Estimation of atmospheric nutrient inputs to the Atlantic Ocean from 50°N to 50°S based on large-scale field sampling: Iron and other dust-associated elements

Atmospheric inputs of mineral dust supply iron and other trace metals to the remote ocean and can influence the marine carbon cycle due to iron's role as a potentially limiting micronutrient. Dust generation, transport, and deposition are highly heterogeneous, and there are very few remote marine locations where dust concentrations and chemistry (e.g., iron solubility) are routinely monitored. Here we use aerosol and rainwater samples collected during 10 large-scale research cruises to estimate the atmospheric input of iron, aluminum, and manganese to four broad regions of the Atlantic Ocean over two 3 month periods for the years 2001–2005. We estimate total inputs of these metals to our study regions to be 4.2, 17, and 0.27 Gmol in April–June and 4.9, 14, and 0.19 Gmol in September–November, respectively. Inputs were highest in regions of high rainfall (the intertropical convergence zone and South Atlantic storm track), and rainfall contributed higher proportions of total input to wetter regions. By combining input estimates for total and soluble metals for these time periods, we calculated overall percentage solubilities for each metal that account for the contributions from both wet and dry depositions and the relative contributions from different aerosol types. Calculated solubilities were in the range 2.4%–9.1% for iron, 6.1%–15% for aluminum, and 54%–73% for manganese. We discuss sources of uncertainty in our estimates and compare our results to some recent estimates of atmospheric iron input to the Atlantic.

Monitoring changes in phytoplankton abundance and composition in the Northwest Atlantic: a comparison of results obtained by Continuous Plankton Recorder sampling and colour satellite imagery

Phytoplankton abundance in the NW Atlantic was measured by continuous plankton recorder (CPR) sampling along tracks between Iceland and the western Scotian Shelf from 1998 to 2006, when sea-surface chlorophyll (SSChl) measurements were also being made by ocean colour satellite imagery using the SeaWiFS sensor. Seasonal and inter-annual changes in phytoplankton abundance were examined using data collected by both techniques, averaged over each of four shelf regions and four deep ocean regions. CPR sampling had gaps (missing months) in all regions and in the four deep ocean regions satellite observations were too sparse between November and February to be of use. Average seasonal cycles of SSChl were similar to those of total diatom abundance in seven regions, to those of the phytoplankton colour index in six regions, but were not similar to those of total dinoflagellate abundance anywhere. Large inter-annual changes in spring bloom dynamics were captured by both samplers in shelf regions. Changes in annual (or 8 months) averages of SSChl did not generally follow those of the CPR indices within regions and multi-year averages of SSChl, and the three CPR indices were generally higher in shelf than in deep ocean regions. Remote sensing and CPR sampling provide complementary ways of monitoring phytoplankton in the ocean: the former has superior temporal and spatial coverage and temporal resolution, and the latter provides better taxonomic information.

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.

Comparison of Zooplankton Sampling Performance of Longhurst-Hardy Plankton Recorder and Bongo Nets

Data on the abundance and biomass of zooplankton off the northwestern Portuguese coast, separately estimated with a Longhurst-Hardy Plankton Recorder (LHPR) and a Bongo net, were analysed to assess the comparative performance of the samplers. Zooplankton was collected along four transects perpendicular to the coast, deployments alternating between samplers. Total zooplankton biomass measured using the LHPR was significantly higher than that using the Bongo net. Apart from Appendicularia and Cladocera, abundances of other taxa (Copepoda, Mysidacea, Euphausiacea, Decapoda larvae, Amphipoda, Siphonophora, Hydromedusae, Chaetognatha and Fish eggs) were also consistently higher in the LHPR. Some of these differences were probably due to avoidance by the zooplankton of the Bongo net. This was supported by a comparative analysis of prosome length of the copepod Calanus helgolandicus sampled by the two nets that showed that Calanus in the LHPR samples were on average significantly larger, particularly in day samples. A ratio estimator was used to produce a factor to convert Bongo net biomass and abundance estimates to equate them with those taken with the LHPR. This method demonstrates how results from complementary zooplankton sampling strategies can be made more equivalent.

