Phenological trends and trophic mismatch across multiple levels of a North Sea pelagic food web

Differential phenological responses to climate among species are predicted to disrupt trophic interactions, but datasets to evaluate this are scarce. We compared phenological trends for species from 4 levels of a North Sea food web over 24 yr when sea surface temperature (SST) increased significantly. We found little consistency in phenological trends between adjacent trophic levels, no significant relationships with SST, and no significant pairwise correlations between predator and prey phenologies, suggesting that trophic mismatching is occurring. Finer resolution data on timing of peak energy demand (mid-chick-rearing) for 5 seabird species at a major North Sea colony were compared to modelled daily changes in length of 0-group (young of the year) lesser sandeels Ammodytes marinus. The date at which sandeels reached a given threshold length became significantly later during the study. Although the phenology of all the species except shags also became later, these changes were insufficient to keep pace with sandeel length, and thus mean length (and energy value) of 0-group sandeels at mid-chick-rearing showed net declines. The magnitude of declines in energy value varied among the seabirds, being more marked in species showing no phenological response (shag, 4.80 kJ) and in later breeding species feeding on larger sandeels (kittiwake, 2.46 kJ) where, due to the relationship between sandeel length and energy value being non-linear, small reductions in length result in relatively large reductions in energy. However, despite the decline in energy value of 0-group sandeels during chick-rearing, there was no evidence of any adverse effect on breeding success for any of the seabird species. Trophic mismatch appears to be prevalent within the North Sea pelagic food web, suggesting that ecosystem functioning may be disrupted.

Reply to Haddock, S. H. D. Reconsidering evidence for potential climate-related increases in jellyfish

Following the publication of our paper (Attrill et al. 2007), we became quickly aware of a couple of errors. We have subsequently been collaborating with Dr. Chris Lynam (Lynam et al. 2004, 2005) to bring together our two datasets, explore the common patterns within our data, and attempt to provide a consensus on how climate is affecting gelatinous plankton in the North Sea. During this reanalysis, two errors within the data were discovered, one involving a transcription error of a column of residuals during de-trended analysis, the other a major data entry error deep in the Continuous Plankton Recorder (CPR) database for sector B2. Here we present a revised version of table 1 from Attrill et al. (2007) to incorporate corrections to these transcription and data entry errors. These corrections alter some of the results in our original data table, mainly to increase and strengthen the number of significant relations we found (e.g., for sector B2 and whole sea area); all previous main results remain robustly significant. Following discussions with Dr. Lynam, two clarifications of statements made in Attrill et al. (2007) are also required. Page 482, Results, last line of first column: ‘‘There were no...robust, consistent relations between jellyfish frequency and any environmental variables for B and D… contrary to the findings of previous shorter time series (Lynam et al. 2005).’’ The Lynam et al. (2004, 2005) papers presented no data for the D sector and found no link in the B sector, contrary to our revised results. Page 482, Discussion, paragraph 1, last sentence: ‘‘… positive association … North of Scotland (Lynam et al. 2005) … does not appear to be maintained.’’ Our paper did not report on any data that covered Lynam et al.’s (2005) North of Scotland area so the statement is not directly supported, although their positive relation North of Scotland, when considered in conjunction with inflow, may agree with the C2 and B2 results of Attrill et al. (2007).

A new procedure to optimize the selection of groups in a classification tree

Agglomerative cluster analyses encompass many techniques, which have been widely used in various fields of science. In biology, and specifically ecology, datasets are generally highly variable and may contain outliers, which increase the difficulty to identify the number of clusters. Here we present a new criterion to determine statistically the optimal level of partition in a classification tree. The criterion robustness is tested against perturbated data (outliers) using an observation or variable with values randomly generated. The technique, called Random Simulation Test (RST), is tested on (1) the well-known Iris dataset [Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann. Eugenic. 7, 179–188], (2) simulated data with predetermined numbers of clusters following Milligan and Cooper [Milligan, G.W., Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179] and finally (3) is applied on real copepod communities data previously analyzed in Beaugrand et al. [Beaugrand, G., Ibanez, F., Lindley, J.A., Reid, P.C., 2002. Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography. Mar. Ecol. Prog. Ser. 232, 179–195]. The technique is compared to several standard techniques. RST performed generally better than existing algorithms on simulated data and proved to be especially efficient with highly variable datasets.

Decadal variability in biogeochemical models: Comparison with a 50-year ocean colour dataset

Assessing the skill of biogeochemical models to hindcast past variability is challenging, yet vital in order to assess their ability to predict biogeochemical change. However, the validation of decadal variability is limited by the sparsity of consistent, long-term biological datasets. The Phytoplankton Colour Index (PCI) product from the Continuous Plankton Recorder survey, which has been sampling the North Atlantic since 1948, is an example of such a dataset. Converting the PCI to chlorophyll values using SeaWiFS data allows a direct comparison with model output. Here we validate decadal variability in chlorophyll from the GFDL TOPAZ model. The model demonstrates skill at reproducing interannual variability, but cannot simulate the regime shifts evident in the PCI data. Comparison of the model output, data and climate indices highlights under-represented processes that it may be necessary to include in future biogeochemical models in order to accurately simulate decadal variability in ocean ecosystems.

