and the supply of propagules; these in turn influence ecosystem functions at the local scale such as nutrient and biogeochemical cycling, supply of food resources, sediment stability (in mixed sediment habitats), habitat provision and population and algae control. The export of biodiversity and organic matter, biodiversity enhancement and biotope stability are the resulting ecosystem functions that occur at the regional to global scale. Features within the models that are most useful for monitoring habitat status and change due to natural variation have been identified, as have those that may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Biological, physical and chemical features of the ecosystem have been identified as potential indicators to monitor natural variation, whereas biological factors and those physical /chemical factors most likely to affect primary production have predominantly been identified as most likely to indicate change due to anthropogenic pressures.

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Human activities within the marine environment give rise to a number of pressures on seabed habitats. Improved understanding of the sensitivity of subtidal sedimentary habitats is required to underpin the management advice provided for Marine Protected Areas, as well as supporting other UK marine monitoring and assessment work. The sensitivity of marine sedimentary habitats to a range of pressures induced by human activities has previously been systematically assessed using approaches based on expert judgement for Defra Project MB0102 (Tillin et al. 2010). This previous work assessed sensitivity at the level of the broadscale habitat and therefore the scores were typically expressed as a range due to underlying variation in the sensitivity of the constituent biotopes. The objective of this project was to reduce the uncertainty around identifying the sensitivity of selected subtidal sedimentary habitats by assessing sensitivity, at a finer scale and incorporating information on the biological assemblage, for 33 Level 5 circalittoral and offshore biotopes taken from the Marine Habitat Classification of Britain and Ireland (Connor et al. 2004). Two Level 6 sub-biotopes were also included in this project as these contain distinctive characterising species that differentiate them from the Level 5 parent biotope. Littoral, infralittoral, reduced and variable salinity sedimentary habitats were excluded from this project as the scope was set for assessment of circalittoral and offshore sedimentary communities. This project consisted of three Phases. • Phase 1 - define ecological groups based on similarities in the sensitivity of characterising species from the Level 5 and two Level 6 biotopes described above. • Phase 2 - produce a literature review of information on the resilience and resistance of characterising species of the ecological groups to pressures associated with activities in the marine environment. • Phase 3 - to produce sensitivity assessment ‘proformas’ based on the findings of Phase 2 for each ecological group. This report outlines results of Phase 2. The Tillin et al., (2010) sensitivity assessment methodology was modified to use the best available scientific evidence that could be collated within the project timescale. An extensive literature review was compiled, for peer reviewed and grey literature, to examine current understanding about the effects of pressures from human activities on circalittoral and offshore sedimentary communities in UK continental shelf waters, together with information on factors that contribute to resilience (recovery) of marine species. This review formed the basis of an assessment of the sensitivity of the 16 ecological groups identified in Phase 1 of the project (Tillin & Tyler-Walters 2014). As a result: • the state of knowledge on the effects of each pressure on circalittoral and offshore benthos was reviewed; • the resistance, resilience and, hence, sensitivity of sixteen ecological groups, representing 96 characteristic species, were assessed for eight separate pressures; • each assessment was accompanied by a detailed review of the relevant evidence; Assessing the sensitivity of subtidal sedimentary habitats to pressures associated with human activities • knowledge gaps and sources of uncertainty were identified for each group; • each assessment was accompanied by an assessment of the quality of the evidence, its applicability to the assessment and the degree of concordance (agreement) between the evidence, to highlight sources of uncertainty as an assessment of the overall confidence in the sensitivity assessment, and finally • limitations in the methodology and the application of sensitivity assessments were outlined. This process demonstrated that the ecological groups identified in Phase 1 (Tillin & Tyler-Walters 2014) were viable groups for sensitivity assessment, and could be used to represent the 33 circalittoral and offshore sediments biotopes identified at the beginning of the project. The results of the sensitivity assessments show: • the majority of species and hence ecological groups in sedimentary habitats are sensitive to physical change, especially loss of habitat and sediment extraction, and change in sediment type; • most sedimentary species are sensitive to physical damage, e.g. abrasion and penetration, although deep burrowing species (e.g. the Dublin Bay prawn - Nephrops norvegicus and the sea cucumber - Neopentadactyla mixta) are able to avoid damaging effects to varying degrees, depending on the depth of penetration and time of year; • changes in hydrography (wave climate, tidal streams and currents) can significantly affect sedimentary communities, depending on whether they are dominated by deposit, infaunal feeders or suspension feeders, and dependant on the nature of the sediment, which is itself modified by hydrography and depth; • sedentary species and ecological groups that dominate the top-layer of the sediment (either shallow burrowing or epifaunal) remain the most sensitive to physical damage; • mobile species (e.g. interstitial and burrowing amphipods, and perhaps cumaceans) are the least sensitive to physical change or damage, and hydrological change as they are already adapted to unstable, mobile substrata; • sensitivity to changes in organic enrichment and hence oxygen levels, is variable between species and ecological groups, depending on the exact habitat preferences of the species in question, although most species have at least a medium sensitivity to acute deoxygenation; • there is considerable evidence on the effects of bottom-contact fishing practices and aggregate dredging on sedimentary communities, although not all evidence is directly applicable to every ecological group; • there is lack of detailed information on the physiological tolerances (e.g. to oxygenation, salinity, and temperature), habitat preferences, life history and population dynamics of many species, so that inferences has been made from related species, families, or even the same phylum; • there was inadequate evidence to assess the effects of non-indigenous species on most ecological groups, and Assessing the sensitivity of subtidal sedimentary habitats to pressures associated with human activities • there was inadequate evidence to assess the effects of electromagnetic fields and litter on any ecological group. The resultant report provides an up-to-date review of current knowledge about the effects of pressures resulting from human activities of circalittoral and offshore sedimentary communities. It provides an evidence base to facilitate and support the provision of management advice for Marine Protected Areas, development of UK marine monitoring and assessment, and conservation advice to offshore marine industries. However, such a review will require at least annual updates to take advantage of new evidence and new research as it becomes available. Also further work is required to test how ecological group assessments are best combined in practice to advise on the sensitivity of a range of sedimentary biotopes, including the 33 that were originally examined.

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Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

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Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.