Can we trust simple marine DMS parameterisations within complex climate models?

Dimethylsulphide (DMS) is a globally important aerosol precurser. In 1987 Charlson and others proposed that an increase in DMS production by certain phytoplankton species in response to a warming climate could stimulate increased aerosol formation, increasing the lower-atmosphere's albedo, and promoting cooling. Despite two decades of research, the global significance of this negative climate feedback remains contentious. It is therefore imperative that schemes are developed and tested, which allow for the realistic incorporation of phytoplankton DMS production into Earth System models. Using these models we can investigate the DMS-climate feedback and reduce uncertainty surrounding projections of future climate. Here we examine two empirical DMS parameterisations within the context of an Earth System model and find them to perform marginally better than the standard DMS climatology at predicting observations from an independent global dataset. We then question whether parameterisations based on our present understanding of DMS production by phytoplankton, and simple enough to incorporate into global climate models, can be shown to enhance the future predictive capacity of those models. This is an important question to ask now, as results from increasingly complex Earth System models lead us into the 5th assessment of climate science by the Intergovernmental Panel on Climate Change. Comparing observed and predicted inter-annual variability, we suggest that future climate projections may underestimate the magnitude of surface ocean DMS change. Unfortunately this conclusion relies on a relatively small dataset, in which observed inter-annual variability may be exaggerated by biases in sample collection. We therefore encourage the observational community to make repeat measurements of sea-surface DMS concentrations an important focus, and highlight areas of apparent high inter-annual variability where sampling might be carried out. Finally, we assess future projections from two similarly valid empirical DMS schemes, and demonstrate contrasting results. We therefore conclude that the use of empirical DMS parameterisations within simulations of future climate should be undertaken only with careful appreciation of the caveats discussed.

Within-Otolith Variability in Chemical Fingerprints: Implications for Sampling Designs and Possible Environmental Interpretation

Largely used as a natural biological tag in studies of dispersal/connectivity of fish, otolith elemental fingerprinting is usually analyzed by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). LA-ICP-MS produces an elemental fingerprint at a discrete time-point in the life of a fish and can generate data on within-otolith variability of that fingerprint. The presence of within-otolith variability has been previously acknowledged but not incorporated into experimental designs on the presumed, but untested, grounds of both its negligibility compared to among-otolith variability and of spatial autocorrelation among multiple ablations within an otolith. Here, using a hierarchical sampling design of spatial variation at multiple scales in otolith chemical fingerprints for two Mediterranean coastal fishes, we explore: 1) whether multiple ablations within an otolith can be used as independent replicates for significance tests among otoliths, and 2) the implications of incorporating within-otolith variability when assessing spatial variability in otolith chemistry at a hierarchy of spatial scales (different fish, from different sites, at different locations on the Apulian Adriatic coast). We find that multiple ablations along the same daily rings do not necessarily exhibit spatial dependency within the otolith and can be used to estimate residual variability in a hierarchical sampling design. Inclusion of within-otolith measurements reveals that individuals at the same site can show significant variability in elemental uptake. Within-otolith variability examined across the spatial hierarchy identifies differences between the two fish species investigated, and this finding leads to discussion of the potential for within-otolith variability to be used as a marker for fish exposure to stressful conditions. We also demonstrate that a 'cost'-optimal allocation of sampling effort should typically include some level of within-otolith replication in the experimental design. Our findings provide novel evidence to aid the design of future sampling programs and improve our general understanding of the mechanisms regulating elemental fingerprints.

Meiobenthos and macrobenthos are discrete entities and not artefacts of sampling a size continuum: Comment on Bett (2013)

Species size distributions for metazoan benthic invertebrates conform to the highly conservative bimodal pattern, regardless of the sieve mesh sizes or numbers of sieves used in their extraction. This pattern is not an artefact of sampling a size continuum as suggested by computer simulations using just 2 fixed mesh sizes in Bett (2013; Mar Ecol Prog Ser 487:1-6). Meiobenthos and macrobenthos are coherent entities, each with a distinct suite of functional attributes, and should not be regarded as a single unit for ecological modelling purposes.