Development and validation of a molecular technique for the analysis of archived formalin-preserved phytoplankton samples permits retrospective assessment of Emiliania huxleyi communities

The coccolithophores, particularly the species Emiliania huxleyi (Lohmann) Hay & Mohler, account for the bulk of global calcium carbonate production and as such play a fundamental role in global CO2 cycling and the carbonate chemistry of the oceans. To evaluate the response of this functional group to the effects of climate change, we undertook a feasibility study to determine whether a retrospective approach could be used on archived coccolithophore datasets. We demonstrate for the first time a technique for the extraction of E. huxleyi nucleic acids from archived formalin-fixed samples of the long-term Continuous Plankton Recorder. Molecular analysis of a nine year old formalin-fixed sample reveals the presence of a diverse population of E. huxleyi genotypes within a developing coccolithophore bloom. In addition, E. huxleyi sequences were amplified from a number of formalin-fixed samples, the earliest of which was collected in August 1972. This molecular assay promises the possibility of studying global variations in the distribution and genetic make-up of E. huxleyi communities over extensive periods of time. (c) 2008 Elsevier B.V. All rights reserved.

Assessing marine plankton community structure from long-term monitoring data with multivariate autoregressive (MAR) models: a comparison of fixed station versus spatially distributed sampling data

We examined how marine plankton interaction networks, as inferred by multivariate autoregressive (MAR) analysis of time-series, differ based on data collected at a fixed sampling location (L4 station in the Western English Channel) and four similar time-series prepared by averaging Continuous Plankton Recorder (CPR) datapoints in the region surrounding the fixed station. None of the plankton community structures suggested by the MAR models generated from the CPR datasets were well correlated with the MAR model for L4, but of the four CPR models, the one most closely resembling the L4 model was that for the CPR region nearest to L4. We infer that observation error and spatial variation in plankton community dynamics influenced the model performance for the CPR datasets. A modified MAR framework in which observation error and spatial variation are explicitly incorporated could allow the analysis to better handle the diverse time-series data collected in marine environments.

Fine-scale nutrient and carbonate system dynamics around cold-water coral reefs in the northeast Atlantic.

Ocean acidification has been suggested as a serious threat to the future existence of cold-water corals (CWC). However, there are few fine-scale temporal and spatial datasets of carbonate and nutrients conditions available for these reefs, which can provide a baseline definition of extant conditions. Here we provide observational data from four different sites in the northeast Atlantic that are known habitats for CWC. These habitats differ by depth and by the nature of the coral habitat. At depths where CWC are known to occur across these sites the dissolved inorganic carbon ranged from 2088 to 2186 μmol kg−1, alkalinity ranged from 2299 to 2346 μmol kg−1, and aragonite Ω ranged from 1.35 to 2.44. At two sites fine-scale hydrodynamics caused increased variability in the carbonate and nutrient conditions over daily time-scales. The observed high level of variability must be taken into account when assessing CWC sensitivities to future environmental change.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

Frequent locations of oceanic fronts as an indicator of pelagic diversity: application to marine protected areas and renewables

Frequent locations of thermal fronts in UK shelf seas were identified using an archive of 30,000 satellite images acquired between 1999 and 2008, and applied as a proxy for pelagic diversity in the designation of Marine Protected Areas (MPAs). Networks of MPAs are required for conservation of critical marine habitats within Europe, and there are similar initiatives worldwide. Many pelagic biodiversity hotspots are related to fronts, for example cetaceans and basking sharks around the Isle of Man, Hebrides and Cornwall, and hence remote sensing can address this policy need in regions with insufficient species distribution data. This is the first study of UK Continental Shelf front locations to use a 10-year archive of full-resolution (1.1 km) AVHRR data, revealing new aspects of their spatial and seasonal variability. Frontal locations determined at sea or predicted by ocean models agreed closely with the new frequent front maps, which also identified many additional frontal zones. These front maps were among the most widely used datasets in the recommendation of UK MPAs, and would be applicable to other geographic regions and to other policy drivers such as facilitating the deployment of offshore renewable energy devices with minimal environmental impact.

Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype

Bacterioplankton of the SAR11 clade are the most abundant microorganisms in marine systems, usually representing 25% or more of the total bacterial cells in seawater worldwide. SAR11 is divided into subclades with distinct spatiotemporal distributions (ecotypes), some of which appear to be specific to deep water. Here we examine the genomic basis for deep ocean distribution of one SAR11 bathytype (depth-specific ecotype), subclade Ic. Four single-cell Ic genomes, with estimated completeness of 55%-86%, were isolated from 770 m at station ALOHA and compared with eight SAR11 surface genomes and metagenomic datasets. Subclade Ic genomes dominated metagenomic fragment recruitment below the euphotic zone. They had similar COG distributions, high local synteny and shared a large number (69%) of orthologous clusters with SAR11 surface genomes, yet were distinct at the 16S rRNA gene and amino-acid level, and formed a separate, monophyletic group in phylogenetic trees. Subclade Ic genomes were enriched in genes associated with membrane/cell wall/envelope biosynthesis and showed evidence of unique phage defenses. The majority of subclade Ic-specfic genes were hypothetical, and some were highly abundant in deep ocean metagenomic data, potentially masking mechanisms for niche differentiation. However, the evidence suggests these organisms have a similar metabolism to their surface counterparts, and that subclade Ic adaptations to the deep ocean do not involve large variations in gene content, but rather more subtle differences previously observed deep ocean genomic data, like preferential amino-acid substitutions, larger coding regions among SAR11 clade orthologs, larger intergenic regions and larger estimated average genome size.

