Lee de Mora

Modelling the effects of climate change on the distribution and production of marine fishes:Accounting for trophic interactions in a dynamic bioclimate envelope model

Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and catches based on bioclimate envelope models are available, but to date they have not considered interspecific interactions. We address this by combining the species-based Dynamic Bioclimate Envelope Model (DBEM) with a size-based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with pelagic species showing the greatest reductions. Goodness-of-fit of biomass data from fish stock assessments in the North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (i) predators and competitors may respond to climate change together; (ii) existing parameterization of the DBEM might implicitly incorporate trophic interactions; and/or (iii) trophic interactions might not be the main driver of responses to climate. Future analyses using ecologically explicit models and data will improve understanding of the effects of inter-specific interactions on responses to climate change, and better inform managers about plausible ecological and fishery consequences of a changing environment.

On the Potential of Surfers to Monitor Environmental Indicators in the Coastal Zone

The social and economic benefits of the coastal zone make it one of the most treasured environments on our planet. Yet it is vulnerable to increasing anthropogenic pressure and climate change. Coastal management aims to mitigate these pressures while augmenting the socio-economic benefits the coastal region has to offer. However, coastal management is challenged by inadequate sampling of key environmental indicators, partly due to issues relating to cost of data collection. Here, we investigate the use of recreational surfers as platforms to improve sampling coverage of environmental indicators in the coastal zone. We equipped a recreational surfer, based in the south west United Kingdom (UK), with a temperature sensor and Global Positioning System (GPS) device that they used when surfing for a period of one year (85 surfing sessions). The temperature sensor was used to derive estimates of sea-surface temperature (SST), an important environmental indicator, and the GPS device used to provide sample location and to extract information on surfer performance. SST data acquired by the surfer were compared with data from an oceanographic station in the south west UK and with satellite observations. Our results demonstrate: (i) high-quality SST data can be acquired by surfers using low cost sensors; and (ii) GPS data can provide information on surfing performance that may help motivate data collection by surfers. Using recent estimates of the UK surfing population, and frequency of surfer participation, we speculate around 40 million measurements on environmental indicators per year could be acquired at the UK coastline by surfers. This quantity of data is likely to enhance coastal monitoring and aid UK coastal management. Considering surfing is a world-wide sport, our results have global implications and the approach could be expanded to other popular marine recreational activities for coastal monitoring of environmental indicators.

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Expanding Aquatic Observations through Recreation

Accurate observations of the Earth system are required to understand how our planet is changing and to help manage its resources. The aquatic environment−including lakes, rivers, wetlands, estuaries, coastal and open oceans−is a fundamental component of the Earth system controlling key physical, biological, and chemical processes that allow life to flourish. Yet, this environment is critically undersampled in both time and space. New and cost-effective sampling solutions are urgently needed. Here, we highlight the potential to improve aquatic sampling by tapping into recreation. We draw attention to the vast number of participants that engage in aquatic recreational activities and argue, based on current technological developments and recent research, that the time is right to employ recreational citizens to improve large-scale aquatic sampling efforts. We discuss the challenges that need to be addressed for this strategy to be successful (e.g. sensor package design, data quality, and citizen motivation), the steps needed to realize its potential, and additional societal benefits that arise when engaging citizens in scientific sampling.

The Assimilation of Phytoplankton Functional Types for Operational Forecasting in the Northwest European Shelf

This paper proposes the use of assimilation of phytoplankton functional types (PFTs) surface chlorophyll for operational forecasting of biogeochemistry on the North‐West European (NWE) Shelf. We explicitly compare the 5‐day forecasting skill of three runs of a physical‐biogeochemical model: (a) a free reference run, (b) a run with daily data assimilation (DA) of total surface chlorophyll (ChlTot), and (c) a run with daily PFTs DA. We show that small total chlorophyll model bias hides comparatively large biases in PFTs chlorophyll, which ChlTot DA fails to correct. This is because the ChlTot DA splits the assimilated total chlorophyll into PFTs by preserving their simulated ratios, rather than taking account of the observed PFT concentrations. Unlike ChlTot DA, PFTs DA substantially improves model representation of PFTs chlorophyll. During forecasting the DA reanalysis skill in representing PFTs chlorophyll degrades toward the free run skill; however, PFTs DA outperforms free run within the whole 5‐day forecasting period. We validated our results with in situ data, and we demonstrated that (in both DA cases) the DA substantially improves the model representation of CO2 fugacity (PFTs DA more than ChlTot DA). ChlTot DA has a positive impact on the representation of silicate, while the PFTs DA seems to have a negative impact. The impact of DA on nitrate and phosphate is not significant. The implications of using a univariate assimilation method, which preserves the phytoplankton stochiometry, and the impact of model biases on the nonassimilated variables are discussed.

Correction: On the Potential of Surfers to Monitor Environmental Indicators in the Coastal Zone.

[This corrects the article DOI: 10.1371/journal.pone.0127706.].