A new approach for objective identification of turns and steps in organism movement data relevant to random walk modelling

1. A first step in the analysis of complex movement data often involves discretisation of the path into a series of step-lengths and turns, for example in the analysis of specialised random walks, such as Lévy flights. However, the identification of turning points, and therefore step-lengths, in a tortuous path is dependent on ad-hoc parameter choices. Consequently, studies testing for movement patterns in these data, such as Lévy flights, have generated debate. However, studies focusing on one-dimensional (1D) data, as in the vertical displacements of marine pelagic predators, where turning points can be identified unambiguously have provided strong support for Lévy flight movement patterns. 2. Here, we investigate how step-length distributions in 3D movement patterns would be interpreted by tags recording in 1D (i.e. depth) and demonstrate the dimensional symmetry previously shown mathematically for Lévy-flight movements. We test the veracity of this symmetry by simulating several measurement errors common in empirical datasets and find Lévy patterns and exponents to be robust to low-quality movement data. 3. We then consider exponential and composite Brownian random walks and show that these also project into 1D with sufficient symmetry to be clearly identifiable as such. 4. By extending the symmetry paradigm, we propose a new methodology for step-length identification in 2D or 3D movement data. The methodology is successfully demonstrated in a re-analysis of wandering albatross Global Positioning System (GPS) location data previously analysed using a complex methodology to determine bird-landing locations as turning points in a Lévy walk. For this high-resolution GPS data, we show that there is strong evidence for albatross foraging patterns approximated by truncated Lévy flights spanning over 3·5 orders of magnitude. 5. Our simple methodology and freely available software can be used with any 2D or 3D movement data at any scale or resolution and are robust to common empirical measurement errors. The method should find wide applicability in the field of movement ecology spanning the study of motile cells to humans.

Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The AlgaRisk demonstration service

Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008–2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed.

Regional ocean-colour chlorophyll algorithms for the Red Sea

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.

GB Non-native Species Information Portal: documenting the arrival of non-native species in Britain

Information on non-native species (NNS) is often scattered among a multitude of sources, such as regional and national databases, peer-reviewed and grey literature, unpublished research projects, institutional datasets and with taxonomic experts. Here we report on the development of a database designed for the collation of information in Britain. The project involved working with volunteer experts to populate a database of NNS (hereafter called “the species register”). Each species occupies a row within the database with information on aspects of the species’ biology such as environment (marine, freshwater, terrestrial etc.), functional type (predator, parasite etc.), habitats occupied in the invaded range (using EUNIS classification), invasion pathways, establishment status in Britain and impacts. The information is delivered through the Great Britain Non-Native Species Information Portal hosted by the Non-Native Species Secretariat. By the end of 2011 there were 1958 established NNS in Britain. There has been a dramatic increase over time in the rate of NNS arriving in Britain and those becoming established. The majority of established NNS are higher plants (1,376 species). Insects are the next most numerous group (344 species) followed by non-insect invertebrates (158 species), vertebrates (50 species), algae (24 species) and lower plants (6 species). Inventories of NNS are seen as an essential tool in the management of biological invasions. The use of such lists is diverse and far-reaching. However, the increasing number of new arrivals highlights both the dynamic nature of invasions and the importance of updating NNS inventories.

Looking backwards to look forwards: the role of natural history in temperate reef ecology

Temperate reefs are superb tractable systems for testing hypotheses in ecology and evolutionary biology. Accordingly there is a rich history of research stretching back over 100 years, which has made major contributions to general ecological and evolutionary theory as well as providing better understanding of how littoral systems work by linking pattern with process. A brief resumé of the history of temperate reef ecology is provided to celebrate this rich heritage. As a community, temperate reef ecologists generally do well designed experiments and test well formulated hypotheses. Increasingly large datasets are being collected, collated and subjected to complex meta-analyses and used for modelling. These datasets do not happen spontaneously – the burgeoning subject of macroecology is possible only because of the efforts of dedicated natural historians whether it be observing birds, butterflies, or barnacles. High-quality natural history and old-fashioned field craft enable surveys or experiments to be stratified (i.e. replicates are replicates and not a random bit of rock) and lead to the generation of more insightful hypotheses. Modern molecular approaches have led to the discovery of cryptic species and provided phylogeographical insights, but natural history is still required to identify species in the field. We advocate a blend of modern approaches with old school skills and a fondness for temperate reefs in all their splendour.